面向多目标的旅游客运车辆生态驾驶策略优化

doi:10.16097/j.cnki.1009-6744.2026.01.019

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (1): 205-216.DOI: 10.16097/j.cnki.1009-6744.2026.01.019

面向多目标的旅游客运车辆生态驾驶策略优化

李琼^a，林若雪^a，汪勇杰^*a，陈艳^b

长安大学，a.运输工程学院；b.学术期刊管理中心，西安710064

收稿日期:2025-11-04 修回日期:2025-12-14 接受日期:2025-12-17 出版日期:2026-02-25 发布日期:2026-02-15
作者简介:李琼（1983—），女，陕西蓝田人，副教授，博士。
基金资助:
长安大学青年学者学科交叉团队建设项目(300104240924)；中央高校基本科研业务费专项资金(300102345103)。

Ecological Driving Strategy Optimization for Tourist Passenger Vehicles with Multi-objective Orientation

LI Qiong^a, LIN Ruoxue^a, WANG Yongjie^*a, CHEN Yan^b

a. School of Transportation Engineering; b. Academic Journal Management Center, Chang'an University, Xi'an 710064, China

Received:2025-11-04 Revised:2025-12-14 Accepted:2025-12-17 Online:2026-02-25 Published:2026-02-15
Supported by:
Research Funds for the Interdisciplinary Projects CHU under Grant(300104240924)；Fundamental Research Funds for the Central Universities of Ministry of Education of China (300102345103)。

摘要/Abstract

摘要： 以燃油车为主导的旅游客运车辆具有高频运行、长时间载客与能耗显著等特征，其能耗机理与城市公交车和货运车辆存在本质差异。为实现旅游客运车辆生态驾驶的精确能耗测算与策略优化，本文提出一种基于灰色关联分析-极端梯度提升（GRA-XGBoost）建模与强化学习优化相结合的多目标生态驾驶方法。首先，基于实测运行数据构建能耗特征数据库，采用灰色关联分析筛选关键影响因子，建立高精度能耗测算模型；其次，基于近端策略优化算法设计生态驾驶策略的状态空间、动作空间和奖励函数，构建涵盖经济性、舒适性、安全性与效率的多目标优化框架，引入安全避撞模块强化决策安全约束；最后，在SUMO仿真平台验证模型有效性。结果表明，本文构建的能耗测算模型均方根误差为0.0061，平均绝对百分比误差为3.1%，相较基准跟驰模型（Krauss）与换道模型（LC2013），低交通流量场景下车辆能耗降低16.88%，高交通流量场景下降低8.86%，且行车平稳性与安全性均显著提升。本文研究为旅游客运车辆的生态驾驶控制与节能优化提供了有益的参考与技术支撑。

关键词: 公路运输, 生态驾驶, 深度强化学习, 旅游客运车辆, 能耗建模, 多目标优化

Abstract: Fuel-dominated tourist vehicles operate at high frequency and for long durations with substantial energy use, fundamentally differing in energy consumption mechanisms from urban buses and freight vehicles. To accurately estimate the energy consumption and optimize eco-driving strategies for tourist passenger vehicles, this paper proposes a multi-objective eco- driving optimization framework integrating grey relational analysis-extreme gradient boosting(GRA-XGBoost) modeling with reinforcement learning. First, an energy consumption feature database is constructed based on real-world operation data, and key influencing factors are identified using Grey Relational Analysis (GRA) to develop a high-accuracy energy consumption estimation model. Then, a Proximal Policy Optimization (PPO)-based eco-driving strategy is designed with explicitly defined state, action, and reward spaces to balance safety, economy, efficiency, and comfort. A collision-avoidance module is incorporated to enhance safety constraints during decision-making. Finally, simulation experiments on the SUMO platform demonstrate that the proposed model achieves a root mean square error of 0.006 1 and a mean absolute percentage error of 3.1%. Compared with the baseline Krauss car-following model and the LC2013 lane-changing model, the proposed strategy reduces energy consumption by 16.88% in low-traffic scenarios and 8.86% in high-traffic scenarios, while maintaining superior driving stability and safety. The study provides useful insights and technical support for eco-driving control and energy optimization of fuel-powered tourist vehicles.

Key words: highway transportation, eco-driving, deep reinforcement learning, tourist passenger vehicles, energy consumption modeling, multi-objective optimization

中图分类号:

U491

李琼, 林若雪, 汪勇杰, 陈艳. 面向多目标的旅游客运车辆生态驾驶策略优化[J]. 交通运输系统工程与信息, 2026, 26(1): 205-216.

LI Qiong, LIN Ruoxue, WANG Yongjie, CHEN Yan. Ecological Driving Strategy Optimization for Tourist Passenger Vehicles with Multi-objective Orientation[J]. Journal of Transportation Systems Engineering and Information Technology, 2026, 26(1): 205-216.

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面向多目标的旅游客运车辆生态驾驶策略优化

Ecological Driving Strategy Optimization for Tourist Passenger Vehicles with Multi-objective Orientation

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