T-CONV: A Convolutional Neural Network for Multi-scale Taxi Trajectory Prediction

Precise destination prediction of taxi trajectories can benefit many intelligent location based services such as accurate ad for passengers. Traditional prediction approaches, which treat trajectories as one-dimensional sequences and process them in single scale, fail to capture the diverse two-dime...

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Bibliographic Details
Published in:International Conference on Big Data and Smart Computing pp. 82 - 89
Main Authors: Lv, Jianming, Li, Qing, Sun, Qinghui, Wang, Xintong
Format: Conference Proceeding
Language:English
Published: IEEE 01.01.2018
Subjects:
Clustering algorithms
Companies
convolutional neural network
multi-scale
Neural networks
Prediction algorithms
Predictive models
Public transportation
Trajectory
ISSN:2375-9356
Online Access:Get full text
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Summary:Precise destination prediction of taxi trajectories can benefit many intelligent location based services such as accurate ad for passengers. Traditional prediction approaches, which treat trajectories as one-dimensional sequences and process them in single scale, fail to capture the diverse two-dimensional patterns of trajectories in different spatial scales. In this paper, we propose T-CONV which models trajectories as two-dimensional images, and adopts multi-layer convolutional neural networks to combine multi-scale trajectory patterns to achieve precise prediction. Furthermore, we conduct gradient analysis to visualize the multi-scale spatial patterns captured by T-CONV and extract the areas with distinct influence on the ultimate prediction. Finally, we integrate multiple local enhancement convolutional fields to explore these important areas deeply for better prediction. Comprehensive experiments based on real trajectory data show that T-CONV can achieve higher accuracy than the state-of-the-art methods.
ISSN:2375-9356
DOI:10.1109/BigComp.2018.00021