Discovering Travelers’ Purchasing Behavior from Public Transport Data

Branda, Francesco; Marozzo, Fabrizio; Talia, Domenico

doi:10.1007/978-3-030-64583-0_63

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…In (Han et al, 2010), the authors use data from smart cards in Changchun, China, to calculate the passenger flow time distribution of different groups of passengers, among other aspects. In (Branda et al, 2020), the authors use data collected from a bus ticketing platform in Italy to discover the factors influencing travelers in booking and purchasing a bus ticket. In (Massobrio and Nesmachnow, 2019), the authors combine several sources of urban data from the public transportation system of Montevideo, Uruguay, to describe mobility patterns in the city.…”

Section: Related Workmentioning

confidence: 99%

Process Mining for Improving Urban Mobility in Smart Cities: Challenges and Application with Open Data

Delgado,

Calegari

2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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Urban mobility presents various challenges to favor urban development. These challenges have been traditionally analyzed using transport network optimization and simulation techniques. Nevertheless, it is possible to think of process mining as a complementary approach allowing, among other things, to discover behavioral transportation models, obtain execution measures and detect bottlenecks.The objective of this article is to analyze how suitable PM is for the analysis of urban mobility problems. We use open data from the Metropolitan Transportation System (STM) of Montevideo, Uruguay, which, among other things, provides the ability to record up-to-date information on its transportation network and trips of its citizens. We apply process mining to process discovery, both from buses and their users, and carry out various analyses linking such data with time information, costs, types of users, and city areas.

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Section: Related Workmentioning

confidence: 99%

Process Mining for Improving Urban Mobility in Smart Cities: Challenges and Application with Open Data

Delgado,

Calegari

2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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Ticket Sales Prediction and Dynamic Pricing Strategies in Public Transport

Branda

Marozzo

Talia

2020

BDCC

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In recent years, the demand for collective mobility services registered significant growth. In particular, the long-distance coach market underwent an important change in Europe, since FlixBus adopted a dynamic pricing strategy, providing low-cost transport services and an efficient and fast information system. This paper presents a methodology, called DA4PT (Data Analytics for Public Transport), for discovering the factors that influence travelers in booking and purchasing bus tickets. Starting from a set of 3.23 million user-generated event logs of a bus ticketing platform, the methodology shows the correlation rules between booking factors and purchase of tickets. Such rules are then used to train machine learning models for predicting whether a user will buy or not a ticket. The rules are also used to define various dynamic pricing strategies with the purpose of increasing the number of tickets sales on the platform and the related amount of revenues. The methodology reaches an accuracy of 95% in forecasting the purchase of a ticket and a low variance in results. Exploiting a dynamic pricing strategy, DA4PT is able to increase the number of purchased tickets by 6% and the total revenue by 9% by showing the effectiveness of the proposed approach.

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Discovering Travelers’ Purchasing Behavior from Public Transport Data

Cited by 2 publications

References 13 publications

Process Mining for Improving Urban Mobility in Smart Cities: Challenges and Application with Open Data

Process Mining for Improving Urban Mobility in Smart Cities: Challenges and Application with Open Data

Ticket Sales Prediction and Dynamic Pricing Strategies in Public Transport

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