Paper Info
Title
How Do Metro Station Crowd Flows Influence the Taxi Demand Based on Deep Spatial-Temporal Network?
Abstract
Forecasting taxi demand is of great significance to the intelligent transportation systems in a smart city. Traditional demand prediction methods mostly considered about inter-regional traffic, events, activities, and weather, while they overlooked the influence of other travel modes, such as metro. In this paper, we propose a Deep Taxi-Metro Spatial-Temporal Network framework, namely TMST-Net, to model the spatiotemporal relationships between the taxi demand and the metro crowd flows. In detail, we apply residual neural networks to model temporal (current, day, and week) properties of the taxi demand in each area. For each feature, we apply residual convolutional units to handle the spatial properties of taxi demand. Likewise, we apply the same method to model the metro crowd flows. TMST-Net learns to assign different weights between taxi and metro by aggregating the output of the three residual neural networks and the external factors to forecast the final taxi demand for each area in the next timestamp. Experimental results on real taxi trajectory and the automatic fare collection (AFC) data in Shanghai show that our approach outperforms the state-of-the-art methods.
Year
DOI
Venue
2018
10.1109/MSN.2018.00031
2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)
Keywords
Field
DocType
taxi demand prediction,deep residual learning,TMST-Net
Residual,Computer science,Real-time computing,Smart city,Timestamp,Intelligent transportation system,Artificial neural network,Trajectory,Distributed computing
Conference
ISBN
Citations 
PageRank 
978-1-7281-0548-2
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Yu Bao1356.69
Yu-e Sun2337.07
Xiaofei Bu300.68
Yang Du4146.47
Xiaocan Wu522.75
He Huang682965.14
Yonglong Luo713922.70
Liusheng Huang847364.55