基于GNSS轨迹数据的公交多能源供需网络调度优化模型

奇格奇; 曹琳琪; 沈益达; 董艳; 何思帆; 杨瑀玎; 关伟

doi:10.3963/j.jssn.1674-4861.2025.03.009

基于GNSS轨迹数据的公交多能源供需网络调度优化模型

doi: 10.3963/j.jssn.1674-4861.2025.03.009

奇格奇^{1, 2,},
曹琳琪¹,
沈益达³,
董艳¹,
何思帆¹,
杨瑀玎⁴,
关伟^5, ,

1.
北京交通大学交通运输学院北京 100044
2.
北京交通大学综合交通运输大数据应用技术交通运输行业重点实验室北京 100044
3.
北京交通大学机械与电子控制工程学院北京 100044
4.
北京交通大学电气工程学院，北京 100044
5.
北京交通大学系统科学学院北京 100044

基金项目:

国家自然科学基金项目 72371021

详细信息

作者简介:
奇格奇（1987—），博士，副教授. 研究方向：智能交通系统、交通大数据. E-mail：gqqi@bjtu.edu.cn

通讯作者:
关伟（1968—），博士，教授. 研究方向：智能交通系统、系统工程. E-mail：weig@bjtu.edu.cn

中图分类号: U491.1
计量
- 文章访问数: 22
- HTML全文浏览量: 10
- PDF下载量: 2
- 被引次数: 0
出版历程
- 收稿日期: 2025-01-24
- 网络出版日期: 2025-10-11

An Optimization Model for Multi-energy Supply and Demand Network Scheduling of Public Transportation Based on GNSS Trajectory Data

QI Geqi^{1, 2
,},
CAO Linqi¹,
SHEN Yida³,
DONG Yan¹,
HE Sifan¹,
YANG Yuding⁴,
GUAN Wei^{5
, ,}

1.
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
2.
Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China
3.
School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
4.
School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
5.
School of Systems Science, Beijing Jiaotong University, Beijing 100044, China

摘要

摘要: 针对多能源类型（包括油、电、气、氢等）公交车混合运营现状，在考虑公交实际运营经验与习惯的基础上，为弥补当前理论研究中通常以单一能源类型为对象的不足，研究了公交混合能源类型网络中能源补充的供需匹配优化问题。以公交车实际全球导航卫星系统（Global Navigation Satellite System，GNSS）数据为基础，挖掘公交在能源补充过程中表现出的空间特性，提出“潜在能源需求点”。基于包括潜在能源需求点的不同能源类型需求点与供给点，建立多能源类型混合调度优化模型，利用能源类型约束、供给点容量约束等条件，使得模型更加符合实际运营情况。提出改进后基于精英策略的遗传算法，类比碱基互补配对原则刻画单条线路存在多类型能源需求情况，结合多项指标求解得到能源补充约束下的最小额外空跑成本、供需网络优化匹配方案及各项线网效能评价。以北京市公交车GNSS长时轨迹数据作为案例进行研究，利用两阶段聚类算法筛选提取潜在能源需求点，提出了公交多能源供需匹配优化策略，并通过随机配置线路能源类型和去除关键节点进行公交多能源供需网络鲁棒性检验。研究结果表明：本文模型相较于基准模型，燃油、氢能源、电动公交线路的能源供需优化匹配成本分别降低7.12%、9.07%、9.82%，优化算法适应度函数提升5.18%，有助于公交能源供给侧优化配置及能源需求的智慧化管理。同时，针对公交车多能源类型混合运营现状，需要对能源需求、能源补充进行协同调整实现供需关系平衡，并关注重要能源补充节点的建设和运营，从而提升网络稳定性与资源利用效率。
- 交通工程 /
- 多能源供需网络 /
- 混合调度优化 /
- 网络鲁棒性 /
- GNSS轨迹数据
Abstract: This study addresses the supply-demand matching optimization problem for energy replenishment in hybrid energy networks of public transit systems, encompassing multiple energy types including oil, electricity, gas, and hydrogen etc. To bridge the limitations of existing theoretical research, which predominantly focuses on single energy types, the study incorporates practical operational experiences and habits of public transit systems. Using real-world GNSS data of buses, the spatial characteristics of energy replenishment behavior are analyzed, and the concept of "potential energy demand points" is proposed. Integrating potential energy demand points with energy supply and demand nodes for different energy types, a multi-energy hybrid scheduling optimization model is developed. The model incorporates constraints such as energy type limitations and supply node capacities, ensuring alignment with real-world operational conditions. An improved genetic algorithm based on the elite strategy is proposed to solve the model, inspired by the principle of base pairing in DNA, to characterize the coexistence of multiple energy demands along a single bus line. Multiple indicators are combined to derive solutions that minimize additional deadhead costs under energy replenishment constraints, optimize the matching scheme of the supply-demand network, and evaluate the efficiency of the transit network. Taking long-term GNSS trajectory data from Beijing's public buses as a case study, a two-stage clustering algorithm is employed to identify potential energy demand points. A multi-energy supply-demand matching optimization strategy for public buses is proposed, alongside robustness tests for the network under scenarios involving random energy type configurations and the removal of critical nodes. The results demonstrate that the proposed model reduces the energy supply-demand matching costs for fuel, hydrogen, and electric bus routes by 7.12%, 9.07%, and 9.82%, respectively, compared to baseline models. Furthermore, the fitness function of the optimization algorithm improves by 5.18%. These findings contribute to the optimization of energy supply-side configurations and the intelligent management of energy demand. Additionally, the study emphasizes the need for coordinated adjustments of energy demand and replenishment in mixed-energy public transit operations to achieve supply-demand balance. The construction and operation of critical energy replenishment nodes are highlighted as essential for enhancing network stability and resource utilization efficiency.
- Traffic engineering /
- multi-energy supply-demand network /
- mixed scheduling optimization /
- network robustness /
- GNSS trajectory data

HTML全文

图 1 公交车不同能源补充线路对比示意图

Figure 1. Schematic diagram illustrating the comparative analysis of diverse bus energy supply routes

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图 2 基于精英策略的遗传算法种群个体编码图

Figure 2. Graphical representation of individual encoding in a population of a genetic algorithm based on elite strategy

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图 3 模型验证效果

Figure 3. Model validation results

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图 4 单日公交轨迹数据可视化

Figure 4. Visualization of daily bus trajectory data

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图 5 潜在能源需求点挖掘

Figure 5. Exploration of potential energy demand points

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图 6 就近匹配和模型优化结果对比

Figure 6. Comparison of the results of nearest matching and model optimization

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图 7 不同算法迭代收敛曲线

Figure 7. Iterative convergence curves of different algorithms

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图 8 多轮次随机选取25、50、75、100条线路改变其能源类型后能源供需网络适应性结果

Figure 8. The result of energy supply-demand network adaptability after multi-round random selection of 25,50,75 and 100 lines to change their energy type

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图 9 鲁棒性分析结果

Figure 9. Robustness analysis result

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图 10 不同补能站点失效情况下评价指标变化

Figure 10. Changes in evaluation indicators under different energy supply station failure conditions

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图 11 公交能源补充网络对车辆和站点能源类型改造的参数响应

Figure 11. Parameter response of bus energy supply network to the transformation of vehicle and station energy types

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表 1 示例网络各能源节点坐标

Table 1. Coordinates of energy nodes in the example network

节点	横坐标	纵坐标
A	4.76	6.75
B	6.21	4.74
C	4.81	0.39
a	6.38	6.44
b	1.73	6.09
c	3.83	4.13
d	2.46	2.94
e	2.67	2.75
f	2.33	1.16
g	5.78	1.46

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表 2 实例网络优化前后各项指标对比

Table 2. Comparison of various metrics before and after the optimization of empirical network

能源类型	优化前				优化后				适应度变化率/%
能源类型	额外空跑距离/（×10³ km）	资源利用度	未过载度	适应度	额外空跑距离/（×10³ km）	资源利用度	未过载度	适应度	适应度变化率/%
混合	3.843	0.017	0.467	0.536	2.580	0.224	0.933	0.934	74.25
燃油	1.573	0.023	0.500	0.597	1.461	0.250	1.000	1.006	68.51
氢能	2.504	0.026	0.429	0.560	2.277	0.500	0.857	1.215	116.96
电能	4.632	0.087	0.700	0.671	4.177	0.258	1.000	0.962	43.37
网络	12.55	0.017	0.560	0.398	10.49	0.169	0.960	0.731	83.67

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表 3 不同方法的分配结果对比

Table 3. Comparison of allocation results of different methods

能源类型	模拟退火算法				改进遗传算法				适应度变化率/%
能源类型	额外空跑距离/（×10³ km）	资源利用度	未过载度	适应度	额外空跑距离/（×10³ km）	资源利用度	未过载度	适应度	适应度变化率/%
混合	3.418	0.196	0.933	0.888	2.580	0.224	0.933	0.934	5.18
燃油	1.358	0.250	1.000	1.008	1.461	0.250	1.000	1.006	-0.20
氢能	1.991	0.316	0.857	1.021	2.277	0.500	0.857	1.215	18.02
电能	4.224	0.258	1.000	0.961	4.177	0.258	1.000	0.962	0.10
网络	10.94	0.156	0.960	0.708	10.49	0.169	0.960	0.731	3.15

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