Big data for decision-making in public transport management: A comparison of different data sources

Abstract

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. To fill this gap, this study first 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, with the aim of understanding whether and to what extent different data sources meet the above requirements.

The findings of this study support transport operators in selecting data sources aligned with different decision-making domains, highlighting related benefits and challenges. This underscores the importance of integrating different data sources to exploit their complementarities.