Spatiotemporal variable and parameter selection using sparse hybrid genetic algorithm for traffic flow forecasting. (June 2017)

• Record Type: Journal Article
• Authors: Chen, Xiaobo; Wei, Zhongjie; Liu, Xiang; Cai, Yingfeng; Li, Zuoyong; Zhao, Feng
• Is Part Of: International journal of distributed sensor networks. Volume 13:Number 6(2017)
• Publication Date: 2017-06
• Subjects: Traffic flow forecasting -- spatiotemporal variable selection -- genetic algorithm -- road traffic network -- machine learning; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147717713376
• Languages: English
• ISSNs: 1550-1329

Abstract

Short-term traffic flow forecasting is a difficult yet important problem in intelligent transportation systems. Complex spatiotemporal interactions between the target road segment and other road segments can provide important information for the accurate forecasting. Meanwhile, spatiotemporal variable selection and traffic flow prediction should be solved in a unified framework such that they can benefit from each other. In this article, we propose a novel sparse hybrid genetic algorithm by introducing sparsity constraint and real encoding scheme into genetic algorithm in order to optimize short-term traffic flow prediction model based on least squares support vector regression. This method can integrate spatiotemporal variable selection, parameter selection as well as traffic flow prediction in a unified framework, indicating that the "goodness, " that is, contribution, of selected spatiotemporal variables and optimized parameters directly depends on the final traffic flow prediction accuracy. The real-world traffic flow data are collected from 24 observation sites located around the intersection of Interstate 205 and Interstate 84 in Portland, OR, USA. The experimental results show that the proposed sparse hybrid genetic algorithm-least square support vector regression prediction model can produce better performance but with much fewer spatiotemporal variables in comparison with other related models.