Article citation information:
Doğan, E. Examining the safety impacts of
transit priority signal systems using simulation techniques. Scientific Journal of Silesian
University of Technology. Series Transport. 2024, 122, 61-71. ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2024.122.4.
Erdem DOĞAN[1]
EXAMINING THE SAFETY IMPACTS OF TRANSIT PRIORITY SIGNAL SYSTEMS USING
SIMULATION TECHNIQUES
Summary. Transit
Priority Signal (TPS) systems are increasingly used to improve traffic
efficiency and reduce passenger waiting times. However, such systems may carry
potential safety risks. This study aims to investigate the safety effects of
TPS at intersections. Our study utilized the SUMO traffic simulation program to
create a road network model containing nine signalized intersections.
Subsequently, the TPS system was applied to selected bus routes within the road
network, and the cases with and without TPS implementation were compared in
terms of safety and performance. In safety-oriented comparisons, surrogate
safety measures were employed, including number of conflict and Time to
Collision (TTC). Signalized intersection performances were measured and
compared in terms of the number and duration of stops. The analysis results
indicate that TPS enhances safety and transportation performance for buses, but
adversely impacts safety and transportation performance for passenger cars.
This study underscores the importance of considering safety aspects in the
implementation of TPS aimed at improving passenger transportation efficiency.
These findings may contribute to the enhancement of public transportation
infrastructure and the implementation of appropriate safety measures.
Keywords: transit
priority signal, traffic safety, SUMO, surrogate safety measures
1. INTRODUCTION
Public transportation plays a vital role in
modern urban mobility, providing a sustainable and efficient means of
transportation for millions of people. To improve the quality of public
transportation services and reduce passenger waiting times, Transit Signal
Priority (TSP) systems have been widely adopted. These systems prioritize
public transit vehicles at traffic signals and aim to reduce passenger delays.
Since buses are the predominant mode of transportation moving on road networks,
this system is also referred to as Bus Priority System (BPS).
Numerous studies have reported that TSP
technology reduces passenger waiting times and enhances the quality of public
transportation services. While the efficiency and potential benefits of Transit Signal Priority (TSP) have been extensively
discussed and researched [1-6], equal attention must be paid to
its impact on road safety. Intersections are known to be areas where traffic
accidents frequently occur, particularly due to rear-end collisions that can
increase with abrupt signal changes. Therefore, it is crucial to
comprehensively assess the safety aspects of these systems to ensure the
well-being of both public transportation users and other road users. Shahla and
others suggest that the use of TSP technology can potentially extend the green
light duration when public transit vehicles approach the intersection during
their green phase, which may confuse drivers [7]. Furthermore, the researcher found
that especially in intersections with long green light durations, pedestrians
tend to violate red lights.
Recent studies focusing on the safety
implications of TSP have yielded mixed results. In the study by [8], microsimulation was employed to
investigate the safety levels of intersections using TSP. Surrogate Safety
Measures (SSMs) were used to enable safety assessments by validating the
relationship between existing accident statistics and SSMs. The simulations
indicated that the TSP system could adversely affect safety performance. Song
and Noyce conducted an experimental Bayesian before-and-after analysis using
TSP application data in King County, Washington [9]. They examined 11 transit corridors
with effective TSP and 75 street segments without TSP. The study claimed a 13%
reduction in total accidents, along with a 5% reduction in fatal and injury
accidents. It was also mentioned that future studies would include pedestrian
and bicycle groups. In a subsequent study, Song and Noyce analyzed accidents
before and after TSP implementation in Oregon using a discontinuous time-series
method [10]. The analysis revealed a 4.5%
reduction in all accidents, but an increase in accidents involving pedestrians
and cyclists was noted. The data from Automatic Vehicle Location devices
installed on buses were used to conduct a new study [11]. In the study, the bus speed fluctuation
metric was used as a SSMs to examine the safety impact of TSP. The results
indicated that buses experienced fewer stops and smoother transitions compared
to other intersections. While it was suggested that this could reduce bus
accidents, no analysis was conducted on its effect on other modes of
transportation. In the studies [12,13], potential safety advantages in
various regions of Florida equipped with the TSP system were explored. The
scientists examined 12 corridors equipped with the TSP system and 29 comparison
corridors without it. The analysis results indicated a 7.2% reduction in total
accidents, although the detected reduction in rear-end collisions was not
statistically significant. Additionally, while various types of accidents were
claimed to have decreased, it was concluded that there was no numerical
reduction in all accident types.
Literature reviews have shown that while some
studies demonstrate positive effects of the TSP system on safety, others argue
the opposite. Furthermore, most of these studies emphasize that safety for all
modes of transportation is not ensured. Based on these findings, it is evident
that research that examines the safety effects of the TSP system from different
perspectives could make a significant contribution. This study analyzes the
safety effects of the TSP system by considering the entire road network rather
than limiting it to a single corridor or signalized intersection. SSMs were
used to compare safety levels in the analysis, and these simulations were
conducted in a microsimulation environment within a hypothetical road network.
Additionally, the waiting times of all buses and other modes of transportation
throughout the study area were compared and discussed. This study provides a
different perspective from previous research by evaluating the impact of TSP on
road safety and analyzing the entire network.
In the following sections, we will first
discuss prominent TSP strategies before explaining the simulation setup,
hypothetical road network and traffic flow scenarios, and data collection
methods used to assess the safety and performance of TSP. The results of these
simulations will provide valuable insights into balancing public transportation
efficiency and road network safety.
2. METHODOLOGY
In this study, a hypothetical road network was
first created using the SUMO traffic simulation program [14] to examine the effects of the Transit Signal Priority (TSP) approach on safety and performance. Subsequently,
traffic flow scenarios determined using the Latin hypercube method were tested
on two different road networks, one utilizing TSP technology and the other not.
The results include Time to Collision (TTC) and collision count values to
determine the safety level, while average waiting times and stopping counts
metrics were used to evaluate the intersection performance of TSP. Fig. 1
illustrates the overall structure of the study.
Fig. 1. Overview of procedures
The analysis began with the calculation of
traffic flow scenarios using the Latin Hypercube Sampling (LHS) method, as
shown in Fig. 1. Then, two road networks, one equipped with the TSP system and
the other without, were created in the SUMO environment, including certain
intersections. Each traffic flow scenario was simulated for these two road
networks using TraCI and Python. At the end of the simulations, safety and
performance metric values for buses and Passenger Cars (PC) were collected and
analyzed. TraCI is an extension that allows access to and control of systems
within the simulation at each simulation step, using Python with SUMO.
2.1. Transit priority
signal system (TSP)
Transit Priority Signal System (TSP) refers to
various methods employed at intersections controlled by traffic signals to
enhance public transportation service and reduce delays. It can be divided into
two main categories: active and passive priority systems [15]. In the active system, the presence
of buses is detected by sensors, while in the passive system, it is assumed
that buses approach the intersection according to a certain statistical
distribution [16]. Under the categories of active and
passive systems, there are specific TSP strategies, which generally work on
principles such as extending the signal on the bus approach arm and prematurely
terminating the green signal on other conflicting arms or making phase changes.
In this study, in our hypothetical network, the
TSP red truncation technique was applied at intersections indicated by red dots
in Fig. 2, in conjunction with an Actuated Signal Control System (ASC). In red
truncation, when the presence of a bus is detected, the red signal on the arm
on which the public transportation vehicle is approaching is prematurely
terminated, and it returns to the green signal. To make the system work, area
detectors along the route were used at intersections where two bus routes pass,
which are located on the central horizontal and vertical axis of the road
network, for the purpose of TSP system detecting buses.
2.2. Surrogate safety and
performance measures
The safety analysis of road segments can be
approached in various ways in today's context.
Traditional methods involve examining data from past accidents to evaluate the
collision risk in an area. On the other hand, collisions are relatively rare
events within the flow of traffic interactions [17]. Therefore, it may take years to
assess an area from a collision perspective using traditional methods. In
contrast to traditional methods, SSMs can be employed to assess safety. SSMs
are based on the concept that accidents result from conflicts, which are situations
where the probability of a collision is high. These models serve as proactive
indicators that provide advance information about the safety of a facility. An
essential term frequently used in these proactive studies is 'conflicts.' A conflict is defined as an
observable situation in which two or more road users come together in time and
space, posing a risk of collision if their movements remain unchanged.
In this study, commonly used concepts from the
literature, Time to Collision (TTC), and the Number of Conflicts (NoC), were
utilized SSMs [18–21]. TTC is defined as the remaining time
until a potential collision if interacting road users do not change their speed
and direction. For two vehicles, TTC is calculated by dividing the distance (
3. SIMULATION SETUP
The use of simulation in traffic engineering is quite common, especially
due to its cost-effectiveness and the ability to facilitate experiments of
various applications before real-world implementation. Additionally,
microsimulation technique enables the modeling of complex vehicle interactions,
allowing for the presentation of comprehensive results. In this study, the
microsimulation program SUMO was employed to investigate the safety
implications of TPS. SUMO was chosen for its reputation for providing reliable
microsimulation capabilities and its compatibility with in-depth analysis
supported by Python.
The purpose of the simulations conducted in this study was to evaluate
whether the implementation of a TPS in a road network raises safety concerns.
Therefore, we aimed to assess the impact of such systems on traffic flow from a
safety perspective, considering the increasing adoption of intelligent
transportation systems in recent years. It is expected that the results of this
research will provide valuable insights for traffic engineers on how much these
systems should influence their design
3.1. Hypothetical road
network and traffic flow scenarios
To conduct simulations, a hypothetical road network consisting of nine
intersections was created (Fig. 2). The connections within this network
consisted of two lanes, and the maximum allowable speed on these lanes was set
at 82 kilometers per hour. The distance between intersections was designed to
be 500 meters. This type of road network was selected due to its relatively
simple configuration, commonly found in large cities.
Fig. 2. Representation of
hypothetical road network and other systems
It was assumed that two types of transportation were used within the road
network: buses and passenger cars (PC). Additionally, it was assumed that bus
routes progressed centrally on the road network. Car routes were configured to
follow a linear path starting from source nodes, and a no-turn rule was assumed
at intersections. Furthermore, pedestrian traffic within the road network was
neglected. This simplification aimed to reduce simulation parameters, the
required number of simulations, and overall complexity. In Fig. 2, source nodes
are highlighted in blue, indicating entry points where both buses and passenger
cars had access to the road network. Other source nodes exclusively served as
entry points for PCs.
To monitor and record the number of buses within the approach lanes,
strategically placed area detectors capable of detecting up to 100 meters
behind the Stop line at intersections were installed, as shown in Fig. 2 in
turquoise. In the SUMO simulation program, these detectors are referred to as
lane area detectors. Additionally, inductive loop detectors placed beneath the
road surface were used for the Adaptive
Signal Control (ASC)
system. The positions of these inductive loop detectors were automatically
determined based on the cycle times used in the active signal control system
employed by SUMO. Information about the parameters used in ASC (min-max green
time, passing time, etc.) and the phase plan is presented in Fig. 2.
3.2. Latin hypercube and traffic flow scenarios
As explained in the simulation setup section, there are a total of 12
source nodes in the hypothetical road network, and it is assumed that passenger
car (PC) traffic originates from all of these nodes. Additionally, it is
assumed that there are bus routes along the roads crossing horizontally and
vertically through the network, and bus traffic originates from the source
nodes along these routes. As a result, a total of 16 traffic flow variables are
created, and these 16 flow variables, taking different values, constitute a
single traffic scenario.
In this study, the existence of 16 traffic flow variables necessitates
considering a large number of traffic scenarios. Therefore, the Latin Hypercube
Sampling (LHS) method, recommended by McKay et al. (1979), aims to reduce the
required number of experiments by creating a set of high-quality samples that
have a distribution similar to the initial distribution. Consequently, the set
of traffic flow scenarios was generated using the LHS method.
For each traffic scenario, a simulation of 3600 seconds was conducted.
After the simulations, total conflict counts within each scenario and the
Time-to-Collision (TTC) durations for each conflict were recorded for use in
safety comparisons, and the average TTC was calculated. As performance metrics,
the total stop counts and stop durations for buses and PC were extracted from
the simulation results, and the averages of stop durations were calculated.
Furthermore, the random-coordinate descent (RCD) optimization method was
selected for the LHS process, resulting in the creation of a total of 500
traffic flow scenarios for simulation.
4. ANALYSIS RESULTS
In this section, the results obtained from the simulations are presented
and discussed with graphs. Different signal control systems were used, and
Figure 3 presents graphs showing how the safety level changes, while Figure 4
presents figures showing how the performance of the road network is affected.
When examining the central tendencies for safety elements in Fig. 3, it
is evident that the average
conflict count (NoC)
is higher for both PC and buses when the bus priority system (TSP) is used
compared to when adaptive signal
control (ASC)
is used. This observation indicates that TSP may lead to more frequent sudden
stops, especially for PC, which could increase the risk of traffic accidents.
Fig. 3. SSMs with TSP+ASC and ASC systems under
different traffic flow scenarios
For TSP usage, it can be observed that some passenger car flow vectors
have more conflicts when only ASC is used. This can be observed particularly
when considering that the upper bound of the TSP box is approximately 70,000,
while the upper bound of ASC is approximately 55,000. A similar situation is
observed when examining the bus graph, especially where lower quartile values
are higher. This could be because buses are relatively fewer compared to PC.
When examining the TTC figures, it is seen that the lower bound for TSP usage
is below the first quartile value determined for ASC usage for PC. However,
some outliers are observed below the first quartile. ASC usage results in a
narrower distribution of TTC values, generally between 1.82 and 1.87 seconds.
On the other hand, TSP usage shows that TTC values are spread over a wider
range, between 1.8 and 1.9 seconds. In other words, while the median value for
TSP usage is slightly higher than ASC, the lower and upper limits are more
dispersed, indicating a higher probability of more severe conflict situations
in some scenarios. Similarly, when examining TTC distributions for buses, it is
observed that TSP usage has a less pronounced distribution compared to ASC
usage. With ASC, TTC values range from 1.7 to 1.7 seconds, while with TSP, they
range from 2 to 1.8 seconds. In other words, TSP usage reduces the probability
of buses experiencing more serious conflicts while potentially increasing the likelihood
of PC encountering more serious conflicts. This result is consistent with the
expected outcome of TSP, which reduces stopping and braking for bus flows.
Fig. 4. Performance measures with TSP+ASC and ASC
The changes in road network performance due to TSP usage are shown in
Figure 4. The figure separately presents waiting and stopping counts per
vehicle for PC and buses. When comparing waiting times for PC, it is clear that
TSP usage increases waiting times compared to ASC usage. This is especially
evident when looking at median values. Waiting times for PC range from 1.3 to
1.7 seconds with TSP usage, while they range from 1 to 1.3 seconds with ASC
usage. Waiting times for buses, on the other hand, are lower on average with
TSP usage, as expected. The average waiting time with TSP is just below 1
second, while with ASC, it exceeds 1.2 seconds. Additionally, when looking at
the lower quartile boundary, it is clearly seen that 25% of the scenarios
require waiting times between 0.3 and 0.7 seconds with TSP usage. On the other
hand, in some traffic flow scenarios where buses are located at distances where
detectors cannot see them within heavy traffic, waiting times may be higher
with TSP usage. As for the average stopping counts, it is observed that PC make
more involuntary stops. With TSP usage, PC make an average of approximately 21
stops, while with only ASC, they make approximately 10 stops. However, the
situation is reversed for buses. With TSP usage, buses make an average of about
3 stops, while with only ASC, they make an average of about 9 stops. When
looking at the lower and upper quartile boundaries, in 75% of the cases, buses
make fewer than 5 stops with TSP usage. On the other hand, with ASC usage, 75%
of the cases require 7 or more stops. In conclusion, TSP usage significantly
increases average waiting times and stopping counts for PC. In contrast, it
significantly reduces waiting times and stopping counts for buses.
To summarize, while TSP enhances bus transportation efficiency by
reducing waiting times and stopping counts, it does raise potential safety
concerns, particularly for passenger vehicles. Decision-makers and
transportation authorities should consider the balance between improving bus
performance and potentially increasing safety risks, especially for PC when
contemplating the implementation of TSP systems. It is evident that more
research and analysis are needed to develop strategies to mitigate safety
concerns associated with TSP and to ensure the safe integration of these
systems into urban transportation networks.
4. CONCLUSION
This study aimed to investigate the effects of Transit
Signal Priority (TSP) systems on traffic safety and efficiency for buses and
PC. TSP is a system that allows public transportation vehicles to receive
priority at traffic signals, potentially enhancing the efficiency of urban
transportation. However, it should be noted that these systems may also raise
potential safety concerns.
Our analyses were conducted using microsimulation
methods, comparing the impacts of TSP strategies' implementation and
non-implementation on safety and performance within a road network. The results
obtained indicate that TSP systems can enhance the transportation efficiency of
buses. It was observed that waiting times and the number of stops for buses
decreased, allowing buses to travel more quickly. This could contribute to
public transportation vehicles providing services in a timelier and reliable
manner.
However, this study also demonstrated that TSP systems
may raise safety concerns, particularly by increasing waiting times and the
number of stops for PC, potentially increasing the risk of traffic accidents.
These concerns reflect the fact that TSP systems may adversely affect traffic
safety for PC.
In conclusion, this study provides valuable guidance
for decision-makers and transportation authorities regarding the implementation
of TSP systems. While these systems have the potential to enhance bus
transportation efficiency, they may also raise safety concerns. Therefore, a
careful balance is required in the implementation and design of TSP systems.
Furthermore, further research is needed to develop strategies to mitigate
safety concerns and integrate these systems into urban transportation networks
safely.
Future studies should focus on exploring how different
TSP strategies impact safety under various traffic conditions and how safety
measures can be improved. Additionally, within this context, there should be a
more detailed examination of the interaction between public transportation
vehicles and private cars.
References
1. Lin
Yiching, Xianfeng Yang, Nan Zou, Mark Franz. 2015."Transit signal
priority control at signalized intersections: a comprehensive review." Transportation
Letters 7(3):168-80.
2. Currie Graham, Amer Shalaby.
2008. "Active transit signal priority for streetcars: experience in
Melbourne, Australia, and Toronto, Canada." Transportation Research
Record 2042(1): 41-9.
3. Kim Wonho, L.R. Rilett.
2005."Improved transit signal priority system for networks with nearside
bus stops." Transportation Research Record. 1925(1):205-14.
4. Ngan Vikki, Tarek Sayed,
Akmal Abdelfatah. 2004. "Impacts of various parameters on transit signal
priority effectiveness. " Journal of Public Transportation 7(3): 71-93.
5. Shaaban Khaled, Mohammad Ghanim.
2018."Evaluation of transit signal priority implementation for bus transit
along a major arterial using microsimulation." Procedia Computer
Science 130: 82-9.
6. Christofa Eleni, Alexander Skabardonis.
2011."Traffic signal optimization with application of transit signal
priority to an isolated intersection." Transportation Research Record
2259(1): 192-201.
7. Shahla Farhad, Amer S.Shalaby,
Bhagwant N. Persaud, Alireza Hadayeghi. 2009. "Analysis of transit safety
at signalized intersections in Toronto, Ontario, Canada." Transportation
Research Record 2102: 108-14. DOI: 10.3141/2102-14.
8. Li Lu, Bhagwant Persaud, Amer
Shalaby. 2017. "Using micro-simulation to investigate the safety impacts
of transit design alternatives at signalized intersections." Accident
Analysis and Prevention 100: 123-32. DOI: 10.1016/j.aap.2016.12.019.
9. Song Yu, David Noyce. 2018. "Assessing
effects of transit signal priority on traffic safety: empirical bayes
before–after study using King County, Washington, Data." Transportation
Research Record 2672(8): 10-8.
10. Song Yu, David Noyce. 2019. "Effects
of transit signal priority on traffic safety: Interrupted time series analysis
of Portland, Oregon, implementations." Accident Analysis &
Prevention 123: 291-302.
11 Song Yu, Hoki Tse, Eric M. Lind,
Madhav V. Chitturi, David A. Noyce. 2021. „Safety Evaluation
of Transit Signal Priority with Bus Speed Volatility as a Surrogate Measure:
Case Study in Minnesota." In: International Conference on
Transportation and Development 2021: 479-90.
12. Ali Sultan, Angela E. Kitali, John H. Kodi,
Priyanka Alluri, Thobias Sando. 2021. "Safety impacts of transit signal
priority using a full bayesian approach." Transportation Research
Record 2675(11): 1189-204. DOI: 10.1177/03611981211025285.
13. Ali M. D. Sultan, Angela E. Kitali, John
Kodi, Priyanka Alluri, Thobias Sando. 2022. "Quantifying the safety
benefits of transit signal priority using full Bayes before–after
study." Journal of Transportation Engineering, Part A: Systems
148(1): 4021102.
14. Lopez Pablo Alvarez, Michael Behrisch,
Laura Bieker-Walz, et al. 2018. „Microscopic traffic simulation
using sumo." In: 21st
International Conference on Intelligent Transportation Systems (ITSC):
2575-82.
15. Imran Nazia Tazin, Anagha Girijan,
Lelitha Devi Vanajakshi. 2022. "A Review on Bus Signal Priority
Systems." Transportation in Developing Economies 8(1): 1-13. DOI:
10.1007/s40890-021-00138-z.
16. Lin Y., X. Yang, N. Zou, M. Franz. 2015. "Transit
signal priority control at signalized intersections: A comprehensive
review." Transportation Letters 7(3): 168-80. DOI:
10.1179/1942787514Y.0000000044.
17. Amundsen F.H., C. Hyden. 1977. Proceedings of first workshop on traffic
conflicts. Oslo, TTI, Oslo, Norway
and LTH Lund, Sweden.
18. Saffarzadeh Mahmoud, Navid Nadimi, Saber Naseralavi,
Amir Reza Mamdoohi. 2013. "A general formulation for time-to-collision
safety indicator." In:
Proceedings of the Institution of Civil Engineers-Transport 166: 294-304.
Thomas Telford Ltd.
19. Zheng Lai, Tarek Sayed. 2019. "Comparison
of traffic conflict indicators for crash estimation using peak over threshold
approach." Transportation Research Record 2673(5): 493-502.
20. Vuong Xuan-Can, Rui-Fang Mou, Trong-Thuat
Vu. 2019. "Safety Impact of timing optimization at mixed-traffic
intersections based on simulated conflicts: a case study of Hanoi,
Vietnam." In: 4th
International Conference on Intelligent Transportation Engineering (ICITE):
247-51. IEEE.
21. Jiang Xiaobei, Wuhong Wang, Klaus Bengler.
2014. "Intercultural
analyses of time-to-collision in vehicle–pedestrian conflict on an urban
midblock crosswalk." IEEE Transactions on Intelligent Transportation
Systems 16(2): 1048-53.
22. Hayward John C. 1972. "Near miss
determination through use of a scale of danger."
Received 03.11.2023; accepted in
revised form 29.12.2023
Scientific Journal of Silesian University of Technology. Series
Transport is licensed under a Creative Commons Attribution 4.0
International License
[1] Faculty of Engineering and
Natural Sciences, Kırıkkale University, Ankara St. 7. Km
Yenişehir, Kırıkkale 71450 Yahsihan/Kırıkkale, Turkey.
Email: edogan@kku.edu.tr. ORCID: https://orcid.org/0000-0001-7802-641X