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AP-R299/07

Microsimulation as a Planning, Operations and Training Aid for Incident

Management

AUSTROADS RESEARCH REPORT

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Microsimulation as a Planning, Operations and Training Aid for Incident Management

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First Published January 2007

© Austroads Inc. 2007

This work is copyright. Apart from any use as permitted under the Copyright Act 1968, no part may be reproduced by any process without the prior written permission of Austroads.

Microsimulation as a Planning, Operations and Training Aid for Incident Management ISBN 1 921139 87 0

Austroads Project No. NS1017

Austroads Publication No. AP–R299/07

Austroads Project Manager Dennis Walsh and Steven Dove

Prepared by

James Luk, Johann Tay and Charles Karl

Published by Austroads Incorporated Level 9, Robell House 287 Elizabeth Street

Sydney NSW 2000 Australia Phone: +61 2 9264 7088

Fax: +61 2 9264 1657 Email: [email protected]

www.austroads.com.au

Austroads believes this publication to be correct at the time of printing and does not accept responsibility for any consequences arising from the use of information herein. Readers should

rely on their own skill and judgement to apply information to particular issues.

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mailto:[email protected]


Sydney 2007

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Austroads profile Austroads is the association of Australian and New Zealand road transport and traffic authorities whose purpose is to contribute to the achievement of improved Australian and New Zealand road transport outcomes by: undertaking nationally strategic research on behalf of Australasian road agencies and

communicating outcomes promoting improved practice by Australasian road agencies facilitating collaboration between road agencies to avoid duplication promoting harmonisation, consistency and uniformity in road and related operations providing expert advice to the Australian Transport Council (ATC) and the Standing

Committee on Transport (SCOT).

Austroads membership Austroads membership comprises the six state and two territory road transport and traffic authorities and the Australian Department of Transport and Regional Services in Australia, the Australian Local Government Association and Transit New Zealand. It is governed by a council consisting of the chief executive officer (or an alternative senior executive officer) of each of its eleven member organisations: Roads and Traffic Authority New South Wales Roads Corporation Victoria Department of Main Roads Queensland Main Roads Western Australia Department for Transport, Energy and Infrastructure South Australia Department of Infrastructure, Energy and Resources Tasmania Department of Planning and Infrastructure Northern Territory Department of Territory and Municipal Services Australian Capital Territory Australian Department of Transport and Regional Services Australian Local Government Association Transit New Zealand

The success of Austroads is derived from the collaboration of member organisations and others in the road industry. It aims to be the Australasian leader in providing high quality information, advice and fostering research in the road sector.

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EXECUTIVE SUMMARY Microsimulation traffic models have in recent years become accepted as useful tools amongst road and transport authorities to develop solutions for traffic and transport management. The technique of microsimulation, or simulating the movement of individual vehicles in a traffic system, has long been used for traffic analysis. The synergy between information technologies and traffic engineering in recent years has enabled a new generation of microsimulation models now available for road and transport managers to analyse complex traffic operations. Traffic incidents such as vehicle crashes, truck load spills, or simply vehicle breakdowns, and planned events such as road works, lead to severe disruptions of traffic flow and are complex situations that warrant the consideration of using microsimulation.

The objective of Austroads Project NS1017 (Improving Incident Management) is to establish processes and resources to enable on-going improvements in traffic incident management by assessing current performance, sharing learnings and developing capability of road authorities and other agencies. One of the tasks under Project NS1017 is to determine the appropriateness of microsimulation for the planning, operation and training of incidents through a literature review and utilising an existing microsimulation traffic model (MSTM).

The benefits of using traffic modelling in general and microsimulation in particular, for incident management are as follows:

reduce disruptions to traffic

reduce the time and cost of evaluating incident management options and improve the decision-making process in prioritising these options

be an embedded part of a signal or freeway control system and allows system performance monitoring and evaluation

be a useful platform for research, education and training. The current generation of MSTMs has the further benefits of the flexibility to model complex traffic situations and has excellent graphical and animation displays that facilitate the explanation and illustration of different scenarios to stakeholders and the public.

The literature reviews in the report show that microsimulation has been actively employed for analysing freeway and arterial incidents using a range of software packages, supported by local and overseas cases studies. A case study of an incident on West Gate Freeway in Melbourne and its potential impact on the freeway and its adjacent arterial roads are described in this report. It satisfactorily demonstrates the use of microsimulation as a useful tool for incident management.

Key principles for the use of MSTMs for incident management have been identified from the literature review and the modelling case study at the West Gate Freeway as follows:

MSTMs are simulations of the real world and have limitations; but they are appropriate for testing different scenarios. The comparison of model outputs would enable the identification of best strategies.

Incidents on freeways have major impact in a corridor relative to those on arterial roads because affected traffic can have more opportunities to bypass an incident, e.g. turning into side-streets. However, freeway incidents should not be analysed in isolation. An MSTM for incident management should include both the freeway and relevant adjacent arterials. Route diversion due to traveller information or other measures can then be adequately analysed.

MSTM performance metrics should include those for a study area as well as separately for freeway and adjacent arterials in that area. A metric for the total network is not detailed

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enough to identify impacts at different parts of the network due to an incident. A comprehensive study of incident strategies should involve analysing MSTM outputs at the levels of a detector, segment (or link), stream (or route) and network.

An MSTM is easy to simulate the blockage of one or more lanes in an incident, and also easy to investigate which lanes are to be blocked on a carriageway. This has implications on incident management policies. At some sites, it may be possible to choose which lane(s) are still opened to traffic, e.g. to facilitate traffic exiting an off-ramp or avoiding excessive weaving actions on a freeway. An MSTM is useful in developing the right decision.

The case study at West Gate Freeway demonstrates the use of a fixed period for the diversion period, which represents the operation of a traffic management centre in switching on and off route diversion information to the public. An MSTM can also implement other more dynamic schemes – adaptively switching on and off of diversion information and dynamic route choice. It is advisable to apply a fixed route, fixed-time management policy as a benchmark before testing other more dynamic schemes.

It is important to carry out sensitivity analysis on incident parameters that include the level of route diversion, duration of an incident, severity of incident (i.e. the choice of lanes and the number of lanes closed), and the start time of the incident. The ease of preparing, processing and reporting of the outputs from different scenarios is an important consideration.

Post-processing of MSTM outputs from current software packages is often required to produce performance metrics that are different from standard outputs and more meaningful to stakeholders and users, at different levels of spatial aggregation (detector, segment, stream or network) and temporally (whole duration of simulation, peak hour or during route diversion).

MSTMs can undertake a much wider range of investigations then those presented in the case study. Freeway incident detection algorithms and variable speed limit signs are two aspects that are particularly appropriate for the study of incident management using microsimulation. Extra Application Programming Interfaces have to be developed in a microsimulation package, and are important areas for further research and development.

In conclusion, microsimulation modelling is a valuable tool for the planning, operation and training of incident management. Significant resources are required in setting up a microsimulation model for incident analysis. Once properly set up and calibrated, the model is a useful tool for the management of incidents and other complex traffic applications.

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CONTENTS

1 INTRODUCTION.................................................................................................................... 1 2 BACKGROUND..................................................................................................................... 2 3 USAGE AND LIMITATIONS OF MICROSIMULATION TRAFFIC MODELS........................ 5 3.1 Benefits of traffic modelling .................................................................................................... 5 3.2 Usage of microsimulation modelling ...................................................................................... 6 3.3 Limitations of microsimulation modelling................................................................................ 7 4 PLANNING, OPERATION AND TRAINING OF INCIDENT MANAGEMENT....................... 9 4.1 Literature review..................................................................................................................... 9

4.1.1 TSIS-CORSIM.......................................................................................................... 10 4.1.2 PARAMICS............................................................................................................... 11 4.1.3 AIMSUN ................................................................................................................... 11 4.1.4 SITRAS .................................................................................................................... 12 4.1.5 Recent developments............................................................................................... 12

4.2 Setting up a model for incident analysis .............................................................................. 13 4.2.1 Site description......................................................................................................... 13 4.2.2 Incident modelling .................................................................................................... 15 4.2.3 Simulation results ..................................................................................................... 16

4.3 Using microsimulation as a tool for incident management .................................................. 23 5 CONCLUSIONS .................................................................................................................. 24 6 REFERENCES .................................................................................................................... 25 7 SUPPORTING DATA FOR CASE STUDY ......................................................................... 26

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TABLES Table 2.1: Objectives for incident detection and response.......................................................... 2 Table 4.1: Microsimulation software packages ......................................................................... 10 Table 4.2: Average travel times for all traffic streams from 4:30 – 5:30 p.m. ............................ 21 Table 7.1: Flow, travel time, delay and density of all scenarios for the Normanby Road

segment.................................................................................................................... 34 FIGURES Figure 2.1: Main processes of traffic incident management ......................................................... 3 Figure 4.1: An aerial photograph of the study site near West Gate Freeway............................. 14 Figure 4.2: Schematic diagram of study site (from AIMSUN NG) .............................................. 14 Figure 4.3: Detailed location of simulated incident ..................................................................... 15 Figure 4.4: Detector counts near the exit from Domain Tunnel to the West Gate Freeway ....... 17 Figure 4.5: Detector speeds near the exit from Domain Tunnel to the West Gate Freeway ...... 18 Figure 4.6: Detector counts on West Gate Freeway downstream of incident............................. 19 Figure 4.7: Detector speeds on West Gate Freeway downstream of incident............................ 19 Figure 4.8: Travel times of the freeway traffic stream ................................................................ 20 Figure 4.9: Traffic flow on Normanby Road segment ................................................................. 22 Figure 4.10: Travel times on Normanby Road segment............................................................... 22 Figure 7.1: West Gate Freeway stream - flow ............................................................................ 27 Figure 7.2: West Gate Freeway stream - travel time.................................................................. 27 Figure 7.3: West Gate Freeway stream – delay ......................................................................... 28 Figure 7.4: Kings Way stream - flow........................................................................................... 28 Figure 7.5: Kings Way stream - travel time ................................................................................ 29 Figure 7.6: Kings Way stream - delay ........................................................................................ 29 Figure 7.7: Power Street stream - flow ....................................................................................... 30 Figure 7.8: Power Street stream – travel time ............................................................................ 30 Figure 7.9: Power Street stream - delay..................................................................................... 31 Figure 7.10: Normanby Road segment - flow............................................................................... 31 Figure 7.11: Normanby Road segment – travel time.................................................................... 32 Figure 7.12: Normanby Road segment – delay............................................................................ 32 Figure 7.13: Normanby Road segment - density.......................................................................... 33

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1 INTRODUCTION Traffic incidents such as vehicle crashes, truck load spills, or simply vehicle breakdowns are frequent occurrences in large urban centres. Planned events such as sporting events, parades and road works are also common non-recurrent incidents. These incidents often result in excessive travel delay and pollutant emission. It is vital that incidents be alerted to relevant local authorities as quickly as possible. Incidents can be on freeways or arterial roads and there are differences between the two types of incidents (see, e.g. Luk et al. 2001).

Traffic incident management can be defined as the systematic use of resources to reduce the impacts of incidents and improve the safety of road users. It requires the coordinated use of human, institutional, mechanical and technical resources. The objective of Austroads Project NS1017 (Improving Incident Management) is to establish processes and resources to enable on-going improvements in traffic incident management in Australia and New Zeeland, by assessing current performance, sharing learnings and developing capability of road authorities and other agencies.

This report presents the results of the task on microsimulation in Austroads Project NS1017. The aim of this report is to describe the appropriateness of microsimulation for the planning, operation and training of incident management. This task involves:

literature reviews of local and overseas work on the use of microsimulation for incident management

utilising a microsimulation traffic models (MSTMs) to investigate and determine the appropriateness of microsimulation as a training aid.

Microsimulation traffic models have in recent years become accepted as useful tools amongst road and transport authorities to develop solutions for traffic and transport management. The technique of microsimulation, or simulating the movement of individual vehicles in a traffic system, has long been used for traffic analysis. The synergy between information technologies and traffic engineering in recent years has enabled a new generation of microsimulation models now available for road and transport managers to analyse complex traffic operations. Traffic incident management is a complex situation that warrants the consideration of using microsimulation, due partly to the difficulty in collecting traffic data that can be ascribed to a specific incident when it occurs.

This report provides the following information:

background on incident management from an earlier literature review for this project (Karl 2005) (Section 2)

usage and limitation of MSTM (Section 3) from another Austroads project on microsimulation (Luk and Tay 2005)

simulation and training of incident management based on a microsimulation case study (Section 4)

conclusions and recommendations (Section 5). The output data for the case study are also reported in detail in Appendix A.

In this report, the terms ‘software package’ and ‘model’ have different meanings. The software package refers to the microsimulation platform on which a model is developed. An MSTM is therefore an application of the software. For example, a freeway incident management model is developed in a software package such as AIMSUN NG or PARAMICS.

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2 BACKGROUND As road networks approach capacity, especially during peak commuting periods, non-recurrent congestion due to an incident or a planned event has the potential to cause a significant impact on total delay and increase the variability of the travel times experienced by motorists. The amount of delay occurring during an incident depends on three primary factors: the nature of the incident, road conditions and the execution of incident clearance. Traffic incident management can play a role in improving the reliability of travel times (or reducing the variability) because incidents are usually a dominant source of such variable traffic conditions.

Karl (2005) found that the field of traffic incident management can be approached from two directions, firstly from the perspective of practitioners responsible for traffic incident management programs and secondly from the perspective of researchers in traffic incident management, encompassing supporting technologies such as incident detection, traveller information and the techniques for evaluation and modelling.

Traffic incident management involves managing multi-agency, multi-jurisdictional responses to road traffic disruptions. As such, practitioners have to deal with the management of operational activities, institutional relationships and also make decisions on technological issues involving incident detection, resources for clearance and recovery, traffic management and information dissemination to the public. Given the impact of traffic incidents on the community, there are many different objectives influencing a variety of stakeholders seeking an improvement in traffic incident management, as shown in Table 2.1 (Charles 2001).

Table 2.1: Objectives for incident detection and response

Stakeholder Objective Road authority Minimise delay, improve safety

Reduce vehicle emissions Limit additional infrastructure Provide information to drivers

Toll operators Reduce travel time to encourage patronage Minimise delays due to incidents

Freight operators Reliable travel times, on-time delivery Reduce operating costs

Public transport providers/ taxis Reduce travel time to encourage patronage Reliable travel times and schedules Minimise delays due to incidents Reduce operating costs

Business travel Reduce travel time – on-time service provision Minimum travel times, increase business time Minimise operating costs

Traffic service providers Rapid detection and response to incidents Emergency services Rapid response to emergencies

Access to incident sites Traveller information service providers Rapid and accurate value-added information Car manufacturers and telematics suppliers Increase sales of products, services and vehicles

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At the same time, advances in computing, information and communications technology has delivered significant improvements to traffic incident management practices from the perspective of incident detection and verification, communications and information delivery to motorists. Additionally legislation and the involvement of the private sector in some services related to incident management have led to improvements in reducing the impact of incidents.

The incident management process can be characterised by seven activities, detection, verification, motorist information, response, site management, traffic management and clearance (Margiotta et al. 2004, Farradyne 2000). Planning, operation and performance monitoring can also be included. The main processes are depicted in Figure 2.1.

Detection Verification Site Mgmt

Clearance

Traffic Mgmt

Motorist information

Response

Planning, evaluation and performance

monitoring

Figure 2.1: Main processes of traffic incident management

In addition to the seven activities listed above, an overall incident management program also includes traffic incident management planning. This forward activity takes into account advanced planning of detour routes, control strategies, alternative signal timing plans and other pre-planned measures. While the most visible part of the incident management program is the activities associated with dealing with the incident and the resultant clearance and traffic management, it is really the planning behind the overall program and its many different facets (as detailed in Figure 2.1) that in the end determines the true benefit of the program itself.

In essence and typical to any business management approach, the incident management program is a cycle of processes that are initiated with a definition of the problem, setting goals and objectives, developing, evaluating, selecting, implementing and then evaluating alternatives. This cycle provides the necessary feedback loop to refine the overall incident management program.

Traffic managers need an effective incident management system. Such systems are essentially hardware and software solutions for transport control centres that assist operators to detect, verify

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and resolve incidents. Such systems are like a human brain that has to rely on senses (from incident detection and feedback during resolution) and reliable communications (to transfer incident information and commands) in order to work the ‘arms and legs’ of traffic control systems including field services such as patrols and recovery vehicles.

The earlier literature review found that while traffic incident management issues are the same in most places, each city has a slightly different emphasis on the major issues and a slightly different approach to dealing with them (Karl 2005). Different jurisdictions have different institutional and legal/legislation arrangement. Increasingly, there seems to be a consistent approach to collaborate through the formation of formal inter-agency groups of responding agencies. These groups deal with a diverse local agenda to improve traffic incident management.

There is also an emphasis to adopt a more proactive approach to managing incidents, not only through in-house means such as service patrols, incident response units, coordination of incident response/traffic management centres but also to use policy and legislative tools to provide more powers to the responding agencies (quick clearance and authority to tow laws).

As a result of the revolution in information and communications technologies (mobile phone, Internet, satellite tracking), there is greater adoption of technology at a number of different levels in traffic incident management, including detection, traveller information, new intelligent transport system (ITS) technologies in dynamic traffic management, (such as ramp metering) and in-vehicle tracking and communication devices.

Microsimulation has received attention in recent years as a useful tool to analyse complex traffic conditions. Incident analysis and planning can be prime applications of microsimulation. The usage and limitations of MSTMs, however, must be noted and are described in Section 3. A literature review, a case study and the principles for employing microsimulation as a planning, operation and training aid for incident management are described in Section 4.

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3 USAGE AND LIMITATIONS OF MICROSIMULATION TRAFFIC MODELS

The management of a road network often requires the forecasting of the impacts of implementing various traffic management measures. The impact involves the road itself, the whole corridor and its abutting areas. These measures include, for example, signal coordination, high-occupancy vehicle (HOV) lanes, one-way systems, different types of intersection control (priority sign, signal or roundabout), signal priority, driver information systems and incident management. Apart from road vehicles, trams, light rails, pedestrians and cyclists can also be simulated. Traffic modelling techniques can be broadly classified into the following four types:

(a) Analytical modelling – this technique relates directly to traffic flow theory and is often a set of equations governing driver behaviour such as gap acceptance, lane changing, car–following, or platoon dispersion. The combination of analytical models can constitute a more complex analytical model for traffic analysis. Individual sets of analytical equations can also act as sub-models in other modelling techniques. Analytical modelling is sometimes also known as microscopic modelling.

(b) Microscopic simulation – the movement of a vehicle in a microscopic simulation is traced through a road network over time at a small time increment of a fraction of a second. A detailed simulation of vehicle-road interaction under the influence of a control measure is therefore possible. This technique is useful for a wide range of applications but requires more computational resources. Random number generators are involved and the calibration of these models requires more effort, and it is difficult to optimise model parameters, e.g. signal settings.

(c) Macroscopic simulation – vehicles in a macroscopic simulation are no longer simulated individually. Vehicle movements are often simulated as packets or bunches in a network with a time step of one or several seconds. An analytical model such as the platoon dispersion model is used to govern the movement of a vehicle platoon along a road link. A macroscopic simulation is deterministic by nature and is useful for network design and optimisation.

(d) Hybrid simulation – this technique combines a detailed microscopic simulation of some key components of a model (e.g. intersection operations) with analytical models (e.g. speed-flow relationships for traffic assignment). It is also possible to interface a microsimulation model with a real-time freeway management system or signal control system - an area of active research and development (see also Section 4.1).

Section 3.1 describes further the benefits of traffic modelling in general. Section 3.2 then describes the usage of MSTMs followed by Section 3.3 on their limitations.

3.1 Benefits of traffic modelling The benefits of traffic modelling are well-known and its use for incident analysis is summarised as follows:

reduce disruptions to traffic – a traffic model provides a means to cheaply estimate the effects of different incident management options prior to full deployment when traffic could be disrupted

reduce time and cost – complex scenarios can be tested in a relatively short time and modelling can be less costly than pilot studies

improve the decision making process – all alternative management options are evaluated in a single, consistent platform; potential impacts can be assessed and the risk of unforeseen effects can be limited; priorities can be set efficiently

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optimise the operation of existing roadway capacity – traffic models, especially macroscopic simulation models mentioned earlier, are very useful for finding the best traffic solution, e.g. the best set of signal timings or the best set of routes for traffic diversion

monitor performance – a traffic model can be an embedded part of a signal or freeway control system and allows system performance monitoring and evaluation

enhance research, education and training – traffic models are useful platforms for research, education and training; the wide range of traffic modelling tools are exciting areas for research and development; these tools are also invaluable for the training of practitioners.

Sections 3.2 and 3.3 will discuss microsimulation modelling in more detail, with a focus upon the specific usage and limitations of MSTMs.

3.2 Usage of microsimulation modelling Microsimulation can offer benefits over traditional traffic analysis techniques in three areas: clarity, accuracy and flexibility as follows (Luk and Tay 2005):

Clarity – a comprehensive real-time visual display and graphical user interface illustrate traffic operations in a readily understandable manner. The animated outputs of microsimulation modelling are easy to understand and simplify checking that the network is operating as expected, and whether driver behaviour is being modelled sensibly.

Accuracy – by modelling individual vehicles through congested networks, the potential exists for more accurate modelling of traffic operations at complex and simple intersections or merges, either on whole networks or along individual roads. Individual vehicles are allowed to make their own decision on speed, lane changing and route choice in a simulation model. On the other hand, conventional techniques such as analytical and macrosimulation modelling provide a simplified representation of reality with input assumptions required on certain parameters such as saturation flow.

Flexibility – a greater range of problems and solutions can be assessed than with conventional methods. Examples include: vehicle-actuated signals, demand dependent pedestrian facilities, queue management, public transport priorities, incidents, toll booths, road works, signalised roundabouts, shock waves, incidents or flow breakdown, or slip road merges.

MSTMs are perceived to be valuable tools by those who have seen some of the visual outputs that they can produce because they provide a medium by which lay and technical people can discuss the respective merits of traffic planning proposals. It is important to recognise that extra modelling effort and costs are inevitable when compared with conventional modelling approaches. These drawbacks as well as the potential benefits should be understood before a decision to develop a microsimulation model for incident analysis is taken. Some pertinent questions to consider include:

what are the purposes and functions of the proposed model?

would a conventional model meet the requirements sufficiently well?

is microsimulation the only available or suitable methodology for this application?

how is the model to be funded, managed, further developed and used?

what is the simplest and cheapest way to obtain the results and usage needed?

what is the nature and quality of the model needed?

In developing any traffic model, including a microsimulation model, it is essential that the model needs to be fit for the purpose. The quality of the model is heavily dependent on the quality of the input data. Model calibration, validation, testing and forecasting procedures, documentation and reporting should follow existing best practices.

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The issues generally accepted as appropriate for analysis using an MSTM include the following (see, e.g. Transport for London 2003):

complex traffic operation schemes, e.g. bus priority, advanced signal control, incident management, different modes of toll collection

significant conflicts amongst different road users, e.g. pedestrians, cyclists, buses

the effect of major road works on traffic movements, e.g. lane closures, one-way system, toll plazas

politically sensitive projects that could benefit from visualisation

planning and design of high-value projects with potential large savings if detailed MSTMs are prepared

emulation of the operation of a dynamic signal control system, with a simulated network driven directly by the control system, with significant saving in signal timing preparation and optimisation

town centre studies

tram and light rail operations.

3.3 Limitations of microsimulation modelling Every modelling technique has its own limitations. MSTMs remain a simplification of reality. This lack of reality is the case for all modelling systems, the difference being that MSTMs simulate the detail directly, and one can argue that it could be closer to reality. Various modelling issues specific to microsimulation have been identified in recent years. Some of these have been resolved but issues that still require attention include:

overtaking has yet to be implemented

driver behaviour in the proximity of intersections

improved modelling of stop-and-go phenomena

improved modelling of pedestrians and cyclists

convergence in dynamic traffic assignment

direct support for roundabouts

wider range of pollutants resulting from vehicle emissions

better route choice following an incident

improved modelling of motorway merges and diverges

improved modelling of collector and lower road classes

the effect of reducing the number of lanes (e.g. due to road works or road crashes).

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In summary, all modelling approaches have limitations. Microsimulation modelling aims to analyse complex, congested traffic conditions and requires more parameters for model development and calibration. MSTMs have some limitations as mentioned in this section and it is important to adopt a fit-for-purpose approach in their usage. It is generally accepted that microsimulation is suitable for analysing traffic incidents and their impact on route diversion in a network.

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4 PLANNING, OPERATION AND TRAINING OF INCIDENT MANAGEMENT

As previously mentioned, traffic incidents of significant impact are on either arterials or freeways. Luk et al. (2001) reported the differences between arterial and freeway incidents. Arterial roads are interrupted by traffic signals and other control devices, whereas vehicles on freeways can move freely and are delayed only through interactions with other vehicles. Other key differences are:

Lane blockage – a lane blocked on a freeway by any incident is a major event with a significant loss of road capacity. On the other hand, roadside friction is common on arterial roads. Alternative routes are available for traffic diversion in a surface street network.

Incident duration – the objective of a freeway incident plan is to detect and remove incidents that normally last for a short duration of, say, less than an hour. The objective of arterial incident detection and management could be the screening out of ‘minor’ events to identify the more dramatic incidents that last over a longer time period.

Spatial impact – the impact of an incident on a freeway is usually localised to a specific freeway route due to the difficulty for traffic to divert out of a freeway. Arterial incidents easily impact upon adjacent streets and even parallel arterial roads. This difference may imply that the detection and management of arterial incidents should cover a larger road network than managing freeway incidents.

In general, arterial incidents have less impact on a road corridor because alternative routes are often available and affected vehicles are easier to reroute. Freeway incidents have more localised impact and it is important to consider freeway incidents and their impact on adjacent arterials. The study area in an MSTM for incident management should include a freeway and its adjacent arterial roads that are deemed suitable for route diversion. This issue is further discussed in the literature review in Section 4.1 and in the case study in Section 4.2.

This section reviews local and overseas research in the use of microsimulation for incident management. Section 4.1 provides a literature review of the potential application of MSTMs. Section 4.2 uses an MSTM based on the Westgate Freeway in Melbourne to demonstrate the appropriateness of microsimulation for the planning, operation and training of incident management. Section 4.3 summarises the principles that should be considered in using microsimulation as a tool for incident management.

4.1 Literature review Many software packages have been developed for the simulation of traffic operations in recent years. The packages that are widely used or actively supported by software developers are listed in alphabetical order in Table 4.1 (Luk and Tay 2005).

Not all the packages in Table 4.1 have been applied and published in the literature for incident management, and this section describes the published applications of those packages for simulating incident management. The packages include: TSIS-CORSIM, PARAMICS, AIMSUN and SITRAS and their applications for incident analysis and planning are described below.

The SITRAS package (Hidas and Behbahanizadeh 1998; Hidas and Awadalla 2001) went through extensive development at the University of New South Wales in Sydney although it has not been supported commercially. SITRAS has been applied for research in arterial and freeway incident management and is included in the following discussion.

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Table 4.1: Microsimulation software packages

Model Internet reference Supplier(s)

AIMSUN NG http://www.aimsun.com Transport Simulation Systems (TSS)

CUBE - Dynasim http://www.citilabs.com/dynasim/ Citilabs

DRACULA http://www.its.leeds.ac.uk/software/dracula/ University of Leeds

MITSIMLab http://mit.edu/its/mitsimlab.html Massachusetts Institute of Technology ITS Program

NGSIM program http://www.ngsim.fhwa.dot.gov Cambridge Systematics

PARAMICS (Q- and S- versions)

http://www.paramics.com Quadstone and SIAS

SITRAS http://www.civeng.unsw.edu.au/research/groups/ transport/projects.htm

University of New South Wales

TRANSIMS http://www.transims.net IBM (commercial version)

TSIS-CORSIM http://www.fhwa-tsis.com McTrans, FHWA

VISSIM http://www.ptvag.com/traffic/vissim.htm PTV

4.1.1 TSIS-CORSIM

Cragg and Demetsky (1995) reported the use of the TSIS-CORSIM package from the US Federal Highway Administration (FHWA) to analyse route diversion strategies from freeways to arterial roads as a form of incident management. It was found that TSIS-CORSIM is a valuable tool in evaluating the effects of incidents on network-wide traffic flow, as well as freeway and arterial road flow. Although the program cannot model every situation explicitly, it is possible to estimate the amount of additional traffic that an alternative route can accommodate.

They found that, for incidents where not all lanes were closed, there was often an optimum diversion percentage beyond which freeway delays increased due to the friction caused by the weaving of vehicles attempting to exit. The physical capacity of on-ramps, off-ramps and weaving sections to accommodate the diverted traffic is critical for successful diversion. If diverted vehicles cannot manoeuvre through a weaving section, excessive queuing on the freeway carriageway would result.

Other researchers have investigated the freeway module of TSIS-CORSIM called FRESIM. Jin et al. (1998) developed a microsimulation model using FRESIM for the evening peak traffic on a Singapore motorway. The model was first used to characterise the impact of incremental input volumes on the average speed and travel time on the motorway. It was found that, with a 12% increase in traffic volume, the average speed of vehicles travelling on the motorway reduced from 75 km/h to 70 km/h. A link-speed analysis was successful in identifying future potential bottlenecks.

Jin et al. then applied the model to simulate incidents and calculate incident delays under different demand scenarios. At a fixed incident duration of 30 min on a four-lane segment of the motorway, it was found that an incident blocking two lanes in the evening peak period caused more than 20 times the traffic delay caused by an incident blocking only one lane. The sensitivity to incident duration was also tested, and it was found that delay to vehicles increased in direct proportion to the duration of the incident for both one blocked lane and two blocked lanes scenarios. The ease of changing the intensity and duration of an incident in an MSTM makes microsimulation an attractive technique for preparing incident management strategies.

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http://www.aimsun.com/

http://www.its.leeds.ac.uk/software/dracula/

http://mit.edu/its/mitsimlab.html

http://www.ngsim.fhwa.dot.gov/

http://www.paramics.com/

http://www.transims.net/

http://www.fhwa-tsis.com/

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4.1.2 PARAMICS

Using PARAMICS, Chu et al. (2004) presented a microsimulation method to evaluate the effectiveness of a number of potential ITS strategies including incident management, adaptive ramp metering, traveller information systems, arterial management, and a combination of these strategies. The strategies were implemented and evaluated in PARAMICS enhanced with various plug-in modules through Application Programming Interfaces (APIs). Performance measures included the efficiency of the overall system, mainline freeway, on-ramp and reliability of the network.

Based on the simulation of eight scenarios, Chu et al. found that all ITS strategies had positive effects on network performance. The findings were as follows:

The most important ITS strategy to relieve traffic congestion caused by an incident was to provide traffic information. The extent of response to traffic information is represented by the compliance rate. In real-world situations, the level of compliance is uncertain and possibly site-specific. The use of simulation is particularly useful to test this issue using different compliance rates.

Adaptive ramp metering did not improve system performance effectively under incident conditions.

The fast removal of the cause of an incident due to a vehicle breakdown or a road crash is important, but a combination of ITS strategies yielded greater benefits in the study.

At a test site in Orange County, California, an integrated control approach involving freeway and other road authorities was found to be desirable for implementing incident management according to simulation results. However, if the arterial signal system was traffic adaptive, an integrated control without the involvement of the road authority that managed arterials could still work well.

4.1.3 AIMSUN

Incident management was investigated in EU under a project called PRIME (Barcelo et al 2002). The objectives of PRIME include:

estimating incident probability in real time based on geometrics, weather and traffic characteristics; this estimate can then be used to activate traffic management strategies to reduce the likelihood of incidents

developing improved systems and algorithms for incident detection

improving the reliability of verifying an incident

exploring the integration of motorway and arterial incident management strategies to increase the effectiveness of incident and traffic management in mixed motorway/arterial networks.

Barcelo et al. (2002) used AIMSUN to investigate incident management strategies. The AIMSUN model was applied to a study area in Barcelona, consisting of an urban motorway section spanning three main interchange nodes. Incident detector measurements were emulated and interfaced to external modules providing the functionalities listed above. Furthermore, an ‘Integrated Incident Management Strategies Module’ was used to determine a strategy to apply to the AIMSUN model in response to an incident. Incident management actions included motorist information using variable message signs (VMS), variable speed limit signs and ramp metering. In the latest version, AIMSUN NG, the simulation of incidents is embedded within the structure of the software package.

The AIMSUN results showed that overall measures of effectiveness improved due to the use of various incident management strategies. However, some strategies had undesirable effects in the

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adjacent arterial network. The impact of rerouting strategies is uncertain because there is so far no valid behavioural model that can accurately account for motorists’ reactions to VMS information. In the simulated scenarios, a conservative 10-15% compliance rate was used based on local knowledge. Nonetheless, an MSTM is particularly suitable for the testing of different scenarios (e.g. compliance rates) and the comparison of results from these scenarios in identifying the best incident management plan.

4.1.4 SITRAS

Apart from TSIS-CORSIM, PARAMICS and AIMSUN, SITRAS from the University of New South Wales went through extensive software development for simulating ITS strategies especially those for incident management. SITRAS was applied initially to incidents on arterial networks (Hidas and Behbahanizadeh1998). It was found that, in a hypothetical network and with some drivers receiving route guidance information, there was an optimal level of guided vehicles in both normal (incident-free) and incident situations. The benefit of travel time information was more significant in an incident situation. Also, the test network as a whole benefited from delay savings at the optimal number of guided vehicles. These results agree with macroscopic modelling using CUBE/TRIPS as reported in Luk and Yang (2003), who found an optimal level of guided vehicles at about 20% in a simplified Singapore network.

SITRAS was also applied to a combination of arterials and motorways in Sydney (Hidas and Awadalla 2001). The test site was a network near the interchange between Windsor Road and the M2 Motorway. Three VMS were implemented in the model to reroute traffic in the case of incidents. SITRAS was found to be useful in preparing incident plans under two incident scenarios – one-lane or two-lane closure on the motorway (M2 has two lanes in each direction at the test site). Simulation results suggested that informing drivers to reroute out of the motorway did not guarantee an overall reduction in network delay. The overall network delay was dependent on whether one or two lanes were blocked and which arterial road received the diverted traffic. The best outcome was when one lane on the motorway remained open to traffic and traffic was diverted away from a congested intersection on the adjacent arterial network.

4.1.5 Recent developments

A recent development in microsimulation in Australia is the use of an interface between a traffic control system, such as SCATS or STREAMS, and a microsimulation package (Millar et al. 2004).

The interface consists of an emulator of a bank of signal controllers (called WINTRAF). It receives signal timings from the control system and processes the (simulated) detector information from a software package such as PARAMICS or AIMSUN that will have its own interface to WINTRAF. This development should enable the realistic simulation of how congestion in a combined arterial-motorway network responds to management scenarios with or without incidents, as well as the reaction of an adaptive traffic control system. This development will further improve the usage of microsimulation for incident analysis, planning and management.

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4.2 Setting up a model for incident analysis At about the time when this project task on microsimulation was carried out, VicRoads was interested in managing the on-ramp flow from Kings Way to the West Gate Freeway in South Melbourne in the evening peak. Some demand and traffic count data in this area of West Gate Bridge was available. A case study on incident management in the same area was regarded as useful to demonstrate the principles of using microsimulation as a tool for managing incidents.

Version 5.0.5.1 of AIMSUN NG was employed to carry out incident analysis on the freeway and the impact on both the freeway and adjacent arterial roads due to route diversion. Note that this case study is for demonstrating the use of microsimulation for incident management, and should not be treated as the preparation of incident management strategies for the study area. Much more resources are required for calibrating and validating a model for the site, which is near the Melbourne CBD. Also, many ITS measures can be investigated using microsimulation and this study has a focus on route diversion due to information provided to drivers.

4.2.1 Site description

The study site is the West Gate Freeway in South Melbourne between the Domain Tunnel portal and the Ingles Street overpass (Figures 4.1 and 4.2). The site is an urban freeway with closely spaced interchanges and high peak period traffic demand. The model was built over 3.2 km of freeway in both directions, although the study only focussed on the westbound direction.

The study site with its many arterials and side-streets is quite complex. For the purpose of demonstrating the use of an MSTM, the modelled arterial network was a simplified version of the real-world arterial network. The model included two surrounding arterial routes that were deemed to be relevant for route diversion purposes. These diversion routes crossed signalised intersections. Although Melbourne uses SCATS to control traffic signals, interfacing a SCATS controller to AIMSUN NG was beyond the scope of this case study and fixed-time control was used to simulate the operation of signalised intersections. The model was then checked to ensure that no uncharacteristic queuing resulted due to the adoption of fixed-time control.

An incident is assumed to occur near the exit of the Domain Tunnel in this case study. Road traffic can exit through an off-ramp that leads to either the Kings Way or the Power Street overpass through the off-ramp (designated as the Power Street off-ramp; see Figures 4.2 and 4.3). The details of these two routes for traffic diverting from the freeway (which is a continuation of the Domain Tunnel) in the case of an incident are as follows:

diversion route 1 - turn off the freeway at the Power Street off-ramp and turn right onto Kings Way then exit onto Normanby Road as above, and re-enter the West Gate Bridge through the Montague Street on-ramp

diversion route 2 - turn off the freeway at the Power Street off-ramp and enter Power Street overpass, continuing along Normanby Road and similarly re-enter the West Gate Bridge through the Montague Street on-ramp.

There is thus a common stretch between the two routes along Normanby Road and the Montague Street on-ramp.

Vehicle route choice was set to the option of fixed-route by minimum travel time. This option does not allow dynamic re-routing or traffic assignment but provides consistent steady-state solutions for comparative analysis. Route diversion due to an incident was totally controlled by diversion scenarios and policies entered into the model (see Section 4.2.2 for more detail on incident modelling).

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Figure 4.1: An aerial photograph of the study site near West Gate Freeway

Figure 4.2: Schematic diagram of study site (from AIMSUN NG)

Incident location

Ingles St overpass

West Gate Freeway

Domain Tunnel exit portal

Kings Way

Power St 500 m

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Normanby Rd

Domain Tunnel

Power St

Kings WayWest Gate Fwy

Montague St

500 m

Power St off ramp

Diversion route 2

Diversionroute 1

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Figure 4.3: Detailed location of simulated incident

Traffic demand on the freeway was derived from an origin-destination (OD) matrix that was made available for this study from VicRoads. The survey was carried out on Tuesday 2 December 2003. Vehicle classification was in two classes for modelling purposes: trucks and cars. The resulting OD matrix provided total counts for the period from 3:45 p.m. to 7:00 p.m. This matrix only provided a traffic demand that did not vary in this period. Some 15 min counts were available on the freeway westbound and a time-dependent profile was prepared for the OD matrix from 3:45 to 6:30 p.m.

The model developed aimed to demonstrate the general impact of incidents and the project budget did not allow detailed calibration that would be necessary for the application of the model to address specific local issues. Nonetheless, visualisation together with traffic demand calibration were carried out to confirm that the model worked properly and provided model counts similar to observed data at various locations. Outputs from detectors located in the study area were also analysed.

Power St overpass

Incident

Power St off ramp Power St

on-ramp 50 m

4.2.2 Incident modelling

The simulated incident took place on the West Gate Freeway westbound 100 m west of the Power Street overpass, and upstream of the Power St on-ramp merge point (Figure 4.3). The middle and right lanes of the three-lane carriageway were blocked from 4:30 to 5 p.m. and the blockage was removed by 5 p.m. in the model.

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A route diversion plan was simulated. Some Domain tunnel traffic under the influence of driver information systems were to divert out of the freeway. The information systems could be radio broadcasts, variable message signs or both. Other more personalised service could be the reception of SMS en-route to the freeway. Current microsimulation packages allow users to specify different levels of diversion under different scenarios or policies.

The diversion plan in this case study was that freeway traffic would divert out of the West Gate Freeway through either Kings Way or Power Street as previously mentioned. To simulate the operation of a traffic management control centre, it was assumed that the diversion scheme was initiated 2 min after the incident occurred, i.e. at 4:32 p.m. and switched off at 5:10 p.m. – drivers no longer received advisory information to divert out of the freeway 10 min after the blockage was cleared. Note that one lane of the three-lane carriageway was open for traffic throughout the incident period and drivers could choose to stay on the freeway in the model.

Previous research by Luk and Yang (2003) on the macroscopic modelling of the impact of driver information systems suggested that the level of diversion varies according to the incident location and the propensity to divert. In this microsimulation study, diversion levels at 15% (representing an average diversion rate) and 30% (representing a high diversion rate) were investigated.

In summary, four scenarios were constructed for this study as follows:

scenario 1 – this is the base case and no incidents occurred in the study period

scenario 2 – an incident occurred on West Gate Freeway 100 m west of the Power Street overpass but with no route diversion; this case represents a situation when drivers are reluctant to change routes or have no knowledge of which alternative routes to use – not an uncommon situation

scenario 3 - an incident (as per scenario 2) occurred on West Gate Freeway with 15% traffic diversion, choosing either Kings Way or Power Street in equal proportions

scenario 4 - an incident (as per scenario 2) occurred on West Gate Freeway with 30% traffic diversion, choosing either Kings Way or Power Street in equal proportions.

The base case scenario was created to verify the normal operation of the freeway. As with all microsimulation models, parameters such as signposting distance, driver reaction time, simulation step, lane changing and route choice models have significant impact on the modelling results. These parameters were kept constant across all scenarios. The reaction time chosen was 0.75 s and the simulation time step used was also 0.75 s; this combination enabled various simulation runs to be completed quite efficiently with consistent results. The case study focussed on the sensitivity of the level of diversion on performance metrics such as traffic flow, travel time, delay and density at different parts of the study area.

4.2.3 Simulation results

Most microsimulation packages provide outputs in the form of flows, travel times, delays and densities. These statistics are often available as time series at the following levels of spatial aggregation:

detector level

segment or link level

stream or route level

network level.

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The network level results were easily available but tended to be an over-aggregated set of results. For the analysis of freeway incidents, it is important to focus on those steams of traffic affected directly by an incident. In this study, traffic on the West Gate Freeway westbound route, the two (one-way) diversion routes and the three routes combined represented the traffic streams that were of concern to managing incidents. All results were based on the average of five simulation runs using different random seed values.

The model was analysed first at the detector level to verify its proper operation. A few detectors were located on the westbound carriageway of the West Gate Freeway. The output from two detectors was analysed – one 720 m upstream of the incident (Figures 4.4 and 4.5) and another 280 m downstream of the incident (Figures 4.6 and 4.7).

Figures 4.4 and 4.5 show the counts (veh/h) and speed (km/h) respectively at the upstream detector station for all four scenarios. The freeway traffic coming from the Domain Tunnel (a tolled road) was fairly constant throughout the study period in the absence of an incident. The counts reached a peak around 5:15 p.m. at 3000 veh/h. When the incident occurred, and in the absence of route diversion (scenario 2), the counts dropped to around 2000 veh/h. The speed (or spot speed measured at the detector) also decreased to about 15 km/h as queuing occurred at the detector station. When the blockage was removed at 5:00 p.m., there was a surge of flow from a backlog of traffic inside the tunnel and traffic flow returned to normal flow soon afterwards.

At a diversion level of 15% (scenario 3), the speed at this upstream detector station was substantially lower than the speed at 30% diversion (Figure 4.5). An MSTM therefore can demonstrate effectively different performance metrics at various levels of diversion – a traffic manager may choose to utilise the media, driver information system and other means to accelerate towards a target level of diversion if it appears that drivers are slow in diverting out of the freeway.

Domain Tunnel detector - 720 m upstream of incident (3 lanes)

0

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15:30 15:45 16:00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 18:15 18:30 18:45

Period commencing

Cou

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h)

No incident Incident - 0% diversionIncident - 15% diversion Incident - 30% diversion

Diversion active

Figure 4.4: Detector counts near the exit from Domain Tunnel to the West Gate Freeway

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Domain Tunnel detector - 720 m upstream of incident (3 lanes)

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Diversion active

Figure 4.5: Detector speeds near the exit from Domain Tunnel to the West Gate Freeway

The traffic flow from the detector downstream of the freeway incident (Figure 4.6) showed clearly the effect of diversion in having progressively less freeway traffic at the three levels of diversion (0%, 15% and 30%). This detector location experienced congestion even under non-incident conditions due to interaction of freeway traffic with heavy flows from the Power St on-ramp. Under incident conditions, this interaction was reduced as freeway flows are constrained by the incident, and Power Street on-ramp traffic entered the freeway more easily, hence higher speeds (Figure 4.7) on the detector during the incident (4:30 p.m. to 5:00 p.m.) Once the incident was cleared, banked up traffic was suddenly released and caused severe congestion. A moderate 15% diversion rate arrested this congestion somewhat, but a higher 30% diversion rate improved speeds on this detector even beyond that of the no-incident scenario, simply due to reduced traffic demand at that point.

These results demonstrate how microsimulation is able to model the outcome of complex scenarios and provide practitioners with insight into the secondary interactions that may affect the outcome of the incident management strategy in question. The above observations are naturally dependent on local site and traffic conditions. The principle of some level of route diversion out of the freeway is a critical component in managing incidents.

Most microsimulation packages also allow analysis at the stream or route level. All vehicles along a route from the Domain Tunnel to the Ingles Street overpass can be analysed as a stream. Figure 4.8 shows the average travel time of the westbound stream on the West Gate Freeway under the four scenarios. Again, a moderate level of 15% diversion rate reduced the maximum time (no diversion) of 750 s to 500 s, i.e. 4 min 10 s less or 33% reduction for the 3.2 km distance. The detailed outputs for the West Gate Freeway stream, the Kings Way stream and the Power Street stream are included in Appendix A.

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West Gate Fwy detector - 280 m downstream of incident between Power St and Kings Way on-ramps (3 lanes)

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Figure 4.6: Detector counts on West Gate Freeway downstream of incident

West Gate Fwy detector - 280 m downstream of incident between Power St and Kings Way on-ramps (3 lanes)

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Diversion active

Figure 4.7: Detector speeds on West Gate Freeway downstream of incident

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Figure 4.8: Travel times of the freeway traffic stream

The standard outputs of a microsimulation package provide a large amount of information but the output may not be sufficiently refined to show the necessary performance metrics in a traffic event such as a freeway incident. Post processing is therefore often required. Relevant post-processing results for this case study are summarised in Table 4.2 and Appendix A.

Table 4.2 summarises the average travel times for all traffic streams. A combined travel time metric for the three streams of traffic was obtained by flow-weighting individual travel times with the corresponding flows. The results are tabulated according to different scenarios. The results are for the period when diversion occurred. Appendix A also provides the full detail of flow, travel time and delay for all streams in time series.

The following observations can be made from Table 4.2:

the travel time on West Gate Freeway between the Domain Tunnel exit portal and the Ingles St overpass on the west end of the study area increased by 50% from 241 s to 362 s due to the incident – an increase of 2 min and 1 s

at a high level (30%) of diversion rate, the travel time on the freeway reduced by about 2 min from 362 s to 253 s, which was similar to the travel time of 241 s under incident-free conditions

the flow-weighted travel time at 30% diversion rate was 307 s, significantly less than the travel time of 362 s at 0% diversion rate (or 1 min 6 s less), but higher than the travel time of 253 s on the freeway; this implies that congestion on the alternative routes has been properly considered in the weighted value

by comparing scenarios 3 and 4, the Power St route was more prone to increased congestion due to diverted traffic in this diversion plan – a factor that needs careful consideration in planning alternative routes for incident management.

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Table 4.2: Average travel times for all traffic streams from 4:30 – 5:30 p.m.

West Gate Freeway stream westbound Kings Way stream Power Street Stream

Flow-weighted (combined)

stream Scenario

Flow (veh/h) Travel time (s) Flow (veh/h) Travel time (s) Flow (veh/h) Travel time (s) Travel time (s)

1 (no incident) 2467 241

(4 min 1 s) 0 332* (5 min 12 s) 0 221*

(3 min 41 s) 241

(4 min 1 s)

2 (incident; no

diversion) 2407 362

(6 min 2 s) 0 346* (5 min 46 s) 0 296*

(4 min 56 s) 362

(6 min 2 s)

3 (incident,

15% diversion)

2200 312 (5 min 12 s) 102 399

(6 min 39 s) 157 387 (6 min 27 s)

319 (5 min 19 s)

4 (incident,

30% diversion)

1994 253 (4 min 13 s) 193 505

(8 min 25 s) 279 567 (9 min 27 s)

307 (5 min 7 s)

* free-flow or uncongested travel times

To demonstrate the use of microsimulation for analysis at the segment or link level, the Normanby Road segment in Figure 4.2 was selected for further analysis. This segment is common to both diversion routes and has a length of about 0.82 km. The traffic flow on this segment was about 2200 veh/h before the onset of the incident on West Gate Freeway (see Figure 4.9). It increased significantly with the level of diversion as expected. The road was simulated as a two-lane road (one-way) and the average maximum flow or capacity was about 3000 veh/h as shown in Figure 4.9. The volume to capacity ratio was about 2200/3000 or 0.73 before the incident and close to 1 during the incident period under scenario 4.

Figure 4.10 further shows the variation of travel times on the Normanby Road segment, which reached a peak of 110 s or a spatial speed of (0.82 x 3600 / 110) or about 20 km/h. According to the Austroads (1988) Guide to Traffic Engineering Practice (Part 2), this speed value corresponds to a Level of Service of E which is near or at capacity. Other segment information on delay and density are shown in Appendix A in graph and table forms.

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Figure 4.9: Traffic flow on Normanby Road segment

Normanby Rd segment

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Figure 4.10: Travel times on Normanby Road segment

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4.3 Using microsimulation as a tool for incident management From the literature review in Section 4.1 and the case study in Section 4.2, it is apparent that microsimulation is a useful tool for the development and analysis of incident management strategies. There are however principles that should be followed in using MSTMs as a planning, operation and training aid for incident management. The following principles are recommended:

MSTMs are simulations of the real world and have limitations; but they are appropriate for testing different scenarios. The comparison of model outputs would enable the identification of best strategies.

Incidents on freeways have major impact in a corridor relative to those on arterial roads because affected traffic can have more opportunities to bypass an incident, e.g. turning into side-streets. However, freeway incidents should not be analysed in isolation. An MSTM for incident management should include both the freeway and relevant adjacent arterials. Route diversion due to traveller information or other measures can then be adequately analysed.

MSTM performance metrics should include those for a study area as well as separately for freeway and adjacent arterials in that area. A metric for the total network is not detailed enough to identify impacts at different parts of the network due to an incident. A comprehensive study of incident strategies should involve analysing MSTM outputs at the levels of a detector, segment (or link), stream (or route) and network.

An MSTM is easy to simulate the blockage of one or more lanes in an incident, and also easy to investigate which lanes are to be blocked on a carriageway. This has implications on incident management policies. At some sites, it may be possible to choose which lane(s) are still opened to traffic, e.g. to facilitate traffic exiting an off-ramp or avoiding excessive weaving actions on a freeway. An MSTM is useful in developing the right decision.

The case study at West Gate Freeway demonstrates the use of a fixed period for the diversion period, which represents the operation of a traffic management centre in switching on and off route diversion information to the public. An MSTM can also implement other more dynamic schemes – adaptively switching on and off of diversion information and dynamic route choice. It is advisable to apply a fixed route, fixed-time management policy as a benchmark before testing other more dynamic schemes.

It is important to carry out sensitivity analysis on incident parameters that include the level of route diversion, duration of an incident, severity of incident (i.e. the choice of lanes and the number of lanes closed), and the start time of the incident. The ease of preparing, processing and reporting of the outputs from different scenarios is an important consideration.

Post-processing of MSTM outputs from current software packages is often required to produce performance metrics that are different from standard outputs and more meaningful to stakeholders and users, at different levels of spatial aggregation (detector, segment, stream or network) and temporally (whole duration of simulation, peak hour or during route diversion).

MSTMs can undertake a much wider range of investigations then those presented in the case study. Freeway incident detection algorithms and variable speed limit signs are two aspects that are particularly appropriate for the study of incident management using microsimulation. Extra Application Programming Interfaces have to be developed in a microsimulation package, and are important areas for further research and development.

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5 CONCLUSIONS This report has highlighted the usage and limitation of using microsimulation in general and as a tool for the planning, operation and training of incident management in particular. The literature review in the report shows that microsimulation has been actively employed for analysing freeway and arterial incidents using a range of software packages, supported by local and overseas case studies. A case study of an incident on West Gate Freeway and its impacts on the freeway and its adjacent arterial roads are described in this report. It satisfactorily demonstrates the use of microsimulation as a tool for incident management.

Key principles in using MSTMs for the planning, operation and training of incident management from the literature review and the West Gate Freeway study are recommended. These include the use of comparative analysis from different simulation scenarios to identify best strategies. Route diversion requires the simulation of alternative routes and can be a complex task in developing the network model and compiling OD traffic data, but it is an important part of an MSTM. Without route diversion and a whole-of-corridor approach, it is not possible to properly estimate the impact of an incident and develop management strategies. Sensitivity analysis should be carried out for various incident parameters in a model – duration of an incident, severity of incident (number and choice of lanes closed), start time of an incident and level of traffic diversion. Dynamic route choice and traffic-adaptive switching on and off of driver information systems are possible in most microsimulation platforms, but it is recommended that a benchmark be set using a scheme based on fixed-route and fixed diversion duration.

It is further recommended that the post-processing of standard MSTM results be used to develop results that can be more useful to stakeholders and users at the appropriate spatial level of aggregation (detector, segment, stream or network) and temporally (whole duration of simulation, peak hour or during route diversion).

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6 REFERENCES Austroads (1988). Guide to Traffic Engineering Practice Part 2 – Roadway Capacity. Publication No. AP-

11.2/88. Austroads, Sydney.

Barcelo, J., Ferrer, J., Casas, J., Montero, L. and Perarnau, J. (2002). Microscopic simulation with AIMSUN for the assessment of incident management strategies.

Charles, P. (2001). Financing incident management. Smart Traffic. Deploying Incident Management Conference. Brisbane. May. www.transportroundtable.com.au.

Chu, L., Liu, H.X. and Recker, W. (2004). Using microscopic simulation to evaluate potential intelligent transportation system strategies under nonrecurring congestion. Transportation Research Record No. 1886, pp. 76-84. National Research Council, Washington, D.C.

Cragg, C.A. and Demetsky, M.J. (1995). Simulation analysis of route diversion strategies for freeway incident management. Report No: VTRC 95-R11. Virginia Transportation Research Council, Charlottesville, Virginia.

Farradyne (2000). Traffic Incident Management Handbook. Prepared for US Federal Highway Administration Office of Travel Management by P.B. Farradyne, November.

Hidas, P. and Awadalla, E. (2001). Deveoping incident managment plans by simualtion: a case study in Sydney. Proc. 8th World Congress on ITS, Sep 30 – Oct 9, 2001, Sydney.

Hidas, P. and Behbahanizadeh, K. (1998). SITRAS: a simuation model for ITS applciations. Proc. 5th World Congress on ITS, October 12-16, 1998, Seoul.

Jin, X., Cheu, R.L., Srinivasan, D., Ng, K.C., Ng, Y.L. and Lee, K.H. (1998). Applications of FRESIM: A microscopic traffic simulation model in an expressway in Singapore. Proceedings of the International Conference on Transportation into the Next Millennium, September, Centre for Transportation Studies, NTU, Singapore

Karl, C.A. (2005). Improving traffic incident management – literature review. ARRB Contract Report RC4354-1 for Austroads, Sydney

Luk, J.Y.K., Chung, E.C.S. and Sin, F.Y.C. (2001). Characterisation of incidents on an urban arterial road, Journal of Advanced Transportation, 35(1), pp. 67-92.

Luk, J.Y.K. and Tay, J. (2005). The use and application of microsimulation traffic models. ARRB Contract Report RC4286-4 for Austroads, Sydney. (in Final Draft; downloadable from http://www.angelfire.com/d20/james_luk)

Luk, J.Y.K. and Yang, C. (2003). Comparing driver information systems in a dynamic modelling framework. Journal of Transportation Engineering, 129(1), pp. 42-50.

Margiotta, R., Voorhies, K. and Lomax, T. (2004). Measuring and communicating the effects of traffic incident management improvements. National Cooperative Highway Research Program. Research Results Digest. May No. 289. Transportation Research Board, Washington D.C.

Millar, G., Tudge, R. and Wilson, C. (2004). Microsimulation evaluation of SCATS coordinated traffic control signals. Compendium of Paper presented at the 83rd TRB Annual Meeting, January 11-15, 2004, Washington, DC (CD ROM).

Transport for London (2003). Microsimulation modelling guidance notes for TfL. TfL, London.

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http://www.transportroundtable.com.au/

http://www.angelfire.com/d20/james_luk


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7 SUPPORTING DATA FOR CASE STUDY This appendix provides some key results from the West Gate Freeway study for all scenarios. The results are for the traffic streams along West Gate Freeway, Kings Way and Power Street, and for the Normanby Road segment. The contents are summarised below.

Captions Page no.

Figure 7.1: West Gate Freeway stream - flow 27

Figure 7.2: West Gate Freeway stream - travel time 27

Figure 7.3: West Gate Freeway stream – delay 28

Figure 7.4: Kings Way stream - flow 28

Figure 7.5: Kings Way stream - travel time 29

Figure 7.6: Kings Way stream - delay 29

Figure 7.7: Power Street stream - flow 30

Figure 7.8: Power Street stream – travel time 30

Figure 7.9: Power Street stream - delay 31

Figure 7.10: Normanby Road segment - flow 31

Figure 7.11: Normanby Road segment – travel time 32

Figure 7.12: Normanby Road segment – delay 32

Figure 7.13: Normanby Road segment – density 33

Table 7.1: Flow, travel time, delay and density of all scenarios for the Normanby Road segment

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Figure 7.1: West Gate Freeway stream - flow


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Figure 7.2: West Gate Freeway stream - travel time

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Figure 7.3: West Gate Freeway stream – delay

Kings Way stream

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Figure 7.4: Kings Way stream - flow

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Kings Way stream

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Figure 7.5: Kings Way stream - travel time

Kings Way stream

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Figure 7.6: Kings Way stream - delay

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Power St stream

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Figure 7.7: Power Street stream - flow

Power St stream

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Figure 7.8: Power Street stream – travel time

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Power St stream

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Figure 7.9: Power Street stream - delay

Normanby Rd segment

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Figure 7.10: Normanby Road segment - flow

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Normanby Rd segment

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Figure 7.11: Normanby Road segment – travel time

Normanby Rd segment

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Figure 7.12: Normanby Road segment – delay

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Normanby Rd segment

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Incident - 15% diversion Incide

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No incident Incident - 0% diversionnt - 30% diversion

Figure 7.13: Normanby Road segment – density

Diversion active

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Table 7.1: Flow, travel time, delay and density of all scenarios for the Normanby Road segment

Flow (veh/h) Travel time (s) Delay time (s) Density (veh/km) Time period s1 s2 s3 s4 s1 s2 s3 s4 s1 s2 s3 s4 s1 s2 s3 s4

16:15-16:20 2030 1960 2080 2071 63 63 63 64 6 5 6 6 17 17 17 17 16:20-16:25 2241 2126 2059 2260 64 64 63 64 6 6 5 6 19 18 17 19 16:25-16:30 2150 2220 2184 2188 63 64 63 64 6 6 6 6 18 19 18 18 16:30-16:35 2073 2097 2196 2253 64 64 64 64 6 6 6 7 18 18 18 20 16:35-16:40 2176 2258 2356 2779 64 63 65 68 6 6 6 9 18 19 20 25 16:40-16:45 2167 2164 2690 2824 64 63 67 69 6 6 8 10 18 18 23 26 16:45-16:50 2239 2349 2582 2894 64 65 66 69 6 7 7 10 19 20 23 26 16:50-16:55 2380 2436 2565 2642 64 65 65 71 7 7 7 13 20 21 23 30 16:55-17:00 2361 2510 2697 2976 64 65 68 104 7 8 9 45 20 21 23 42 17:00-17:05 2388 2328 2666 3223 65 64 67 101 7 7 9 42 20 20 24 40 17:05-17:10 2376 2328 2743 2822 64 64 69 107 7 7 10 49 20 20 25 44 17:10-17:15 2313 2424 2846 3067 64 64 67 119 7 7 9 61 19 20 24 47 17:15-17:20 2304 2407 2380 3264 64 65 65 94 7 7 7 36 20 21 20 35 17:20-17:25 2344 2368 2476 2572 64 64 65 67 7 6 7 9 20 20 21 23 17:25-17:30 2356 2462 2397 2560 64 65 65 67 7 7 8 10 20 21 20 22 17:30-17:35 2407 2371 2412 2383 65 64 65 66 7 7 7 8 20 20 21 21 17:35-17:40 2416 2352 2491 2476 65 64 65 65 7 6 7 8 21 20 21 21 17:40-17:45 2299 2354 2481 2606 64 64 65 65 7 7 7 8 20 20 21 23 17:45-17:50 2227 2205 2160 2294 64 64 64 64 7 6 6 6 18 18 18 19 17:50-17:55 2222 2308 2164 2232 64 64 64 64 6 7 6 6 19 19 18 19 17:55-18:00 2196 2085 2284 2160 64 64 64 63 6 6 7 6 18 18 19 18 18:00-18:05 2196 2241 2126 2114 64 64 64 63 6 7 6 6 19 19 18 18 18:05-18:10 2085 2308 2234 2198 63 64 64 64 6 7 7 6 17 19 19 18 18:10-18:15 2157 2109 2220 2172 64 63 64 64 6 6 6 6 18 17 19 18

Average 2254 2282 2395 2543 64 64 65 74 7 7 7 16 19 19 20 25

Note: s1 = no incident; s2 = incident with 0% diversion; s3 = incident with 15% diversion; s4 = incident with 30% diversion

Licensed to Mr zhaofeng tian on 30 Dec 2009. Personal use licence only. Storage, distribution or use on network prohibited.

INFORMATION RETRIEVAL

Austroads (2007), Microsimulation as a Planning, Operations and Training Aid for Incident Management, Sydney, A4, 44pp, AP-R299/07

Keywords:

Modelling, Incident, Microsimulation, Traffic Model, Software, Delay, Travel Time, Congestion, Roadworks, Events, Training, Driver Information

Abstract:

This report investigates the appropriateness of microsimulation modelling for the planning, operation and training of incident management in a road network. The methodology for this study includes a literature review and a modelling case study of the West Gate Freeway in Melbourne. A microsimulation model was set up using AIMSUN NG for the study area. It is concluded that significant resources are required in setting up a microsimulation model for incident analysis. Once set up and calibrated, the model is a useful tool for the planning, operation and training of incident management and other complex traffic applications.

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9. P

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se li

cenc

e on

ly. S

tora

ge, d

istr

ibut

ion

or u

se o

n ne

twor

k pr

ohib

ited.

AP-R299/07 Microsimulation as a Planning, … CD/Menu Files...Microsimulation as a Planning,...

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