  In this paper, we target at recovering the exact routes taken by commuters
inside a metro system that arenot captured by an Automated Fare Collection
(AFC) system and hence remain unknown. We strategicallypropose two inference
tasks to handle the recovering, one to infer the travel time of each travel
link thatcontributes to the total duration of any trip inside a metro network
and the other to infer the route preferencesbased on historical trip records
and the travel time of each travel link inferred in the previous inferencetask.
As these two inference tasks have interrelationship, most of existing works
perform these two taskssimultaneously. However, our solutionTripDecoderadopts a
totally different approach. To the best of ourknowledge,TripDecoderis the first
model that points out and fully utilizes the fact that there are some
tripsinside a metro system with only one practical route available. It
strategically decouples these two inferencetasks by only taking those trip
records with only one practical route as the input for the first inference
taskof travel time and feeding the inferred travel time to the second inference
task as an additional input whichnot only improves the accuracy but also
effectively reduces the complexity of both inference tasks. Twocase studies
have been performed based on the city-scale real trip records captured by the
AFC systems inSingapore and Taipei to compare the accuracy and efficiency
ofTripDecoderand its competitors. As expected,TripDecoderhas achieved the best
accuracy in both datasets, and it also demonstrates its superior efficiencyand
scalability.
