The paper aims to define an algorithm capable of building the origin-destination matrix from the check-in data collected in the extra-urban area of Torino, Italy, where thousands of people commute everyday, 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, the transport demand has been matched with the existing 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 in accordance with user needs. Indeed, tailoring public transport to user needs attracts both more customers and the latent demand, diverting it from car and making transport more sustainable.
|Titolo:||Smart card data mining to analyse mobility patterns in suburban areas|
|Data di pubblicazione:||2018|
|Digital Object Identifier (DOI):||10.3390/su10103489|
|Appare nelle tipologie:||1.1 Articolo in rivista|