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Evaluation Framework for Multi-Modal Public Transport Systems Based on Connectivity and Transfers at Stop Level

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6 Citations (Scopus)
34 Downloads (Pure)

Abstract

Multi-modal public transport (PT) networks within metropolitan areas are often characterized by complexity resulting mainly from their infrastructure, design, operations, and demand. This complexity leads to a significant amount of effort on behalf of the transit agencies to properly evaluate their performance at certain locations and proceed with improvements. This study proposes a methodology based on clustering techniques that facilitates the evaluation of PT networks. The evaluation framework refers to the comparison between the levels of supply and demand at a certain stop. Service supply is quantified through an existing connectivity index, whereas demand is considered through the number of transfers that are performed at each stop. Transfers are critical within multi-modal mobility and often serve as a hindrance for choosing PT. The case study here is the Helsinki PT network in Finland. General Transit Feed Specification (GTFS) data are used for quantifying connectivity and a dataset deriving from smartphone ticketing application for quantifying transfers. Results include the evaluation for each PT mode and for the overall multi-modal PT network. Focusing on the evaluation of the overall multi-modal PT network, connectivity and transfers levels for 75.60% of stops are found to be well aligned. Therefore, these stops could be eliminated from the list of candidate stops for performing improvements. Of the remaining stops, 19.73% belongs to the case of higher connectivity than transfers and 4.67% to the case of lower connectivity than transfers. Stops included in these two cases require further attention and prioritization during planning processes.

Original languageEnglish
JournalTransportation Research Record
Early online date2024
DOIs
Publication statusPublished - 3 Mar 2024
Publication typeA1 Journal article-refereed

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The work of C. Sipetas was supported by the FinEst Twins Center of Excellence (H2020 Grant 856602). Z. Huang is supported by the NetResilience consortium funded by the Strategic Research Council at the Academy of Finland (grant numbers 345188 and 345183) and Guangdong Science and Technology Strategic Innovation Fund (the Guangdong-Hong Kong-Macau Joint Laboratory Program), Project No.: 2020B1212030009. The work of A. Espinosa Mireles de Villafranca was supported by the Academy of Finland.

FundersFunder number
Smart City Center of Excellence (Finest Twins)
Guangdong Science and Technology Strategic Innovation Fund
Guangdong-Hong Kong-Macau Joint Laboratory Program2020B1212030009
Horizon 2020 Framework Programme856602
Strategic Research Council at the Research Council of Finland345188, 345183
Finnish Strategic Research Council

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 11 - Sustainable Cities and Communities
      SDG 11 Sustainable Cities and Communities

    Keywords

    • GTFS
    • management and performance
    • performance measures
    • public transportation
    • transformative trends in transit data

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Civil and Structural Engineering
    • Mechanical Engineering

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