Chapter
Aug 12, 2020
Personalized Travel Time Estimation Based on Collaborative Block Term Decomposition
Authors: Haiyang Yu, Shuai Liu, Chenyang Liu, Yilong Ren [email protected], and Can YangAuthor Affiliations
Publication: CICTP 2020
ABSTRACT
Travel time is an inevitable and significant parameter in urban transportation planning and management. Due to the limitations of detectors and missing data, it is difficult to get complete travel time information in urban road networks. Here, we treat travel time estimation as a tensor completion problem, and propose a collaborative block term decomposition model using monitoring data in Ruian City. We model different drivers’ travel time on road segments in various time slots with a three dimensional tensor. Meanwhile, a historical travel time tensor is built to help to discovery underlying information and relieve the problem of data sparsity. Then, three feature matrices are extracted to capture the contextual information of travel time. The three matrices and the historical tensor are aided by the object function to solve the travel time estimation problem. Finally, experiments show that our model is an effective approach.
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© 2020 American Society of Civil Engineers.
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Published online: Aug 12, 2020
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Haiyang Yu
1School of Transportation Science and Engineering, Beihang Univ., Beijing 100191, China; National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology, Beijing 100191, China; Beijing Advanced Innovation Center for Big Data and Brain Computing, Beijing 100191, China.
Shuai Liu
2School of Transportation Science and Engineering, Beihang Univ., Beijing 100191, China; National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology, Beijing 100191, China.
Chenyang Liu
3School of Transportation Science and Engineering, Beihang Univ., Beijing 100191, China; National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology, Beijing 100191, China.
4School of Transportation Science and Engineering, Beihang Univ., Beijing 100191, China; National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology, Beijing 100191, China; Beijing Advanced Innovation Center for Big Data and Brain Computing, Beijing 100191, China. Email: [email protected]
Can Yang
5Hefei Innovation Research Institute, Beihang Univ., Xinzhan Hi-Tech District, Anhui 230013, China.
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