交通大数据背景下的公交问题诊断与优化综述

刘骜; 钟绍鹏; 李茜瑶; 姜宇; 王仲

doi:10.16503/j.cnki.2095-9931.2023.05.004

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交通运输研究 ›› 2023, Vol. 9 ›› Issue (5) : 40-51. DOI: 10.16503/j.cnki.2095-9931.2023.05.004

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交通大数据背景下的公交问题诊断与优化综述

刘骜 ¹ ,
钟绍鹏 ¹^,² ,
李茜瑶 ³ ,
姜宇 ⁴^,⁵ ,
王仲 ¹

作者信息 +

Review on Public Transportation Problem Diagnosis and Optimization in Context of Transportation Big Data

LIU Ao ¹ ,
ZHONG Shaopeng ¹^,² ,
LI Xiyao ³ ,
JIANG Yu ⁴^,⁵ ,
WANG Zhong ¹

Author information +

文章历史 +

摘要

为深化对交通大数据背景下公交问题诊断与优化的理解，推动城市公共交通系统良性发展，围绕出行需求估计、出发时间与出行路径选择、出行时间估算及公交系统优化4类核心问题进行了综述研究。采用系统性文献综述法，对近年来的公交问题诊断与优化研究进行了全面回顾和分析，以总结既有研究的主要贡献和存在的缺陷，并探讨了未来研究的可能方向。文献梳理结果显示，以往研究存在过度关注居民个体出行行为刻画、忽视公共交通与其他出行方式的联系、未能充分考虑潜在客流需求、忽视公交线网设计与时刻表制定之间相互影响4个主要问题。从研究趋势来看，未来公交系统的分析与优化将更加关注多源数据融合和人工智能技术。这些技术将深化对公交乘客和小汽车用户出行规律的分析、解析小汽车用户向公交系统转移的作用机理，并推动公交系统协同优化模型的开发，以推进城市交通系统向更高效、环保和可持续的方向发展。

Abstract

To augment comprehension of problem diagnosis and optimization of public transportation systems in context of transportation big data, and promote healthy development of urban public transportation system, this study conducted a meticulous review and investigation focusing on four pivotal topics: travel demand estimation, departure time and travel route selection, travel time estimation and public transportation system optimization. Employing a systematic literature review methodology, this paper offered a comprehensive analysis of current research concerning the problem diagnosis and optimization of public transportation system. The objective is to summarize the principal contributions and limitations of previous research and to delineate prospective directions of future research. The results from the literature review reveal four major issues existing in previous research: an overemphasis on the individual behaviors of passengers, the neglect of the relationship between public transportation and alternative travel modes, the failure to fully consider potential passengers′ travel demand, and the disregard for the interplay between bus network design and timetable formulation. As for research trends, the analysis and optimization of public transportation systems will increasingly focus on integration of multi-source data and application of artificial intelligence technologies. These technologies will deepen the analysis of travel patterns of bus passengers and car users, elucidate the mechanism of car commuters transitioning to bus systems, and promote the development of collaborative optimization models for bus systems, in order to promote the development of urban transportation systems towards more efficient, environmentally friendly, and sustainable directions.

导出引用

刘骜, 钟绍鹏, 李茜瑶, 等. 交通大数据背景下的公交问题诊断与优化综述[J]. 交通运输研究. 2023, 9(5): 40-51 https://doi.org/10.16503/j.cnki.2095-9931.2023.05.004

LIU Ao, ZHONG Shaopeng, LI Xiyao, et al. Review on Public Transportation Problem Diagnosis and Optimization in Context of Transportation Big Data[J]. Transport Research. 2023, 9(5): 40-51 https://doi.org/10.16503/j.cnki.2095-9931.2023.05.004

中图分类号： U491.1

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