Hybrid Scheduling Method for Conventional Bus and Demand-responsive
 Transit Based on Eco-driving Technology

doi:10.16097/j.cnki.1009-6744.2025.02.021

Abstract

Abstract: In response to the insufficient energy efficiency in the joint operation mode of conventional bus and demand responsive transit, this paper proposes an energy-saving oriented transit scheduling decision and ecological speed collaborative optimization method by introducing eco-driving technology. With the purpose of minimizing total operational cost and the penalty cost caused by asynchronous schedules, a mixed integer optimization model was developed with the constraints of passenger travel time window preferences and schedule coordination. Feasible range of conventional bus eco-speed are derived for four signal phase and timing scenarios, and the conventional bus eco-speed, actual arriving time, and demand responsive bus fleet size, route, schedule, and eco-speed are synthetically optimized. A three-stage mixed heuristic algorithm is designed based on the model properties. To verify the effectiveness of the proposed method, a case study is conducted in the Chongqing University town area, simulating the actual scenario of joint operation of conventional and demand-responsive buses. The results show that compared with traditional scheduling method, the proposed method can reduce the total system cost by more than 16.2%. Through adjusting eco-speed, the idle waiting at signalized intersection is avoided, and the arrival punctuality and the goal of energy saving are taken into account. In addition, this method also improves the turnover rate of demand responsive-bus, then reduces the fleet size, increases the timetable synchronization by 81%, fully meeting the passengers' time window preference and hybrid scheduling requirement.

Key words: urban traffic, hybrid scheduling, hybrid heuristic algorithm, demand responsive transit, eco-driving technology

摘要： 针对常规公交和需求响应公交联合运营模式下存在的同步性差和能耗高的问题，本文通过引入生态驾驶技术，提出一种低能耗导向的公交混合调度决策和生态车速协同优化方法。以最小化公交运营成本和时刻表不同步所产生的惩罚成本为目标，考虑乘客出行时间窗偏好和时刻表协同等关键约束条件，构建混合整数优化模型，推导4种信号相位配时场景下的公交生态车速可行区间，协同优化常规公交的生态车速、实际到站时间，以及需求响应公交车队规模、路径、时刻表和生态车速，并根据模型性质设计三阶段混合启发式算法。为验证所提模型和算法的有效性，选取重庆大学城片区开展案例分析，模拟常规公交和需求响应公交联合运营的实际场景。结果表明，与传统混合调度方法相比，本文提出的方法能够降低系统总成本约16.2%。通过调整行驶车速，避免常规公交在信控交叉口的怠速等待，兼顾到站准时和能耗节约目标。此外，该方法还提升了需求响应公交单车周转率，减少了车队规模，接驳站点处时刻表同步性提升81%，充分满足乘客出行时间窗需求和混合调度要求。

关键词: 城市交通, 混合调度, 混合启发式算法, 需求响应公交, 生态驾驶

CLC Number:

U121

XU Weihan, LI Xin, WANG Tianqi. Hybrid Scheduling Method for Conventional Bus and Demand-responsive Transit Based on Eco-driving Technology[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 227-240.

徐伟汉, 李欣, 王天奇. 基于生态驾驶的常规和需求响应公交混合调度方法[J]. 交通运输系统工程与信息, 2025, 25(2): 227-240.

Add to citation manager EndNote|Ris|BibTeX

URL: http://www.tseit.org.cn/EN/10.16097/j.cnki.1009-6744.2025.02.021

http://www.tseit.org.cn/EN/Y2025/V25/I2/227

References

[1] LI X, WANGT, XUW, et al. A novel model and algorithm for designing an eco-oriented demand responsive transit (DRT) system[J]. Transportation ResearchPart E: Logistics andTransportationReview, 2022,157:102556.

[2]卢小林,潘述亮,邹难.复杂路网下灵活接驳公交路径优化研究[J].交通运输系统工程与信息,2016,16(6): 128-134. [LUXL, PANSL, ZOUN. Flexiblefeeder transit routedesign incomplex roadnetwork[J]. Journal of TransportationSystemsEngineering and Information Technology,2016,16(6):128-134.]

[3]李欣,乔靖元,李炎皓,等.环形放射状城市常规与需求响应公交联合优化研究[J].交通运输系统工程与信息,2023,23(4):155-163. [LI X, QIAO J Y, LIYH. et al. Joint optimization of conventional transit and demand responsive transit for ring-radial cities[J]. Journal of Transportation Systems Engineering and InformationTechnology,2023,23(4):155-163]

[4] WANGZ,YUJ,HAOW, etal.Designinghigh-freedom responsive feeder transit systemwithmultitypevehicles [J]. Journal ofAdvancedTransportation, 2020, 2020(6): 1-20..

[5]常玉林,蔡宇航,孙超,等.异质需求下响应型接驳公交车速引导与路径规划协同研究[J].交通运输系统工程与信息,2023, 23(5): 83-95. [CHANG Y L, CAI Y H, SUN C, et al. Collaborative study of speed guidance and path planning for responsive feeder transit with heterogeneous demands[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(5): 83-95.]

[6]孙倩,胡大伟,钱一之,等.考虑车辆随机到站时间的动态需求响应型接驳公交线路优化[J].交通运输系统工程与信息,2022,22(5): 196-204, 292. [SUN Q, HU D W, QIAN Y Z, et al. Dynamic bus routing optimization for demand-responsive feeder transit considering stochastic bus arrival time[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(5): 196-204, 292.]

[7]王正武,宋名群.多换乘点响应型接驳公交运行线路的协调优化[J]. 中国公路学报,2019, 32(9): 164-174. [WANG Z W, SONG M Q. Coordinated optimization of operation routes for responsive feeder transit systems with multiple transfer points[J]. China Journal of Highway and Transport, 2019, 32(9): 164-174.]

[8] HAO P, WU G, BORIBOONSOMSIN K, et al. Eco Approach and Departure (EAD) application for actuated signals in real-world traffic[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(1): 30-40.

[9] JIANG H, HU J, AN S, et al. Eco approaching at an isolated signalized intersection under partially connected and automated vehicles environment[J]. Transportation Research Part C: Emerging Technologies, 2017, 79: 290 307.

[10] 珠兰, 马潇,刘卓凡.时间依赖型绿色车辆路径问题研究[J]. 交通运输系统工程与信息, 2021, 21(6): 187 194. [ZHU L, MA X, LIU Z F. Time-dependent green vehicle routing problem[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(6): 187-194.]

[11] LI X, XU W, WANG T, et al. Optimizing integrated eco driving control and holding strategy for real-time bus bunching mitigation[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(7): 7568-7582.

[12] DENG Y J, LIU X H, HU X, et al. Reduce bus bunching with a real-time speed control algorithm considering heterogeneous roadway conditions and intersection delays [J]. Journal of Transportation Engineering, Part A: Systems, 2020, 146(7): 04020048.

[13] LI X, WANG T, XU W, et al. A novel model and algorithm for designing an eco-oriented demand responsive transit (DRT) system[J]. Transportation Research Part E: Logistics and Transportation Review, 2022, 157: 102556.

[1]	JIN Zeqian, YU Chengcheng, YE Xin. Flood Evacuation Decision-making Behavior Under Hybrid Utility and Regret Rules [J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 16-25.
[2]	ZHOU Yuyang, ZHAO Congying, LI Jingkun, CHEN Yanyan, LIU Di, WANG Shuling. Comparison on Influence of Job-housing and Commuting Status on Travel Mode Choice in Multiple Types of Cities [J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 26-35.
[3]	PENG Qiyuan, LIU Siyuan, JIANG Shan, FENG Tao, CHEN Yao, ZHANG Yongxiang. Collaborative Optimization of Suburban Railway and Metro Train Operation Plans Under Interconnection Operational Mode [J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 36-47.
[4]	JIANG Yangsheng, YE Xiaofu, XU Weiyao, LIU Hongxun, HU Lu. Dynamic Operation of Autonomous Shared Electric Vehicles Considering Relay and Ridesplitting [J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 58-68.
[5]	ZHAO Xueting, HU Liwei, ZHOU Jun. Cascading Failure and ResilienceAssessment of Urban Traffic Congestion Risk Fields [J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 146-156.
[6]	WU Huirong, WANG Yuying, SHENG Chunting. Urban Freight Bus Route Planning Model Considering Dynamic Demand [J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 214-226.
[7]	LI Hao, CHEN Shaokuan, XU Bin, SHI Mengtong, XIAO Di, CHEN Ziqi. Urban Rail Transit Crew Rostering Optimization Method Considering Cross-line Operation and Crews Capacity Difference [J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 253-260.
[8]	XUE Feng, XIAO En, YANG Ying, WANG Jincheng, LUO Jian. Optimization of Metro Crew Scheduling Plans Based on Traveling Salesman Problem Modeling [J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 261-272.
[9]	ZHANG Peng, WU Zhaojie, SUN Chao, LI Wenquan. An Optimization Model for Minimizing Arterial Red Wave Bandwidth with Mixed Release at Intersections [J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 293-303.
[10]	XU Qi, QIN Beining, REN Peng, CHEN Yue, LAI Jinxuan. Heterogeneous Effects of Built Environment on Ridership of Integrated Use of Bus and Metro [J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 352-363.
[11]	YAO Ming, WANG Yuhang, CAO Shuchao, MA Luhan. Pedestrian Movement Characteristics with Mobile Phone Distractions [J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 364-372.
[12]	WENG Jiancheng, ZHOU Huiyuan, ZHANG Mengyuan, YU Jiangbo. Effectiveness of New Energy Vehicle Incentive Strategies Considering Urban and Population Heterogeneity [J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(1): 2-14.
[13]	LI Xiying, LU Meiyan, HE Zhaocheng, SU Shuyan, PANG Shumin. Traffic State Recognition Based on Vehicle Dynamic Behavior Characteristics [J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(1): 44-55.
[14]	WANG Lianzhen, MA Zhifei, CHENG Guozhu, ZHENG Fushui. Urban Road Dedicated Lane for Connected and Automated Vehicles [J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(1): 56-66.
[15]	ZUO Zhongyi, LIU Zeyu, YANG Guangchuan. Key Node Identification of Rail Transit Network Based on Gravity Influence Model [J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(1): 102-112.

Hybrid Scheduling Method for Conventional Bus and Demand-responsive Transit Based on Eco-driving Technology

基于生态驾驶的常规和需求响应公交混合调度方法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics