Cooperative Transit Signal Priority Considering Bus Stops Under Adaptive Signal Control

Zhang, Changlong; Yang, Xiaodong; Wei, Jimin; Yang, Shuo; Dai, Jingang; Qu, Shibo

doi:10.1109/access.2023.3290992

Cited by 4 publications

(5 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using V2I communications, Zeng et al developed a strategy in which the data on general traffic and PT vehicle arrivals are fed into the model to minimize passenger delay at intersection level [28]. Another ATSP strategy was proposed by Zhang et al taking advantage of V2I communications to improve the PT vehicle travel time prediction and subsequently passenger delay [29]. Regarding the novel solution methods, fuzzy logic [30,31], Q-learning [32], deep reinforcement learning [33,34], and metaheuristics [25,29,35,36] are among the methods used in the ATSP strategies to empower the optimization process.…”

Section: Introductionmentioning

confidence: 99%

“…Another ATSP strategy was proposed by Zhang et al taking advantage of V2I communications to improve the PT vehicle travel time prediction and subsequently passenger delay [29]. Regarding the novel solution methods, fuzzy logic [30,31], Q-learning [32], deep reinforcement learning [33,34], and metaheuristics [25,29,35,36] are among the methods used in the ATSP strategies to empower the optimization process. The maximization of passenger throughput has been also targeted in some of the ATSP strategies showing superior performance compared to the delay-based strategies, especially at high congestion levels [37][38][39].…”

Section: Introductionmentioning

confidence: 99%

“…Although PT vehicle dwell time at stops is a determinant parameter on travel time estimation, this parameter is limitedly included in these models and is mostly assumed constant [13,54]. The available data on the number of passengers at the stop were used in some studies to estimate the exact PT vehicle dwell time [29,40]. However, despite the importance of developing methods to calculate the dwell time of two PT vehicles arriving close to each other at the stop [5], this subject has not yet been addressed in the related studies.…”

Section: Introductionmentioning

confidence: 99%

“…Another ATSP strategy was proposed by Zhang et al. taking advantage of V2I communications to improve the PT vehicle travel time prediction and subsequently passenger delay [29]. Regarding the novel solution methods, fuzzy logic [30, 31], Q‐learning [32], deep reinforcement learning [33, 34], and metaheuristics [25, 29, 35, 36] are among the methods used in the ATSP strategies to empower the optimization process.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Passenger‐based adaptive transit signal priority for BRT systems with multiple loading areas

Behbahani,

Poorjafari

2024

IET Intelligent Trans Sys

View full text Add to dashboard Cite

An adaptive transit signal priority strategy is presented in this paper with the objective of passenger delay minimization at isolated intersections serving conflicting bus rapid transit (BRT) routes. The proposed passenger‐based adaptive signal priority for BRT systems (PASPB) is designed to optimize both green times and phase sequences at the start of each cycle and for a prespecified decision horizon. Since a public transportation (PT) vehicle travel time model capable of estimating the dwell time at stops with multiple loading areas has not yet been developed, PT vehicle dwell time is modeled in this study by analyzing the cases of passenger service by single or double PT vehicles. The problem is formulated as a mixed‐integer nonlinear program (MINLP) and at each execution, the optimization is conducted by genetic algorithm. The model is deployed to a real‐field intersection with conflicting BRT routes under the SUMO microsimulation environment. The results show that PASPB outperforms the SYNCHRO optimal solution and phase insertion strategy regarding PT passenger delay. Besides, the sensitivity analysis proves that at high demand levels of the PT system or general traffic, PASPB presents the best performance in terms of general traffic, PT, and total passenger delay compared to other models.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Passenger‐based adaptive transit signal priority for BRT systems with multiple loading areas

Behbahani,

Poorjafari

2024

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…To minimize vehicle delays in coordinated arterial TSP, other studies have proposed mathematical models targeting bus schedules [10], bus headway [2], and bus priority [11]. The original ATSP factors are bus stops [12], pedestrians [13], and buses [14]. This paper, based on research of the original adaptive transit signal priority, has added the consideration of private vehicles, with the aim of reducing private vehicle delays caused by transit signal priority.…”

Section: Introductionmentioning

confidence: 99%

Coordination Optimization of Real-Time Signal Priority of Self-Driving Buses at Arterial Intersections Considering Private Vehicles

Li,

Zhang

2023

Applied Sciences

View full text Add to dashboard Cite

Transit Signal Priority (TSP) is a system designed to grant right-of-way to buses, yet it can lead to delays for private vehicles. With the rapid advancement of network technology, self-driving buses have the capability to efficiently acquire road information and optimize the coordination between vehicle arrival and signal timing. However, the complexity of arterial intersections poses challenges for conventional algorithms and models in adapting to real-time signal priority. In this paper, a novel real-time signal-priority optimization method is proposed for self-driving buses based on the CACC model and the powerful deep Q-network (DQN) algorithm. The proposed method leverages the DQN algorithm to facilitate rapid data collection, analysis, and feedback in self-driving scenarios. Based on the arrival states of both the bus and private vehicles, appropriate actions are chosen to adjust the current-phase green time or switch to the next phase while calculating the duration of the green light. In order to optimize traffic balance, the reward function incorporates an equalization reward term. Through simulation analysis using the SUMO framework with self-driving buses in Zhengzhou, the results demonstrate that the DQN-controlled self-driving TSP optimization method reduces intersection delay by 27.77% and 30.55% compared to scenarios without TSP and with traditional active transit signal priority (ATSP), respectively. Furthermore, the queue length is reduced by 33.41% and 38.21% compared to scenarios without TSP and with traditional ATSP, respectively. These findings highlight the superior control effectiveness of the proposed method, particularly during peak hours and in high-traffic volume scenarios.

show abstract

Evaluating the performance of implementing regionally coordinating bus priority signals under different control schemes

Al-Hyasat,

Alhadidi

2024

Comput.Urban Sci.

View full text Add to dashboard Cite

Bus Rapid Transit (BRT) proves its effectiveness in alleviating traffic congestion, especially in urban areas. The implementation of Transit Signal Priority (TSP) for BRT has shown a significant reduction in delays. However, in densely populated urban areas, this priority can inadvertently cause additional delays for other modes of transportation. In this paper, we propose a control strategy for Regionally Coordinating Bus Priority Signals Control (RCBPSC) at urban intersections. The aim is not only to reduce bus delays but also to consider minimizing delays for pedestrians and other vehicles. To achieve this, we modeled two consecutive intersections along the Amman BRT. Essentially, we evaluated three different control scenarios in addition to the current base scenario. These scenarios include adaptive traffic signal control, RCBPSC with no signal timing optimization, and RCBPSC with signal optimization. Simulation results indicate that the adaptive traffic signal timing has the worst operational performance in terms of average delay and Level of Service (LOS) compared to the base scenario. Additionally, the results show that BRT delays significantly decrease at both intersections when we implement RCBPSC scenarios. When implementing RCBPSC with optimization scenarios, the results show an average reduction of more than 60% in intersection delay, a decrease in emissions of more than 50%, and an improved LOS for system users compared to the base scenario. The findings of this work can help agencies improve the current operational condition of BRT when implementing RCBPSC.

show abstract

Cooperative Transit Signal Priority Considering Bus Stops Under Adaptive Signal Control

Cited by 4 publications

References 30 publications

Passenger‐based adaptive transit signal priority for BRT systems with multiple loading areas

Passenger‐based adaptive transit signal priority for BRT systems with multiple loading areas

Coordination Optimization of Real-Time Signal Priority of Self-Driving Buses at Arterial Intersections Considering Private Vehicles

Evaluating the performance of implementing regionally coordinating bus priority signals under different control schemes

Contact Info

Product

Resources

About