A tool for measuring and visualizing connectivity of transit stop, route and transfer center in a multimodal transportation network
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- 作者：Sabyasachee Mishra (1)
Timothy F. Welch (2)
Paul M. Torrens (3)
Cheng Fu (3)
Haojie Zhu (4)
Eli Knaap (5)
1. Department of Civil and Environmental Engineering and Intermodal Freight Transportation Institute ; University of Memphis ; 3815 Central Avenue ; Memphis ; TN ; 38152 ; USA
2. School of City and Regional Planning ; Georgia Institute of Technology ; 245 4th Street NW ; Suite 204 ; Atlanta ; GA ; 30332 ; USA
3. Department of Geographical Sciences and Institute for Advanced Computer Studies ; University of Maryland ; 1104 LeFrak Hall ; College Park ; MD ; 20742 ; USA
4. Department of Geographical Sciences ; University of Maryland ; 1104 LeFrak Hall ; College Park ; MD ; 20742 ; USA
5. National Center for Smart Growth Research and Education ; 054 Preinkert Fieldhouse ; University of Maryland ; College Park ; MD ; 20742 ; USA - 关键词：Transit connectivity ; Graph theory ; Public transportation ; Multimodal transportation system ; GIS
- 刊名：Public Transport
- 出版年：2015
- 出版时间：March 2015
- 年：2015
- 卷：7
- 期：1
- 页码：77-99
- 全文大小：15,762 KB
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- 出版者：Springer Berlin Heidelberg
- ISSN：1613-7159
文摘
Agencies at the federal, state and local level are aiming to enhance the public transportation system (PTS) as one alternative to alleviate congestion and to cater to the needs of captive riders. To effectively act as a viable alternative transportation mode, the system must be highly efficient. One way to measure efficiency of the PTS is connectivity. In a multimodal transportation system, transit is a key component. Transit connectivity is relatively complex to calculate, as one has to consider fares, schedule, capacity, frequency and other features of the system at large. Thus, assessing transit connectivity requires a systematic approach using many diverse parameters involved in real-world service provision. In this paper, we use a graph theoretic approach to evaluate transit connectivity at various levels of service and for various components of transit, such as nodes, lines, and transfer centers in a multimodal transportation system. Further, we provide a platform for computing connectivity over large-scale applications, using visualization to communicate results in the context of their geography and to facilitate public transit decision-making. The proposed framework is then applied to a comprehensive transit network in the Washington-Baltimore region. Underpinning the visualization, we introduce a novel spatial data architecture and Web-based interface designed with free and open source libraries and crowd-sourced contextual data, accessible on various platforms such as mobile phones, tablets and personal computers. The proposed methodology is a useful tool for both riders and decision-makers in assessing transit connectivity in a multimodal transit network in a number of ways such as the identification of under-served transit areas, prioritization and allocation of funds to locations for improving transit service.