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.

Attention-Based Sequence Learning Model for Travel Time Estimation

“…It is common practice to ignore the time crossing the intersection in existing works [10], [11]. However, the delay time on the intersections is just as important for there are always a lot of cars up at the crossroads, in particular, the peak commuting, which causes the overall travel time of a trip highly depends on some key intersections involved.…”

“…1(b) represents the detailed structure of an intersection the path involved, the travel delay of car A and car B is obviously different for the effect of traffic lights and the traffic condition of the next road segment even they stand in the same intersection. Besides, it is intuitive to understand the time crossing the road segments makes up a large part of the overall travel time of a path in most cases, which is the reason that intersections always with less discussion in many works [10], [11]. Nevertheless, congestion usually happens in the surrounding of intersections during rush hours.…”

A Hybrid Framework Combining Vehicle System Knowledge with Machine Learning Methods for Improved Highway Trajectory Prediction

“…2) Data-based component: To choose a DB model suitable for vehicle motion prediction, the nature and complexity of the maneuvers need to be considered. For this task, the authors of [4] and [27] show that machine learning methods, such as Long Short-Term Memory (LSTM) ANNs show potential. This model is also chosen here since LSTMs are well known for sequence to sequence mapping [28], hence also suitable for our trajectory prediction problem, i.e.…”

Deep Graph Convolutional Networks for Incident-Driven Traffic Speed Prediction

“…Traffic speed prediction has been a challenging problem for decades, which has a wide range of traffic planning and related applications, including congestion control [17], vehicle routing planning [14], urban road planning [28] and travel time estimation [9]. The difficulty of the prediction problem comes from the complicated and highly dynamic nature of traffic and road conditions, as well as a variety of other unpredictable, ad hoc factors.…”