Pre-trip reservation enabled route guidance and signal control cooperative method for improving network throughput

Wang, Jiawen; You, Liangzhi; Hang, Jiayu; Zhao, Jing

doi:10.1016/j.physa.2022.128405

Cited by 6 publications

(7 citation statements)

References 24 publications

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“…However, a greedy signal preemption method can lead to a substantial negative impact on ordinary vehicles (OVs) from conficting directions, thereby signifcantly degrading overall trafc efciency. Benefting from the development of connected and autonomous vehicles (CAV) [13][14][15][16], real-time optimization of vehicle trajectories and signal schemes has become feasible [17][18][19]. In the CAV environment, the new method involves controlling the trajectory of OVs to yield the right of way to EV, thereby reducing hindrances they encounter from OVs.…”

Section: Introductionmentioning

confidence: 99%

A Collaborative Control Framework: Achieving Emergency Vehicle Priority While Minimizing Negative Impact on Ordinary Vehicles at Signalized Intersection

Long,

Zhang,

Gao

et al. 2024

Journal of Advanced Transportation

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When an emergency vehicle (EV) passes through an isolated signal intersection, it is crucial to ensure the efficient passage of the EV while minimizing the negative impact on ordinary vehicles (OVs), particularly in high-traffic flow scenarios. Given the constraints on temporal and spatial resources within intersection areas, OVs ahead of EV often face challenges in finding safe gaps for giving way, resulting in significant obstructions to OVs. This research introduces a novel collaborative control framework to jointly optimize dynamic emergency lane settings and signal schemes, considering EV priority and OVs benefits for a single signalized intersection. Firstly, we propose a dynamic emergency lane control algorithm to help obstructed EV in roadway segments by extending and reallocating temporal and spatial resources for vehicles. Then, we establish a collaborative control model considering EV priority and OVs benefits. Assigning the highest priority to the emergency priority phase, this model optimizes signal schemes to prevent interphase conflict, taking into account OVs benefits. Finally, our collaborative control framework also employs an Eco-Driving algorithm for the optimization of OV speed to reduce fuel consumption. The case study results reveal that in comparison to other baseline methods, our proposed model significantly reduces EV travel time, simultaneously lowering the travel time and fuel consumption of OVs. Sensitivity analysis of varying traffic flow scenarios reveals that, as vehicle volumes increase, our proposed method demonstrates more pronounced reductions in both EV and OV travel time. In addition, there is a progressive increase in the proportion of dynamic emergency lane utilization, with activation occurring at earlier locations.

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Section: Introductionmentioning

confidence: 99%

A Collaborative Control Framework: Achieving Emergency Vehicle Priority While Minimizing Negative Impact on Ordinary Vehicles at Signalized Intersection

Long,

Zhang,

Gao

et al. 2024

Journal of Advanced Transportation

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“…This approach has found applications in various domains, including hospital outpatient departments, highways, buses, metros, the management of visitor flow at tourist destinations, and intelligent parking systems [17][18][19][20][21][22][23][24]. The scope of research on travel reservations encompasses aspects such as travel route reservations, intersection reservations, dedicated lanes, and the construction of comprehensive travel reservation systems [25].…”

Section: Introductionmentioning

confidence: 99%

“…The travel reservation strategy: in accordance with Wang's methodology [25], passengers with reservations take the fast track, and those without reservations take the main track. It is assumed that a certain proportion (denoted by p R ) of passengers at entrance C will use travel reservations and enter the station through the fast track after making a reservation in advance; different reservation proportions have different effects.…”

mentioning

confidence: 99%

Increasing Passenger Efficiency and Minimizing Infection Transmission in Chinese Metro Stations during COVID-19: A Simulation-Based Strategy Analysis

Xue,

Zhang,

Shiwakoti

2023

Systems

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This study addresses the challenging problem of increasing passengers’ travel efficiency while lowering the infection transmission risk at metro stations during COVID-19 pandemic. To achieve this objective, we deploy Anylogic software and formulate an infection risk model. As a case study, this study focuses on a transfer metro station in Xi’an, China. Firstly, by utilizing Anylogic software, three distinct strategies are simulated: flow-control fences, travel reservation, and the collaborative use of travel reservations and flow-control fences. Secondly, the passenger density and average dwell time under these strategies are assessed while constructing an infection risk model to quantify the risk faced by passengers. Thirdly, when compared to the absence of any strategy, the results are as follows: (1) The flow-control fences strategy: implementing flow-control fences can effectively reduce the risk of passenger infection when the length of the flow-control fences is fixed at 47.5 m, but comes at the cost of a 20.15% decrease in passenger travel efficiency; however, excessively long flow-control fences will neither alleviate congestion nor reduce the infection risk. (2) The travel reservation strategy: the adoption of travel reservations, along with a fast track for reserved users, when the reservation proportion is 40%, leads to a remarkable 29.05% improvement in travel efficiency and reduces the risk of passenger infection by 67.12%. (3) The combined strategy: the combined utilization of travel reservations and flow-control fences enhances travel efficiency by 15.80% and reduces the risk of passenger infection by 56.77% when the reservation proportion is set at 30%. When the reservation proportion is between 10 and 30%, its infection risk reduction effect is better than that of the travel reservation strategy, but this is not necessarily true for their effects on travel efficiency. Finally, this study was compared to an existing study that proposed a new strategy by combining travel reservations with departure intervals, analyzing the effect of the implementation of the strategy with different departure intervals. The findings from this study have implications for developing appropriate strategies to optimize passenger flow without significantly compromising the transmission of infection risk during the pandemic.

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“…For network trafc signal control, it focuses on determining optimal signal timing plans that can trigger better equilibrium fow patterns with optimal network performance. It is also called the combined trafc assignment and signal control problem [7][8][9][10][11][12] or anticipatory network trafc control [13][14][15][16] because the signal control anticipates the efect of route choice response.…”

Section: Introductionmentioning

confidence: 99%

Metamodel-Based Optimization Method for Traffic Network Signal Design under Stochastic Demand

Wei

Zhang

Cheng

et al. 2023

Journal of Advanced Transportation

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Traffic network design problems (NDPs) play an important role in urban planning. Since there exist uncertainties in the real urban traffic network, neglecting the uncertainty factors may lead to unreasonable decisions. This paper considers the transportation network signal design problem under stochastic origin-destination (OD) demand. In general, solving this stochastic problem requires a large amount of computational budget to calculate the equilibrium flow corresponding to a certain demand distribution, which limits its real applications. To reduce the computational time in calculating the equilibrium flow under stochastic demand, this paper proposes a metamodel-based optimization method. First, a combined metamodel that integrates a physical modeling part and a model bias generic part is developed. The metamodel is used to approximate the time-consuming average equilibrium flow solution process, hence to improve the computational efficiency. To further improve the convergence and the solution optimality performance of the metamodel-based optimization, the gradient information of traffic flow with respect to the signal plan is incorporated in the optimization model. A gradient-based metamodel algorithm is then proposed. In the numerical example, a six-node test network is used to examine the proposed metamodel-based optimization method. The proposed combined metamodel is compared with the benchmark method to investigate the importance of incorporating a model bias generic part and the traffic flow gradient information in the combined metamodel. Although there is a reduction in solution optimality since the metamodel is an approximation of the original model, the metamodel methods greatly improve the computational efficiency (the computational time is reduced by 4.84 to 13.47 times in the cases of different initial points). By incorporating the model bias, the combined metamodel can better approximate the original optimal solution. Moreover, incorporating the gradient information of the traffic flow in the optimization search algorithm can further improve the solution performance. Numerical results show that the gradient-based metamodel method can effectively improve the computation efficiency while slightly reducing the solution optimality (with an increase of 0.09% in the expected total travel cost).

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Pre-trip reservation enabled route guidance and signal control cooperative method for improving network throughput

Cited by 6 publications

References 24 publications

A Collaborative Control Framework: Achieving Emergency Vehicle Priority While Minimizing Negative Impact on Ordinary Vehicles at Signalized Intersection

A Collaborative Control Framework: Achieving Emergency Vehicle Priority While Minimizing Negative Impact on Ordinary Vehicles at Signalized Intersection

Increasing Passenger Efficiency and Minimizing Infection Transmission in Chinese Metro Stations during COVID-19: A Simulation-Based Strategy Analysis

Metamodel-Based Optimization Method for Traffic Network Signal Design under Stochastic Demand

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