Integrating public transit signal priority into max-pressure signal control: Methodology and simulation study on a downtown network

Xu, Te; Barman, Simanta; Levin, Michael W.; Chen, Rongsheng; Li, Tianyi

doi:10.1016/j.trc.2022.103614

Cited by 34 publications

(7 citation statements)

References 34 publications

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“…The framework proposed in this study and the study findings have practical implications. For instance, estimated vehicle volume based on MDLD can be leveraged in safety risk exposure analysis and can support traffic signal controls (53)(54)(55). In particular, the proposed estimation method can particularly be beneficial for safety risk exposure and crash analysis with respect to vulnerable road users (e.g., pedestrians and bicyclists).…”

Section: Discussionmentioning

confidence: 99%

Big-Data Driven Framework to Estimate Vehicle Volume Based on Mobile Device Location Data

Yang

Luo

Ashoori

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Vehicle volume serves as a critical metric and the fundamental basis for traffic signal control, transportation project prioritization, road maintenance planning, and more. Traditional methods of quantifying vehicle volume rely on manual counting, video cameras, and loop detectors at a limited number of locations. These efforts require significant labor and cost for expansions. Researchers and private sector companies have also explored alternative solutions, such as probe vehicle data, although this still suffers from a low penetration rate. In recent years, along with the technological advancement in mobile sensors and mobile networks, the quantity of mobile device location data (MDLD) has been growing dramatically in spatiotemporal coverage of the population and its mobility. This paper presents a big-data driven framework that can ingest terabytes of MDLD and estimate vehicle volume over a larger geographical area with a larger sample size. The proposed framework first employs a series of cloud-based computational algorithms to extract multimodal trajectories and trip rosters. A scalable map matching and routing algorithm is then applied to snap and route vehicle trajectories to the roadway network. The observed vehicle counts on each roadway segment are weighted and calibrated against ground truth control totals, that is, annual vehicle-miles traveled and annual average daily traffic. The proposed framework is implemented on the all-street network in the State of Maryland using MDLD for the entire year of 2019. The results demonstrate that our proposed framework produces reliable vehicle volume and also its transferability and generalization ability.

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Section: Discussionmentioning

confidence: 99%

Big-Data Driven Framework to Estimate Vehicle Volume Based on Mobile Device Location Data

Yang

Luo

Ashoori

et al. 2023

Transportation Research Record: Journal of the Transportation R

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“…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]. Another recent focus area of the ATSP strategies can be attributed to developing methods based on the minimization of environmental impacts of the PT priority process [40,41].…”

Section: Introductionmentioning

confidence: 99%

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

Behbahani,

Poorjafari

2024

IET Intelligent Trans Sys

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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.

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“…With the continuous development of the urban economy and scale, traffic volumes have sharply increased. A series of traffic problems, such as traffic congestion and environmental pollution, have emerged [1]. Public transport (PT) has the advantage of a large passenger capacity, resulting in a fraction of energy consumption and exhaust emission compared with social vehicles in terms of per capita.…”

Section: Introductionmentioning

confidence: 99%

A cooperative control method combining signal control and speed control for transit with connected vehicle environment

Teng,

Liu,

Liu

et al. 2023

IET Control Theory & Appl

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Transit operation efficiency and service quality can be enhanced through the implementation of signal and speed control. Previous studies prefer to change driving speed in priority to alleviate the adverse effects of signal timing adjustment on social vehicles. The driving safety and fuel consumption of transit are ignored. To this end, a cooperative control method consisting of three models is proposed. The cooperative control strategy model provides optimal schemes for allocating transit priority time. Based on this, the adjustment of phase time and the transit speed trajectory with the lower fuel consumption are calculated by signal control model and speed control model, respectively. Especially, the signal control model is established in the background of green wave coordinated control to further protect the travelling benefits of social vehicles. The simulation is performed in SUMO to demonstrate the effectiveness of the proposed method. The results show that the cooperative control method improves the crossing efficiency and enhances the fuel economy of transit under different arrival speeds and lengths of control area. Compared with the general signal control, the proposed method can minimize traffic interference, which is particularly obvious in a higher degree of saturation.

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Integrating public transit signal priority into max-pressure signal control: Methodology and simulation study on a downtown network

Cited by 34 publications

References 34 publications

Big-Data Driven Framework to Estimate Vehicle Volume Based on Mobile Device Location Data

Big-Data Driven Framework to Estimate Vehicle Volume Based on Mobile Device Location Data

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

A cooperative control method combining signal control and speed control for transit with connected vehicle environment

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