Journal article
Urban Traffic Dynamics Prediction-A Continuous Spatial-temporal Meta-learning Approach
ACM transactions on intelligent systems and technology, Vol.13(2), pp.1-19
04/30/2022
DOI: 10.1145/3474837
Abstract
Urban traffic status (e.g., traffic speed and volume) is highly dynamic in nature, namely, varying across space and evolving over time. Thus, predicting such traffic dynamics is of great importance to urban development and transportation management. However, it is very challenging to solve this problem due to spatial-temporal dependencies and traffic uncertainties. In this article, we solve the traffic dynamics prediction problem from Bayesian meta-learning perspective and propose a novel continuous spatial-temporal meta-learner (cST-ML), which is trained on a distribution of traffic prediction tasks segmented by historical traffic data with the goal of learning a strategy that can be quickly adapted to related but unseen traffic prediction tasks. cST-ML tackles the traffic dynamics prediction challenges by advancing the Bayesian black-box meta-learning framework through the following new points: (1) cST-ML captures the dynamics of traffic prediction tasks using variational inference, and to better capture the temporal uncertainties within tasks, cST-ML performs as a rolling window within each task; (2) cST-ML has novel designs in architecture, where CNN and LSTM are embedded to capture the spatial-temporal dependencies between traffic status and traffic-related features; (3) novel training and testing algorithms for cST-ML are designed. We also conduct experiments on two real-world traffic datasets (taxi inflow and traffic speed) to evaluate our proposed cST-ML. The experimental results verify that cST-ML can significantly improve the urban traffic prediction performance and outperform all baseline models especially when obvious traffic dynamics and temporal uncertainties are presented.
Details
- Title: Subtitle
- Urban Traffic Dynamics Prediction-A Continuous Spatial-temporal Meta-learning Approach
- Creators
- Yingxue Zhang - Worcester Polytechnic InstituteYanhua Li - Worcester Polytechnic InstituteXun Zhou - University of IowaJun Luo - Lenovo Group Limited, Quarry Bay, Hong KongZhi-Li Zhang - University of Minnesota
- Resource Type
- Journal article
- Publication Details
- ACM transactions on intelligent systems and technology, Vol.13(2), pp.1-19
- Publisher
- Assoc Computing Machinery
- DOI
- 10.1145/3474837
- ISSN
- 2157-6904
- eISSN
- 2157-6912
- Number of pages
- 19
- Grant note
- Safety Research using Simulation University Transportation Center (SAFER-SIM) 69A3551747131 / U.S. Department of Transportation's University Transportation Centers Program CNS-1952085; CMMI-1831140; CNS-1901103; DGE-2021871; IIS-1942680 / NSF; National Science Foundation (NSF)
- Language
- English
- Date published
- 04/30/2022
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380425402771
Metrics
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