Published Project ReportPPR541

A new railway system capacity model

C Roberts, F Schmid, P Connor, V Ramdas and J Sharpe

Transport Research Laboratory

PROJECT REPORT PPR541

A new railway system capacity model A method of assessing capability trade-offs on railway capacity using a system-wide approach

by C Roberts, F Schmid (University of Birmingham), P Connor (PRC Consulting), V Ramdas and J Sharpe (TRL)

Prepared for: Project Record: NN-RS-DK-090727-04(Capacity)

Rail capability trade-off study

Client: Department for Transport, Rail Systems

(Duncan Kemp)

Copyright Transport Research Laboratory November 2010

This Published Report has been produced by TRL Limited, as part of a contract placed by the Department for Transport.

The views expressed are those of the authors and not necessarily those of the Department for Transport.

Name Date

Approved

Project Manager

Vijay Ramdas 17/11/2010

Technical Referee

Vijay Ramdas 17/11/2010

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When purchased in hard copy, this publication is printed on paper that is FSC (Forest Stewardship Council) and TCF (Totally Chlorine Free) registered.

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Contents

Executive summary vii

1 Introduction 1

2 Railway capacity definitions 3

3 Capacity assessment 7

3.1 UIC 405 method 7

3.2 UIC 406 method 8

3.3 British method 13

4 Common practice in improving railway capacity 15

5 System views of railway capacity 17

5.1 Current views 17

6 Railway capacity definitions and assessment 19

7 Railway capacity workshop 21

7.1 Measurement and definition of capacity 21 7.1.1 Matching policy and objectives 21 7.1.2 Metrics 21 7.1.3 Summary 22

7.2 Capacity loss 22 7.2.1 Rolling stock 22 7.2.2 Signalling 23 7.2.3 Timetabling 23 7.2.4 Stations 23 7.2.5 Infrastructure 23 7.2.6 Operational practice and misalignments 24 7.2.7 Summary 24

7.3 Capacity assessment tools 24 7.3.1 Network modelling 25 7.3.2 Timetable development 25 7.3.3 Summary 25 7.3.4 Post-workshop outputs 25

8 A Matrix of capability interdependencies 27

8.1 Model structure 27 8.1.1 Capacity functions 28 8.1.2 System components 28 8.1.3 Analysing a section 29

8.2 Developing the impact factors 29

8.3 Impact factors of railways system components/attributes 29 8.3.1 Adding tracks 30 8.3.2 Adding platforms at stations 30 8.3.3 Improving signalling 30 8.3.4 Increasing line and train speed 31 8.3.5 Improving horizontal track curvature 31 8.3.6 Improving track gradient 32

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8.3.7 Increasing number of carriages per train 32 8.3.8 Improving train speed heterogeneity 32 8.3.9 Improving other components and attributes of a railway

system 32

8.4 Feasibility of improving railway components 33

9 Example trade-offs analysis – Thameslink Route 35

9.1 Thameslink central section 35

9.2 Parsons Railway Integrated Modelling Environment (PRIME) 35

9.3 PRIME model 36

9.4 PRIME results 37

9.5 Matrix model inputs 38

9.6 Matrix model results 39

9.7 Discussion 40

9.8 Conclusions 40

10 Conclusions and recommendations 41

10.1 General conclusions 41

10.2 Recommendations 41

Acknowledgements 43

References 45

Appendix A Workshop attendees 47

Appendix B Submission from ProRail 49

Appendix C Matrix of capacity interdependencies 53

Appendix D Capacity impact factor lookup tables 55

Appendix E Matrix of capacity interdependencies (Thameslink route) 65

Appendix F Highway capacity (USA) 69

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Executive summary Railway capacity is now an increasingly prominent issue for government, infrastructure managers and operators. A range of definitions and explanations for railway capacity have been published, together with models and proposals for improvement but none of these have managed to include the railway as a complete system. They have tended in general to concentrate on operational or train control systems.

The first part of this report covers a review of current knowledge of railway capacity, comprising a literature review and a workshop with stakeholders:

1 Railway capacity is generally defined as the ability of a railway either to accommodate trains or to transport passengers and goods;

2 However, there is no absolute definition of capacity – the selection of an appropriate metric depends on the objectives of the service;

3 There are many different metrics already available for the measurement of capacity once an objective has been agreed, although not all of these directly address the definition given in 1 and some are too complex to be applied in day-to-day railway operations; and

4 Although historically the railway has been heavily compartmentalised, there is increasing recognition of the need to examine the railway with whole systems approaches.

The second part of this report examines a specific multi-train simulator, PRIME, and compares it with a newly-developed tool, Matrix model for modelling railway capacity as a complete system, including infrastructure, vehicles and operations.

• PRIME (Parson’s Railway Integrated Modelling Environment) is a Capacity Planning Tool that allows a high level evaluation of capacity impacts within a flexible modelling environment that is adaptable to different railway characteristics. For this study, capacity studies on the central part of the Thameslink route were carried out to examine the effects on headways of new signalling, different speed profiles and train lengths.

• The Matrix model comprises an Excel spreadsheet that decomposes the railway system into the individual components and operational systems that have an effect on capacity. The model is arranged in a matrix, providing the facility to vary components and their scores individually. Each component is provided with a numerical score that ranks its importance to system capacity. The model shows how varying the system attributes from their existing state by different levels affects capacity. The tool also evaluates the feasibility of the proposed changes in terms of context, cost and technical difficulty. The output will provide a system-wide view of the effects of the effects of proposals for improving capacity.

The preliminary analysis in this study has demonstrated the feasibility of a systems approach to examining the benefits of capability trade-offs. The Matrix model is in its early stage of development and further work is required to improve and validate the default values used in the model.

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1 Introduction Capacity and how to improve it are currently among the most significant concerns of many railways worldwide. However, whilst the term railway capacity is used frequently, it has neither a standard definition nor a standard method of measurement. Whether a line has reached its capacity and which are the attributes most critical to its capacity constraints are not obviously apparent.

The question of route capacity is a prominent issue for the modern railway network, especially since a railway is a fixed guidance system, requiring a combination of civil, electrical, mechanical and environmental engineering to construct and maintain and a widely spread, diverse control and management organisation to operate. All of these require substantial planning, building and operating resources. In addressing railway capacity, the combination of these wide-ranging factors must be considered in a way that ensures that the effects of all of them upon the system are drawn into the debate and realistically assessed. The consideration must also include the feasibility of proposals provide increased capacity in terms of the cost, technical difficulty and efficiency of the proposed changes for the railway system as a whole.

TRL and the Railway Research Centre at the University of Birmingham, with financial support from Network Rail, have now conducted research into these aspects. The objective of the task was to provide a reasonably comprehensive review of railway capacity definitions and develop a system based method to assess the impact on capacity of changes to different railway components.

A two part study has been carried to achieve the above objectives:

• An extensive literature review of definitions and methods of assessment of railway capacity in the UK and elsewhere was conducted. Those used for highway capacity were also briefly reviewed. Sources include railway standards and specification, journal and conference papers, books, research and project reports, and online publications.

Additionally, a workshop with stakeholders from across the rail industry was held to gather information from across the industry on the key drivers of capacity constraints based on their understanding and experience.

• Development of a simple and easy-to use methodology for determining the most effective ways to improve railway capacity and to ensure that the scope of capacity assessments treats the railway as a system. The work is designed to develop a model that would allow a number of the elements affecting railway capacity to be included in a preliminary assessment of proposals to improve capacity over a section of line or route. The assessment is designed to allow a ranking of features in accordance with their effects on route/line capacity. In broad terms, the objective of the model is to widen the scope of railway capacity assessment and ensure that system interactions are taken on board in the decision making process

This report presents the outputs of the two stages of the study.

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2 Railway capacity definitions It is generally accepted that railway capacity is an elusive concept that is not easily defined or quantified (Kozan & Burdett, 2004; Kozan & Burdett, 2005; Krueger, 1999; UIC, 2004). Therefore, railway capacity is often understood and defined differently according to the context. An example of various views is shown in Figure 1.

Market

(customer needs)

Infrastructure planning

Timetable planning Operations

Expected number of train paths (peak)

Expected mix of traffic and speed (peak)

Infrastructure quality need

Journey times as short as possible

Translation of all short- and long-term market-induced demands to reach optimised load

Expected number of train paths (average)

Expected mix of traffic and speed (average)

Expected conditions of infrastructure

Time supplements for expected disruptions

Maintenance strategies

Requested number of train paths

Requested mix of traffic and speed

Existing conditions of infrastructure

Time supplements for expected disruptions

Time supplements for maintenance

Connecting services in stations

Requests out of regular interval timetables (system times, train stops, etc.)

Actual number of trains

Actual mix of traffic speed

Actual conditions of infrastructure

Delays caused by operational disruptions

Delays caused by track works

Delays caused by missed connections

Additional capacity by time supplements not needed

Figure 1: Different views of capacity (UIC, 2004)

Ultimately, the capacity of a railway can be considered to be the quantity of passengers and goods that the railway can transport over a given time period. It is often expressed in terms of the number of passenger kilometres per year and freight tonne kilometres per year or passengers per hour and freight tonnes per hour. This relates to the carrying capacity of the railway and reflects both infrastructure capacity and train capacity. However, while this concept is often used to express the scale of a railway in comparison with other railways or with other modes of transport, it is rarely used in day-to-day railway operations.

In practice, railway capacity is often associated more with the ability of infrastructure to accommodate train traffic. Below are examples of this type of definition.

According to Kozan & Burdett (2004, 2005), “the simplest approximation and the most prevalent encountered is that the capacity of a single line is the total number of standard train paths that can be accommodated across a critical section in a given time period, where a standard train is defined as the most prevalent type to traverse the corridor”.

The International Union of Railways (UIC) has attempted to provide a definition of railway capacity which is supposed to work for as broad a spectrum of scenarios as possible (UIC, 2004):

“The capacity of any railway infrastructure is:

- the total number of possible paths in a defined time window, considering the actual path mix or known developments respectively and the Infrastructure Manager’s own assumptions;

- in nodes, individual lines or part of the network;

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- with market-oriented quality.”

Krueger (1999) of the Canadian National Railway adopted the following general definition:

“Capacity is a measure of the ability to move a specific amount of traffic over a defined rail line with a given set of resources under a specific service plan.”

He also provided various specific definitions and measures of capacity as follows:

“Theoretical (Physical) Capacity: This is the theoretical maximum upper boundary of capacity. It assumes all trains are the same, with the same train consist, equal priority, and are evenly spaced throughout the day with no disruptions. It ignores the effects of variations in traffic and operations that occur in reality.”

“Practical Capacity: The practical limit of “Representative” traffic volume that can be moved on a line while achieving a defined performance threshold. “Representative” traffic reflects actual train mix, priorities, consists, power to weight, and traffic bunching.”

“Used Capacity: The actual traffic volume occurring over the territory. Reflects actual variation in traffic and operations that occur on the line.”

“Available Capacity: The difference between Used and Practical Capacity. It is an indication of the additional traffic volume that could be handled while maintaining the predefined performance threshold.”

According to Krueger, practical capacity is the most significant measure of track capacity since it relates the ability of a specific combination of plant, traffic and operations to move the most volume within an expected service level.

The definitions of railway capacity provided in a report prepared for the Washington State Department of Transportation (HDR Engineering, 2001), are given in Table 1.

Table 1: Types of railroad capacity (HDR Engineering, 2001)

Capacity type

Definition

Theoretical The number of trains per day that could run over a route in a strictly perfect, mathematically-generated environment. This number is useful because it is relatively easy to generate. For example, if the longest running time between two sidings were one hour, that implies that it would take at least two hours between trains to travel in each direction. This would imply a capacity of 12 trains travelling east and 12 trains travelling west each day (or 24 trains per day).

Practical It’s not possible to actually run the number of trains you work out mathematically. Things will happen – one train doesn’t have enough locomotive power, the rail is slippery, there is wind or fog, or the engineer is a little slow on his train handling. A reasonable and slightly reduced figure for what the real world might produce is 75% of the theoretical capacity. Using this relationship for practical capacity makes it possible to produce a reasonable estimate fairly easily.

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Capacity type

Definition

Commercial Commercial capacity is simply the practical capacity available during the times when business needs would actually want shipments to move. Practical capacity is the number of trains you could reasonably expect to run in a day, but using all of it would require you to run trains when you don’t need them. Suppose that the Seattle area could practically accept one train an hour and send out one train per hour. However, shippers want to receive their shipments before 6am, so they can be ready for the day’s business, and they want to send shipments after a day of loading cars (say, after 6pm).

In effect, the commercial capacity in this very simple example is six trains per day outbound from 6pm to midnight and six trains per day inbound from midnight to 6am. Shippers might want to increase their rail business to a level that would need ten trains, but since their businesses only accept or send out trains at certain times, the commercial capacity is much less than the practical capacity.

In the context of signalling, Woodland, in his PhD thesis (2004), has adopted the following definitions of railway capacity:

• Train Following Capacity: The maximum throughput at a particular point on the railway network, such as a signal position, if all trains were to follow each other at line speed and with a minimum of braking distance separation, no allowance being made for station stops.

• Point Capacity: The maximum throughput at a particular point on the railway network, such as a station platform, accounting for station stops and actual train speeds.

• Theoretical Line Capacity: Indicates the theoretical maximum throughput of a railway line when all trains complete more than one round trip.

• Sustainable Line Capacity: Indicates the sustainable throughput of a railway line when all trains complete more than one round trip, in accordance with the time tabled service pattern.

• Optimum Line Capacity: Indicates the sustainable throughput when passenger / goods travel times and comforts are optimised.

For transit systems (e.g. light rail) Vuchic (2005) adopted two types of capacity:

(1) Static Capacity: Total number of spaces or persons a vehicle can accommodate.

(2) Dynamic Capacity: The maximum number of transit units, vehicles, spaces or persons that can be transported on a transit line past a fixed point in one direction per unit of time (usually one hour).

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3 Capacity assessment As shown in Section 2 above, the majority of railway capacity definitions are about the traffic volume or throughput (number of trains passing a given section over a period of time). Interestingly, except for the UIC 405 method which is now superseded, most of the common capacity assessment methods do not directly address this. Instead they concern capacity consumption or utilisation.

3.1 UIC 405 method

Before the current railway capacity code (UIC, 2004a) was issued, another method had been provided by the International Union of Railways (UIC, 1983). Although this code is now superseded, it is still worth reviewing since it provides a direct assessment of capacity in terms of the number of trains per given time period (day or hour).

The UIC 405 basic formula is:

� � ���������

where � is the capacity of a line section in number of trains in period � (the reference period in minutes), �� is the average duration of minimum train headway time (minutes), � is the extra time margin (minutes) and �� is an additional time (minutes).

• The average duration of minimum train headway time �� is calculated from the headway of all trains running on the line section. There are two different methods for determining ��: dependent and independent of the running schedule.

• The extra time margin � is a “breathing space” provided after each minimum train headway to reduce the risk of the occurrence of a build-up of delays.

• The additional time �� is another additional period of time allowed after each train headway to ensure more or less the desired quality of service over the whole line section also when a number of sections of line are involved.

UIC 405 provides a detailed guide to defining each of the parameters in the formula, and an example of the capacity calculation. It is reported (Viegas et al., 2003;, Moreira etal., 2003; Moreira et al., 2004) that the UIC 405 formula has been used by Swiss Federation Railways in its CAPACITY model to analyse different long term scenarios quickly and to determine bottlenecks for the whole Swiss railway network with ease.

The formula has also been used in the development of CAP1 (capacity model: one directional flow) and CAP2 (capacity model: bi-directional flow) under the European project IMPROVERAIL (Viegas et al., 2003;, Moreira et al., 2003; Moreira et al., 2004). These two models can be seen as further development of the UIC 405 formula and the CAPACITY model, and have been used for a capacity study of a 336km section of the North Line in Portugal (Figure 2).

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Figure 2: Calculated capacity on North Line, Portugal (Moreira et al., 2003)

The capacity in Figure 2 was calculated for five scenarios:

• Capacity limit - The maximum capacity of a railway section, obtained with all trains running in same direction (one section of double track), all equally spaced at the minimum headway;

• Capacity with a mix of trains - Capacity with a mix of trains with different running times between two stations and without the possibility of overtaking;

• Capacity with different train services – taking operational patterns of passenger and freight trains into account. Typically, passenger trains are not passed in stations due to the additional stop time required, but for freight trains the additional stop time is not so relevant.

• Capacity with network effect – taking into account the fact that not all trains have the same path or end in the same station, where shorter trains and also convergence or divergence of trains generate unusable capacity.

• Used capacity - Actual capacity used.

3.2 UIC 406 method

The UIC 406 – Capacity (UIC, 2004a) was issued after three years of study and consultation within many European railways. Railway capacity is assessed though the capacity consumption as shown in Figure 3.

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Figure 3: UIC Method of

The formula for determining capacity consumption is as follows:

where is the total consumption timetime is the supplement for single(all values in minutes). Then:

where is the percentage(again, in minutes).

The timetable compression method, which reveals the time shares of a nontimetable and of a compressed timetable, can be used to determine the capacity consumption components in the above equation. For compression purposes, all single train paths are pushed together up to the minimum theoretical headway according to their timetable order, without recommending any buffer time. This compression can be done by constructing graphical analysis (see analytical calculation.

9

f Determination of Capacity Consumption (UIC, 2004)

capacity consumption is as follows:

ption time, is the infrastructure occupationsingle-track lines and is any supplements for maintenance

n:

capacity consumption and is the chosen time window

method, which reveals the time shares of a nonessed timetable, can be used to determine the capacity n the above equation. For compression purposes, all single ether up to the minimum theoretical headway according to ut recommending any buffer time. This compression can be hical analysis (see Figure 4), using suitable tools, or by

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on (UIC, 2004)

on, is the buffer ts for maintenance

osen time window

a non-compressed mine the capacity urposes, all single dway according to ompression can be table tools, or by

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Figure 4: Concept of timetable compression on a double line (adapted from UIC, 2004)

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There is a relationship between infrastructure occupation time (% of time window) and the risk of congestion. Based on the assessment of about 3000km of lines on several European rail networks, UIC has proposed recommended values for capacity consumption index (see Table 2). Capacity consumptions above these values may significantly increase the risk of congestion if delays occur.

Table 2: UIC recommended capacity consumption index (UIC, 2004)

Type of line Peak hour (%)

Daily period (%)

Comment

Dedicated suburban passenger traffic

85 70 The possibility to cancel some services in case of delays allows for high levels of capacity

utilisation

Dedicated high-speed line

75 60

Mixed-traffic lines 75 60 Can be higher when number of trains is low (fewer than 5 per hour) with strong

heterogeneity

The UIC 406 method has been used widely in many European countries (Wahlborg, 2004; Landex et al., 2006; UIC, 2004) to assess railway capacity. Figure 5 shows the results of a study undertaken in Sweden by Banverket.

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Figure 5: Capacity consumption on Swedish Banverket rail network (Wahlborg, 2005)

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3.3 British method

In Great Britain the Capacity Utilisation Index (CUI) has been adapted as a measure of capacity utilisation. The CUI currently measures the utilisation of track sections only, and not of junctions. Its concept, which is fairly similar to the UIC’s capacity consumption, is illustrated in Figure 6.

Figure 6: Graphical representation of the Capacity Utilisation Index

The CUI can therefore be determined as:

��� � �� � � � �� �� � � � � � �� � 100%

Based on this method, Network Rail has established a map of the peak CUI of the national (GB) rail network (see Figure 7). Analyses by the Strategic Rail Authority (SRA) (SRA, 2003) suggests that 75% is the maximum CUI beyond which benefits arising from the operation of further services on a route are likely to be outweighed by the effect of worsening performance.

It is worth noting that CUI does not reflect the number of trains (or train paths) per hour. For example, a timetable for trains with identical speeds and stopping patterns might allow 20 trains per hour at a CUI level of 67%, whereas a timetable over the same route accommodating trains with very diverse speeds and stopping patterns might reach the same CUI level with just eight trains per hour (SRA, 2003).

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Figure 7: Network Rail peak CUI map (Network Rail, 2006)

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4 Common practice in improving railway capacity There are many factors affecting railway capacity. However, in practice, efforts are often focused on several areas that the railway industry believes to be most significant. With reference to the track, according to Patch (2004), the capacity of a railway line or a part of a network could be improved by:

• Modifying the timetable or the operating procedure,

• Removing slow speed sections,

• Modifying the signalling arrangement,

• Modifying the track layout.

In the UK, the Strategic Rail Authority (SRA) has suggested the following measures (SRA, 2003):

• Increasing load factors (where crowding is not an issue),

• Lengthening trains,

• Improving train path take up arrangements,

• Changing the pattern and mix of train services (timetables focussed on achieving higher throughput rather than highly diverse services),

• Reducing timetable ‘fragility’ (e.g. more robust plans for crew and stock movements),

• Better train regulation (revisiting prioritisation rules, class regulation practices and the use of passing facilities by passenger services).

The most common methods used to improve railway capacity are probably timetable and signalling solutions. For Network Rail, improving the use of existing capacity is a central element of its route utilisation strategies (Network Rail, 2006b), and the first priority is to address lines with capacity constraints by timetable solutions (Hansen, 2003). In fact, improving timetabling is considered a very effective way of increasing capacity utilisation (UIC, 2004; Pachl, 2004; Hansen, 2003; Hansen, 2004). Network Rail’s recent re-timetabling of the Settle and Carlisle line has been reported to have created significant additional capacity for freight traffic (Network Rail, 2006b). However, there does not appear to be a straightforward method of optimising a timetable (or even assessing whether a timetable is optimised).

Station capacity is also an area that has attracted much attention. Several sophisticated algorithms and models (Yuan & Hansen, 2007; Carey & Carville, 2003; Carey & Crawford, 2007) have been developed to improve the capacity of stations or networks with busy complex stations.

As signalling systems define the headway (time interval) between following trains, their improvement can significantly improve the railway capacity.

It is worth noting that within the UK rail industry, railway capacity allocation and charges are also a problem. However this is not within the scope of this project. Reviews and discussions of various methods can be found e.g. in Gibson (2003) and Watson et al. (2003).

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5 System views of railway capacity

5.1 Current views

Recently more effort has been concentrated on examining the railway system and its problems with whole system approaches. Railway capacity has also attracted attention. By considering railway capacity in systems terms, a more comprehensive picture of the factors which may affect it can be established.

The London Underground has initially produced a number of diagrams showing railway capacity in relation to the factors affecting it. The London Underground perception of the capacity relationship is illustrated by the diagram in Figure 8 which shows a breakdown (decomposition) of the railway line capacity. However, it does not clearly distinguish the capacity functions from the railway components.

Figure 8: Drivers affecting line capacity and possible technological solutions - London Underground's perception

Woodland, in his PhD thesis (2004), has suggested a system breakdown of the “Achieved Line Capacity” in the context of signalling control (Figure 9). This diagram is much more informative and logical than that of the London Underground (Figure 8) and clearly distinguishes the capacity functions (rounded corner shapes) from the railway components (rectangle shapes).

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Figure 9: Factors affecting achieved line capacity (Woodland, 2004)

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6 Railway capacity definitions and assessment Railway capacity is generally defined as the ability of a railway either to accommodate trains or to transport passengers and goods1. However, most of the railway capacity assessment/measurement methods (e.g. UIC 406 and Network Rail CUI) do not directly address either definition. These methods mainly concern the railway infrastructure occupation time. In other words, the typical measure of capacity is the proportion of time that trains occupy the infrastructure over the total time window. The measure is normally used to assess existing train timetables. It is worth noting that although Network Rail’s CUI method is mentioned widely (e.g. in SRA, 2003; Network Rail, 2006; Network Rail, 2005a; Network Rail, 2005b; Network Rail, 2004), no formal document about this method (standards or specifications) is available.

As mentioned earlier, high CUI does necessarily mean high traffic volume or large quantity of passengers/goods. While the CUI is already over 70% on a large proportion of the network (Figure 7), the UK railway efficiency is still much lower than that of China, Japan South Korea and the USA (see Table 3).

Table 3: Comparison of rail network scale and efficiency (Hatano, 2005)

The capacity consumption/utilisation concept is useful as capacity measured in terms of the number of trains per hour may not provide the information on the actual track occupancy (and thus whether more trains can be put in the line) unless the line is used by trains of similar (homogenous) speeds and stopping patterns. However, depending on the circumstances, other concepts (i.e. in terms of trains per hour or passengers per hour and freight tonnes per hour) should also be used.

There are also some methods reported that are able to determine “absolute” railway capacity (e.g. Burdett & Kozan, 2005; Burdett & Kozan, 2006). However, these involve fairly complex mathematical algorithms and modelling techniques, and thus would not be easily applied in day-to-day railway operations.

1 The former (e.g. number of trains per hour) is similar to the concept of highway capacity (see Appendix F)

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7 Railway capacity workshop To gather information from across the industry, the project team organised a workshop of railway experts representing a broad spectrum of railway industry mainly from the UK but with some representation from Europe. The key areas that formed the basis of the discussion were:

1. Measurement/definition of capacity

2. Capacity loss:

a. Where, why and how are we losing capacity on the railways?

b. Which factors can be changed?

3. Tools that are available to assess capacity

This section summarises the discussion and highlights the key themes. A list of workshop attendees is provided in Appendix A.

7.1 Measurement and definition of capacity

The first area considered was the measurement of capacity, looking at the different metrics available and how they could be applied to accurately and usefully measure the capacity of the network or a part of it.

7.1.1 Matching policy and objectives

The first concern is that to accurately measure utilisation of the railway it must be decided what the railway is for; goals for the railway’s use will inform the appropriate method of measuring progress towards those goals.

For instance, the Swiss Rail 2000 program set out with the overarching aim of achieving modal change (i.e. switching passengers and freight from road to rail); these broad objectives suggest that capacity should be measured in the number passengers or tonnes of freight per hour. Alternatively, from the point of view of an infrastructure owner, the business objective is to sell capacity to train operating companies (TOCs), suggesting that an appropriate measurement of capacity would be train paths per hour.

This raises the question of whether there should be a one-size-fits-all capacity assessment or a “differentiated railway”, where different routes use different measures. Again, this is down to the goals of individual routes. On top of this, either for each route or the network as a whole, a balance of priorities and political objectives should be chosen; for instance, should the focus be on passengers, freight or a mixture of the two?

This policy-first method can impact many of the factors which affect capacity. For instance, London Underground (LU) carries more passengers by running the trains more slowly, more frequently and closer together. Similarly, a decision could be made for main line railways to sacrifice station-to-station speed for capacity increases if this met appropriate objectives.

At the moment, DfT’s Towards a Sustainable Transport System policy (Department for Transport, 2007) aims to contribute to economic growth, reduce environmental impacts, improve safety and promote social issues (e.g. integration). However, in the railway context, these aims may be mutually exclusive. The current “supermarket railway” approach leaves the railway as a whole without a clear objective.

7.1.2 Metrics

Broadly speaking, there are a number of possible metrics which could be used to assess railway capacity, including:

• Trains per hour (TPH);

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o 14 TPH is Network Rail’s defined capacity, although there is no definitive knowledge of the system’s capability and some lines currently achieve more;

• Number of passengers and/or tonnes of freight moved;

• Number of train paths for dominant vehicle – standard paths; and

• Point-to-point (node-to-node) journey times.

7.1.3 Summary

There is no single definition of capacity; it is linked to policy, planned objectives and operational philosophy. A policy-driven approach with clear objectives (i.e. defining what you want and what is being offered, how and by whom) is fundamental to defining “capacity”.

There are standard metrics available; once an aim has been selected these can be applied appropriate to give a measure of how well the aim is being achieved.

7.2 Capacity loss

The second area considered was the factors that affect network capacity. The discussion covered both what these factors are and which of them could most easily be altered to increase capacity.

To understand the system capacity of a mixed railway, parameters including signalling, timetabling and operations need to be clearly understood. However, the impacts of these factors will vary according to the specific local conditions of the line being considered. The broad factors which affect capacity include:

• Rolling stock;

o Train power (acceleration and top speed);

o Train length (per-train capacity, platform lengths); and

o Braking systems (braking distances, block size, headway);

• Signalling;

• Timetabling;

o Stopping patterns;

• Stations;

o Platform availability; and

o Dwell times; and

• Track infrastructure;

o Switches and crossings.

The Swiss approach to dealing with capacity at a system level started with analysing the infrastructure capacity, producing a concept timetable to bring in operational and policy factors then designing the signalling to match the timetable and appropriate rolling stock.

7.2.1 Rolling stock

The impact of fleet delays has been greatly reduced; for example, by improving the reliability of rolling stock. Stock design still has an impact on capacity, though, especially through its effect on station dwell times.

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7.2.2 Signalling

When designing signalling, a risk-averse culture leads to excessive safety margins. Current signalling systems assume that a given train needs to stop before it reaches where the back of the train in front is at that instant (i.e. that the train in front could stop dead) and allow at least 200m for a conflicting point. There are many layers of safety which overlap, leading to an unnecessary level of safety which has negative impacts on capacity.

7.2.3 Timetabling

Once infrastructure is fixed, a concept timetable brings together policy, operational requirements, nodal influences and other factors affecting capacity. However, there are problems within this process.

The performance measures and incentives in place for the TOCs lead to the inclusion of too much “padding” in the tables, to minimise penalty costs. It is accepted that recovery time is necessary, but it is not fully understood how much is necessary and where it should be placed.

Where the capability of the infrastructure is poorly understood or actually inappropriate for the service patterns necessary for the line there can be mismatch, leading to sub-optimal timetabling. The risk-averse culture of the railway means that often symptoms are addressed in these cases, rather than the root causes.

A further problem is misalignment between adjacent franchises; as contracts are set at different times, each franchise is effectively working around others’ fixed timetables. This could be alleviated, either by synchronising the franchises or specifying them down to timetables; both of these, though, would be major changes to the current system and require significant political input.

Longer or less highly-specified franchises would also be an option, but there would be a need to reconcile with a sensible overall plan; for instance the high-level output specification (HLOS) or route utilisation strategies (RUS). As an example, the Docklands Light Railway (DLR) is already moving towards longer, less prescriptive contracts

7.2.4 Stations

There are several issues surrounding stations which can affect capacity, including:

• Selective door opening (on stations with short platforms);

• Dwell times;

• Platform entrances and exits at the same place at each station, leading to bunching of passengers on the service; and

• Platform occupation (i.e. how long the train spends at the platform and how rapidly the platform can be re-occupied by the next train.

7.2.5 Infrastructure

7.2.5.1 Maintenance

The maintenance regime of the infrastructure has both direct and indirect impacts on capacity; indirectly if the maintenance is ineffective and infrastructure failure causes delays and directly when the maintenance is being carried out.

There is a balance between the use of preventative and reactive maintenance regimes; for instance, the Shinkansen in Japan runs for 18 hours per day then is maintained every night for six hours, to ensure smooth performance the following day. Preventative maintenance may be better for reducing indirect delays but, particularly with restrictive

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work practices due to worker safety, performing the maintenance means stopping on re-routing trains.

7.2.5.2 Switches and crossings

The design of switches and crossings (S&C) leads inevitably to lowered train speeds through turnouts (or huge space requirements for suitably wide-radius turns); in turn, this leads to reduced capacity as headways have to be increased.

There is also an issue with interlocking: a switch has to be locked into the appropriate position before the block will be considered free and the train is allowed to enter the block.

7.2.6 Operational practice and misalignments

On top of the factors already discussed, there are overall operational misalignments that lead to a reduction in available capacity.

Operational issues include:

• A lack of top-level approach, resulting in different sections of the network being modified in isolation;

• Institutional issues (e.g. ORR relationship with Network Rail and the emphasis on performance) and industry fragmentation (multiple parties with different objectives);

• Conflicts resulting from different parties using the same line (e.g. ECML);

• Misalignments between operational concept and actual demand;

• Disconnect between planning and operations (e.g. seconds used for design while timetable is in minutes);

• Crewing policy and plans (e.g. split/join trains, crew location, poor communication and understanding and lack of correct tools); and

• Poor operational understanding of the differences between passenger and freight requirements.

7.2.7 Summary

There are many factors which affect capacity, from individual train performance to high-level policy decisions; however, while many of the factors are understood there is little knowledge of the overall picture and how trade-offs could best be made. There are, though, some factors which were agreed to be easy to alter.

These “low-hanging fruit” identified by the attendees were:

• Excess recovery time in the timetable;

• Defensive driving;

• Dwell time and door operation;

• Signalling decisions;

• Train control (communications, advice to drivers); and

• Timetable effectiveness (train mixes).

7.3 Capacity assessment tools

The third area discussed concerned the available tools for assessing capacity.

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7.3.1 Network modelling

There are several tools available for modelling the passenger rail network; those discussed included:

• Network Modelling Framework (NMF);

• VISION, used on Thameslink;

• PLANET, DfT’s standard rail demand forecasting model;

• DeltaRail’s MERIT;

• RailSys; and

• OpenTrack.

It was also noted that many consultants have their own in-house modelling tools.

The models have different perspectives, with some being detailed, bottom-up simulations (which tend to be require very detailed input data and perform well with typical situations but struggle with service breakdown or population shifts) or broader strategic modelling (which are less precise, but not necessarily less accurate). The models also varied in the detail of their approach; for instance, some work with service frequencies where others handle timetables.

Econometric freight demand modelling can be carried out by the Great Britain Freight Model (GBFM, developed by MDS Transmodal), which takes input from Network Rail’s freight billing system and can provide forecasting of demand based on trends or scenarios (e.g. change of land use). However, there are no timetable-based freight models.

7.3.2 Timetable development

Software tools for automating timetabling are also being developed; currently, timetables are drawn up by analysts rather than calculated and take in the region of 18 months to develop. Mathematical solutions to the problem of complex timetabling are a subject of active academic research and it is possible that, in between five and seven years, systems will be faster and more accurate than human analysts,

For the time being, however, there are different tools to perform different parts of the task and there would have to be a transition from manual to automatic timetabling. As yet there is no tool to optimise the use of capacity from a timetabling perspective and competition and commercial investment will be required to drive an industry-wide solution.

7.3.3 Summary

There are various tools available that can be used to calculate network capacity and, by extension, examine possible methods for increasing capacity, from strategic demand forecasting tools to very detailed local simulations of smaller networks. Work is ongoing to develop tools to calculate timetables, which could also have an effect on network capacity utilisation.

7.3.4 Post-workshop outputs

One idea used during the workshop was that of the “network diagram”, demonstrating the overall capacity available, where capacity is lost and what factors contribute what proportions of the lost capacity. Following the workshop, several stakeholders submitted their own capacity breakdowns, giving their opinions on how capacity is being lost on the network. These are shown, in no particular order, in Figure 10. It is clear that there is no single view on the key drivers of capacity constraints, and that the changes that would

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deliver capacity improvements are strongly influenced by the particular characteristics of a line/route.

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8 A Matrix of capability interdependencies The previous approaches to capacity assessment that have been discussed in the Literature Review and the stakeholder workshop are useful integrated views of railway capacity in relation to perceived key drivers. However, the collated information does not provide opportunities for impact factors (qualitative or quantitative) to be assigned to the railway components. Consequently, it is not possible to identify components or attributes which critically affect the capacity of a specific line (section). To overcome these drawbacks, a matrix of capacity interdependencies is proposed (Figure 11; a larger image is included in 0).

Figure 11: Matrix of capacity interdependencies

8.1 Model structure

The impact of changing the components of a railway system on capacity, as well as the resultant effect on overall capacity, can to some extent be quantified using a matrix approach. The procedure for building the matrix is as follows:

1. Since the impact of each capacity function element on the overall capacity is not the same (e.g. altering minimum headway time has a larger effect on total capacity than altering buffer time) they are each assigned an element impact factor (score) which is shown immediately below the element heading. More details are presented in Section 8.1.1.

2. The impact factors of improving the existing railway system components/attributes on the cross-linked capacity elements also need to be defined and then put into the corresponding (white) cells of the matrix. These impact factors may have negative values when improvement of the railway system components/attributes may result in reduction in capacity, e.g. stricter safety rules may lead to longer minimum headway time. The method of defining the factors (scores) is discussed in detail in Section 8.2.

3. The overall impact of improving a railway system component is represented by its overall impact score, which is the sum of the products of the railway component scores and their related capacity scores. These overall impact scores can be used to rank the railway system components/attributes according to the impact on capacity of improving them.

4. The feasibility of improving the railway system components/attributes is also considered by giving each component/attribute a feasibility score (see Section 8.4).

5. A combined score can then be calculated as the product of the overall impact and feasibility scores. These combined scores are the final scores, which can be used to define the most favourable railway component or attribute to improve, based on both its impact on the capacity and the feasibility of improving it.

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8.1.1 Capacity functions

The column headings of the matrix are the capacity functions derived from the decomposition of overall capacity. Here, the overall capacity is defined as the quantity of passengers and goods that the railway (or line) can transport over a given time period. It is defined by the capacity of two main elements:

• the capacity of the railway infrastructure to carry trains (e.g., trains per hour); and

• the capacity of the trains to accommodate passengers and goods (e.g., passengers per train)

The impact of the various capacity function elements on the overall railway line capacity are not the same. Each element is therefore assigned an impact factor between 0 and 10 (Table 4)2.

Table 4: Impact factors of capacity function elements

Capacity functions Description Factor

Infrastructure Capacity

Usable capacity

Capacity that is available for usage. 10

Minimum line headway time

The minimum time interval between a pair of trains, so that a following train is not affected by the train

ahead throughout its run.

3

Regular recovery time

A time supplement that is added to the pure running time to enable a train to make up small

delays.

1

Dwell time The total elapsed time from the time that a train stops in a station until the time it resumes

movement.

2

Waiting time Scheduled delays due to running & dwell time margins, adjustment of departure times and

network synchronisation

1

Buffer time An extra time that is added to the minimum line headway to avoid the transmission of small delays.

2

Special recovery time

A supplement time for maintenance 1

Train Capacity Train capacity Number of carriages or wagons in a train 5

Carriage / Wagon capacity

Number of spaces per carriage or net tonnes per wagon

3

8.1.2 System components

The row headings are the railway system components and attributes derived from the decomposition of the railway system. The railway system is considered to consist of three main components:

• railway infrastructure;

2 The impact factors of the capacity function elements on the overall line capacity in Table 4 are generic; this means that they are independent of the conditions and attributes of a particular railway line. The relative impacts of conditions and attributes of a particular railway line are considered in Section 8.3.

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• vehicle fleet; and

• operational attributes.

Each of these main components is then further decomposed into individual elements of the system, including platform length, train heterogeneity and signalling systems.

8.1.3 Analysing a section

To model a railway system, the system must be decomposed into heterogeneous sections, within which every system component has the same value.

Where appropriate, each of the fixed railway system components is given a value according to the current state of the existing facility or system. To represent the platform length, for example, the proportion of the platform current being utilised is set to match that of the section being analysed.

The white boxes in Figure 11 show the capacity functions which can be affected by each system component; for instance, changing the platform length could impact usage capacity, minimum headway time, dwell time, waiting time and train capacity.

Based on look-up tables and the current state of components within the system, the impact a set alteration would have on capacity is calculated; if, for instance, the platform was lengthened by 20% with a current platform utilisation of 110% (i.e. over capacity), the overall impact score for this change would be 106. Alternatively, if the current platform utilisation were 90% (i.e. close to but under capacity), the same change in platform length would have a lower impact score, 44 (see Table 19).

8.2 Developing the impact factors

Impact factors could be incorporated into a set of predefined “standard” look-up tables in accordance with the “existing” or “pre-improved” conditions/specifications. With this procedure, inconsistency in assigning impact factors can be eliminated because, once the look-up tables have been built, the impact factors would be generated directly from the look-up tables based on the input values of the existing conditions/specifications of a specific railway section (and not depending on the judgment of the analyst). The look-up tables can be developed by:

1. An individual railway capacity analyst; or

2. A group of experienced experts in railway capacity.

With the first method, the scoring system entirely depends on the knowledge and experience of the individual analyst and thus could be very subjective. In the second method, the scoring system should be less subjective as it is based on a consensus on the views of a number of experienced experts.

To help with the development of the scoring system, certain quantitative and qualitative analyses of the impacts can be conducted. The sections below show some examples.

8.3 Impact factors of railways system components/attributes

This section describes the analysis of the impacts of improving existing railway system components/attributes on the related capacity elements. The impact factors can be positive or negative, where negative values mean that improving the railway system component/attribute would potentially reduce the existing capacity. The lookup tables are presented in 0.

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8.3.1 Adding tracks

Obviously, the number of tracks isline capacity. However, adding more tracks to a line only increases the actual capacity if the existing line has reached or is approaching its maximum utilisation, i.e. it is difficult to put more trains into the timetable. Line utilisation can be categorised based on the Capacity Utilisation Index, CUI (if known) or simply based on the number of trains per track per hour (see Table 10

8.3.2 Adding platforms at stations

The number of platforms at a station plays an important part in the capacity of a railway line as it affects the headway, dwell and waiting times. Similar to the tracks, adding more platforms at a station ohave reached or are approaching their maximum utilisation. Platform utilisation can be categorised based on the number of trains per platform per hour (see

8.3.3 Improving signalling

Signalling is the most significant factor affecting headway time, and thus line capacity. In addition, improving signalling systems is much simpler and less costly than impromany other aspects of infrastructure, such as adding more tracks or platforms. Therefore it has often been considered the most effective way of improving line capacity.

Figure 12 shows how significantly different signalling systems affect the headway (Note: headway is defined here as the maximum throughput of trains that the signalling system will permit, so that the train ahead does not affect a following train, mper hour, tph).

Figure 12: Theoretical achievable headways (Woodland, 2004)

Figure 12 illustrates the effect of different signalling systems and line speed on headway for an optimal signal layout. It is clear that there exists an optimum line speed for any given signalling system and that generally the more sophisticated systems achieve higher headway. However the situation in mixed use railways may not be as straightforward. Signal positioning is determined by the braking characteristics of the

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racks is the single most important factor impacting railway ng more tracks to a line only increases the actual capacity if d or is approaching its maximum utilisation, i.e. it is difficult timetable. Line utilisation can be categorised based on the

CUI (if known) or simply based on the number of trains per ).

ms at stations

a station plays an important part in the capacity of a railway way, dwell and waiting times. Similar to the tracks, adding

only increases the actual capacity if the existing platforms aching their maximum utilisation. Platform utilisation can be mber of trains per platform per hour (see T

alling

ficant factor affecting headway time, and thus line capacity. lling systems is much simpler and less costly than impro

structure, such as adding more tracks or platforms. Therefore the most effective way of improving line capacity.

cantly different signalling systems affect the headway (Note: the maximum throughput of trains that the signalling system n ahead does not affect a following train, m

etical achievable headways (Woodland, 2004)

ct of different signalling systems and line speed on headway . It is clear that there exists an optimum line speed for any d that generally the more sophisticated systems achieve

the situation in mixed use railways may not be as tioning is determined by the braking characteristics of the

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impacting railway e actual capacity if n, i.e. it is difficult ised based on the mber of trains per

pacity of a railway the tracks, adding existing platforms utilisation can be

Table 11).

thus line capacity. tly than improving atforms. Therefore apacity.

he headway (Note: e signalling system measured in trains

d, 2004)

speed on headway line speed for any systems achieve may not be as

aracteristics of the

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vehicles permitted to use the route and therefore several compromises are introduced in the design stage which may limit the absolute capacity of the line.

Capacity may be improved by resignalling, but there is also a requirement to have a general trend in increasing homologation of the rolling stock performance. This can be transformed into impact factors as shown in Table 12.

8.3.4 Increasing line and train speed

The effect of line speed on capacity is intimately linked with the layout of the signalling system. As indicated in Figure 12, there exists an optimum line speed for any given signalling system. If line speed is therefore increased, or indeed decreased from this optimum, then capacity will be lost. The question is whether the methods used to derive the data in Figure 12 are relevant to a mixed railway in which the acceleration and braking performances of the rolling stock differ widely. The complex relationship between capacity and train performance, signalling, line speed, etc., can be explored through the matrix analysis proposed within this work. An initial attempt to determine the interactions between these features is presented in Table 13.

8.3.5 Improving horizontal track curvature

A sharp curve may warrant a speed limit (see Figure 13), which may affect the headway time and, thus, the line capacity.

Figure 13: Relationship between horizontal curve radius and speed (ref.)

However, as shown earlier (Figure 12), speed reduction does not always reduce the capacity, particularly in cases where the signal layout is optimised. This means that, unless existing curvature warrants a speed limit below about 55mph, improving the curvature would not improve the capacity. Of course, improving the curvature is only practical in a small number of situations and, although it may lead to a decrease in journey time, it may not have a capacity benefit. This is reflected by the impact factors given in Table 14.

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8.3.6 Improving track gradient

Steep uphill gradient track may reduce the train speed which may affect the headway time and thus the line capacity. This is likely to happen more to freight trains than to passenger trains.

However, as shown earlier (see Figure 12), speed reduction does not always reduce the capacity. This means that unless existing gradient reduces the speed to below about 55mph improving the gradient would not improve the capacity. The impact factors may be given values as in Table 15.

8.3.7 Increasing number of carriages per train

Increasing the number of carriages in a train would, without doubt, increase the total number of passenger seats and the number of passengers that could be accommodated on that line. However, adding more carriages to a train only increases the actual line capacity if the existing train has reached or is approaching its maximum capacity utilisation, i.e. most of the seats/spaces are already taken during journeys. In addition, adding more carriages will lengthen the train and thus increase the headway, dwell and possibly waiting times, and adversely affect the line capacity. Impact factors for increasing the number of carriages per train in accordance with existing train capacity utilisation (seat occupancy rate) are given in Table 16.

Similarly, increasing the number of seats or spaces in a carriage would potentially increase line capacity. However, adding more seats to a carriage only increases the actual capacity if the existing train has reached or is approaching its maximum capacity utilisation, i.e. most of the seats/spaces are taken up during journeys. Although adding more seats to carriages may not increase the number of seats per train as much as adding more carriages (unless using double-deck carriages), it has the advantages of not lengthening the train and thus not adversely affecting the line capacity (see Table 17).

8.3.8 Improving train speed heterogeneity

The speed difference (heterogeneity) between following trains increases the headway time (see Figure 14). Improving this (making the speeds more homogenous) would reduce the headway time and thus increase the capacity.

The trend might be that, the greater the heterogeneity of existing speeds, greater the impact of increased homogeneity on capacity. Therefore, the impact factors may be given values as in Table 18.

8.3.9 Improving other components and attributes of a railway system

As shown in the matrix of capacity interdependencies, there are a number of components and attributes of railway systems that have an impact on railway capacity, however small. Nonetheless, in specific cases their improvement may have significant impacts; e.g. improving timetabling of a route with a poorly designed timetable or improving passenger handling facilities (station gate, signs, stairs, elevators and escalators) in a large and busy station.

It is not straightforward, though, to quantify the impacts of improving these components on capacity. As an initial guide, possible trends are presented in Table 5.

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Figure 14: Heterogeneous (a) and homogeneous (b) timetables (Landex et al, 2006)

8.4 Feasibility of improving railway components

When considering improvement to railway capacity through changes to railway system components or attributes, it is essential to consider its feasibility (i.e. contextually, financially and technically). This is because only if an improvement option is feasible will its impact on capacity be meaningful.

Similar to the impact of improvement, the feasibility of improvement can be given a score. The feasibility score takes a value between zero and one; the total score for a given component is the product of overall impact score and the feasibility factor.

The combination of the impact and feasibility scores can be used to define the most favourable railway component or attribute to improve, taking into account both its impact on the capacity and the feasibility of improving it.

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Table 5: Impact factors of improving railway capacity

Railway System Components and

Attributes

Impact of Improving on Railway Capacity

Platform Length Improving platform length may not directly increase capacity unless the existing platform is shorter than the requirement. However,

platform length should be taken into consideration when examining the feasibility of adding more carriages to trains.

Passenger Handling Facilities

Improving passenger handling facilities (station gate, signs, ticketing, stairs, elevators and escalators) may reduce dwell time

and thus increase capacity.

Junction Characteristics Improving junction characteristics may ease speed limits and thus increase capacity (but only for speeds <55mph). The signalling

techniques used at junctions may also be worthy of further analysis.

Distance between Stations/Junctions

Increasing distance between stations/junctions may reduce headway time, recovery time, buffer time and waiting time and

thus increase capacity.

Power Supply Power supply capability will limit capacity. Upgrading will therefore have an impact in situations where the power supply is limited.

Door Characteristics Improving train door characteristics (number of doors, width or operating technology) may reduce dwell time and thus increase

capacity.

Braking System (braking rate)

Improving the braking rate may reduce headway time thus increasing capacity. This is however controlled very strictly.

Safety Rules Improving certain safety requirements may increase headway time and thus reduce capacity.

Priority Rules Changing the priorities of train services changes the order in which trains run and thus affects capacity either positively or negatively.

KPI Targets Need to meet Key Performance Indicator targets (e.g. punctuality and reliability) may lead to increased recovery time and buffer

time and thus reduce capacity.

Environment Protection Rules

Rules preventing freight trains from operating during night-time because of noise disturbance will put these trains on day-time

timetables and potentially reduce capacity.

Station Stops Station stops can influence capacity. Homogenising the stopping pattern, as is practised in metro operations tends to improve

capacity.

Timetabling Techniques Improving timetabling techniques may reduce headway time and waiting time and thus increase capacity.

Maintenance Strategy Improving infrastructure maintenance strategy may reduce special delivery time and thus increase capacity.

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9 Example trade-offs analysis – Thameslink Route A preliminary (example) capacity evaluation of the central part of the Thameslink Route has been carried out to illustrate the use of the systems approach to examining the impacts of changes to a number of components of that route. The objective was to compare the outputs from the models, PRIME and the capability trade-off Matrix model, to get an understanding of the effectiveness of the relatively simple methodology used in the Matrix model.

9.1 Thameslink central section

The route modelled is illustrated in, Figure 15.

Figure 15: Thameslink Route

First Capital Connect currently operates passenger services on the route. The peak frequency is 15 trains per hour (tph) in either direction. This is equivalent to an operating headway of 4 minutes. The service uses mainly 8- and 4-car class 319 trains. An 8-car class 319 train has a capacity of about 440 seated passengers. Thus the peak hour capacity in any section is 6600 seated passengers per hour. Allowing for standing, this figure could be rounded up to about 10000.

The route is currently being upgraded with the objective of increasing this capacity by 140%. This is planned to be achieved by:

• Introducing a new fleet of trains • Extending platforms to accommodate 12 car trains • Introducing cab based signalling to the European ETCS Level 2 standard • Introducing Automatic Train Operation (ATO) overlaid on ETCS Level 2 signalling • Upgrading power supplies to accommodate 24 high performance trains per hour.

9.2 Parsons Railway Integrated Modelling Environment (PRIME)

PRIME, a Capacity Planning tool, developed by Parsons, provides a flexible and adaptable modelling environment, giving a full profile of signalling headways at each point on the line, simulating target service patterns to evaluate the actual headways that can be achieved and evaluating different operational strategies on a number of railway projects.

For instance, this model has recently been used (Ramdas et al., 2010) to evaluate the capacity benefits of upgrading signalling to ERTMS (European Rail Traffic Management System). Capacity benefits were evaluated using a combination of PRIME with domain knowledge and judgement. Although PRIME has a wide range of functionality, in that study only the headway evaluation capabilities were employed. With typical internal processing parameters drawn from current practice, and using moving block principles, the analysis showed that with the same block lengths as 4 aspect signalling, ERTMS

Finsbury Park

Kentish TownSt PancrasInternational

FarringdonCity Thameslink Blackfriars Elephant & Castle

London Bridge

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Level 2 signalling provides improved capacity and ERTMS Level 3 would give further capacity improvements.

9.3 PRIME model

The key parameters used in the simulation were:

• The route as shown in Figure 15 with geographic data, including gradients. • Existing 4 aspect signalling data – signal locations, block lengths and overlaps. • Class 319 performance data. • Assumed station dwell times.

PRIME provides a number of outputs. An example is the speed profile diagram shown in Figure 16.

Figure 16: Southbound Thameslink Train Speed Profile

The speed profile illustrates:

• The 48 kph (30 mph) speed limit over most of the section. • A limit of 24 kph (15 mph) over Blackfriars bridge. (Note: the train cannot

accelerate until its rear is clear of the bridge.) • Slower acceleration between City Thameslink and Blackfriars due to the gradient.

The most useful output for basic capacity estimation is the signalling headway plot shown in Figure 17.

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Figure 17: Thameslink Central Section Signalling Headways3

Signalling headways are dependent on a number of factors, which leads to the complex curves shown by Figure 17. The important value is in identifying the worst case bottleneck identified by the highest value of signalling headway.

In this baseline case, the limit value is 191 seconds. It is not realistic to timetable the service to the signalling headway with no margin for run times, dwell times or other minor delays. A margin of around 30 seconds is typically used for this “recover margin”. Adding the recovery margin we get a value of 221 seconds corresponding to 16.2 trains per hour. Thus the PRIME output shows that the signalling is limiting the capacity to just over the current timetable.

A further PRIME run was performed with signalling upgraded to ETCS Level 2. The result was an improved signalling headway of 100.2 seconds. With the same recovery margin this gives a capacity of 27 trains per hour.

This achieves the headway objective but we also need an increase in train capacity to achieve the overall ridership objective. Increasing the train length will also have an impact on dwells. To test this on PRIME the train length was increased to 12 cars and the dwells from to 45 seconds (the Thameslink upgrade assumption). The result is a headway increase to 120.3 seconds and a reduction to 23.9 tph. The capacity is now just short of the objective.

The introduction of new trains with higher acceleration is intended to counter this shortfall. A PRIME run based on the Thameslink train specification shows a headway improvement to 111.7 seconds and a capacity of 25.4 which meets the objective.

The combination of the extra power required for the higher performance and heavier 12-car Thameslink and the increase in train frequency will require a upgrade to the traction power supply system. Modelling of the traction power network is, however, outside the scope of this study.

9.4 PRIME results

The PRIME model is typical of its type in that it models the possible train throughput along a route based on a fixed track infrastructure. The results offer a technical output in terms of a signalling headway against a particular train performance. The platform reoccupation times are included with a fixed dwell time.

Additional operating parameters have to be applied separately. The operating parameter applied to the model in this case is a 30s margin to provide an allowance for variances that may occur in dwell times or driver performance. Such variables are referred to in

3 Northbound headways are shown as negative only for convenience of plotting on the chart

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some literature as “buffer time” (UIC, 2004a). Within the headway time under review here, 30s is considered a suitable margin. However, a percentage might be more appropriate where improvements in signalled headway are proposed so as to provide a fixed ratio.

In the context of a capacity upgrade, the simulator shows that a combination of new signalling and longer, more powerful trains is required to achieve the desired 140% increase in capacity along the central sections of the Thameslink route. The improvement in signalling retains the existing block sections but reduces the safety margin between trains by use of the continuous supervision available in ETCS.

The report has included a 50% increase in dwell time coupled with the increase in train length. Such a large increase in dwell time may not be considered appropriate on the basis of a train length increase only. It is also suggested that an improved power supply system may be required to allow longer, more powerful trains to achieve the required performance.

9.5 Matrix model inputs

The Matrix model was populated with route data from the central area of Thameslink and the results evaluated. The central section of Thameslink was divided into three subsections in order to ensure that the different characteristics of each could be isolated and accurately defined. The subsections are: Kentish Town to St. Pancras, St. Pancras to Blackfriars and Blackfriars to London Bridge. Their characteristics were included as in the existing condition, selected from a look-up table.

The input characteristics used in the evaluation (and corresponding to the outputs discussed in Section 9.6) are given in Appendix E, tables, 29, 30 and 31. A summary of some of the key inputs are listed in Table 6 and the feasibility scores are provided in Table 7.

Table 6: Summary of Matrix model inputs – existing characteristics

System

Existing condition value

Kentish T – St. P St. P - Blackfriars Blackfriars – London Bridge

Power Supply 100-125% 100-125% >125%

Junction Characteristics

Flat Flat Flat

Signalling 4 aspect 2 aspect 2 aspect

Train Utilisation

(Seat occupancy)

120% 100-120% 100-120%

Station Stops N/A N/A N/A

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Table 7: Summary of Matrix model inputs – feasibility

System

Feasibility4 (Contextual/Financial/Technical)

Kentish T – St. P St. P - Blackfriars Blackfriars –

London Bridge

Power Supply Good/Low/Easy Bad/High/Easy Good/Medium/Easy

Junction Characteristics

Indifferent/High/Difficult Bad/High/Difficult Good/High/Difficult

Signalling Good/Medium/Easy Indifferent/Medium/ Easy

Good/Medium/Easy

Train Utilisation

(Seat occupancy)

Indifferent/Medium/ Difficult

Indifferent/Medium/ Difficult

Good/Medium/ Difficult

Station Stops N/A N/A N/A

9.6 Matrix model results

Selected outputs from the Matrix model are shown in Table 8. The selection of the systems was arranged, as far as is possible, to match the systems included in the PRIME model so as to obtain a broad comparison. Two outputs from the Matrix model, one for the overall impact score and the other a total score that combines the impact and feasibility scores, are given in Table 8.

Table 8: Summary of Matrix model results

Systems

Kentish T – St. P St. P - Blackfriars Blackfriars – London Bridge

Overall Impact Score

Total Score,

including Feasibility

Overall Impact Score

Total Score,

including Feasibility

Overall Impact Score

Total Score,

including Feasibility

Power Supply 170 170 170 36 220 107.8

Junction Characteristics

82 12.14 82 8 82 17.35

Signalling 16 11.2 80 39.2 80 56

Train Utilisation

(Seat occupancy)

58.6 13.2 54.6 12.3 54.6 17.6

Station Stops N/A N/A N/A N/A N/A N/A

The results from the Matrix model show that all the components listed produce positive scores. However, based on the current population of default data in the model, the 4 Contextual feasibility is rated as Good [1], Indifferent [0.7] or Bad [0.46], financial feasibility as High [0.46], Medium [0.7] or Low [1] (High is a high cost of improvement) and technical feasibility as either Easy [1] or Difficult [0.46]; overall feasibility is the product of these three values and the total score is the product of the overall feasibility and the overall impact scores.

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impact of power supply changes with the highest score appears to be the key component for the lines being considered. It is well known that failure to upgrade the power supply will impact a capacity upgrade where increases in train length or performance are implemented; the power supply may, therefore, not provide an effective capacity upgrade as a stand-alone system, but it has an important impact when combined with other improvements.

9.7 Discussion

The results in Table 8 demonstrate the differences in outputs between the PRIME and Matrix models, e.g. the scores representing the relative impacts of changes in power supply and signalling. PRIME is a mature tool designed to show what level of improvement can be obtained with certain systems, principally signalling and rolling stock. The Matrix model on the other hand does not produce train headways in the way that the PRIME model does. Instead, it takes a wider view, attempting to cover all components that could impact capacity and to include a factor for representing the feasibility of implementing the change. The Matrix model shows, for example, a difference between the impact of alternative signalling types over the three sections of the route; similar evaluations of the three sections could also be done by the PRIME model, but these analyses would not include consideration of feasibility and would be more time-consuming than the Matrix analyses.

It should be noted that the Matrix model currently assumes that the timetable, numbers of passengers, dwell times and buffer times remain constant against changes to the infrastructure. In the case of Junction Characteristics, therefore, the impact of a flat junction on route capacity will have little or no impact on a timetable with one or two trains per hour, but it will have a significant impact on a service of 14tph. In any case, eliminating it will reduce the possibility of train movement conflict at that location by at least 50%.

At present, the Matrix model does not have a value specifically for train length. This is contained in the cells for Train Utilisation (Seat Occupancy) and demonstrates the impact of adding one vehicle. However, the impact will vary according to the existing train length and this is not currently recognised by the model.

9.8 Conclusions

The two models examined in this work are quite different and are designed to provide different technical outputs. Direct comparison between them was therefore not appropriate. However, it is clear that the two models can be used in parallel to evaluate the benefits of options for upgrading a route’s capacity. In this case, for Thameslink, the PRIME model can show headway improvements resulting from a signalling upgrade and the effects on the headway of train lengthening. The data could then be used to underpin the scores in the Matrix model for parts of the signalling and rolling stock improvement options.

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10 Conclusions and recommendations

10.1 General conclusions

The literature search and the stakeholder workshop confirmed that there is no single accepted definition of railway capacity. Expert views on the key drivers that impact on capacity are also diverse and influenced by their particular experiences. While there are a number of reported approaches to defining and measuring the capacity of a line/route, there is no reported study on the use of a systems approach that looks at the interdependencies of different components and the benefits of trade-offs between different capabilities when looking for a solution to improve the capacity of a particular line. There is therefore the risk that changes implemented may not deliver the expected increases in capacity.

This preliminary study has shown that the Matrix model is capable of enabling a system-wide approach to route upgrade options. It allows an initial assessment to identify and highlight the key components on a particular line that need to be investigated in greater detail. It is recognised that the model itself is in a preliminary stage of development and there is a need to refine and validate the numerical inputs that produce the overall impact score representing the effect on capacity of particular changes to system components of a line/route. This could be done by examining previous upgrades, reviewing existing methods for developing business cases for upgrade projects and the use of technical data produced by multi-train simulators like PRIME for improvements.

It may also be appropriate to drill down further into some of the systems so as to offer subsets of options, e.g. automatic or manual driving for signalling, provision of platform edge doors, location of station exits, reversing facilities etc.

10.2 Recommendations

It is recommended that:

1. A validation process for the Matrix model is set up. This will involve research and expert reviews to ensure a robust, evidence-based argument is provided for each system score;

2. The tool be fully populated, particularly for operational features, which may show cost-effective gains;

3. Specific areas of the Matrix model should be refined to allow more accurate assessment, e.g. train length variations, varieties of signalling, and specific infrastructure improvements;

4. Further assessment of the model outputs is carried out to see if system components should be further sub-divided to provide data on issues such as the value of automatic or manual driving for signalling, seating versus standing space in carriages, provision of platform edge doors, location of station exits, interchange facilities, and in-route reversing facilities;

5. The delta scores should be expanded and developed to provide an agreed range for each system;

6. Further development of the railway system components and attributes (Table 5) should be undertaken to provide a more accurate output set;

7. A study be undertaken to determine the value of linking the matrix model directly to other more specific, single-system models such as PRIME, a power supply model or a cost model.

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Acknowledgements The work described in this report was carried out by the Transport Research Laboratory (Asset Management Group) and the University of Birmingham (Centre for Railway Research and Education).

The authors wish to acknowledge David Fisher (Parsons), for carrying out PRIME modelling and providing expert input, the attendees of the capacity workshop (see Appendix A) and all additional input received from those who could not attend on the day, including Jelle van Luipen (ProRail), who provided an update on the tools and options that are being used in the Netherlands to increase capacity, increase punctuality and save energy (Appendix B).

This research project was funded by the Department for Transport.

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References

Burdett R L and Kozan E (2006). Techniques for Absolute Capacity Determination in Railways. Transportation Research Part B, 40 (2006) , 616-632.

Carey M and Carville S (2003). Scheduling and Platforming Trains at Busy Complex Stations. Transportation Research Part A 37(3) , 195-224.

Carey M and Crawford I (2007). Scheduling Trains on a Network of Busy Complex Stations. Transportation Research Part B 41(2) , 159-178.

Colorado DOT (n.d.). Retrieved from Colorado Department of Transportation: http://www.dot.state.co.us/designsupport/Design%20Guide%202005/DG05%20Ch%2002%20Design%20Controls.pdf

Department for Transport (2007). Towards a Sustainable Transport System. Available: http://www.official-documents.gov.uk/document/cm72/7226/7226.asp. Last accessed 6th December 2010.

Gibson S (2003). Allocation of Capacity in the Rail Industry. Utilities Policy 11(2003) , 39-42.

Hansen I A (2003). Capacity Increase through Optimised Timetabling. London: AGRRI Network Capacity Seminar.

Hansen I A (2004). Increase of Capacity through Optimised Timetabling. Computers in Railways IX , 529-538.

Hatano L (2005). The International Benchmarking Project. Railway Strategies, 2005 (January - February) , 70-72.

HDR Engineering (2001). Everett to Blaine Commuter Rail Preliminary Feasibility Study. Washington: Report preparted for the Washington State Department of Transportation and Snohomish County. HDR Engineering, Inc.

Kozan E and Burdett R (2005). A Railway Capacity Determination Model and Rail Access Charging Methodologies. Transportation Planning and Technology , 28(1), 27-45.

Kozan E and Burdett R (2004). Capacity Determination Issues in Railway Lines. Conference on Railway Engineering (CORE - 2004) (pp. 31.1 - 31.6). Darwin.

Krueger H (1999). Parametric Modelling in Rail Capacity Planning. Proceedings of the 1999 Winter Simulation Conference (pp. 1194-1200). INFORMS Simulation Society.

Landex A, Klaas A H, Schittenhelm B and Schneider-Tilli J (2006). Evaluation of Railway Capacity. Trafikdage (Traffic Days). Aalborg, Denmark.

Moreira N, Garcia L and Catarrinho P (2004). Network Capacity. Computers in Railways IX , 36-43.

Moreira N, Viegas J M, Macário R and Garcia L (2003). Capacity Evaluation Models for Medium Term Planning and Capacity Evolution Modelling for Long Term Planning. World Congress on Railway Research, (pp. 1044-1059). Edinburgh, UK.

Network Rail (n.d.). Map of the National Rail Network. Retrieved from http://www.nationalrail.co.uk/system/galleries/download/print_maps/uk.pdf

Network Rail (2004). Network Capability (Section 11 of 2004 Technical Plan). Network Rail.

Network Rail (2005a). Report on Capacity Analysis Study on the East Coast Main Line. Draft - Version 3. Network Rail.

Network Rail (2005b). Route Utilisation Strategies - Technical Guide. Network Rail.

Network Rail (2006a). Network Rail Business Plan. Network Rail.

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Network Rail (2006b). Network Rail Initial Strategic Business Plan - Control Period 4. Network Rail.

Network Rail (2006c). Track Design Handbook NR/SP/TRK/0049. Network Rail.

Pachl J (2004). Railway Operation and Control. Mountlake Terrace: VTD Rail Publishing.

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SRA (2003). Capacity Utilisation Policy: Network Utilisation Strategy. Strategic Rail Authority (SRA).

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TRB (2000). Highway Capacity Manual. Washington D.C.: Transportation Research Board (TRB), National Research Council.

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Viegas, J. (2003). IMPROVERAIL - Deliverable 6: Methods for Capacity and Resource Management. Lisbon.

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Wahlborg M (2004). Benverket Experience of Capacity Calculations according to the UIC Capacity Leaflet. Computers in Railways IX , 665-673.

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Watson R, Gillingwater D and Boodoo A (2003). Using Network Capacity Effectively Where Train Operators Compete for Paths - the UK Experience. (pp. 1060-1066). Edinburgh, UK: World Congress on Railway Research.

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Appendix A Workshop attendees

Table 9: List of workshop attendees

Name Representing

John Armstrong

Chris Bouch

University of Southampton

University of Birmingham

Piers Connor PRC Rail Consulting

Felix Laube

Gary Mewis

Emch + Berger AG (Switzerland)

Rail Safety and Standards Board

Chris Nash

Susie Northfield

Vijay Ramdas

Paul Richards

Clive Roberts

Neil Russell

Jonathan Sharpe

Oskar Stalder

University of Leeds

Department for Transport

Transport Research Laboratory

Network Rail

University of Birmingham

Parsons

Transport Research Laboratory

Oskar Stalder Consulting (Switzerland)

Jonathan Tyler Passenger Transport Networks

Robert Watson RWA Rail

Chris Wilson Department for Transport

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Appendix B Submission from ProRail Key points from the submission to the Railway capability trade-off workshop received from Prorail(jelle.vanluipen@prorail.nl) are described below.

• The innovation department of ProRail has been working on a series of tools to increase thesituation awareness of operational personnel (drivers, guards, dispatchers, trackside workers)and shorten the execution time of key processes, such as departing and overtaking of slow trainsby fast trains.

• The ProRail control loop diagram (Figure 18) makes the complex issue of capacity expansion byoperational innovation simple again:,

o From better control of the dispatcher - driver - train guard loopo To better control of the traffic management loopo To better time-table resolutiono Operational innovations being considered in the rail infrastructure expansion projects as

an equal variant, next to the conventional expansion of railways (" Concrete & steel").

Workshops by ProRail on 17 and 19 November 2009 The workshops were organised as part of an attempt to put operational innovations into an equal variant for capacity expansion.The goals of workshops were:

1. Knowledge transfer of operational innovations to rail developers, signalling experts, projectmanagers and other people involved in the daily work processes of developing plans forexpanding infrastructure

2. Developing a methodology for applying innovations as an equal variant3. Where possible, applying these innovations into concrete projects

Bridge opening for boats: concrete case was the Vecht bridge With the water authorities, openings of the bridge to allow boats through have been put into the planswhereby every hour there is a window of 7 minutes to allow the opening of the bridge. This is also inseasons when the bridge is not being opened. On the busiest day the bridge is opened at most 8times per day.At the moment 15 trains per hour pass the bridge in each direction, this will be going up to 23 trains in2018. Then the 7 minute window is not feasible any longer,Innovations were considered whereby:

1. The bridge opening times are reduced: the winch that powers the bridge is improved, thelocking becomes electronic, the waiting boats get a countdown to allow quick departure whenthe bridge is opened. This will decrease by about 2 minutes the necessary window to 5minutes.

2. Another way of looking at this is to open the bridge in a time window. An agreement is madethat within a certain half hour, for 5 minutes the bridge will be opened. The exact moment ischosen at the last moment, to allow minimisation of delays. For this, the dispatcher needsadvanced tooling to have a good situation awareness to choose the right moment for opening.

Free crossing (fly-overs) In the new high-frequency timetable, the number of trains triggers the need to build fly-overs at keyjunctions. This is to eliminate hindering of trains crossing each other paths. Using the approach in theworkshop, functions of a fly-over were looked at.These were:

- Facilitate driving times differences on number of trains- Separate trains which occupy the crossing point at the same time- Sort slow and fast trains

Given these functions, the participants were able to map the operational innovations onto thesefunctions, and make an expert assessment of their impact: High, Middle of Low. Sometimes people

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will venture into a percentage number: a measure in which the operational innovation fulfils thefunction.

Expanding double tracks lines to four track lines, or alternatively local overtaking tracks Again, here the functions were listed:

- Facilitate capacity for different train types- Create passing possibilities for fast trains- The application of operational innovations was comparable with the fly-overs-

Lessons Learned from the workshops - In the total planning process, involve operational innovations as soon as possible- Make this an interactive process, whereby you begin with high-level alternatives and in the

course of the capacity study zoom in on the specific case- The current models for planning and operational modelling do not allow the effect of

operational innovations to be modelled- And the most surprising lesson: it is a lot of fun, and not just for the innovators.

Proje

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PPR541

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fact

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53

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56

111

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52.5

355.5

70-8

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122

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(<9)

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Project

Rep

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56

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Tab

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1:

Imp

act

facto

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Existin

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Proje

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PPR541

Tab

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7:

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te(%

)

Imp

act

fact

ors

of

incr

easi

ng

nu

mb

er

of

seats

per

carr

iag

eb

y1

0%

Overa

llIm

pact

Sco

reU

sab

leca

paci

tyM

inim

um

head

way

tim

e

Reg

ula

rre

covery

tim

e

Dw

ell

tim

eW

ait

ing

tim

eB

uff

er

tim

eS

peci

al

reco

very

tim

e

Tra

inca

paci

tyC

ar-

wag

on

cap

aci

ty

10

31

11

21

53

≥120

44

16

100-1

20

34

15

90-1

00

24

14

75-9

01

413

<75

04

12

Tab

le1

8:

Imp

act

fact

ors

of

imp

rovin

gh

ete

rog

en

eit

y

Exis

tin

gm

ax/

min

speed

rati

o

Imp

act

fact

ors

of

imp

rovin

gh

ete

rog

en

eit

y(e

.g.

speed

rati

oto

<1

.2)

Overa

llIm

pact

Sco

reU

sab

leca

paci

tyM

inim

um

head

way

tim

e

Reg

ula

rre

covery

tim

e

Dw

ell

tim

eW

ait

ing

tim

eB

uff

er

tim

eS

peci

al

reco

very

tim

e

Tra

inca

paci

tyC

ar-

wag

on

cap

aci

ty

10

31

11

21

53

≥3

12

44

4136

2-3

83

33

92

1.5

-25

22

258

1.2

-1.5

21

11

24

<1.2

00

00

0

Project

Rep

ort

TRL

60

PPR541

Tab

le1

9:

Imp

act

facto

rso

fin

creasin

gp

latfo

rmle

ng

th

Pro

po

rtion

of

pla

tform

len

gth

use

dat

pre

sen

t

Imp

act

facto

rso

fin

creasin

gth

ele

ng

tho

fp

latfo

rms

by

20

%O

vera

llIm

pact

Sco

reU

sab

leca

pacity

Min

imu

mh

ead

way

time

Reg

ula

rre

covery

time

Dw

ell

time

Waitin

gtim

eB

uffe

rtim

eS

pecia

lre

covery

time

Tra

inca

pacity

Car-

wag

on

cap

acity

10

31

11

21

53

≥120

12

55

6160

100-<

120

83

34

106

80-<

100

42

20

44

<80

01

10

2

Tab

le2

0:

Imp

act

facto

rso

fin

creasin

gp

asse

ng

er

statio

nca

pacity

Existin

gp

erce

nta

ge

Imp

act

facto

rso

fin

creasin

gp

asse

ng

er

statio

nca

pacity

by

50

%O

vera

llIm

pact

Sco

reU

sab

leca

pacity

Min

imu

mh

ead

way

time

Reg

ula

rre

covery

time

Dw

ell

time

Waitin

gtim

eB

uffe

rtim

eS

pecia

lre

covery

time

Tra

inca

pacity

Car-

wag

on

cap

acity

10

31

11

21

53

150

10

10

125

77

100

55

75

33

Proje

ctRep

ort

TRL

61

PPR541

Tab

le2

1:

Imp

act

fact

ors

of

incr

easi

ng

jun

ctio

ncl

ass

ific

ati

on

Jun

ctio

ncl

ass

ific

ati

on

Imp

act

fact

ors

of

incr

easi

ng

jun

ctio

ncl

ass

ific

ati

on

by

on

ele

vel

Overa

llIm

pact

Sco

reU

sab

leca

paci

tyM

inim

um

head

way

tim

e

Reg

ula

rre

covery

tim

e

Dw

ell

tim

eW

ait

ing

tim

eB

uff

er

tim

eS

peci

al

reco

very

tim

e

Tra

inca

paci

tyC

ar-

wag

on

cap

aci

ty

10

31

11

21

53

Sin

gle

trac

k6

66

696

Sin

gle

lead

10

810

10

154

Flat

46

88

82

Fly-

ove

r0

00

00

Tab

le2

2:

Imp

act

fact

ors

of

decr

easi

ng

dis

tan

ceb

etw

een

stati

on

san

dju

nct

ion

s

Dis

tan

ceb

etw

een

stati

on

san

dju

nct

ion

s

Imp

act

fact

ors

of

decr

easi

ng

po

pu

lati

on

by

on

ele

vel

Overa

llIm

pact

Sco

reU

sab

leca

paci

tyM

inim

um

head

way

tim

e

Reg

ula

rre

covery

tim

e

Dw

ell

tim

eW

ait

ing

tim

eB

uff

er

tim

eS

peci

al

reco

very

tim

e

Tra

inca

paci

tyC

ar-

wag

on

cap

aci

ty

10

31

11

21

53

<1km

33

33

351

1≤

2km

33

33

351

2≤

4km

33

33

351

4≤

10km

33

33

351

10≤

30km

33

33

351

>30km

33

33

351

Project

Rep

ort

TRL

62

PPR541

Tab

le2

3:

Imp

act

facto

rso

fd

ecre

asin

gp

eak

po

wer

load

Peak

po

wer

load

Imp

act

facto

rso

fd

ecre

asin

gp

eak

po

wer

load

to7

5%

Overa

llIm

pact

Sco

reU

sab

leca

pacity

Min

imu

mh

ead

way

time

Reg

ula

rre

covery

time

Dw

ell

time

Waitin

gtim

eB

uffe

rtim

eS

pecia

lre

covery

time

Tra

inca

pacity

Car-

wag

on

cap

acity

10

31

11

21

53

>125%

15

15

20

-53

220

100≤

125%

10

15

20

-53

170

75≤

100%

55

5-3

10

114

≤75%

00

00

00

Tab

le2

4:

Imp

act

facto

rso

fim

pro

vin

gd

oo

r-to-v

eh

iclele

ng

thra

tio

Do

or-to

-veh

iclele

ng

thra

tio

Imp

act

facto

rso

fim

pro

vin

gd

oo

r-to-v

eh

iclele

ng

thra

tiob

y1

0%

Overa

llIm

pact

Sco

reU

sab

leca

pacity

Min

imu

mh

ead

way

time

Reg

ula

rre

covery

time

Dw

ell

time

Waitin

gtim

eB

uffe

rtim

eS

pecia

lre

covery

time

Tra

inca

pacity

Car-

wag

on

cap

acity

10

31

11

21

53

10%

10

10

12.5

%8

8

15%

55

20%

00

Proje

ctRep

ort

TRL

63

PPR541

Tab

le2

5:

Imp

act

fact

ors

of

incr

easi

ng

bra

kin

gra

te

Bra

kin

gra

te

(ms-2

)

Imp

act

fact

ors

of

incr

easi

ng

bra

kin

gra

teto

1.4

ms-2

Overa

llIm

pact

Sco

reU

sab

leca

paci

tyM

inim

um

head

way

tim

e

Reg

ula

rre

covery

tim

e

Dw

ell

tim

eW

ait

ing

tim

eB

uff

er

tim

eS

peci

al

reco

very

tim

e

Tra

inca

paci

tyC

ar-

wag

on

cap

aci

ty

10

31

11

21

53

0.6

10

30

0.8

824

16

18

1.2

412

Tab

le2

6:

Imp

act

fact

ors

of

imp

rovin

gP

PM

of

ad

jace

nt

netw

ork

Exis

tin

gP

PM

of

ad

jace

nt

netw

ork

Imp

act

fact

ors

of

imp

rovin

gP

PM

of

ad

jace

nt

netw

ork

by

2%

Overa

llIm

pact

Sco

reU

sab

leca

paci

tyM

inim

um

head

way

tim

e

Reg

ula

rre

covery

tim

e

Dw

ell

tim

eW

ait

ing

tim

eB

uff

er

tim

eS

peci

al

reco

very

tim

e

Tra

inca

paci

tyC

ar-

wag

on

cap

aci

ty

10

31

11

21

53

>97%

86

8102

95≤

98%

43

451

90≤

95%

22

226

90≤

85%

11

113

75≤

85%

00

00

≤75%

00

00

Project

Rep

ort

TRL

64

PPR541

Tab

le2

7:

Imp

act

facto

rso

fen

han

cing

tractio

nty

pe

Existin

gtra

ction

typ

e

Imp

act

facto

rso

fen

han

cing

tractio

nty

pe

by

on

ele

vel

Overa

llIm

pact

Sco

reU

sab

leca

pacity

Min

imu

mh

ead

way

time

Reg

ula

rre

covery

time

Dw

ell

time

Waitin

gtim

eB

uffe

rtim

eS

pecia

lre

covery

time

Tra

inca

pacity

Car-

wag

on

cap

acity

10

31

11

21

53

Diesel

loco

hau

led8

66

36

DM

U6

44

26

Electric

loco

hau

led4

22

16

DEM

U2

11

8

EM

U0

00

0

Tab

le2

8:

Feasib

ilitysco

res

Good

Ind

iffere

nt

Bad

Hig

hM

ed

ium

Low

Easy

Difficu

lt

10

.70

.46

0.4

60

.71

10

.46

Proje

ctRep

ort

TRL

65

PPR541

Ap

pen

dix

EM

atr

ixo

fca

paci

tyin

terd

ep

en

den

cies

(Th

am

esl

ink

rou

te)

Tab

le2

9.

Th

am

esl

ink

sect

ion

-K

en

tish

To

wn

toS

tP

an

cras

Usa

ble

Cap

acity

Min

imum

head

way

time

Reg

ular

reco

very

time

Dw

ell

time

Wai

ting

time

Buf

fer

time

Spe

cial

reco

very

time

Trai

nca

paci

ty(C

arria

ges-

Wag

ons/

trai

n)

Car

riage

-Wag

onca

paci

ty(S

pace

s/ca

rria

geor

Tonn

es/w

agon

)10

31

11

21

53

21

122

11

0.46

10.1

255

.50

Trac

kS

truc

ture

(Lin

eS

peed

,mph

)-2

-21

1-0

.5-2

50.

460.

460.

46-2

.433

42

-38

Min

Cur

ve(m

)5

11

520.

460.

460.

465.

0614

7274

20

Max

Gra

dien

t3

31

139

0.46

0.46

0.46

3.79

6104

5721

22

41

0.7

12.

88

0

-

33

10.

461

1.38

5

46

88

820.

70.

460.

4612

.145

8415

40

33

33

351

0.46

0.46

0.46

4.96

4136

5151

51

1015

20-5

317

01

11

170

220

114

0

12

161

0.7

111

.238

0-1

6

21

11

241

0.7

116

.858

0

-0.5

-0.8

142

1058

.60.

70.

70.

4613

.208

4454

.652

.6

Car

riage

Util

isat

ion

(Sea

tOcc

upac

y)4

416

0.7

0.7

0.46

3.60

6415

14

Doo

rC

hara

cter

istic

s8

80.

70.

71

3.92

105

0

824

10.

71

16.8

3018

12

- - - - -

11

10.

71

1-

260

0

- -

Ove

rall

Imp

act

Sco

reH

igh

er

De

lta

Bra

king

Sys

tem

(bra

king

rate

)

Ove

rall

Imp

act

Sco

reL

ow

er

De

lta

1

Ove

rall

Imp

act

Sco

reL

ow

er

De

lta

2

Trai

nH

eter

ogen

eity

(Max

/Min

Spe

edR

atio

)

Dis

tanc

ebe

twee

nS

tatio

ns/J

unct

ions

Pow

erS

uppl

y

Sig

nallin

g

Junc

tion

Cha

ract

eris

tics

Sta

tion

Rai

lway

Lin

eC

apac

ity

Exis

tin

gC

on

dit

ion

(Val

ue

)

Trai

nC

apac

ity(P

asse

nger

sor

Tonn

es/T

rain

)In

fras

truc

ture

Cap

acity

orTi

met

able

Cap

acity

(Tra

ins/

hour

)Fe

asib

ility

for

Imp

rove

me

nt

Co

nte

xtu

al(

G/I

/B)

Fin

anci

al(

H/M

/L)

Ope

ratio

ns

Mai

nten

ance

Str

ateg

y

Saf

ety

Rul

es

Prio

rity

Rul

es

Envi

ronm

entP

rote

ctio

nR

ules

Tim

etab

ling

Tech

niqu

es

KPI

Targ

ets

Sta

tion

Sto

ps

Publ

icPe

rfor

man

ceM

easu

reof

Feed

ing

Rai

lway

Infr

astr

uctu

re

Trac

kU

tilis

atio

n(C

UIo

rTr

ains

/trac

k/ho

ur)

Plat

form

Util

isat

ion

(Tra

ins/

plat

form

/hou

r)

Trac

kco

nditi

onTr

ack

Veh

icle

Flee

t

No

tes

Rai

ls

yste

mC

ar/W

agon

Cha

ract

eris

tics

Trai

nch

arac

teris

tics

Ove

rall

Imp

act

Sco

re

Trai

nU

tilis

atio

n(S

eatO

ccup

ancy

)

Plat

form

Leng

th

Co

mb

ine

dIm

pac

tan

dFe

asib

ility

Sco

re

Pass

enge

rH

andl

ing

Faci

litie

s

Te

chn

ical

(E

/D)

70

-<8

045

-<

53

50-

<

1-

<1.

5

8-

<12

4as

pec

1.2

-<1

=12

0

=12

0

200

75 flat

4=

10k

100

=1

0.1

25

0.8 85

=90

Project

Rep

ort

TRL

66

PPR541

Tab

le3

0.

Th

am

eslin

kse

ction

-S

tP

an

cras

toB

lack

friars

Usable

Capacity

Minim

umheadw

aytim

e

Regular

recoverytim

e

Dw

elltim

eW

aitingtim

eB

uffertim

e

Special

recoverytim

e

Traincapacity

(Carriages-

Wagons/train)

Carriage-W

agoncapacity

(Spaces/carriage

orTonnes/w

agon)10

31

11

21

53

21

122

0.460.46

0.462.141392

55.50

TrackS

tructure(Line

Speed,m

ph)5

54

4-2

690.46

0.460.46

6.71618429

2M

inC

urve(m

)7

22

740.46

0.460.46

7.202864106

5220

Max

Gradient

45

22

570.46

0.460.46

5.54815275

3921

22

40.46

0.70.46

0.592488

0

-

1010

0.70.7

0.462.254

75

46

88

820.46

0.460.46

7.981552154

0

33

33

351

0.460.46

0.464.964136

5151

51

1015

20-5

3170

0.460.46

135.972

220114

0

510

800.7

0.71

39.238

16

21

11

241

11

2458

0

-0.5-0.8

102

1054.6

0.70.7

0.4612.30684

58.652.6

50.6

Carriage

Utilisation

(SeatO

ccupacy)4

416

10.7

0.465.152

1514

Door

Characteristics

88

10.7

15.6

105

0

824

10.7

116.8

3018

12

-----

00

0-

130

--

Ove

rallIm

pact

Sco

reH

igh

er

De

lta

Braking

System

(brakingrate)

Ove

rallIm

pact

Sco

reL

ow

er

De

lta1

Ove

rallIm

pact

Sco

reL

ow

er

De

lta2

TrainH

eterogeneity(M

ax/Min

Speed

Ratio)

Distance

between

Stations/Junctions

Power

Supply

Signalling

JunctionC

haracteristics

Station

Railw

ayL

ine

Cap

acity

Existin

gC

on

ditio

n(V

alue

)

TrainC

apacity(Passengers

orTonnes/Train)

InfrastructureC

apacityor

Timetable

Capacity

(Trains/hour)Fe

asib

ilityfo

rIm

pro

vem

en

t

Co

nte

xtual

(G

/I/B)

Finan

cial(

H/M

/L)

Operations

Maintenance

Strategy

Safety

Rules

PriorityR

ules

EnvironmentProtection

Rules

Timetabling

Techniques

KPITargets

Station

Stops

PublicPerform

anceM

easureof

Feeding

Railw

ayInfrastructure

TrackU

tilisation(C

UIor

Trains/track/hour)

PlatformU

tilisation(Trains/platform

/hour)

Trackcondition

Track

Vehicle

Fleet

No

tes

Rail

sys

tem

Car

/Wagon

Characteristics

Traincharacteristics

Ove

rallIm

pact

Sco

re

TrainU

tilisation(S

eatOccupancy)

PlatformLength

Co

mb

ine

dIm

pact

and

Feas

ibility

Sco

re

PassengerH

andlingFacilities

Te

chn

ical(

E/D

)

70-

<80<

25200

-<

1.5-

<2

8-

<12

2aspec

1.2-

<1

100-

<

=120

200

150

flat

1=

2km

100=

1

0.125

0.8

75=

85

Proje

ctRep

ort

TRL

67

PPR541

Tab

le3

1.

Th

am

esl

ink

sect

ion

-B

lack

fria

rsto

Lo

nd

on

Bri

dg

e

Usa

ble

Cap

acity

Min

imum

head

way

time

Reg

ular

reco

very

time

Dw

ell

time

Wai

ting

time

Buf

fer

time

Spe

cial

reco

very

time

Trai

nca

paci

ty(C

arria

ges-

Wag

ons/

trai

n)

Car

riage

-Wag

onca

paci

ty(S

pace

s/ca

rria

geor

Tonn

es/w

agon

)10

31

11

21

53

52.

53

55.5

0.7

0.46

0.46

8.22

066

111

220

Trac

kS

truc

ture

(Lin

eS

peed

,mph

)2

23

3-1

.529

0.46

0.46

0.46

2.82

2744

692

-25

Min

Cur

ve(m

)7

22

740.

460.

460.

467.

2028

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TRL 68 PPR541

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Appendix F Highway capacity (USA) The most widely used Highway Capacity Manual (HCM), published by the US Transportation Research Board (TRB, 2000), defines capacity as "the maximum hourly rate at which persons or vehicles can reasonably be expected to traverse a point or a uniform section of a lane or roadway during a given time period, under prevailing roadway, traffic and control conditions."

• Roadway Conditions refer to the physical aspects of the roadway, such as lane-width, number of lanes, bike lanes, shoulder width, lateral clearance, vertical and horizontal alignments and any other aspect of the roadway.

• Traffic Conditions refer to the characteristics of the traffic stream, such as its composition, vehicles’ characteristics and speeds.

• Control Conditions refer to the types of control (at-grade or grade, unsignalled or signalled junctions), characteristics of control devices (timing, phasing and actuation of the signal system) and traffic regulations (speed limits, etc.).

The HCM provides methods for estimating the capacity of different types of roadways. Figure 20 presents the speed–volume curves determined from field measurements for ideal conditions: no heavy vehicles, level terrain, and drivers familiar with the roadway. Flow rate is given in number of passenger car per hour per lane (pc/h/ln), and reasonable weather conditions are presumed. These curves end at different capacity values for different free-flow speeds.

Figure 20: Speed-volume relationships of ideal conditions (TRB, 2000)

There are several situations in which the capacity for ideal conditions is not suitable. Drivers who are unfamiliar with the road tend to drive more cautiously and reduce capacity. Heavy vehicles, particularly on steep gradients, tend to move slower and observe greater distances from other vehicles. Lane width and lateral clearance also affect the driver perception, thus the speed. The capacity of the roadway strongly depends on the number of lanes (Tarko, 2003). To take into account these factors various adjustments have been made available within HCM for different types of road conditions (motorway, two lane rural road,..), junctions and upgrade and downgrade road sections. Table 32 to Table 36 show some examples of the adjustments for motorway conditions.

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Table 32: Passenger car equivalents on extended motorway segments (TRB, 2000)

Factor Type of Terrain

Level Rolling Mountainous

!� (for HGVs and buses)

1.5 2.5 4.5

!" (for recreational vehicles)

1.2 2.0 4.0

Table 33: Adjustments for lane width (TRB, 2000)

Lane width (feet) Reduction in free-flow speed, #$% (mph)

12 0.0

11 1.9

10 6.6

Table 34: Adjustments for right-shoulder lateral clearance (TRB, 2000)

Right-shoulder lateral

clearance (feet)

Reduction in free-flow speed, #$& (mph)

Lanes in one direction

2 3 4 ≥5

≥6 0.0 0.0 0.0 0.0

5 0.6 0.4 0.2 0.1

4 1.2 0.8 0.4 0.2

3 1.8 1.2 0.6 0.3

2 2.4 1.6 0.8 0.4

1 3.0 2.0 1.0 0.5

0 3.6 2.4 1.2 0.6

Table 35: Adjustments for number of lanes (TRB, 2000)

Lanes in one direction Reduction in free-flow speed, #' (mph)

≥5 0.0

4 1.5

3 3.0

2 4.5

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Table 36: Adjustments for junction density (TRB, 2000)

Junctions per mile Reduction in free-flow speed, #() (mph)

0.50 0.0

0.75 1.3

1.00 2.5

1.25 3.7

1.50 5.0

1.75 6.3

2.00 7.5

To assess the degree of congestion on a highway facility the level of service (LOS) is used. It is a qualitative measure describing operational conditions and their perception by drivers. It is intended to capture factors such as speed and travel time, freedom to manoeuvre, and safety. The Highway Capacity Manual defines six levels of service, from A to F, with LOS A representing the best operating condition, LOS E representing the volume being at the capacity level (volume/capacity ratio (v/c) = 1.0) and LOS F representing the volume exceeding the capacity (Figure 21 and Table 37). Detailed criteria for LOS for motorway segments are shown in Table 38. The HCM also provides separate methods applicable to other types of road sections, including multilane motorways and two-lane rural roads; different capacity factors are considered for these roads.

Table 37: General operational conditions of Levels of Service

Level of Service General Operational Conditions

A Free flow

B Reasonably free flow

C Stable flow

D Approaching unstable flow

E Unstable flow (traffic volume is at or near the capacity level)

F Forced or breakdown flow (traffic volume exceeds the capacity)

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Figure 21: Illustrations of level of service (Colorado DoT, n.d.)

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TRL 73 PPR541

Table 38: LOS criteria for basic motorway segments (TRB, 2000)

The two most important applications of the highway capacity analysis are: to assess the degree of congestion on a highway facility for a given traffic volume through LOS (operational analysis); and to define the number of lanes required to accommodate a given traffic volume at a desired LOS (design analysis).

To determine level of service of a motorway road section the following steps are often taken:

1. Determine flow rate from the given traffic volume and road conditions. For this, adjustments may be required to take into account heavy vehicle (buses, trucks and recreational vehicle), number of lanes, peak hour factor and driver familiarity.

2. Determine free flow speed (FFS). This is based on the base free flow speed (design speed) and adjusted for lane width, lateral clearance, number of lanes and junction density.

3. Determine level of service (LOS) by comparing the calculated flow rate and free flow speed with those in Table 37.

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For design analysis of a new motorway (to determine the number of lanes required) the process is the same as operational analysis above, except the number of lanes is increased (i.e. start with two lanes) until an acceptable level of service is achieved (University of Wisconsin-Milwaukee, n.d.).

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R5

41

A new railway system capacity model

Railway capacity is now an increasingly prominent issue for government, infrastructure managers and operators. Although a range of definitions and explanations for railway capacity have been published, together with models and proposals for improvement, none of these have managed to include the railway as a complete system. They have tended in general to concentrate on operational or train control systems.

The first part of this report covers a review of current knowledge of railway capacity, comprising a literature review and a workshop with a wide range of stakeholders. The second part of this report reports on a newly-developed tool, Matrix model for modelling railway capacity as a complete system, including infrastructure, vehicles and operations. This tool is designed to support early high level decisions on the rail system attributes influencing capacity on particular routes or lines. The report also presents the results of a preliminary analysis carried out using PRIME, a capacity planning tool that allows a high level evaluation of capacity impacts within a flexible modelling environment, to examine the effects on headways of new signalling, different speed profiles and train lengths on the central part of the Thameslink route.

Other titles from this subject area

PPR193 Channel corridor community transport study: Final Report. A Davies, C Burke, K Townley and S Reid. 2007

PPR194 Evaluating congestion caused by abnormal loads. Final report. N Taylor, T Rees, P Sanger, K Alexander and D Savage. 2007

PPR195 Evaluating congestion caused by abnormal loads. Summary report. N Taylor, T Rees, P Sanger, K Alexander and D Savage. 2007

PPR196 Modelling congestion caused by abnormal loads. N Taylor. 2007

PPR173 Development of a Speed Limit Strategy for the Highway Agency - Proposed strategy. A Fails, D Lynam, R Gorell and P Wells. 2006

PPR139 Access to air travel for disabled people: 2005 monitoring study. J Sentinella. 2006

PPR106 In-depth interviews with van drivers and managers of van drivers. B Lang. 2006

PPR113 Literature review on van use in the UK. B Lang and L Rehm. 2006