A Real-Time Transit Signal Priority Control Model Considering Stochastic Bus Arrival Time

Zeng, Xiaosi; Zhang, Yunlong; Balke, Kevin; Yin, Kedong

doi:10.1109/tits.2014.2304516

Cited by 60 publications

(45 citation statements)

References 19 publications

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“…Some systems have optimized signal settings by minimizing some weighted combination of passenger delay, car delay, bus delay, and bus schedule delay (Chang et al, 1996;Vasudevan, 2005;Li et al, 2008;Stevanovic et al, 2008;Ma et al, 2013a) and others by reducing bus travel time or passenger waiting time at the downstream bus stop while minimizing the impact these priority strategies have on the rest of the traffic (Ma et al, 2013b;Lin et al, 2013;Zeng et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Arterial traffic signal optimization: A person-based approach

Christofa

Ampountolas

Skabardonis

2016

Transportation Research Part C: Emerging Technologies

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This paper presents a real-time signal control system that optimizes signal settings based on minimization of person delay on arterials. The system's underlying mixed integer linear program minimizes person delay by explicitly accounting for the passenger occupancy of autos and transit vehicles. This way it can provide signal priority to transit vehicles in an efficient way even when they travel in conflicting directions. Furthermore, it recognizes the importance of schedule adherence for reliable transit operations and accounts for it by assigning an additional weighting factor on transit delays. This introduces another criterion for resolving the issue of assigning priority to conflicting transit routes. At the same time, the system maintains auto vehicle progression by introducing the appropriate delays associated with interruptions of platoons. In addition to the fact that it utilizes readily available technologies to obtain the input for the optimization, the system's feasibility in real-world settings is enhanced by its low computation time. The proposed signal control system is tested on a four-intersection segment of San Pablo Avenue arterial located in Berkeley, California. The findings show the system's capability to outperform pretimed (i.e., fixed-time) optimal signal settings by reducing total person delay. They have also demonstrated its success in reducing bus person delay by efficiently providing priority to transit vehicles even when they are traveling in conflicting directions.

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Section: Introductionmentioning

confidence: 99%

Arterial traffic signal optimization: A person-based approach

Christofa

Ampountolas

Skabardonis

2016

Transportation Research Part C: Emerging Technologies

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“…Christofa et al considered the total delay on arterials composed of the delay of social vehicles and buses, and presented a real-time signal control system that optimizes signal settings based on minimization of passenger delay on arterials [1]. Zeng et al proposed a stochastic mixed-integer nonlinear programming (SMINP) model to produce a good transit signal priority (TSP) timing considering the bus stop dwell time and the delay caused by standing vehicle queues [2]. Ghanim and Abu-Lebdeh developed a realtime tra c signal control method integrating tra c signal timing optimization and TSP control using genetic algorithms (GA) and arti cial neural networks (ANN) [3].…”

Section: Literature Reviewmentioning

confidence: 99%

Bus Priority Signal Control Considering Delays of Passengers and Pedestrians of Adjacent Intersections

Liu

Yang

et al. 2020

Journal of Advanced Transportation

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In this paper, a bus priority signal control (BPSC) method based on delays of passengers and pedestrians at adjacent intersections, is proposed. The influences of BPSC on passenger and pedestrian delay at adjacent intersections under the condition of coordinated control of green waves are studied. The implementation of BPSC at intersections not only reduces the delay of bus passengers, social vehicle passengers and pedestrians, but also improves the traffic flow of priority buses and social vehicles at downstream intersections. This study takes the green phase extension as an example of the active BPSC strategy, and analyzes three cases of priority vehicles reaching downstream intersection. Firstly, passenger and pedestrian delays at adjacent intersections are calculated under different traffic situations. Secondly, models with the goal of maximizing the reduced total delays are established. Thirdly, three algorithms are used to solve the problem to obtain the optimal signal timing adjustment scheme at upstream intersections. Ultimately, the result shows that the BPSC can effectively reduce pedestrian delays at intersections, protect the rights and interests of pedestrians, reduce the delays of priority vehicles, and maximize the reduced total delay.

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“…Li et al optimized green splits to minimize a weighted delay of both buses and other vehicles (7). Zeng et al proposed a stochastic mixed-integer TSP model for explicitly modeling random bus arrival times (8). He et al proposed a platoon-based formulation for optimizing arterial signal timings based on clustered signal requests with different priority levels (9).…”

Section: With Onboard Passenger Information the Papscci Model Computmentioning

confidence: 99%

Person-Based Adaptive Priority Signal Control with Connected-Vehicle Information

Zeng¹,

Sun

Zhang

et al. 2015

Transportation Research Record

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The goal for transit signal priority (TSP) strategies is to improve the efficiency of urban transportation systems by promoting fast passage of system users. However, because conventional vehicle detection technologies require TSP strategies to be vehicle based, TSP may not lead to optimal results for person delay. This paper proposes a signal control model called PAPSCCI (person-based adaptive signal priority control with connected-vehicle information). First, by using vehicle speed and location information available from connected-vehicle technologies, the model explicitly computes individual vehicle delay. In this way the model avoids assumptions about vehicle arrivals, which often are inevitable in a delay calculation derived from a queuing model. Furthermore, in the model approach, the computation of delays for private vehicles is no different from that for public buses except in the priority level and unifies the two types of vehicles. With onboard passenger information, the PAPSCCI model computes person delay for every vehicle running through the intersection and offers a more accurate basis for person delay minimization. The performance of the PAPSCCI model is evaluated in a traffic simulation environment. Compared with the optimized timing from SYNCHRO, the PAPSCCI model produces 39%, 49%, and 30% decreases in bus passenger delays for one, two, and three conflicting bus routes, respectively. In addition, general automobiles experience about an 8% to 11% decrease in person delays, showing the potential of PAPSCCI as a general adaptive signal control model. Finally, a penetration rate study shows that the PAPSCCI model can consistently perform reasonably well even when only about 30% of vehicles are equipped with connected-vehicle technology.

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A Real-Time Transit Signal Priority Control Model Considering Stochastic Bus Arrival Time

Cited by 60 publications

References 19 publications

Arterial traffic signal optimization: A person-based approach

Arterial traffic signal optimization: A person-based approach

Bus Priority Signal Control Considering Delays of Passengers and Pedestrians of Adjacent Intersections

Person-Based Adaptive Priority Signal Control with Connected-Vehicle Information

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