Contextualized Spatial–Temporal Network for Taxi Origin-Destination Demand Prediction

Lingbo Liu · Zhilin Qiu · Guanbin Li · Qing Wang · Wanli Ouyang · Liang Lin

287 citations0/0 files storedIEEE Transactions on Intelligent Transportation Systems

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Traffic Prediction and Management Techniques Transportation Planning and Optimization Data Management and Algorithms

Abstract

Taxi demand prediction has recently attracted increasing research interest due to its huge potential application in large-scale intelligent transportation systems. However, most of the previous methods only considered the taxi demand prediction in origin regions, but neglected the modeling of the specific situation of the destination passengers. We believe it is suboptimal to preallocate the taxi into each region-based solely on the taxi origin demand. In this paper, we present a challenging and worth-exploring task, called taxi origin-destination demand prediction, which aims at predicting the taxi demand between all-region pairs in a future time interval. Its main challenges come from how to effectively capture the diverse contextual information to learn the demand patterns. We address this problem with a novel contextualized spatial-temporal network (CSTN), which consists of three components for the modeling of local spatial context (LSC), temporal evolution context (TEC), and global correlation context (GCC), respectively. First, an LSC module utilizes two convolution neural networks to learn the local spatial dependencies of taxi, demand respectively, from the origin view and the destination view. Second, a TEC module incorporates the local spatial features of taxi demand and the meteorological information to a Convolutional Long Short-term Memory Network (ConvLSTM) for the analysis of taxi demand evolution. Finally, a GCC module is applied to model the correlation between all regions by computing a global correlation feature as a weighted sum of all regional features, with the weights being calculated as the similarity between the corresponding region pairs. The extensive experiments and evaluations on a large-scale dataset well demonstrate the superiority of our CSTN over other compared methods for the taxi origin-destination demand prediction.

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References

2017Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction 2017Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach 2013Predicting Taxi–Passenger Demand Using Streaming Data 2012Prediction of urban human mobility using large-scale taxi traces and its applications 1988A unified framework for estimating or updating origin/destination matrices from traffic counts 2011Where to find my next passenger 2017Real-Time Prediction of Taxi Demand Using Recurrent Neural Networks

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