多尺度地铁网络属性对高峰时段车站客流和流量平衡的影响

IF 1.6 4区 工程技术 Q4 TRANSPORTATION
Haixiao Pan, Miao Hu, Xiyin Deng, Ailing Liu
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引用次数: 0

摘要

分析车站客流和客流的平衡,对于了解车站区域和整个网络范围内的职住平衡和建筑环境,以及提高城市轨道交通运营效率至关重要。本文以上海轨道交通网络为例,定义了基于特定空间范围的多尺度子网络(MSSN)。通过提取车站和 MSSN 的网络特征和建筑环境要素,分析影响高峰小时车站客流和区域网络客流失衡的因素。研究表明,在距离车站 6-8 公里的范围内进行小型 MSSN 分析,可以从整个网络的角度提供有价值的结果,而不是仅仅关注单个车站区域或整个网络。在 MSSN 范围内,就业与住房平衡的区域属性和交通条件对车站客流和人流失衡都有很大影响。这项研究为城市规划者和决策者制定有效的城市轨道交通网络战略提供了理论依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effect of multiscale metro network-wide attributes on peak-hour station passenger and flow balancing
Analyzing the balance of station passenger and passenger flow is essential for understanding jobs-housing balance and built environment in station areas and network-wide range as well as for enhancing the efficiency of urban rail transit operations. Taking the Shanghai rail transit network as a case study, this paper defines the Multiscale Subnetwork (MSSN) based on a specific spatial scope. By extracting the network features and built-environment elements of the stations and the MSSN, this study analyzes the factors affecting the peak-hour station passenger and the imbalance of regional network passenger flow. The research suggests that the small MSSN analysis, within 6-8 km from a station, can provide valuable results from a network-wide perspective, rather than solely focusing on individual station areas or the entire network. The regional attributes of jobs-housing balance and the transportation conditions in the MSSN range have great impact on both station passengers and flow imbalance. This research provides theoretical insights for urban planners and policymakers to formulate effective strategies for urban rail transit networks.
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来源期刊
CiteScore
3.40
自引率
5.30%
发文量
34
审稿时长
30 weeks
期刊介绍: The Journal of Transport and Land Usepublishes original interdisciplinary papers on the interaction of transport and land use. Domains include: engineering, planning, modeling, behavior, economics, geography, regional science, sociology, architecture and design, network science, and complex systems. Papers reporting innovative methodologies, original data, and new empirical findings are especially encouraged.
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