Optimization Method for Mixed Vehicle Bus Scheduling Considering Scenario Differences

被引：0

作者：

Weng, Jiancheng ^{[1
]}

Qiao, Runtong ^{[1
]}

Wang, Maolin ^{[3
]}

Lin, Pengfei ^{[2
]}

Liu, Dongmei ^{[4
,5
]}

Zhang, Xiaoliang ^{[4
,5
]}

机构：

[1] Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing

[2] Faculty of Information Technology, Beijing University of Technology, Beijing

[3] Longling Highway Branch of Baoshan Highway Bureau, Yunnan, Baoshan

[4] Key Laboratory of Intelligent Transportation Systems Technologies, Beijing

[5] Research and Development Center of Transport Industry of Big Data Processing Technologies and Application for Comprehensive Transport (ZHONG LU GAO KE), Beijing

来源：

Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | 2024年 / 24卷 / 04期

基金：

中国国家自然科学基金;

关键词：

bus scheduling optimization; genetic algorithm; mixed bus types; pure electric bus; scheduling plan; urban traffic;

D O I：

10.16097/j.cnki.1009-6744.2024.04.017

中图分类号：

学科分类号：

摘要：

Pure electric bus has become an important option for the electric transformation of vehicles due to its low-carbon, energy-saving, and environmental protection characteristics. However, pure electric buses still face challenges such as performance degradation under low-temperature conditions and reduced mileage due to battery aging in actual operation. The mixed use of fuel buses and pure electric buses in operation helps to improve the performance degradation of pure electric buses in specific scenarios, and to enhance the efficiency and service quality of bus operation. This paper proposes a segmented optimization model for bus timetables considering the dynamic operation characteristics of buses. With the optimized frequency as input, a bus fleet scheduling planning compilation model is developed under mixed bus operation conditions. An improved genetic algorithm is designed to solve the model. Taking the bus routes in Beijing as an example, the case studies were conducted in different typical operational scenarios such as single-line operation, remote charging, and regional centralized scheduling to verify the applicability and optimization effect of the model under differentiated operational scenarios. The results indicate that compared to local charging scenarios, operational costs increased by 5.15% and the number of operating vehicles increased by 5.88% in remote charging scenarios. In the regional centralized scheduling scenario where multiple routes are jointly scheduled, operational costs decreased by 4.68% compared to single-line operation scenarios. Under the condition of given bus types proportion threshold, the effectiveness of mixed vehicle operation surpasses single vehicle type operation, effectively reducing operational costs and carbon emissions. This study provides a support for public transport enterprises to create scientific and flexible electric bus operation scheduling schemes based on different operation scenarios. © 2024 Science Press. All rights reserved.

引用

页码：176 / 187

页数：11

共 24 条

[1]

Statistical bulletin on the development of the transportation industry in 2022

[2]

WANG Y, SHEN J Q., Single-track bus combined scheduling model based on departure schedule, Computer Science, 44, 10, pp. 269-275, (2017)

[3]

LIU C., Research on single-line bus scheduling problem based on hybrid heuristic algorithm, (2016)

[4]

WENG J C, WANG M L, LIN P F, Et al., Optimization method for cross line combination scheduling of public transportation based on passenger flow characteristic identification, Journal of South China University of Technology (Natural Science), 50, 9, pp. 39-48, (2022)

[5]

YAO E J, LIU T, XUN N, Et al., Optimization of departure interval and vehicle configuration of conventional bus routes, Journal of Beijing Jiaotong University, 44, 4, pp. 86-93, (2020)

[6]

WANG H, SHEN J, LIU Z., Ant colony algorithm for transit vehicle scheduling problem with route time constraint, Proceedings of the Fifth International Conference on Traffic and Transportation Studies, (2006)

[7]

SASSI O, OULAMARA A., Electric vehicle scheduling and optimal charging problem: Complexity, exact and heuristic approaches, International Journal of Production Research, 55, 2, pp. 519-535, (2016)

[8]

GARCIA-ALVAREZ J, GONZALEZ M, VELA C, Et al., Electric vehicle charging scheduling by an enhanced artificial bee colony algorithm, Energies, 11, 10, (2018)

[9]

GARCIA-ALVAREZ J, GONZALEZ M, VELA C., Metaheuristics for solving a real-world electric vehicle charging scheduling problem, Applied Soft Computing, 65, pp. 292-306, (2018)

[10]

YANG Y, GUAN W, MA J H., Research on optimization of electric bus dispatch plan based on column generation algorithm, Transportation Systems Engineering and Information, 16, 5, pp. 198-204, (2016)

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