Transit Performance Evaluation at Signalized Intersections of Bus

Rapid Transit Corridors

Robel Desta

1 a

, Tewodros Dubale

and János Tóth

Department of Transport Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering,

Budapest University of Technology and Economics, Muegyetem rkp. 3, 1111 Budapest, Hungary

Department of Civil Engineering, Institute of Technology, Hawassa University, P.O. Box 1528, Hawassa, Ethiopia

Keywords: Bus Rapid Transit, Delay, ITS, PTV VISSIM, Transit Signal Priority.

Abstract: Bus Rapid Transit (BRT) is one of the mass transportation solutions consisting of infrastructures integrating

dedicated bus lanes and smart operational service with different ITS technologies like Transit Signal Priority

(TSP). Delay at an intersection is among the major factors for poor transit performance. This study examines

the performance of buses at intersections of BRT corridors, which are privileged with Signal Priority on the

dedicated lane. Simulation models were developed for the selected intersection together with the real-time

calibration and validation. Statistical comparisons were conducted to test the alternative scenarios aimed at

visualizing the deployment advantages. TSP options were evaluated by using PTV VISSIM with VisVAP

add-on simulation tool. Alternative scenarios with and without TSP were tested to measure the performance

of BRT buses along with impact assessment on the general traffic. TSP reduces travel time and control delay,

improves travel speed and the results depicted a reduction in average passenger delay by 10–20%. The

improvement on travel speed at an intersection of BRT vehicles were determined to be 6–8%. Prioritizing

buses has diminutive impact on the general traffic, nonetheless, it is the easiest way of improving transit

performance.

1 INTRODUCTION

Many of the cities around the world are experiencing

alertly growing traﬃc congestion in urban areas and

motorway networks. The impact of congestion could

be calmed down by optimizing the performance of the

traﬃc infrastructure through various traﬃc

management and operational strategies. The

eﬀectiveness of the proposed mitigation measures are

examined in carefully designed experiments for a real

highway stretch with real demand scenarios.

Furthermore, the behaviour of the proposed

intervention schemes is examined and evaluated

through microscopic simulation models along with

refined validation (Ziaei and Goharpour, 2019;

Papadopoulou et al., 2017; Gunawan et al., 2014).

Bus Rapid Transit (BRT) is one of the efficient and

sustainable solution enhancing mass transportation

performance. It is a high quality bus-based transit

system that provides dedicated lanes for buses and is

therefore considered to be fast, safe, comfortable, and

https://orcid.org/0000-0002-8055-7137

cost effective. It requires an improvement in the

infrastructure like BRT dedicated lane, integration of

the service and operation with different intelligent

transport technologies like Transit Signal Priority

(TSP), because of this, it is considered to be an

effective and cheap way of improving transit service

reliability and efficiency (Prayogi and Satwikasari,

2019; Raj et al., 2013; Deng et al., 2013).

TSP is an Intelligent Transportation System (ITS)

component that modifies the normal signal operation

process to better accommodate transit vehicles. It

aims to reduce the delay and travel time of transit

vehicles, thereby increasing the quality of a transit

service, meanwhile, it should attempt to provide these

benefits with minimal impact on other road users

(Shaaban and Ghanim, 2018; Parr et al., 2014;

Albright and Figliozzi, 2012). Various cities are

implementing BRT for making public transport an

attractive travel option; nevertheless, it is better to

develop virtual models in order to visualize the

impact of TSP performance, which should be done

618

Desta, R., Dubale, T. and Tóth, J.

Transit Performance Evaluation at Signalized Intersections of Bus Rapid Transit Corridors.

DOI: 10.5220/0010474506180625

In Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2021), pages 618-625

ISBN: 978-989-758-513-5

before implementation on signalized intersections of

BRT corridors. It aids in envisaging the real-world

characteristics of the traffic operational conditions. It

also assists in visualizing traffic performance impacts

under different situation and scenarios, which help

decision makers to choose the best approach during

deployment period (Prayogi and Satwikasari, 2019;

Gunawan et al., 2014; Krajzewicz et al., 2012).

Various microsimulation software’s are available

on the market and used as tools for the evaluation of

traffic management and control. Released in 1992,

PTV VISSIM (Planung Transport Verkehr - Verkehr

In Städten - SIMulations model) is a microscopic,

time step and behaviour-based simulation model

developed to model urban traffic and public transit

operations. It can operate with the analyses of various

traffic and transit operations under various conditions

and aid the assessment of traffic impacts of physical

and operational alternatives in transportation

planning. VISSIM itself can be considered as a very

technical software to work with, due to its complexity

and versatility. Application of the simulation tool

include functions of testing TSP timing alternatives

(Papageorgiou et al., 2006; Smith et al., 2005; Ngan

et al., 2004; Park and Schneeberger, 2003).

Operational measures like TSP may have positive

as well as negative impact on the general traffic. In

general, TSP helps the public transport vehicles to

easily pass the congested signalized intersection.

However, due to lack of different operational and

technical problems, the efficiency of this technology

could be highly distorted and to cope with this, the

responsible authority shall assess its effectiveness in

regular basis. It is always better to see the impact or

the possible problem, which may be affecting the

operation of transportation systems (Shaaban and

Ghanim, 2018; Deng et al., 2013; Ngan et al., 2004).

Since such infrastructure could be costly and may

cause impedance to the general traffic, investigations

should be conducted to evaluate their possible

impacts behind their expected benefits. In this regard,

this study provides additional insights as it focuses on

the evaluation of TSP performance at signalized

intersection along the BRT corridor with the

developed microsimulation models, together with the

real-time calibration and validation.

2 LITERATURE REVIEW

TSP can be an effective method for improving transit

service, efficiency, and reliability despite increasing

congestion. However, unless the results of this

method are measured and evaluated, the system will

never actually contribute its maximum potential

benefit. Ultimately, the goal of TSP is to improve

transit performance; nevertheless, from local routes to

BRT, understanding of how TSP affects the

performance of a system is prerequisite to

maximizing the benefit of such measures. The

primary aspects of transit service that are exaggerated

by TSP include travel time, speed, and reliability.

Four primary performance measures that can be used

to determine travel speed and reliability are average

speed, statistical variability in travel time, percentage

of buses arriving on time and frequency/variance of

headway (Shaaban and Ghanim, 2018; Smith et al.,

2005).

TSP technologies can be used to extend or

advance green times or allow left turn swaps to allow

buses that are behind schedule to get back on track,

improving schedule adherence, reliability, and speed.

The technology requires traffic signal controllers with

imbedded software, TSP capable equipment on the

transit vehicle and equipment at the intersection for

identifying the transit vehicle and generating low

priority request when appropriate (Albright and

Figliozzi, 2012; Ngan et al., 2004; Baker et al., 2002).

TSP strategy is used to extend the green interval by

up to a preset maximum value if a transit vehicle is

approaching. Detectors are located so that any transit

vehicle that would just miss the green light (by no

more than the specified maximum green extension

time) receive extended green time and is able to clear

the intersection rather than waiting through an entire

red interval (Parr et al., 2014; Smith et al., 2005).

Green Extension provides a benefit to a relatively

small percentage of buses (only the delayed buses that

arrive during a short time window). However, the

reduction in delay for those buses that do benefit is

large (an entire red interval). This strategy is used to

shorten the conflicting phases whenever a bus arrives

at a red light in order to return to the bus's phase

sooner. The conflicting phases are not ended

immediately like they are for emergency vehicle

preemption systems, but are shortened by a

predetermined amount. Early green benefits a large

portion of buses (any bus that arrives at a red light),

but provides a relatively modest benefit to those

buses. Early green can be combined with green

extension at the same intersection to increase the

average benefits for transit (Shaaban and Ghanim,

2018; Wolput et al., 2015; Parr et al., 2014; Albright

and Figliozzi, 2012; Dion et al., 2004; Baker et al.,

2002).

VisVAP (VISual Vehicle Actuated

Programming) is an optional add-on module of PTV

VISSIM for the simulation of programmable phase or

Transit Performance Evaluation at Signalized Intersections of Bus Rapid Transit Corridors

619

stage-based traffic actuated signal program controls.

The control logic is described in a text file using a

simple programming language. During VISSIM

simulation runs or in the text mode, VisVAP

interprets the control logic commands and creates the

signal control commands for the VISSIM network. At

the same time, various detector variables reflecting

the current traffic situation are retrieved from the

simulation and processed in the logic. In signal

priority logic, various studies disclosed that early

green and extended green or red truncation of TSP

strategies are the most used approaches (Smith et al.,

2005; Dion et al., 2004; Ngan et al., 2004; Baker et

al., 2002).

3 CASE STUDY AND METHODS

In this study, the project of the line B2 of the BRT

network (Fig. 1) was considered, which is developing

along an axis crossing Addis Ababa city from North

to South, the entire B2 BRT line stretches nearly 20

km. The standard cross-section has two directions

central lane bus ways having 3.5m lane width per

direction with passing lanes of 3.5m width on the

adjacent sides for the general traffic. Along the

corridor, the cross-section converted was between 25-

40 meters width for works related to both the BRT

corridor and the mixed traffic.

Figure 1: The proposed BRT corridor (B2 line) in Addis

Ababa city (Source: (LTPA, 2010)).

This study describes the relationship and impact

on traffic performance of the BRT-B2 operation at

selected intersection (Mexico square signalized

intersection), which is considered for TSP study (Fig.

2). Actual field data on classified intersection volume

at each leg by vehicle type, traffic signal cycle

lengths, traffic composition, road geometry,

categorized average vehicle sizes, mid-block traffic

volume, spot speeds and observation data on traffic

operation were accumulated for developing the

simulation models in VISSIM.

Moreover, additional data were collected from

field for samples of vehicles based on standard

literature. Such data include actual vehicle

categorized travel time (Garber and Hoel, 2008), time

headways (Shawn et al., 1998), categorized total

traffic delays (Garber and Hoel, 2008; Bhavsar et al.,

2007), queue lengths (Shawn et al., 1998) and speed

data (Garber and Hoel, 2008; Currin, 2001) to verify

the validity of the simulation results with the actual

traffic conditions. Accordingly, the summarized data

collected based on the above sampling strategies were

utilized in the development of the simulation models.

Figure 2: BRT direction at Mexico square signalized

intersection near Ras Mekonen avenue.

The Mexico square signalized intersection is the

junction where the BRT is given priority and

expected to pass the intersection with insignificant

impact on the general traffic. The BRT direction is

North to South, which will be receiving signal priority

over the East to West general traffic (Fig. 2).

Basic measurements are taken at the intersection,

which are preliminary geometric data input for the

VISSIM model.

Intersection volume and spot speed study were

conducted on each approach of the intersection to

determine the distribution along the intersection (Fig.

3). Furthermore, the speed data are also used for

calibrating the PTV VISSM model to ensure the

correct representation of the field conditions.

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Figure 3: Total hourly volume on each approach and

average travel speed including all delay effects (km/h).

The signal data for the four phases were recorded

for creating the signal program in VISSIM (Table 1),

which facilitates the creation of a realistic base model.

Table 1: Fixed time signal cycle lengths at Mexico square

signalized intersection.

3.1 Developing the Base Micro

Simulation Model

According to LTPA (2010), the proposed BRT B2

works with a virtual loop detector installed on the bus

lane. When a bus arrives in the virtual loop, it detects

the bus arrival and sends a signal to the traffic signal

controller at the junction. The controller will initiate

the bus priority signal cycle, which will reduce the

green signal time for other arms of the junction or

extend the green signal for the bus lane to prioritize

the transit operation. Green Extension of a buffer time

less than or equal to 10-25 s, whereas Red Truncation

/Early Green/ of termination less than or equal to 10-

25 s are considered in the BRT B2 corridor. In this

study, fixed Green extension and Early green time are

taken as 15s.

In developing the model with PTV VISSIM,

scaled background map of the intersection along the

BRT dedicated lane ensures accurate geometric

representation. The selected Mexico square

signalized intersection was modelled on VISSIM

using the data obtained from field (geometric data

like lane width, approach length, segregated lane, and

section of the BRT-B2 corridor) and secondary data

sources (Fig. 4). Validation of the PTV VISSIM

model were conducted using speed data from the field

and data from the software output with justified

confidence interval. Afterwards, evaluation of the

TSP setups was performed at the intersection.

Figure 4: Snapshot of simulation model with the links,

splined connectors, and reduced speed areas at Mexico

square signalized intersection.

Overall, in the model development, PTV VISSIM

annex software (VisVAP) were used to program the

actuated signal controller. Scenario 1 will be with No

Green Extension /No Early Green, where the Existing

fixed cycle time is adopted as it is. In Scenario 2, the

program consists of green extension of 15 s, early

green time of 15 s and the interruption of cycle time

based on the call from the BRT buses reaching the

detectors.

3.2 Routing Decision and Vehicle

Attributes

Combining inputs of vehicle attributes, vehicle route

per direction, speed distribution and inputs of public

transport characteristics for the BRT, general routing

of BRT buses was made following the traffic

behavior in the dedicated lane. Defining vehicle

routes (static vehicle route decision) were configured

based on the actual field conditions by considering

the direction of the link flows (Fig. 5). Then, link

relative flows were entered in each direction for

separate movement types of the intersection. Each

routing decisions show the branches of possible

movements with their corresponding relative

flows/traffic volumes. Directional traffic flow at the

intersection was assigned in VISSIM with relative

flow of vehicles along with the vehicle types, each

volume input was calibrated later for better level of

Transit Performance Evaluation at Signalized Intersections of Bus Rapid Transit Corridors

621

accuracy in the model. Before inserting the relative

vehicle volumes at each leg, new vehicle

compositions were defined for those vehicle types

that does not exist by default in VISSIM to represent

the actual vehicle types in the corridor.

Figure 5: Snapshot of simulation model with the vehicle

routes at Mexico square signalized intersection.

3.3 Signal Controller

A detector was placed on the BRT dedicated lane near

the signalized intersection (Fig. 6). Signal control on

the intersection were based on the existing signal

scheme and priority given via the sensor, which is

initiated by a call by the BRT buses approaching at an

intersection. By interrupting the fixed signal cycle, an

optimum green extension and red truncation was

incorporated in to the system.

Modelling traffic signal control to emulate the

‘before’ case and to deploy a ‘green extension/early

green’ priority logic to scrutinize the ‘after’ case is

the main element of this study. Therefore, it was very

important to outdo the signal control data and to

program the model for deploying the priority strategy.

In this research, VisVAP based approach was chosen

to model selected intersections signal program in

VISSIM, which includes different signal head groups

attributed to different lane geometric configurations.

One of the primary importance in this research

was guaranteeing minimum disruption of traffic

signal operations. This feature was especially

important to the selected study junction, where the

main corridor is a major urban road carrying high

traffic volumes in peak hours; moreover, the

deployment is planned for green extension.

Therefore, the study incorporated a 15 s green

extension and 15 s red truncation to evaluate the

possible outcomes of the strategies.

Figure 6: Signal and detector coordination in VISSIM.

3.4 Validation of the Model

The validation and calibration of the model is an

important process in the simulation process, since it

delivers credibility to the results by closely

representing the actual conditions. Calibration

involves adjusting the default values of PTV VISSIM

software, which may not be the representative of the

driving and geometric characteristics of the study

segment. The validation process involves comparing

and justifying the result (field and VISSIM output)

with level of accuracy given by confidence interval

(Raj et al., 2013; Vedagiri & Jain, 2012; Park and

Schneeberger, 2003). Typical calibration measures

include the consideration of traffic parameters like

volume, delay, and travel speeds. In this study,

average operating speed (spot speed) was used as a

measure of effectiveness parameter for the transit

network.

Average spot speed of vehicles within the speed

distribution ranges from 5 to 55 km/h. VISSIM allows

the operating speed of a class of vehicles to be

controlled during the simulation process. The average

operating speed was in the range of 5 to 60 km/h,

which closely relate with the field data. Furthermore,

the signal time distributions for ‘No TSP’ case

observed from the VISSIM output are similar to the

inputs from field conditions, which provides further

support for the signal control. The speed distribution

in VISSIM and in the field favourably compare with

one another with 95% confidence (Fig. 7). Therefore,

the model is closely related to the real traffic

behaviour in the field.

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Figure 7: Comparison of speed distribution from PTV

VISSIM with field measurement.

3.5 Simulated Signal Scenarios and

Scenario Management

Two scenario groupings were implemented in

simulation runs that can be distinguished by certain

characteristics being simulated. The two scenarios

analyzed were the No Transit Signal Priority

(scenario I) and with Transit Signal Priority (scenario

II). The change in signal phases coordination (either

Green extension or early Green) within the two

scenarios brought considerable alteration in the

performance of transit operation (Fig. 8).

Figure 8: Green Extension and/or early Green on

intersection phase in the two scenarios.

To investigate the impact of signal priority on the

performance of BRT B2 buses and general traffic,

scenario management were considered during the

simulation to facilitate the assessment of traffic

parameters. The basic conditions of TSP plan and

way of detecting the bus arrival at an intersection,

green extension of 15 s (GE-15), and red truncation

/early green 15 s (EG-15) are critical dimensions in

the scenarios. In general, 15-20 s of GE and EG

extension was fused in the simulation network. The

above signal modification of green extension and/or

early green was made on the existing signal to

experiment on the variations that occur. The changes

in the signal coordination was made with the help of

VisVAP logic, which was used for programming the

detector call.

4 RESULTS AND DISCUSSIONS

The extracted output data from PTV VISSIM result

directory include travel time, delay, and queue length

of BRT buses, and the general traffic. For the

assigned approach crossing segment at the

intersection (North to South), the vehicles travel time

were extracted from VISSM output directory for both

scenarios (Fig. 9). There is a significant improvement

in the travel time of BRT the North to South line by

an average of 2.83% decrease in the overall travel

time.

Figure 9: Travel time of BRT vehicles from North bound

Shebelle approach to South bound Kera approach.

Data extracted for the South to North flow

indicated that there is a significance improvement in

the travel time of BRT with an average 4.78%

decrease in the overall travel time (Fig. 10).

Figure 10: Travel time of BRT vehicles from South bound

Kera approach to North bound Shebelle approach.

The simulation result of average queue length from

VISSIM results directory were summarized for

average values based on vehicle composition and

category of the general traffic (Table 2). The priority

direction in Kera and Shebelle approaches has

improvement in queue length, which was reduced by

1.5%-1.7%, whereas the non-priority side of the

intersection the queue length increased by 1.1%-2.5%.

Transit Performance Evaluation at Signalized Intersections of Bus Rapid Transit Corridors

623

Table 2: Summary of average queue length at the different

approaches of the intersection.

The simulation results from PTV VISSIM

indicated that for the selected BRT corridor, the travel

speed increased by an average of 7.25%. The Speed

variability for BRT vehicles with and without transit

signal priority was significant.

From the overall results of the simulation

scenarios, the average passenger delay for the

multiple simulation cases signposted a positive result,

which is an average of 10%-20% in delay reduction

in comparison to the no priority case (Fig. 11).

Figure 11: Average Delay of BRT vehicle passengers in the

No TSP and With TSP scenarios.

5 CONCLUSIONS

This study presented crucial insights in developing a

simulation model with relative comparison of various

scenarios for experimenting transit signal priority

approaches at intersections of BRT corridors. The

study particularly addressed the performance of buses

running on a segregated lane and receiving signal

priority at junctions over the general traffic.

Moreover, the influence on the general traffic

performance was examined to evaluate the pre- and

post-deployment conditions of the Transit Signal

Priority on BRT corridors. Based on the experimental

investigations, the following conclusions are made:

• the PTV VISSIM simulation output and

statistical comparisons indicated that there is a

significant improvement in the performance of

BRT buses at an intersection level crossing

because of the TSP;

• the introduction of TSP resulted in an average

travel time reduction by a minimum of nearly

4% for BRT buses;

• the average delay reduction for passengers of

the BRT line was found to be 10-20% and the

travel speed of BRT vehicles was increased by

7-8%;

• TSP may reduce the queue length in the priority

direction (parallel to the BRT lane) by nearly

2%, while in the non-priority direction, the

queue may increase by a maximum of 3%.

Overall, implementation TSP have little impact on

the traffic performance of the general traffic, while it

can bring significant operational improvement on the

vehicles of the BRT lane.

ACKNOWLEDGEMENTS

The authors wish to acknowledge all the professionals

who contributed to this work; especially, Transport

Programs Management Office’s (TPMO’s), Traffic

Safety and management (TSM) division staffs and

BRT unit staffs for their technical assistance and

provisions of the license to run PTV VISSIM. The

authors extend their sincere appreciation for the

Ethiopian Roads Authority (ERA) and Hawassa

University for funding this research work.

CONFLICT OF INTEREST

The authors declare that they have no competing

interest.

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