A new study explores the potential of innovative data sources to optimise planning, operations and performance in the public transport sector.

The conventional data used to support public transport management have inherent constraints related to scalability, cost, and the potential to capture space and time variability. These limitations underscore the importance of exploring innovative data sources to complement more traditional ones. For public transport operators, who are tasked with making pivotal decisions spanning planning, operation, and performance measurement, innovative data sources are a frontier that is still largely unexplored.

The exploration of big data sources for public transportation management is the focus of a study recently published by Valeria Maria Urbano, Marika Arena and Giovanni Azzone from POLIMI School of Management, Politecnico di Milano, in the Research in Transportation Business & Management entitled Big data for decision-making in public transport management: A comparison of different data sources.

The study is the result of a long-term research program aimed at exploring the potential of novel data sources and addressing emerging challenges in public transport. The research program includes four projects carried out by the research team in partnerships with two primary public transport operators (Azienda Trasporti Milanesi S.p.A. and Trenord S.r.l.) operating in northern Italy for five years over a five-year period (2019–2023).

The study establishes a framework for evaluating innovative data sources, highlighting the specific characteristics that data should have to support decision-making in the context of transportation management. Second, a comparative analysis is conducted, using empirical data collected from primary public transport operators in the Lombardy region, including smart card data, mobile phone data and automatic vehicle data, with the aim of understanding whether and to what extent different data sources meet the above requirements.

This study can support public transport operators in selecting data sources that are more coherent with the three primary decision-making domains, highlighting the potential benefits and key challenges associated with big data sources in public transport management. In addition to providing individual data source evaluations, this study underscores the pivotal role of data integration in improving understanding of travel behaviour, optimizing operational processes, and assessing performance metrics.

For more information: https://www.sciencedirect.com/science/article/pii/S2210539525000136