使用基于密度的出行率度量来评估需求响应型交通服务

IF 1.6 4区 工程技术 Q4 TRANSPORTATION Journal of Transport and Land Use Pub Date : 2021-04-14 DOI:10.5198/JTLU.2021.1796
Benjamin Kaufman, Abraham Leung, M. Burke
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引用次数: 2

摘要

需求响应式交通(DRT)作为向低密度人口提供公共交通的一种手段,正引起越来越多的关注。本研究旨在提供一套具有低数据要求和广泛可用性的评估指标,以便运营商、资助者、监管机构和从业者能够更好地评估DRT服务的性能。行程数可以除以许多可用变量(周期、行程长度、人口和密度),以创建许多派生度量。通过将这些变量应用于澳大利亚洛根市三个不同的DRT服务区,其中其他关键因素保持不变,可以看到不同的配方如何导致DRT系统性能的非常不同的读数。结果证实了低密度环境中服务提供的成本效率与公平的困境。本文还强调了当前数据的局限性,并呼吁更好地收集数据,以促进DRT服务的新评估方法和可用于服务间比较的新复合度量的发展。
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Evaluating demand responsive transit services using a density-based trip rate metric
Demand responsive transit (DRT) is attracting increased attention as a means to provide public transit to low-density populations. This research aims to provide a suite of evaluation metrics with low data requirement and widespread availability, so that operators, funders, regulators, and practitioners can better evaluate the performance of DRT services. Trip numbers can be divided by a number of available variables (period, trip length, population, and density) to create a number of derived metrics. By applying these variables across three different DRT service areas in Logan City, Australia, where other key factors are held constant, one can see how different formulations lead to very different readings of DRT system performance. The results confirm the dilemma of cost efficiency versus equity in service provision in low-density environments. This paper also highlights current data limitations and calls for better data collection to facilitate the development of new evaluation methods for DRT services and a new composite metric that can be used for inter-service comparison.
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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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