TY - JOUR
T1 - Multiple-factor based sparse urban travel time prediction
AU - Zhu, Xinyan
AU - Fan, Yaxin
AU - Zhang, Faming
AU - Ye, Xinyue
AU - Chen, Chen
AU - Yue, Han
N1 - Funding Information:
Acknowledgments: This work was supported by National Natural Science Foundation of China No. 41271401; the National 863 project, Multi-source Information Real-time Access and Heterogeneous Information Autonomous Loading Technology under the Unified Spatiotemporal System, No. 2013AA122301; Pan-information map fusion technology Based on the Internet superposition protocol, No. 2013AA12A203; National Science and Technology Support Plan, the Key Technology and Applications of Location-based Sensor Network and Pan-information Map, No. 2012BAH35B03; and National Science Foundation, Nos. 1416509, ACI-1535031,and 1535081; National Key R&D Program of China (Grant No.2016YFB0502204), and Opening research fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing. Finally, the authors thank Steve for his proofreading.
Publisher Copyright:
© 2018 by the authors.
PY - 2018/2/12
Y1 - 2018/2/12
N2 - The prediction of travel time is challenging given the sparseness of real-time traffic data and the uncertainty of travel, because it is influenced by multiple factors on the congested urban road networks. In our paper, we propose a three-layer neural network from big probe vehicles data incorporating multi-factors to estimate travel time. The procedure includes the following three steps. First, we aggregate data according to the travel time of a single taxi traveling a target link on working days as traffic flows display similar traffic patterns over a weekly cycle. We then extract feature relationships between target and adjacent links at 30 min interval. About 224,830,178 records are extracted from probe vehicles. Second, we design a three-layer artificial neural network model. The number of neurons in input layer is eight, and the number of neurons in output layer is one. Finally, the trained neural network model is used for link travel time prediction. Different factors are included to examine their influence on the link travel time. Our model is verified using historical data from probe vehicles collected from May to July 2014 in Wuhan, China. The results show that we could obtain the link travel time prediction results using the designed artificial neural network model and detect the influence of different factors on link travel time.
AB - The prediction of travel time is challenging given the sparseness of real-time traffic data and the uncertainty of travel, because it is influenced by multiple factors on the congested urban road networks. In our paper, we propose a three-layer neural network from big probe vehicles data incorporating multi-factors to estimate travel time. The procedure includes the following three steps. First, we aggregate data according to the travel time of a single taxi traveling a target link on working days as traffic flows display similar traffic patterns over a weekly cycle. We then extract feature relationships between target and adjacent links at 30 min interval. About 224,830,178 records are extracted from probe vehicles. Second, we design a three-layer artificial neural network model. The number of neurons in input layer is eight, and the number of neurons in output layer is one. Finally, the trained neural network model is used for link travel time prediction. Different factors are included to examine their influence on the link travel time. Our model is verified using historical data from probe vehicles collected from May to July 2014 in Wuhan, China. The results show that we could obtain the link travel time prediction results using the designed artificial neural network model and detect the influence of different factors on link travel time.
KW - Artificial neural networks
KW - Big probe vehicles data
KW - Data sparsity
KW - Link travel time prediction
KW - Multi-factor influences
KW - Spatiotemporal relationships
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U2 - 10.3390/app8020279
DO - 10.3390/app8020279
M3 - Article
AN - SCOPUS:85042044896
SN - 2076-3417
VL - 8
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 2
M1 - 279
ER -