基于文献计量的交叉口车路协同研究综述

doi:10.13229/j.cnki.jdxbgxb.20240853

摘要/Abstract

摘要：

为了解交叉口车路协同领域的研究现状及未来研究热点，以Web of Science和CNKI数据库作为数据源，检索了2000年1月至2024年9月的427篇核心期刊文献。通过文献计量分析，从发文趋势、文献来源和关键词共词分析等角度进行了统计和可视化。统计结果显示，近年来此领域的发文数量呈增长趋势，且样本文献具有较强的代表性。关键词共现分析表明，此领域的研究方向主要包括交叉口管控、路径与速度引导、信号-车辆协同优化、建模与仿真、多车交互安全、单一车辆安全、弱势道路使用者安全、网络通信和路侧感知。对以上研究方向的发展脉络以及具体方法进行了梳理和总结，并基于此展望了交叉口车路协同领域的未来研究热点。

关键词: 交通运输系统工程, 车路协同, 文献计量, 交叉口, 可视化分析

Abstract:

To understand the research status and future research hotspots in the field of vehicle-infrastructure integration at intersections， the Web of Science and CNKI databases were used as data sources to retrieve core journal articles from January 2000 to September 2024， totaling 427 articles. Through bibliometric analysis， statistics and visualization were performed from the perspectives of publication trends， literature sources， and keyword co-word analysis. The statistical results show that the number of publications in this field has been increasing in recent years， and the sample documents are highly representative. Keyword co-occurrence analysis shows that the research directions in this field mainly include intersection control， path and speed guidance， signal-vehicle collaborative optimization， modeling and simulation， multi-vehicle interaction safety， single vehicle safety， vulnerable road user safety， network communication， and roadside perception. The development context and specific methods of the above research directions were sorted out and summarized， and based on this， the future research hotspots in the field of intersection vehicle-infrastructure integration were prospected.

Key words: engineering of communication and transportation system, vehicle-infrastructure integration, bibliometrics, intersection, visual analysis

中图分类号:

U491.1

慈玉生,黄轶康. 基于文献计量的交叉口车路协同研究综述[J]. 吉林大学学报(工学版), 2026, 56(2): 313-332.

Yu-sheng CI,Yi-kang HUANG. Overview of intersection vehicle-infrastructure integration based on bibliometrics[J]. Journal of Jilin University(Engineering and Technology Edition), 2026, 56(2): 313-332.

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期刊	文章数 /篇	占比 /%	总被引/次	平均被引/次
IEEE Transactions on Intelligent Transportation Systems	40	11.33	1 794	44.85
Transportation Research Record	34	9.63	432	12.71
IEEE Access	22	6.23	369	16.77
Transportation Research Part C： Emerging Technologies	18	5.10	1 089	60.50
Sensors	17	4.81	175	10.29

期刊	文章数/篇	占比 /%	总被引/次	平均被引/次
中国公路学报	8	10.81	190	23.75
交通信息与安全	7	9.46	220	31.43
交通运输工程学报	5	6.76	30	6.00
交通运输系统工程与信息	4	5.41	152	38.00
系统仿真学报	4	5.41	10	2.50

优化算法分类	优化算法	算法特性
精确解法	穷举法分支定界法	能够得到精确解，但是要求模型简单或控制参数取值固定，求解效率难保证
数值解法	迭代网格搜索法	求解效率高，但是要求模型简单，并可能陷入局部最优
启发式算法	遗传算法免疫遗传算法智能树搜索算法	求解效率高，可求解NP难问题，但是可能陷入局部最优，依赖人工参数调优
机器学习算法	强化学习方法	可求解NP难问题，可输出最优规则，可能陷入局部最优，对数据规模和计算资源要求高，结果可解释性差
模型预测方法	模型预测方法	可求解NP难问题，可预测系统状况提高控制准确性，但是需要构建系统预测模型