Paper Info
Title
Predicting Fine-Grained Traffic Conditions via Spatio-Temporal LSTM.
Abstract
Predicting traffic conditions for road segments is the prelude of working on intelligent transportation. Many existing methods can be used for short-term or long-term traffic prediction, but they focus more on regions than on road segments. The lack of fine-grained traffic predicting approach hinders the development of ITS. Therefore, MapLSTM, a spatio-temporal long short-term memory network preluded by map-matching, is proposed in this paper to predict fine-grained traffic conditions. MapLSTM first obtains the historical and real-time traffic conditions of road segments via map-matching. Then LSTM is used to predict the conditions of the corresponding road segments in the future. Breaking the single-index forecasting, MapLSTM can predict the vehicle speed, traffic volume, and the travel time in different directions of road segments simultaneously. Experiments confirmed MapLSTM can not only achieve prediction for road segments based a large scale of GPS trajectories effectively but also have higher predicting accuracy than GPR and ConvLSTM. Moreover, we demonstrate that MapLSTM can serve various applications in a lightweight way, such as cognizing driving preferences, learning navigation, and inferring traffic emissions.
Year
DOI
Venue
2019
10.1155/2019/9242598
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
Field
DocType
Volume
Ground-penetrating radar,Computer science,Real-time computing,Global Positioning System,Traffic volume,Intelligent transportation system,Traffic prediction,Travel time,Traffic conditions,Distributed computing
Journal
2019
ISSN
Citations 
PageRank 
1530-8669
1
0.35
References 
Authors
7
6
Name
Order
Citations
PageRank
Xiaojuan Wei121.71
Jinglin Li215030.39
Quan Yuan35511.07
Kaihui Chen450.78
Ao Zhou518728.14
Fangchun Yang6108290.49