Abstract
Public transport has become one of the major transport options, especially when it comes to reducing motorized individual transport and achieving sustainability while reducing emissions, noise and so on. The use of public transport data has evolved and rapidly improved over the past decades. Indeed, the availability of data from different sources, coupled with advances in analytical and predictive approaches, has contributed to increased attention being paid to the exploitation of available data to improve public transport service. In this paper, we review the current state of the art of public transport data sources. More precisely, we summarize and analyze the potential and challenges of the main data sources. In addition, we show the complementary aspects of these data sources and how to merge them to broaden their contributions and face their challenges. This is complemented by an information management framework to enhance the use of data sources. Specifically, we seek to bridge the gap between traditional data sources and recent ones, present a unified overview of them and show how they can all leverage recent advances in data-driven methods and how they can help achieve a balance between transit service and passenger behavior.
Highlights
Public transport provides an essential service whose relevance is increasingly recognized
Before delving into the various studied issues, it should be noted that several reviews on big data in public transport have recently been published, which we summarize below
Data), this information can be distributed spatially over a network. We note that this is among the advantages of automated fare collection (AFC) beyond surveys as illustrated, for example, in [51], which aims to understand the spatio-temporal dynamics of passenger travel behavior in the context of a public transport network
Summary
Public transport provides an essential service whose relevance is increasingly recognized. Before delving into the various studied issues, it should be noted that several reviews on big data in public transport have recently been published, which we summarize below. [7], which appears to be the most comprehensive and up-to-date public transport big data review, aims to categorize research on this topic, with respect to its applications, into three areas, namely passenger behavior analysis, operation optimization and policy making. We notice that these reviews rather aim to show different applications of big data in public transport than to focus on the data sources themselves. For those interested in data sources, the various data challenges are not extensive.
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