Traffic network design model with trip production-attraction and supply uncertainty

Xinxin Yu; Changzhi Bian; Xianke Han; Guoyi Tang; Yu Wang; Heling Liu; Ying Liu; Yi Liu; Jie Shao; Shu Cui

doi:10.1117/12.3039449

28 August 2024 Traffic network design model with trip production-attraction and supply uncertainty

Xinxin Yu, Changzhi Bian, Xianke Han, Guoyi Tang, Yu Wang, Heling Liu, Ying Liu, Yi Liu, Jie Shao, Shu Cui

Proceedings Volume 13251, Ninth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024); 132510H (2024) https://doi.org/10.1117/12.3039449
Event: 9th International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024), 2024, Guilin, China

Abstract

Traffic network design is the core field in transportation planning, and it is also the key point and difficulty. The traditional traffic network design model assume that demand and supply are deterministic which can result in unreasonable planning scheme. This paper assumes that the traffic supply and trip production-attraction are random variables which aims to build new transport network design method with uncertainty theory. A bi-level model is established with combined trip distribution/traffic assignment model to improve the accuracy of predictions. The essence of combinatorial optimization for network design problems has been solved with Monte Carlo simulation, genetic algorithm, and traffic assignment algorithm. The results of the Nguyen Dupuis network show that the parameter of the combined trip distribution/traffic assignment model and degrees of uncertainty in trip production-attraction have an important impact on the decision-making of network design.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinxin Yu, Changzhi Bian, Xianke Han, Guoyi Tang, Yu Wang, Heling Liu, Ying Liu, Yi Liu, Jie Shao, and Shu Cui "Traffic network design model with trip production-attraction and supply uncertainty", Proc. SPIE 13251, Ninth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024), 132510H (28 August 2024); https://doi.org/10.1117/12.3039449

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KEYWORDS

Design

Network architectures

Genetic algorithms

Algorithms

Stochastic processes

Computer programming

Gallium

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