考虑场景差异性的混合车型公交调度优化方法

doi:10.16097/j.cnki.1009-6744.2024.04.017

交通运输系统工程与信息 ›› 2024, Vol. 24 ›› Issue (4): 176-187.DOI: 10.16097/j.cnki.1009-6744.2024.04.017

考虑场景差异性的混合车型公交调度优化方法

翁剑成^1a，乔润童^1a，王茂林²，林鹏飞^*1b，刘冬梅^3,4，张晓亮^3,4

1. 北京工业大学，a.交通工程北京市重点实验室，b.信息学部，北京100124；2.保山公路局龙陵公路分局，云南保山678300；3.智能交通技术交通运输行业重点实验室，北京100088；4.综合交通运输大数据处理及应用技术交通运输行业研发中心(中路高科)，北京100088

收稿日期:2024-04-07 修回日期:2024-05-29 接受日期:2024-06-05 出版日期:2024-08-25 发布日期:2024-08-22
作者简介:翁剑成(1981- )，男，浙江金华人，教授，博士。
基金资助:
国家自然科学基金 (52072011，52302381)；北京市教育委员会科学研究计划项目(KM202310005025)。

Optimization Method for Mixed Vehicle Bus Scheduling Considering Scenario Differences

WENGJiancheng,QIAO Runtong,WANG Maolin,LIN Pengfei,LIU Dongmei,ZHANG Xiaoliang

1a. Beijing Key Laboratory of Traffic Engineering, 1b. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; 2. Longling Highway Branch of Baoshan Highway Bureau, Baoshan 678300, Yunnan, China; 3. Key Laboratory of Intelligent Transportation Systems Technologies, Beijing 100088, China; 4. Research and Development Center of Transport Industry of Big Data Processing Technologies and Application for Comprehensive Transport (ZHONGLUGAOKE),Beijing 100088, China

Received:2024-04-07 Revised:2024-05-29 Accepted:2024-06-05 Online:2024-08-25 Published:2024-08-22
Supported by:
NationalNaturalScienceFoundation of China (52072011，52302381)；目/R&DProgramofBeijingMunicipal Education Commission (KM202310005025)。

摘要/Abstract

摘要： 纯电动公交因其低碳和节能环保的特性，已成为车辆电动化转型的必然选择，但纯电动公交车在实际运营中仍面临低温条件下性能下降和电池老化导致续航里程降低等挑战。考虑在运营中混合使用燃油车和电动车，以弥补纯电动公交车在特定场景下的性能下降，提升公交运营效率和服务质量。本文考虑公交动态运行特征建立公交时刻表分段优化模型，以优化后的车次为输入，构建混合车型运营条件下的公交行车计划编制优化模型，并设计改进的遗传算法实现模型求解。最后，以北京市公交线路为例，选取单线路运营、异地充电及区域集中调度等不同典型运营场景开展案例研究，验证优化模型在差异化运营场景条件下的适用性和优化效果。结果表明，对比本地充电场景，异地充电场景下的运营成本增加5.15%，运营车辆数量增加5.88%；在多线路联合编制行车计划的区域集中调度场景下，运营成本较单线路运营场景降低4.68%；在给定的车型比例阈值下，使用混合车型运营效果优于使用单一车型运营，有效降低运营成本和碳排放。本文研究为公共交通企业结合不同运营场景，制定科学灵活的电动公交运营调度方案提供了重要支撑。

关键词: 城市交通, 公交调度优化, 遗传算法, 混合车型, 纯电动公交, 行车计划

Abstract: 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.

Key words: urban traffic, bus scheduling optimization, genetic algorithm, mixed bus types, pure electric bus, scheduling plan

中图分类号:

U121

翁剑成, 乔润童, 王茂林, 林鹏飞, 刘冬梅, 张晓亮. 考虑场景差异性的混合车型公交调度优化方法[J]. 交通运输系统工程与信息, 2024, 24(4): 176-187.

WENGJiancheng, QIAO Runtong, WANG Maolin, LIN Pengfei, LIU Dongmei, ZHANG Xiaoliang. Optimization Method for Mixed Vehicle Bus Scheduling Considering Scenario Differences[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(4): 176-187.

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http://www.tseit.org.cn/CN/Y2024/V24/I4/176

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考虑场景差异性的混合车型公交调度优化方法

Optimization Method for Mixed Vehicle Bus Scheduling Considering Scenario Differences

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