  Travel time estimation from GPS trips is of great importance to order
duration, ridesharing, taxi dispatching, etc. However, the dense trajectory is
not always available due to the limitation of data privacy and acquisition,
while the origin destination (OD) type of data, such as NYC taxi data, NYC bike
data, and Capital Bikeshare data, is more accessible. To address this issue,
this paper starts to estimate the OD trips travel time combined with the road
network. Subsequently, a Multitask Weakly Supervised Learning Framework for
Travel Time Estimation (MWSL TTE) has been proposed to infer transition
probability between roads segments, and the travel time on road segments and
intersection simultaneously. Technically, given an OD pair, the transition
probability intends to recover the most possible route. And then, the output of
travel time is equal to the summation of all segments' and intersections'
travel time in this route. A novel route recovery function has been proposed to
iteratively maximize the current route's co occurrence probability, and
minimize the discrepancy between routes' probability distribution and the
inverse distribution of routes' estimation loss. Moreover, the expected log
likelihood function based on a weakly supervised framework has been deployed in
optimizing the travel time from road segments and intersections concurrently.
We conduct experiments on a wide range of real world taxi datasets in Xi'an and
Chengdu and demonstrate our method's effectiveness on route recovery and travel
time estimation.
