New Opportunities in Urban Transport Data : Methodologies and Applications

Abstract: The deployment of Information and Communication Technologies (ICT) is growing in transportation which may contribute to a more efficient and effective service. The data acquired from ICT based systems could be used for many purposes such as statistical analysis and behavior learning and inference. This dissertation addresses the question of how transportation data that was collected for a specific application can be used for other applications. This thesis consists of five separate papers, each addressing a subset of the topic.The first paper estimates a route choice model using sparse GPS data. This paper demonstrates the feasibility of an Indirect Inference based estimator in a model with random link costs, allowing for a natural correlation structure across paths, where the full choice set is considered.The second paper presents an estimator for the mean speed and travel time at network level based on indirect inference when the data are spatially and temporally sparse.The third paper proposes an evaluation framework which outlines a systematic process to quantify and assess the impacts of public transport preferential measures on service users and providers in monetary terms, using public transport data sources.In the fourth and fifth papers, a methodology is developed and implemented for integrating different prediction models and data sources while satisfying practical requirements related to the generation of real-time information. Then the performance of the proposed prediction method is compared with the prediction accuracy obtained by the currently deployed methods.