Abstract
This paper aims to define an algorithm capable of building the origin-destination matrix from check-in data collected in the extra-urban area of Torino, Italy, where thousands of people commute every day, using smart cards to validate their travel documents while boarding. To this end, the methodological approach relied on a survey over three months to record smart-card validations. Peak and off-peak periods have been defined according to validation frequency. Then, the origin-destination matrix has been estimated using the time interval between two validations to outline the different legs of the journey. Finally, transport demand has been matched with existing bus services, showing which areas were not adequately covered by public transport. The results of this research could assist public transport operators and local authorities in the design of a more suitable transport supply and mobility services in accordance with user needs. Indeed, tailoring public transport to user needs attracts both more customers and latent demand, reducing reliance on cars and making transport more sustainable.
Highlights
To achieve a sustainable transport system requires understanding transport demand, which is a key element in transport planning
The new technologies developed for intelligent transport systems (ITS) increasingly facilitate data collection, and, to this end, automated fare collection (AFC) systems can play a key role
Data coming from AFC systems are useful for analyzing passenger mobility patterns [6,7,8], as well as spatiotemporal information on boarding and alighting [9,10,11]
Summary
To achieve a sustainable transport system requires understanding transport demand, which is a key element in transport planning. It is a challenging task due to the high costs of travel surveys. The new technologies developed for intelligent transport systems (ITS) increasingly facilitate data collection, and, to this end, automated fare collection (AFC) systems can play a key role. Data coming from AFC systems are useful for analyzing passenger mobility patterns [6,7,8], as well as spatiotemporal information on boarding and alighting [9,10,11]
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