Preprint Article Version 1 This version is not peer-reviewed

Predicting Freeway Travelling Time Using Multiple-Source Data

Version 1 : Received: 24 October 2018 / Approved: 25 October 2018 / Online: 25 October 2018 (10:48:45 CEST)

A peer-reviewed article of this Preprint also exists.

Long, K.; Yao, W.; Gu, J.; Wu, W.; Han, L.D. Predicting Freeway Travel Time Using Multiple- Source Heterogeneous Data Integration. Appl. Sci. 2019, 9, 104. Long, K.; Yao, W.; Gu, J.; Wu, W.; Han, L.D. Predicting Freeway Travel Time Using Multiple- Source Heterogeneous Data Integration. Appl. Sci. 2019, 9, 104.

Journal reference: Appl. Sci. 2018, 9, 104
DOI: 10.3390/app9010104

Abstract

Freeway travelling time is affected by many factors including traffic volume, adverse weather, accident, traffic control and so on. We employ the multiple source data-mining method to analyze freeway travelling time. We collected toll data, weather data, traffic accident disposal logs and other historical data of freeway G5513 in Hunan province, China. Using Support Vector Machine (SVM), we proposed the travelling time model based on these databases. The new SVM model can simulate the nonlinear relationship between travelling time and those factors. In order to improve the precision of the SVM model, we applied Artificial Fish Swarm algorithm to optimize the SVM model parameters, which include the kernel parameter σ, non-sensitive loss function parameter ε, and penalty parameter C. We compared the new optimized SVM model with Back Propagation (BP) neural network and common SVM model, using the historical data collected from freeway G5513. The results show that the accuracy of the optimized SVM model is 17.27% and 16.44% higher than those of the BP neural network model and the common SVM model respectively.

Subject Areas

support vector machine; travelling time; intelligent transportation system; artificial fish swarm algorithm; big data

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