文章编号： 1672-5328（2025）03-0045-09

韩子韬1, 2，宋程1, 2，易斌1, 2
(1. 广州市交通规划研究院有限公司，广东广州510030；2. 广东省可持续交通工程技术研究中心，广东广州 510030)

摘要： 研究城市道路交叉口电动自行车行驶特征能够为拥堵治理和渠化改善提供量化依据。受设施 环境、交通量及驾驶人行为等因素影响，电动自行车在停驻等候、启动加速和通过交叉口3 个阶段 呈现显著差异性特征。基于无人机航拍数据，采用Data From Sky 交通轨迹分析软件提取基础数 据，建立了包括停驻面积、机动车启动延误、电动自行车行驶速度和行驶轨迹膨胀度等关键参数的 量化分析方法。以汕头市金砂路4 个典型交叉口为例进行应用分析，结果表明：停驻等候阶段，停 驻数量与面积呈非线性函数关系；启动加速阶段，当等候区位于机动车前方时，电动自行车停驻数 量与机动车启动延误显著正相关，每10 辆电动自行车造成0.6~0.9 s 机动车延误；通过交叉口阶段， 电动自行车85%位速度为23.5 km·h-1，且行驶轨迹膨胀度呈先增后稳的变化趋势。

关键词： 电动自行车；城市道路交叉口；停驻面积；启动延误；行驶速度；行驶轨迹膨胀度

中图分类号： U491.2+6

文献标识码：A

Identification of E-Bike Riding Characteristics and Optimization Strategies at Urban Road Intersections: A Case Study of Jinsha Road in Shantou

HAN Zitao1, 2, SONG Cheng1, 2, YI Bin1, 2
(1. Guangzhou Transport Planning Research Institute Co., Ltd., Guangzhou Guangdong 510030, China; 2. Guangdong Sustainable Transportation Engineering Technology Research Center, Guangzhou Guangdong 510030, China)

Abstract： Understanding the e-bike riding characteristics at urban road intersections provides a quantitative basis for congestion mitigation and channelization design improvements. This paper presents a systematic analysis of e-bike riding characteristics at urban road intersections, revealing distinct patterns across three key stages—queuing, starting/accelerating, and passing through intersections—influenced by infrastructure environment, traffic volume, and rider behavior. Using drone footage data and the Data From Sky trajectory analysis software, this paper extracts basic data and develops a quantitative analysis methodology based on key parameters such as queuing area, motor vehicle start-up delay, e-bike travel speed, and trajectory expansion. The approach is applied to four representative intersections along Jinsha Road in Shantou. The results suggest a nonlinear functional relationship between the number of stopped e-bikes and the occupied area during the queuing stage. At the starting/accelerating stage, when e-bikes queue ahead of motor vehicles, their number is significantly and positively correlated with vehicle start-up delays—each additional 10 e-bikes may cause a delay of approximately 0.6 to 0.9 seconds for motor vehicles. At the stage of passing intersections, 85% of e-bikes travel at a speed of 23.5 kilometers per hour, with trajectory expansion initially increasing then stabilizing.

Keywords： e-bikes; urban road intersections; queuing area; start-up delay; travel speed; trajectory expansion