Transfer-based customized modular bus system design with passenger-route assignment optimization

被引:65
作者
Gong, Manlin [1 ]
Hu, Yucong [1 ]
Chen, Zhiwei [2 ]
Li, Xiaopeng [2 ]
机构
[1] South China Univ Technol, Dept Transportat Engn, Guangzhou 510640, Guangdong, Peoples R China
[2] Univ S Florida, Dept Civil & Environm Engn, Tampa, FL 33620 USA
基金
美国国家科学基金会;
关键词
Customized bus; Network design; Transfer; Passenger-route assignment; Modular vehicle; PARTICLE SWARM OPTIMIZATION; RESPONSIVE TRANSPORTATION SERVICES; NETWORK DESIGN; SIMULTANEOUS PICKUP; GENETIC ALGORITHM; DEMAND; DELIVERY; HEURISTICS; VEHICLES; DISPATCH;
D O I
10.1016/j.tre.2021.102422
中图分类号
F [经济];
学科分类号
02 ;
摘要
Customized bus (CB) is an increasingly popular mode of transportation in many cities around the world. However, studies on CB network design have mostly overlooked three options that may further improve system performance: passenger-route assignment, passenger transfer, and modular vehicles. To bridge this gap, this paper proposes to design a transfer-based CB network with a modular fleet while simultaneously optimizing the passenger-route assignment. To solve the optimal network structure with this new design paradigm, we formulate the network design problem into a nonlinear mixed integer optimization model. A linearization approach and a particle swarm optimization (PSO) algorithm are proposed to solve the exact and near-optimal solution(s) to the model, respectively. Numerical experiments are conducted on the Sioux Falls network and a large-scale network in Chengdu, China. Results show that the customized PSO algorithm efficiently provides high quality near-optimal solutions compared with CPLEX, the genetic algorithm, and the simulated annealing algorithm. Results also show that incorporating passenger-route assignment optimization and the transfer operation produces a more costeffective CB operational network with less operational costs and higher service quality. The benefit increases as the passenger demand grows.
引用
收藏
页数:24
相关论文
共 86 条
[1]  
[Anonymous], TRANSPORT ANAL GUIDA
[2]  
Baldacci R, 2008, OPER RES COMPUT SCI, V43, P3, DOI 10.1007/978-0-387-77778-8_1
[3]   A new hybrid genetic algorithm for the capacitated vehicle routing problem [J].
Berger, J ;
Barkaoui, M .
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 2003, 54 (12) :1254-1262
[4]  
Berger J, 2003, LECT NOTES COMPUT SC, V2723, P646
[5]  
Blair A., 1958, STUDY NUMERICAL SOLU
[6]   A differential evolution for simultaneous transit network design and frequency setting problem [J].
Buba, Ahmed Tarajo ;
Lee, Lai Soon .
EXPERT SYSTEMS WITH APPLICATIONS, 2018, 106 :277-289
[7]  
Bullnheimer B., 1997, SFB Adaptive Information Systems and Modelling in Economics and Management Science
[8]  
Cao Y., 2017, Mathematical Problems in Engineering, V2017, P1, DOI [10.1155/2017/7914753, DOI 10.1155/2017/7914753]
[9]   Day-to-day market evaluation of modular autonomous vehicle fleet operations with en-route transfers [J].
Caros, Nicholas S. ;
Chow, Joseph Y. J. .
TRANSPORTMETRICA B-TRANSPORT DYNAMICS, 2021, 9 (01) :109-133
[10]   AN IMPROVED ANT COLONY SYSTEM ALGORITHM FOR THE VEHICLE ROUTING PROBLEM [J].
Chen, Chia-Ho ;
Ting, Ching-Jung .
JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING, 2006, 23 (02) :115-126