网联混行下城市干道动态公交专用道管控策略

doi:10.16097/j.cnki.1009-6744.2026.01.013

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (1): 135-147.DOI: 10.16097/j.cnki.1009-6744.2026.01.013

• 智能交通系统与信息技术 • 上一篇下一篇

网联混行下城市干道动态公交专用道管控策略

单肖年^*1，胡颖¹，成嘉琪²，田大新³

1. 河海大学，土木与交通学院，南京210024；2.上海市政工程设计研究总院(集团)有限公司，上海200092；3. 北京航空航天大学，交通科学与工程学院，北京102206

收稿日期:2025-11-09 修回日期:2025-12-12 接受日期:2025-12-23 出版日期:2026-02-25 发布日期:2026-02-15
作者简介:单肖年（1990—），男，江苏盐城人，副教授，博士。
基金资助:
江苏省自然科学基金(BK20242054)；江苏省青年科技人才托举工程(JSTJ-2025-645)。

Dynamic Bus Lane Management Strategies for Urban Arterials Under Connected Mixed Traffic

SHAN Xiaonian^*1, HU Ying¹, CHENG Jiaqi², TIAN Daxin³

1. College of Civil and Transportation Engineering, Hohai University, Nanjing 210024, China; 2. Shanghai Municipal Engineering Design Institute (Group) Co LTD, Shanghai 200092, China; 3. School of Transportation Science and Engineering, Beihang University, Beijing 102206, China

Received:2025-11-09 Revised:2025-12-12 Accepted:2025-12-23 Online:2026-02-25 Published:2026-02-15
Supported by:
Natural Science Foundation of Jiangsu Province (BK20242054)；The Jiangsu Youth Science and Technology Talent Support Program (JSTJ-2025-645)。

摘要/Abstract

摘要： 智能网联汽车（Connected and Automated Vehicles, CAVs）可实时获取周边车辆行驶状态信息，能在保障公交车辆优先通行的基础上充分利用道路设施资源。本文首先剖析混行交通流跟驰模式，并利用Waymo数据集进行参数标定；提出融合风险感知的CAV换道模型，构建城市干道混行交通流仿真模型，分析动态公交专用道场景下混行交通流运行效率与风险特征；进一步考虑CAV渗透率、普通车道交通需求，以及公交站点位置变化的影响，探讨车道管控策略的适应性。研究结果表明，考虑风险的智能专道场景，公交车行程时间增加了4.8%，交通流人均行程时间降低了23.1%，CAV换道次数较无风险智能专道场景减少了50.8%。当CAV渗透率在[0.5,0.8)、普通车道流量在[600,900]pcu·h^-1及公交站点位置位于[50,250]m范围内，或当CAV渗透率处于[0.8,1.0]且普通车辆流量在(900,1400]pcu·h^-1及公交站点位置位于[50,450]m时，智联专道管控策略具有更好的交通流运行效益。

关键词: 智能交通, 车道管控, 混行交通流仿真, 动态公交专用道, 风险感知, 公交优先

Abstract: The Connected and Automated Vehicles (CAVs) can acquire real-time information on the driving states of surrounding vehicles, enabling them to make more efficient use of roadway infrastructure while ensuring priority passage for buses. This study analyzes the car-following patterns in mixed traffic flow and calibrates model parameters using the Waymo dataset. A CAV lane- changing model incorporating risk perception is then proposed. Building upon this model, a mixed traffic flow simulation framework for urban arterial roads is developed to examine the operational efficiency and risk characteristics of mixed traffic under dynamic bus lane scenarios. The adaptability of lane management strategies is evaluated in consideration of CAV penetration rates, general traffic demand, and bus stop locations. The results show that under the risk-aware intelligent lane scenario, bus travel time increases by 4.8%, while the average travel time per person decreases by 23.1%, and the number of CAV lane changes is reduced by 50.8%, compared to the non-risk-aware scenario. The intelligent lane management strategy achieves optimal performance when the CAV penetration rate is within [0.5, 0.8) and general traffic demand is within [600, 900] passenger car unit per hour with bus stops located between [50, 250] meters, or when the CAV penetration rate is within [0.8, 1.0] and traffic demand is within (900, 1 400] passenger car unit per hour with bus stops between [50, 450] meters.

Key words: intelligent transportation, lane control, mixed traffic flow simulation, dynamic bus lane, risk perception, bus priority

中图分类号:

U495

单肖年, 胡颖, 成嘉琪, 田大新. 网联混行下城市干道动态公交专用道管控策略[J]. 交通运输系统工程与信息, 2026, 26(1): 135-147.

SHAN Xiaonian, HU Ying, CHENG Jiaqi, TIAN Daxin. Dynamic Bus Lane Management Strategies for Urban Arterials Under Connected Mixed Traffic[J]. Journal of Transportation Systems Engineering and Information Technology, 2026, 26(1): 135-147.

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网联混行下城市干道动态公交专用道管控策略

Dynamic Bus Lane Management Strategies for Urban Arterials Under Connected Mixed Traffic

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