Effect of Side Friction Parameter on Urban Road Traffic ...

Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019

314

Effect of Side Friction Parameter on Urban Road Traffic: Under Mixed

Traffic Scenario

Ritvik CHAUHANa, Ashish DHAMANIYAb, Shriniwas ARKATKARc, Prasanta K. SAHUd,

Durgesh VIKRAMe

aResearch Scholar, Department of Civil Engineering, Sardar Vallabhbhai National Institute

of Technology, Surat, 395007, India; E-mail: [email protected]

bAssociate Professor, Department of Civil Engineering, Sardar Vallabhbhai National Institute

of Technology, Surat, India

Email b: [email protected]

Email c: [email protected]

dAssistant Professor, Department of Civil Engineering, Birla Institute of Technology and

Science - Pilani, Hyderabad, India; Email: [email protected]

e Assistant Professor, Department of Civil Engineering, Birla Institute of Technology and

Science - Pilani, Pilani, India; Email: [email protected]

Abstract: US Highway Capacity Manual and Indian Highway Capacity Manual define road

capacity as the maximum sustainable flow rate at which vehicles or persons can reasonably be

expected to traverse a point under the given roadway, geometric, traffic, environmental, and

control conditions. The capacity of the roadway system often gets reduced by the roadside

activities called side friction. In India, side friction prevails predominantly in urban areas due

to a significant rise in population and economic activities. These activities restrain the urban

road system to serve at full operating capacity. In this study, the impact of a side friction

parameter (on-street parking, and non-motorized vehicles) on the performance of an urban

road has been measured. Secondarily, the product limit method of capacity estimation has

been implemented to overcome the randomness observed in capacity value. A

microsimulation model is developed to validate the results.

Keywords: On-street parking; Capacity estimation; Mixed traffic condition; Product limit

method; Fundamental diagram

1. INTRODUCTION

1.1 General

Surplus amount of economic activities are observed in the urban areas of all the

communities of the world. Road transportation and Infrastructure acts as a backbone for all

the activities. In terms of logistics, more emphasis is given towards the development of road

infrastructure and network as it plays a major role in the economic growth and development

of the communities. Despite the observed increase in the road infrastructure, it has failed to

cope with the traffic growth rate and transportation demand. Unavailability of space, as most

of the urban areas are already built-up, makes it difficult to carry on any significant

expansion. In addition, it might not be economical to increase the road space in the instance,

though it might be required in the future, following the traffic growth. Even so, the primary

goal should be the optimization of the road network, to increase its efficiency in terms of

traffic movement instead of expansion. Studies conducted in developing countries have stated

mailto:[email protected]




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that significant amount of activity is present alongside and on the roads, which affects the

operation on the carriageway(Bang, 1995; Chiguma and Bang, 2006; Marler et.al. 1994). This

interference to the smooth flow of traffic is “side friction”. US Highway Capacity Manual

(HCM 2010) and Indian Highway capacity manual (Indo-HCM 2017)specifies various

factors, including roadside activities that reduce capacity and affect speed-flow relationships.

Karl Bang (1995) defined side friction as a composite variable describing the degree of

interaction between the traffic flow and activities along the side(s) and sometimes across or

within the travelled way. Activities likely to disrupt traffic flow include blockage of travel

way (public transport vehicles, pedestrian crossing or movement, non-motorized or slow

moving vehicles), Roadside parking and un-parking activities, vehicles leaving or entering

roadside premises, roadside vendors, etc. Studying these factors and analyzing their effect on

the operational performance of the road network of the urban areas can be termed as studying

the effect of side friction on urban roads in broader terms.

Capacity is defined as the maximum sustainable flow rate at which vehicles can be

expected to traverse a point or uniform section of a lane or roadway during a given time

period under prevailing roadway, traffic and control conditions (Indian Road Congress

106:1990; HCM 2010).Some studies have stated that the maximum flow value may not

necessarily indicate or coincide with the transition of traffic from uncongested state to

congested state(Asgharzadeh and Kondyli, 2018; Brilon et.al. 2005; Lorenz and Elefteriadou,

2001; Minderhoud et.al. 1997). To overcome such randomness in capacity Breakdown

probability models have been developed. Following the studies related to comparison and

applicability of capacity estimation methods (Asgharzadeh and Kondyli, 2018; Ben-Edigbe et

al. 2013; Li and Laurence, 2015; Minderhoud et.al. 1997; Suresh and Umadevi, 2014), two

methods, namely macroscopic fundamental diagram method and Product Limit Method were

found to be the best and reliable.

1.2 Problem Statement

In India, the number of side friction parameters are too many and their effect on the

traffic flow is substantial. Similar situation is also observed in countries with similar traffic

and demographic characteristics. Even if we follow, the methodologies prescribed in the US

HCM (2010) there are factors, which have not been considered. Factors like on-street trading,

nonmotorized vehicles, on-street parking’s, irregular stopping of public transport vehicles for

boarding and alighting of passengers, pedestrian movement, un-designated crossing, etc. All

these activities are highly observed to be plying on the Indian roads. But among all the

parameters on-street parking is found to be the most dominant(Lim et.al. 2012; Peprah et.al.

2014; Yousif, 1999). The traffic is heavily interruptedbut this, and hence reduction in the

performance of the road infrastructure is observed. Slow moving vehicles (non-motorized

vehicle class) can also prove to be a form of side friction as these vehicles move at

comparatively slower speeds and if present in higher concentration, may block some part of

lanes in the carriageway. The main aim of the study is to find the impact of on-street parking,

non-motorized trafficand Motorized two-wheeler concentration in traffic stream on

performance and capacity of the Urban Road link. Secondarily this study also aims to test the

stochastic approach of capacity estimation using the product-limit method.

2. LITERATURE REVIEW

Some of the initial work concerning side friction was laid down by Karl. L. Bang

(1995). He suggested that importance should be given to corporate the effect of side friction


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explicitly into procedures for calculation of speed and capacity. The factors that could be

considered as side friction parameters were properly described and summarized in the study.

Furthermore, side friction is also classified based upon the level of impact it had on the traffic.

The analysis done in the study is based on the HCM model and the author tried to determine

the adjustment factor for the free flow speed calculation. Reduction factors related to

carriageway width reduction and shoulder width reduction have been determined for different

road types and class of side friction. Chiguma and Bang(2006)analyzed side friction impacts

on the urban road links based on a case study for Dar-es-salaam a city in Tanzania, they

studied the effect of side friction on the microscopic and macroscopic level. The effects on

speed and capacity are aggregated for the macroscopic analysis whereas each side friction

parameter is studied on an individual basis for the microscopic analysis. The impact is

represented as a reduction in average speed caused by each individual side friction factor on

the corresponding type of road.

Capacity is defined precisely in the Highway capacity manual, buta misunderstanding in

the interpretation of the derived value is prone to occur. According to the study “Assessment

of Roadway Capacity Estimation method”(Minderhoud et.al. 1997), this problem is caused

due to the presence of different types, approaches, and procedures for capacity estimation. To

simplify future proceedings, the authors have classified various capacity estimation methods

into the direct empirical and indirect empirical method. An overview of various methods

containing the most important of their characteristics is presented in a tabulated manner. The

characteristics may serve as criteria for adoption of the method suitable for the need, analysis,

and data availability. Based on the above findings Product limit method for capacity

estimation is utilized in this study. Developed recently, the method is useful in tackling the

randomness in capacity. The product-limit method is based on the survival analysis method

by Kaplan and Meier. The method is used to find the probability of breakdown or survival of

traffic condition corresponding to the flow value. Though other studies (Li and Laurence,

2015; Shao, 2011) have been conducted, precise description defining breakdown or

breakdown probability value has not been specified. (Asgharzadeh and Kondyli, 2018; Shao,

2011), studied the product limit method and compared the results achieved with the simulated

results validating the use of the method.

Simulation is recognized as a versatile tool for traffic flow modelling by various

researchers. But modelling the heterogeneous traffic proves to be rather challenging than

modelling of homogeneous traffic as several factors like driver aggressiveness, lane

discipline, driver behaviour for a different class of vehicles and varying traffic mix causes

challenges in the calibration of the model to the field like condition. Studies have provided

efficient steps and parameter measures, methodology and to calibrate and simulate mix traffic

conditions (Ashish Dhamaniya and Chandra, 2016; Bains et.al. 2012; Siddharth and

Ramadurai, 2013). Although several studies have been done for calibration of Indian Traffic,

unfortunately, the studies and values presented in them cannot be used to full extent. Studies

are useful, to set some general benchmark of calibration values for different traffic conditions,

road infrastructures, systems or networks. This will help in achieving faster calibration of the

models. Heterogeneous traffic composition, irregular geometry, loose lane discipline make it

difficult to analyze the traffic and arrive at precise results. Micro-simulation tools offer vivid

flexibility in studying the behaviour of such traffic condition. Being widely used in

transportation operations and management, researchers say that analysis is safer, less

expensive and much faster than field implication and testing (Daamen et al. 2014). It

effectively analyses and evaluates proposed improvements and alternatives. Moreover, any

variation in geometric or traffic conditions can be analyzedeasily. Traffic model in VISSIM

needs to be calibrated, to efficiently represent the field condition. In addition, for the


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calibration, the required parameters to model and simulate the field conditions must be known

in advance.

3. STUDY AREA AND DATA COLLECTION

3.1 Site Selection

A preliminary survey was conducted in the city of Jaipur from the state of Rajasthan in

India. Possible locations were scouted after inquiring about the traffic condition with local

residents and traffic authorities stationed in various parts of the city. Following

comprehensive visits to the sites during different times of the day, observing traffic,

geometric and surface condition of the roads the study stretch was finalized. It is desirable

that the study section meets the following characteristics to provide ease in classification and

analysis of the side friction parameter. Only a single type of side friction parameter (SFP) is

present on the site. There is no disturbance to the site from either merging/diverging traffic or

any other different type of side friction parameter. The SFP exists for a considerable distance

and prolonged duration. The traffic condition in the congested state is observed on the site to

help determine the capacity.

Figure 1a

Figure 1b:

Figure 1 Site Details. (1a: Satellite view showing on-street parking at the site. 1b: Base

Geometry of the site when side friction is absent)

(Figure 1 a, b) The road section selected is a sub-arterial road part of MI Road in Jaipur

near Collectorate Circle and adjacent to the District Court. Road section is a two-way divided

road with a median, restricted access and located in a straight flat terrain in urban areas where

mobility is of primary importance. Raised kerb path exists on both the side of the carriageway

for pedestrian movement. Road width is constant; the surface is paved and in good condition,


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lane markings are present and clearly visible. The research is limited to studying a specific

factor on a single road stretch. In the presentscenario, on-street parking is considered.

Average carriageway width without side friction is 8.4m, whereas when side friction exists

the average carriageway width is reduced to 6.4m.

3.2 Data Collection and Extraction

Video-graphic data is collected on a bright clear sunny day. The data is collected for

nearly five hours on three working weekdays. The time for data collection is decided such that

it captures data for the time duration when side friction parameter prevails and when it

diminishes completely. During this period the traffic’s, transition from uncongested to

congested state when moving from far upstream section to SFP affected area is observed.

Geometric data required in model creation for simulation is obtained manually from field

measurement.

The recorded video was replayed on a large screen in the laboratory. The observed

heterogeneous traffic is classified into seven classes. The non-motorized vehicle, two-

wheelers, three-wheelers, small cars, big cars, Bus/Truck, commercial vehicles. Traffic

composition is shown in Figure 2. Classified volume count and its corresponding average

traffic speed for every 5-min interval are extracted. Volume counts for every vehicle class are

converted to PCU using their passenger car equivalents calculated using the dynamic PCU

estimation method(Chandra et al. 1997). After addition, total PCU count for every 5-min

interval is converted to its equivalent hourly volume. Space mean speed is calculated for each

corresponding 5-min interval. Figure 3 shows the detailed speed volume plot.

Figure 2. Traffic composition

1.43%3.2%

1.18%

4.92%

26.47%8.75%

54.05% Two-Wheelers

Auto Rickshaws

Passenger Cars

SUV's

Commercial Vehicles

Bus/Heavy

Non-Motorized


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Figure 3.Speed Flow Data

4. METHODOLOGY

4.1 Effect of On-Street Parking

As the preliminary objective is to determine the capacity of the selected road section

while the side friction parameter prevails and when its effect is negligible or absent, the

analysis is performed for both the conditions using the methods of Product limit and

Simulation. The collected data is separated into two parts for each day. Division of data was

done such that the time duration when on-street parking is present is considered as data when

side friction prevails. While the time duration when the presence of on-street parking

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diminishes, such that no vehicle is affected during its traversing through the section is

considered as the data when side friction is absent. We would like to specify that the time of

this division is not constant on each day. The division is done completely based on

observations made from the video data.

4.1.1 Product-limit method

Based on the Survival Analysis by Kaplan and Meir is one of the methods to develop

the breakdown probability models.Maximum number for volume or PCU count may not

necessarily represent the transition of traffic state from congested to the non-congested

condition. Hence, various researchers have tried to identify as to what value of capacity

should be used. Volume at transition point or the maximum volume before breakdown.

Therefore, it is necessary if we are to pursue capacity value. For breakdown to occur a section

with bottleneck where frequent congestions are observed is to be selected. Also, the transition

from uncongested to the congested state would signify that the capacity is reached. However,

it may not define the carrying capacity of the upstream section, since the congestion is due to

the spill-back and queuing caused due to bottleneck. The bottleneck characterizes the capacity

of the downstream section. However, for this study, we are precisely looking for such kind of

section, where congestion is due to the bottleneck created by side friction parameter (on-street

parking)

Table 1 Comparison of lifetime survival with capacity survival

Characteristics Lifetime analysis Capacity analysis

Parameter Time t Traffic volume q

Failure event Death at time t Breakdown at volume q

Lifetime variable Lifetime T Capacity c

Censoring Lifetime T is longer than the Duration

of the experiment

Capacity c is greater than

Traffic demand

Survival function S(t) = 1 – F(t) Sc(q) = 1 – Fc(q)

Capacity has been defined as a random variable due to traffic breakdown occurring at various

flow rate levels [5]. Capacity probability function Fc(q) can be expressed as:

Fc(q) = P(C<q) (1)

Where,

q:maximum flow rate

C: Capacity of the road section.

So when the flow rate reaches the capacity value (i.e. Q=C) breakdown will occur. Therefore,

Fc(q) will denote the probability at the corresponding flow rate q, the breakdown will occur.

Contrarily survival probability function is given as Sc(q), which can be expressed as

Sc(q) = P(C>q) (2)

Therefore, as long as flow rate does not reach capacity value the traffic will move in

uncongested state and the breakdown does not occur. So,

Sc(q) = 1- Fc(q) (3)

Thus, the estimator of product limit for the survival function is given as

𝑆(𝑞) = ∏Ki−di

Ki𝑖:𝑞𝑖≤𝑞 , i∈ 𝐵𝑖 (4)


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Where,

(Ki): No of intervals with traffic volumeqi ≤ q;

(Di)no of intervals with traffic volume qi > 𝑞 ;

(B) Breakdown Interval sets {B1, B2, B3….}

As described earlier analysis for conditions when side friction prevails and it is absent,

are done separately. As the procedure goes the first part is to define and identify the

breakdowns from the data. The breakdown is defined as the transition of operations from non-

congested to congested conditions. Kondyli and Brilon(2011)concluded that speed as a

measure can be used to identify the Breakdown. Lorenz and Elefteriadou (2001)defined

breakdown as occurring when the average speed of all lanes drops below a specified threshold

for a period of at least 5 minutes. Brilon et.al.( 2005)defined breakdown as a reduction of

average below a threshold of 70 Km/h for one particular 5-min interval can be defined as a

breakdown. Elefteriadou and Lertworawanich(2002) during their study on-ramp merging

proposed that a breakdown is defined when the speed decrease between two consecutive time

intervals exceeds a pre-specified threshold, and this lower speed is sustained for a predefined

length of time. Another study on freeways in Germany (Brilon et.al. 2005)defined breakdown

as occurring when the average speed of all lanes drops below 90 km/ h for a period of at least

5 min. Hence, following such findings we have specified two conditions to define and identify

a breakdown. Firstly, the average speed of all lanes drops for a period of at least 5 min. before

traffic recovers to its original condition prior to speed reduction (Table 2). The second

condition is thatthe low-speed condition is below a specified threshold and is sustained for at

least 5-min. Threshold speed is considered as the maximum of post-breakdown speeds

observed among all the identified probable breakdowns that meet the first condition.

Table 2. Probable Breakdowns satisfying condition 1

Time Pre-Breakdown

Speed

Post Breakdown

speed

Speed

Difference

Duration of

Congested Traffic

1 (D2) 11:05-11:10 23 9 14 35 min.

2 (D2) 13:25-13:30 24 9 15 10 min.

3 (D2) 12:55-13:00 25 16 9 20 min.

4 (D2) 10:45-11:00 23 14 8 15 min.

5 (D3) 09:25-09:30 21 15 6 15 min.

6 (D3) 11:25-11:40 21 9 12 30 min.

7 (D3) 11:10-11:15 16 10 6 10 min.

8 (D3) 09:55-10:15 16 8 8 45 min.

9 (D3) 12:05-12:10 26 9 17 10 min.

10 (D3) 12:25-12:30 19 11 8 25 min.

The absence of specific literature that defines threshold speed value for a breakdown on

urban roads, makes the task difficult. In this case, considering 15 percentile speed does not

reflect the actual conditions observed during the breakdown. As the speeds observed

throughout the peak period when side friction exists are consistently low. But considering all

the initially identified breakdown for the calculation will not justify the process as the

magnitude of speed difference in pre-breakdown and post-breakdown speed might not be

significant. Hence for this study, the threshold speed value is considered as a maximum of the

post-breakdown speeds observed among all the identified probable breakdown conditions


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(i.e., 16 Km/Hr.). With this, we can eliminate the probable breakdowns whose effect on the

traffic stream may be insignificant. After shortlisting of actual breakdowns (Figure 4), the

calculation to find corresponding breakdown probability is carried out. Similarly, breakdowns

were identified for the duration when side friction is absent and the calculations are

performed. Figure 4 shows the Breakdown probability against the flow. It can be seen that the

probability goes on increasing at the maximum flow observed during the time period when

side friction exists and when it is absent. This can be attributed to the breakdown observed at

the maximum flow values. Following the methodology, if we substitute corresponding values

in Eq.4 to calculate survival probability. For the condition when side friction exists, survival

probability obtained will be zero. And thereafter using Eq. 1 we get the probability of

breakdown equal to 1 or the 0.7 as in the case when side friction is absent

Figure 2. Final Breakdown Identified after assigning Threshold Value

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(a) (b)

Figure 3Breakdown probability Flow; (a) Side friction present; (b) Side friction absent

4.1.2 Simulation

VISSIM is used for simulating the traffic conditions. As for model creation, Geometric

data is collected manually. An aerial view screenshot of the study site is acquired from

Google maps and used as a background image in VISSIM. The image is set to proper scale.

The background image is used for reference purpose and does not affect the geometry of the

modelled created using the field data. Point to note is that model creation, calibration and

validation are done using the data when side friction is absent. Sufficient Buffer length is

provided at the upstream of study location for the model to prime and achieves better results

from the model. Traffic composition observed over the duration of 3 days is averaged and

used as input for vehicle composition. Desired speed distribution curves for each vehicle class

are generated from the speed data extracted. Link behaviour for the road section and driver

behaviour for each vehicle class is defined. Calibration of driver behaviour parameters is done

by trial and error basis. The model is considered to be calibrated when for a given volume; the

traffic composition, average speed of each vehicle class and flow values are found similar to

the corresponding values observed in the field. The time period for which each simulation

shall run is set to 4300 seconds, and time duration of for which the data measurement point

shall start and stop recording the data is set from 600 sec. to 4200 sec. (1 hr.) This is done to

allow the simulation of a priming period of 600 sec. (10 mins.) to reach field like conditions

After the calibration of the model, the side friction effect of capacity is analysed. Side

friction is introduced by reducing carriageway width at a set of regular intervals. Beginning

from the actual field conditions when no on-street parking is present and uniform carriageway

width is available throughout the stretch. In such a case, road width reduction is said to be

0%. Variation in average traffic speed and flow values are observed by increasing the input

vehicle volume at a regular interval of 100 vehicles. Results of vehicles passing the data

collection point during the simulation are converted to their passenger car equivalent and

added to define the per hour flow for the given conditions. After one such cycle, carriageway

width is reduced by 10% and a similar procedure is followed for every cycle until 30% of

road width reduction. It should be noted that after starting of simulation, the run is allowed to

prime for 10 minutes before beginning the data collection at data collection point defined in

the model.

Simulations were performed at increasing input volumes while varying the carriageway

width in a pre-specified and cyclic order described earlier (Figure 6).

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Figure 4 Geometric Model for parking condition in VISSIM

4.2 Effect of Non-MotorizedVehicles

Although, the concentration of non-motorized traffic in urban areas is less, in some

cases, it has been observed to be more than 5%. Moreover, with the idea of sustainable

transport development taking root, studying the effect of increasing non-motorized transport

share in the traffic is warranted. Some studies have already been conducted in these

aspects(RAHMAN et.al. 2014; Shi and Li, 2008). Although studies have gone as to estimate

results for up to 95% (Rahman et al. 2003) of non-motorized vehicles in traffic composition,

for the present study a maximum compositional share of 10% for NMV’s has been

considered. Since such conditions and data is scarcely observed in urban areas, moreover on

urban arterial roads, a simulation-based approach is used to measure the effect of NMV on the

carriageway capacity. To tackle this hurdle microsimulation approach is used. Simulation

model used to measure the effect of on-street capacity was used, with the base condition, i.e.

actual carriageway width was used without any reduction. Traffic composition was changed

for non-motorized vehicles and standard cars accordingly.

It is found that the traffic composition also affects the capacity of the

carriageway(Chandra et al. 2016). Hence, the composition of the dominant vehicle class of

2W was varied accordingly. The 2Wcomposition was changed and respective alterations were

made to passenger car composition as it is the standard measure for volume. But, this change

was made only for side friction affected section. Means, the original microsimulation model

with reduced carriageway width was considered. Capacity for all cars condition was

determined and subsequently, the 2W composition was increased from 0% to 50%.

5 RESULTS

5.1 Effect of On-street parking

From the results obtained using Product limit method and simulation, it is evident that

side friction results in a reduction of operational performance of urban roads. 10% Breakdown

probability (90% Survivable Probability) (Brilon et.al. 2005; Kondyli and Brilon, 2011; Li


325

and Laurence, 2015)is considered as a threshold value for capacity estimation. Following the

field condition, it is observed that 21% carriageway width reduction exists. Table 3 and

Figure 7 shows theresults from PLM and simulation are compared.

(a) (b)

Figure 7: Simulation Results for decreasing carriageway width. (a) Flow variation with respect to

increasing influx volume. (b) Speed Flow relation

Table 3: Result of MFD, PLM and Simulation

On-Street

parking PLM Method Simulation % Reduction

Capacity Present 4200 PCU/Hr. 4159 PCU/Hr. 11.56%

Absent 4749PCU/Hr. 4708PCU/Hr. 11.66%

Average

Speed

Present 14 Km/Hr. 13 Km/Hr. 46.15%

Absent 26 Km/Hr. 24 Km/Hr. 45.83%

5.2 Effect of Non-Motorized Vehicles

1000 1500 2000 2500 3000 3500 4000 4500 5000

20

25

30

35

40 0% NMV

1.72% NMV

5% NMV

10% NMV

Avera

ge S

peed (

Km

/Hr.

)

Flow (PCU/hr)

Figure 8 Speed Flow plot of simulated results for varying Non-motorized vehicles composition

Generally non-motorized vehicles are found to traverse along the extreme end from the

carriageway median and hence do not usually block the traffic on the carriageway, but when

3000 3500 4000 4500 5000 5500 60003000

3200

3400

3600

3800

4000

4200

4400

4600

4800

5000

Flo

w (

pcu

/h)

Input flow value (vehicles)

0% Width Reduction

10% Width Reduction

20% Width Reduction

30% Width Reduction

0% Width Reduction

10% Width Reduction

20% Width Reduction

30% Width Reduction

3000 3250 3500 3750 4000 4250 4500 4750 50000

5

10

15

20

25

30

35

Av

g.

Sp

eed

(K

mp

h)

Flow (pcu/h)


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the composition increases, it might happen that an NMV to be traversing along the middle of

the lane or right end (Left-Hand Traffic) and end up creating a temporary moving bottleneck.

Simulation results have been specified in figure 8 and table 4.

Table 4 Results for varying NMV composition

Non-Motorized Vehicles Simulated Capacity Average Speed

0% 4855 PCU/Hr. 28 Km/Hr.

1.72% (Field Observed) 4708 PCU/Hr. 24Km/Hr.

5% 4489 PCU/Hr. 21 Km/Hr.

10% 4242 PCU/Hr. 21Km/Hr.

5.3 Effect of Change in Traffic Composition

0 1000 2000 3000 4000 5000

15

20

25

30

35

40

All Cars

0% 2W

10% 2W

20% 2W

30% 2W

40% 2W

50% 2W

Av

era

ge S

peed

(K

m/H

r.)

Flow (PCU/Hr.)

Figure 9 Speed flow plot for varying 2W composition in the presenceof on-street parking

Multiple simulationruns with increasing traffic volume for respective traffic composition were

carried out. Due to higher manoeuvrability and less space, it was expected that the capacity of

the carriageway should also increase following the findings by (Ashish Dhamaniya and

Chandra 2016; Chandra et al. 2016; Meena et al. 2018), but the results are found to be

conflicting.

Table 5 Results for varying 2W composition in the presence of on-street parking

2W Composition Simulated Capacity Average Speed

0% 4619 PCU/Hr. 25 Km/Hr.

10% 4532 PCU/Hr. 24 Km/Hr.

20% 4544 PCU/Hr. 24 Km/Hr.

30% 4244 PCU/Hr. 25 Km/Hr.

40% 4185 PCU/Hr. 24 Km/Hr.

50% 3979 PCU/Hr. 25 Km/Hr.

All Cars Scenario 4559 PCU/Hr. 30 Km/Hr.


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Following the trends from results, when compared for all the compositions, increasing 2W

traffic composition shows a pattern towards an increase in capacity of the carriageway but

until a certain point and then goes on the reverse with increasing traffic volume at the same

composition as seen in figure 9. The final results are specified in table 5.

6 SUMMARY AND CONCLUSION

From the results, it is evident that the fringe conditions on the carriageway, affect the

performance of the road and results in increased discomfort to the travellers. Therefore, the

study analyzing their effect is much needed to help policymakers in proper decision making in

terms of design, operation, and enforcement to effectively manage the traffic plying on the

road network and mitigate the effect of side friction. Capacity estimation methods generally

used, provide a deterministic and discrete value, but they fail to consider the stochastic nature

of traffic. With the application of Product Limit Method, we have attempted to take into

account the random nature of capacity value. Instead of providing a specific value, the method

provides a distribution curve, for the researcher or planner to consider the value as per his

requirements or the intended purpose of the analysis. But, the implementation of the method

has some challenges, primarily what is perceived to be a breakdown scenario during the

observation and the breakdown probability value that should be considered to represent the

capacity of the selected road section.

a) The breakdown has been defined differently by various authors. However, it can be said

that it will largely depend on the location of the study. The speed threshold defining the

breakdown at freeways may be much higher than the threshold defining breakdowns in

urban roads.

b) Based on the Breakdown probability considered, the capacity value may also vary. Higher

breakdown probability might represent higher capacity value.

So, either a systematic and well-defined set of the condition should be made to identify

and define breakdowns. Alternatively, a questionnaire survey can be carried out at the site to

consider the traveller's perception of breakdown [8]. For the breakdown probability, it may

vary as per the demand of the study. Higher breakdown probability may be considered for

traffic management purposes, while lower breakdown probability may be applied for

designing purposes considering the future growth of the traffic. Again, the analysis and result

will vary based on the purpose of the study. As the breakdown volume considered may be

different for designing or management purposes.

Coming to the effect of side friction parameters, it was found that the presence of on-

street parking resulted in around 12% reduction in capacity of the carriageway causing around

an average delay of 60 secs. in travel times.A 13% decrease in operational capacity was

observed for a traffic composition with 10% NMV. Traffic composition always affects the

capacity of the carriageway. When studied for side friction affected area (reduced carriageway

width due to on-street parking), it was found that the dominant vehicle class of 2W caused a

reduction in the capacity. This is in contrast to the prediction and results by (Chandra, 2004;

Meena et.al.. 2018) where, contrarily to base capacity with all cars condition, increase in 2W

vehicle composition causes increase in capacity. Following the results in figure 9 the theory

was found to be valid till a certain breakpoint(around 2900PCU/Hr. in this study) but with

increasing traffic flow, the trend reverses and any increase in 2W vehicle composition in

traffic reduces the volume carrying capacity of the carriageway. This might be attributed to

the higher manoeuvring tendancyof 2W vehicle class and smaller lateral gaps causing

increase in conflicts at the bottleneck (onset of on-street parking as shown in figure 6). Till


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the breakpoint it can be said that the 2W traverse around the carriageway with higher

manoeuvrability and better Level of service conditions causing faster dispersing of traffic. But

after the breakpoint it can be said that the absence of lane following, the higher manoeuvring

tendancy of 2W vehicle class, smaller lateral gaps and tendency to prioritize self through

movement, results in increase of conflicts and interruption to other vehicles thus increasing

congestion and delay, subsequently resulting in a decreased capacity. It should also be noted

that this condition was observed in simulation based side-friction affected scenario, which is

the existence of bottleneck (reduced carriageway width due to on-street parking) might be the

key reason for such results. In absence of such situation (i.e. base condition) the results by

(Ashish Dhamaniya and Chandra, 2016; Chandra et.al.. 2016; Meena et.al.. 2018)should hold

true, this can be verified by further comparison of the two scenarios with varying 2W

compositional share in traffic.

This study presents the effect of three parameters namely on-street parking, non-

motorized traffic and increasing two-wheeler composition in side-friction affected area on the

performance of the carriageway. Though presented individually, it should be noted that the

on-field conditions might be different. Generally, a single side friction parameter may not

exist on the field but another additional SFP also is affecting the traffic performance. So the

decrease in capacity might be more than specified results. In any degree, side friction hinders

smooth and efficient operation of traffic on the carriageways. The notion of proper design of

facility, education to use the existing facility and enforcement of for wrongful usage should

be cyclically self-evolving, leading towards better and safer design and operation of a traffic

infrastructure. Vehicular traffic oriented deigns often lead towards neglecting of other road

users and hence resulting in rise of side friction parameters, which as this study states,

deteriorates the performance of traffic infrastructure. With this study the authors aim to

emphasis on the negative effect of side friction parameter on the urban traffic infrastructure so

as to help designers and policymakers towards efficient and safer traffic infrastructure design

and operation.


329

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