needs-gap analysis of street space allocation

IF 1.6 4区 工程技术 Q4 TRANSPORTATION
Gabriel Lefebvre-Ropars, C. Morency, Paula Negron-Poblete
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引用次数: 5

Abstract

Streets have long been designed to maximize motor vehicle throughput, ignoring other street users. Many cities are now reversing this trend and implementing policies to design more equitable streets. However, few existing tools and metrics enable widescale assessment, evaluation, and longitudinal tracking of these street space rebalancing efforts, i.e., assessing how equitable the current street design is, how it can be improved, and how much progress has been made. This paper develops a needs-gap methodology for assessing the discrepancy between transportation supply and demand in urban streets using existing datasets and automated methods. The share of street space allocated to different street users is measured in 11 boroughs of Montreal, Canada. Travel survey data is used to estimate the observed and potential travel demand in each borough in the AM peak period. A needs-gap analysis is then carried out. It is found that bus riders and cyclists face the greatest needs-gap across the study area, especially in central boroughs. The needs-gap also increases if only trips produced or attracted by a borough are considered. This shows the potential of applying an equity-based framework to the automated assessment of street space allocation in cities using large datasets.
街道空间配置的需求差距分析
长期以来,街道的设计都是为了最大化机动车辆的吞吐量,而忽略了其他街道使用者。许多城市正在扭转这一趋势,并实施政策,设计更公平的街道。然而,很少有现有的工具和指标能够对这些街道空间再平衡工作进行大规模的评估、评估和纵向跟踪,即评估当前街道设计的公平程度、如何改进以及取得了多大进展。本文开发了一种需求缺口方法,用于使用现有数据集和自动化方法评估城市街道交通供需之间的差异。分配给不同街道使用者的街道空间份额在加拿大蒙特利尔的11个行政区进行了测量。旅游调查数据用于估计每个行政区在AM高峰期的观察和潜在旅游需求。然后进行需求差距分析。研究发现,公交乘客和骑自行车的人在整个研究区域面临最大的需求差距,特别是在中心行政区。如果只考虑由一个行政区产生或吸引的旅行,需求差距也会扩大。这显示了将基于公平的框架应用于使用大型数据集自动评估城市街道空间分配的潜力。
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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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