Abstract
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.
Highlights
Travel time is one of the main indexes that reflect the traffic operation level of a freeway, and it is the basis for Advanced Traveler Information System (ATIS), Traffic Guidance System (TGS), and Traffic Control System (TCS)
The first step in the optimization process of the artificial fish swarm algorithm is to feed in the training value and the training target through the Support Vector Machine (SVM) model to calculate the fitness of the individual
Back Propagation (BP) neural network, SVM, and optimized SVM were used for the prediction
Summary
Travel time is one of the main indexes that reflect the traffic operation level of a freeway, and it is the basis for Advanced Traveler Information System (ATIS), Traffic Guidance System (TGS), and Traffic Control System (TCS). Many flow detectors and video detection equipment are on the freeway, captured data are incompatible, redundant, and include error or loss. The first aspect is the prediction of travel time by map navigation providers using their personalized GPS data. Bai-du, Gao-de, and other Chinese map providers collect real-time GPS data from users while providing map navigation services. A correlation algorithm is proposed to obtain the travel time prediction result at road sections, which depends on the market share of the map navigation service. As people use the navigation service with greater frequency, the GPS data will be more complete and the prediction accuracy will be higher. Information Service system uses inaccurate data, it cannot recommend optimal travel routes nor warn of potential traffic congestion, and users cannot determine optimal departure times nor estimate their arrival times. Theoretical research on freeway travel time prediction can be divided into two categories based on single source data and multi-source data
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