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Title: DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation
Estimating the travel time for a given path is a fundamental problem in many urban transportation systems. However, prior works fail to well capture moving behaviors embedded in paths and thus do not estimate the travel time accurately. To fill in this gap, in this work, we propose a novel neural network framework, namely Deep Image-based Spatio-Temporal network (DeepIST), for travel time estimation of a given path. The novelty of DeepIST lies in the following aspects:1) we propose to plot a path as a sequence of -generalized images"which include sub-paths along with additional information, such as traffic conditions, road network and traffic signals, in order to harness the power of convolutional neural network model (CNN)on image processing; 2) we design a novel two-dimensional CNN, namely PathCNN, to extract spatial patterns for lines in images by regularization and adopting multiple pooling methods; and 3) we apply a one-dimensional CNN to capture temporal patterns among the spatial patterns along the paths for the estimation. Empirical results show that DeepIST soundly outperforms the state-of-the-art travel time estimation models by 24.37% to 25.64% of mean absolute error (MAE) in multiple large-scale real-world datasets.  more » « less
Award ID(s):
1717084
PAR ID:
10170812
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Proceedings of the 28th ACM International Conference on Information and Knowledge Management
Page Range / eLocation ID:
69 to 78
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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