Inferring passenger-level bus trip traces from schedule, positioning and ticketing data: methods and applications.

Abstract

As a result of the recent and fast rise in urban population, mobility has emerged as one of the most problematic and fast-evolving urban problems of the 21st century. With the advent of the Internet of Things, gigabytes of data are generated every day by Public Transportation Systems around the world, including bus GPS/speed records, and passenger boarding registries. Although this data has the potential to help improve mobility, the vast amount, dynamicity and diversity of data produced by different systems with different goals and constraints poses difficulties to integrate and analyze it and help the system’s users, operators and administrators. This study addresses this problem, more specifically the one of using bus schedule data, raw GPS and smart card records to reconstruct trips at passenger-level. We use data from the Curitiba bus system in Brazil to devise an analysis pipeline that combines and extends consolidated heuristics found in literature. Experiments demonstrate the utility of the proposed solution in two applications scenarios: a) the estimation of an Origin-Destination Matrix for Public Transport users, which was validated by a comparison to a recent Origin-Destination Survey performed in the city; and b) an analysis of the (in)efficiency of passenger itinerary choice, conducted by contrasting the estimated itinerary choice (extracted from trip reconstruction) to the set of available and feasible itineraries at the time of boarding.