DEEPTRAVEL: A neural network based travel time estimation model with auxiliary supervision

• Main Authors: ZHANG, Hanyuan, WU, Hao, SUN, Weiwei, ZHENG, Baihua
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Complex factors; Real data sets; Road segments; Supervision models; Trajectory data; Trajectory points; Travel time estimation; Urban mobility; Travel time; Databases and Information Systems; Numerical Analysis and Scientific Computing; Transportation
• Online Access: https://ink.library.smu.edu.sg/sis_research/4107; https://ink.library.smu.edu.sg/context/sis_research/article/5110/viewcontent/DeepTravel.pdf
• Institution: Singapore Management University

Estimating the travel time of a path is of great importance to smart urban mobility. Existing approaches are either based on estimating the time cost of each road segment or designed heuristically in a non-learning-based way. The former is not able to capture many cross-segment complex factors while the latter fails to utilize the existing abundant temporal labels of the data, i.e., the time stamp of each trajectory point. In this paper, we leverage on new development of deep neural networks and propose a novel auxiliary supervision model, namely DeepTravel, that can automatically and effectively extract different features, as well as make full use of temporal labels of the trajectory data. We have conducted comprehensive experiments on real datasets to demonstrate the out-performance of DeepTravel over existing approaches.