Efficient Planning of Urban Public Transportation Networks

Planning efficient public transport is a key issue in modern cities. When planning a route for a bus or the line for a tram or subway it is necessary to consider the demand of the people for this service. In this work we presented a method to use existing crowdsourcing data (like Waze and OpenStreetMap) and cloud services (like Google Maps) to support a transportation network decision making process. The method is based the Dempster-Shafer Theory to model transportation demand and uses data from Waze to provide a congestion probability and data from OpenStreetMap to provide information about location of facilities such as shops, in order to predict where people may need to start or end their trip using public transportation means. The paper also presents an example about how to use this method with real data. The example shows how to analyze the current availability of public transportation stops in order to discover its weak points.

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