Predicting Freeway Travel Time Using Multiple- Source Heterogeneous Data Integration

Applied Sciences

Wei Wu¹  Wukai Yao¹  LeeD. Han²  Kejun Long²  Jian Gu³ 

[1] Technology, Changsha 410004, China;;Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science &;School of Traffic and Transportation Engineering, Changsha University of Science &

关键词: Support Vector Machine;    semantic technology;    travel time;    intelligent transportation system;    artificial fish swarm algorithm;    big data;   

DOI  :  10.3390/app9010104

来源: DOAJ

【 摘 要 】

Freeway travel time is influenced by many factors including traffic volume, adverse weather, accidents, traffic control, and so on. We employ the multiple source data-mining method to analyze freeway travel time. We collected toll data, weather data, traffic accident disposal logs, and other historical data from Freeway G5513 in Hunan Province, China. Using the Support Vector Machine (SVM), we proposed the travel time predicting model founded on these databases. The new SVM model can simulate the nonlinear relationship between travel time and those factors. In order to improve the precision of the SVM model, we applied the 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 the Back Propagation (BP) neural network and a 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.

【 授权许可】

Unknown