4.5 Article

Bus passenger path choices after consulting ubiquitous real-time information

期刊

TRAVEL BEHAVIOUR AND SOCIETY
卷 23, 期 -, 页码 226-239

出版社

ELSEVIER
DOI: 10.1016/j.tbs.2021.01.001

关键词

Ubiquitous real-time information; Travel behaviour; Path choice; PT demand distribution

资金

  1. Edinburgh Napier University's Transport Research Institute

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The study shows that URTPI has an impact on bus passenger path choice, with changes in departure and boarding times being the most popular actions taken. This can potentially alter the demand distribution for bus runs and lines, advocating for transport planners and operators to consider the impact of URTPI for better predictions of PT demand distribution.
Ubiquitous real-time passenger information (URTPI) enables public transport (PT) users to make better travel choices at both pre-trip and en-route stages. A significant amount of URTPI usage is evident in the existing literature. This study investigates the impact of URTPI on bus passenger path choice. To this end, a bus passenger survey was conducted in the City of Edinburgh, UK, and a total of 1645 completed responses were collected. More than half of the survey participants used at least one source of ubiquitous information. The survey results reveal that about 55% of the URTPI users changed at least one aspect of their trip. Changing the time of departure from the start and boarding time are the two most popular actions taken by bus passengers after consulting URTPI. Passengers' decisions are influenced by information on bus arrival time, bus route, and walking distance. The study demonstrates the potential impact of the change in passenger choices on PT demand distribution. We find that the demand distribution for bus runs could potentially be changed by 17% and for bus lines by 15%. The overall network demand distribution could be affected in 42% of cases as a result of consulting URTPI. This study advocates that transport planners and operators should take the potential impact of URTPI into account to make better predictions of PT demand distribution.

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