Aggressive Driving Saves More Time? Multi-task Learning for
Customized Travel Time Estimation
Abstract
Estimating the origin-destination travel time is a
fundamental problem in many location-based services for vehicles, e.g., ride-hailing, vehicle dispatching, and route planning. Recent work has
made significant progress to accuracy but they
largely rely on GPS traces which are too coarse to
model many personalized driving events. In this
paper, we propose Customized Travel Time Estimation (CTTE) that fuses GPS traces, smartphone
inertial data, and road network within a deep recurrent neural network. It constructs a link traffic
database with topology representation, speed statistics, and query distribution. It also uses inertial data to estimate the arbitrary phone’s pose in car, and
detects fine-grained driving events. The multi-task
learning structure predicts both traffic speed at public level and customized travel time at personal level. Extensive experiments on two real-world traffic
datasets from Didi Chuxing have demonstrated our
effectiveness