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In this paper, we target at recovering the exact routes taken by commutersinside a metro system that arenot captured by an Automated Fare Collection(AFC) system and hence remain unknown. We strategicallypropose two inferencetasks to handle the recovering, one to infer the travel time of each travellink thatcontributes to the total duration of any trip inside a metro networkand the other to infer the route preferencesbased on historical trip recordsand the travel time of each travel link inferred in the previous inferencetask.As these two inference tasks have interrelationship, most of existing worksperform these two taskssimultaneously. However, our solutionTripDecoderadopts atotally different approach. To the best of ourknowledge,TripDecoderis the firstmodel that points out and fully utilizes the fact that there are sometripsinside a metro system with only one practical route available. Itstrategically decouples these two inferencetasks by only taking those triprecords with only one practical route as the input for the first inferencetaskof travel time and feeding the inferred travel time to the second inferencetask as an additional input whichnot only improves the accuracy but alsoeffectively reduces the complexity of both inference tasks. Twocase studieshave been performed based on the city-scale real trip records captured by theAFC systems inSingapore and Taipei to compare the accuracy and efficiencyofTripDecoderand its competitors. As expected,TripDecoderhas achieved the bestaccuracy in both datasets, and it also demonstrates its superior efficiencyandscalability.

TRIPDECODER: Study Travel Time Attributes and Route Preferences of Metro Systems from Smart Card Data