Joint road toll pricing and capacity development in discrete transport network design problem

Xu, Meng; Wang, Guangmin; Grant-Muller, Susan; Gao, Ziyou

doi:10.1007/s11116-015-9674-2

Cited by 20 publications

(8 citation statements)

References 61 publications

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“…In stage 2, a reasonable road OD traffic demand for the object road is set. Following Xu et al [48] and Tennøy et al [49], the study estimates the capacity of the object road. For each lane, it is set that the morning peak time is 3 h and the traffic flow is 1700 pcu/h; the common time is 8 h and the traffic flow is 1200 pcu/h; the evening peak time is 3 h and the traffic flow is 1550 pcu/h.…”

Section: Datamentioning

confidence: 99%

QPNet: Lane‐changing trajectory planning combining quadratic programming and neural network under the convex optimization framework

Wang

Wei

2022

IET Intelligent Trans Sys

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Lane-changing is a basic driving behaviour, which largely impacts on traffic safety and efficiency. With the development of technology, the automated lane-changing system has attracted extensive attention. Among it, the trajectory planning part is a challenging problem owing to the complexity and diversity of the driving situations. The planner requires the real-time capability to produce safe and comfortable trajectories for coping with the dynamically changing environment. Based on this, the paper proposes a lane-changing trajectory planning model in dynamic driving environments. The model constructs a neural network to predict the end position of the ego vehicle, and then adopts the mathematical programming method to solve the optimal lane-changing trajectory that guarantees the ride safety and comfort. With the assistance of the neural network, the lane-changing trajectory planning problem is converted into a quadratic programming (QP) model, thereby achieving rapid solution of the model. Moreover, to train the proposed algorithm, a novel approach for generating the scenario data is designed, which can generate rich and diverse traffic scenarios at a low cost. The simulation results show that the proposed model can plan a lane-changing trajectory quickly and effectively, and the ego vehicle can follow the planned trajectory safely and comfortably.

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Section: Datamentioning

confidence: 99%

QPNet: Lane‐changing trajectory planning combining quadratic programming and neural network under the convex optimization framework

Wang

Wei

2022

IET Intelligent Trans Sys

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“…Although the authors show that the linear-linear bilevel program is strongly NP-hard, special cases of the problem are solved in polynomial time, for example as a mixed integer program with a small number of binary variables. Bilevel programs have been further extended to the continuous transport network design problem in Chiou (2005) and the optimal capacity and toll problem in Xu, Wang, Grant-Muller, and Gao (2017) .…”

Section: Bilevel Programmingmentioning

confidence: 99%

A review of transport market modeling using game-theoretic principles

Adler

Brudner

Proost

2021

European Journal of Operational Research

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“…Congestion mitigation, environmental protection, and better travel quality provided on the road network is still an ever increasing concerned problem in the urban transportation system. Road congestion pricing has been viewed as an effective means to alleviate traffic congestion [26][27][28][29].…”

Section: Conclusion and Further Studiesmentioning

confidence: 99%

A Simplified C-Logit Stochastic User Equilibrium Model on Bimodal Transportation Network

Liu

Zhang

2020

Mathematical Problems in Engineering

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This paper investigates the C-logit stochastic user equilibrium (SUE) problem on a bimodal transportation network with road and rail travel modes. The C-logit model captures the overlapping effect among the different paths via commonality factors; sequentially, it has ability to obtain a more realistic traffic flow distribution pattern. In this paper, when we redefine the link travel cost functions and employ a binary Logit model for the mode split, the bimodal C-logit SUE model can be simplified into an unconstrained nonlinear mathematical programming formulation. Such model is verified to satisfy the bimodal C-logit SUE conditions at its stationary point and can be solved by existing algorithms. So, the simplified model can be convenient to be used on the general bimodal transportation network.

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Joint road toll pricing and capacity development in discrete transport network design problem

Cited by 20 publications

References 61 publications

QPNet: Lane‐changing trajectory planning combining quadratic programming and neural network under the convex optimization framework

QPNet: Lane‐changing trajectory planning combining quadratic programming and neural network under the convex optimization framework

A review of transport market modeling using game-theoretic principles

A Simplified C-Logit Stochastic User Equilibrium Model on Bimodal Transportation Network

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