Crowdsourcing information for knowledge-based design of routes for unscheduled public transport trips

The purpose of this paper is to devise a crowdsourcing methodology for acquiring and exploiting knowledge to profile unscheduled transport networks for design of efficient routes for public transport trips.

This paper analyzes daily travel itineraries within Mexico City provided by 610 public transport users. In addition, a statistical analysis of quality-of-service parameters of the public transport systems of Mexico City was also conducted. From the statistical analysis, a knowledge base was consolidated to characterize the unscheduled public transport network of Mexico City. Then, by using a heuristic search algorithm for finding routes, public transport users are provided with efficient routes for their trips.

The findings of the paper are as follows. A crowdsourcing methodology can be used to characterize complex and unscheduled transport networks. In addition, the knowledge of the crowds can be used to devise efficient routes for trips (using public transport) within a city. Moreover, the design of routes for trips can be automated by SmartPaths, a mobile application for public transport navigation.

The data collected from the public transport users of Mexico City may vary through the year.

The significance and novelty is that the present work is the earliest effort in making use of a crowdsourcing approach for profiling unscheduled public transport networks to design efficient routes for public transport trips.