A Multi-Criteria Multi-Modal Predictive Trip Planner: Application on Paris Metropolitan Network

Abstract

Public transport route planning is of growing interest in smart cities and especially in metropolitan areas where congestions and traffic jams are frequently recorded. The availability of multiple data sources, such as passenger load in trains or ticketing logs, provides an interesting opportunity to develop decision support tools to help passengers better plan their trips around the city and to enhance their travel experience. We present, in this paper, a multi-criteria journey planner that incorporates train load predictions as criteria. To this end, on the one hand, we enrich the proposed routes with predictive indicators of passenger flow such as the load on board the trains. These indicators are computed for each section of the itinerary using machine learning algorithms. On the other hand, we design a journey planner that incorporates the predicted load in its search criteria.

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