Introduction. Current methods for determining passenger ridership, due to their complexity and limited efficiency, do not allow monitoring transport demand at the proper level (in terms of comprehensiveness and continuity). Nowadays, technologies based on the collection, integration and analysis of big data (Urban computing, Big data, Internet of things) are used to create effective solutions in many aspects of our lives (including for urban transit).Materials and methods. Within the framework of this approach, a methodology for determining the correspondence of transit passengers from the operations of validating any kind of electronic travel tickets: smart cards, transport cards, magnetic cards has been developed. Each operations details are recorded in the Automated Fare Collection (AFC) system during the validation.This methodology based on the definition and evaluation the set of acceptable options of passenger trips sequences, which form the passenger correspondence, taking into account many factors that effect on the route choice by a passenger. For example, in contrast to previous studies, the practice of paying for trip at any point on the route, not necessarily immediately after boarding the vehicle, was taken into account.Results. It was proved that passenger correspondence calculated using the developed methodology statistically corresponds to the general set of transit passenger ridership within acceptable errors. The chaacteristics of transit demand is provided in the results.Discussion and conclusion. The application of the developed methodology makes it possible to organize continuous monitoring of passenger flows, technical and operational indicators of the functioning of transit system and thus implement the concept of sustainable development of public transport by designing public transit supply that meets demand.


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    Title :

    Methodology for determining passengers correspondence by public transport from electronic tickets validation operations


    Contributors:


    Publication date :

    2022




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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