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Journey planning is the key to an efficient andsustainable transportation system in a smart city. A good journeyplanner is expected to help commuters travel safely, comfortablyand quickly, as well as keep the whole transportation networkrunning efficiently. In modern cities, it should be able to combinea wide range of private and public transport modes, and moreimportantly, react to real-time events...
We analyze the work of urban trip planners and the relevance of trips they recommend upon user queries. We propose to improve the planner recommendations by learning from choices made by travelers who use the transportation network on the daily basis. We analyze a large collection of individual travelers' trips collected from the automated fare collection systems; we convert the trips into pair-wise...
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