Abstract
Multistep prediction of traffic state is a key technology for advanced transportation information system. The research results based on the principle of multistep prediction can provide more information about the traffic operating quality in advance. Considering that prediction error increases with the increasing numbers of multistep predictions, this research proposes the concept of dynamic predictability that is related to the characteristic of historical traffic flow data used. The traffic flow is characterized by randomness, regularity, and volatility according to the traffic flow theory. Therefore, three key indexes are firstly calculated to measure the characteristics of reliability series. Then a two-phase model is established based on wavelet neural network optimized by particle swarm optimization. The upper phase is a model to estimate the number of predictable steps, and the lower phase is the multistep prediction model of reliability. Compared with that of backpropagation neural network and support vector machine, results show that the convergence time of the wavelet neural network optimized by particle swarm optimization is the lowest, which only costs 256 and 291 seconds in both two-phase models under the same conditions. The average relative error of multistep prediction reached the lowest value, 8.91% and 12.01%, respectively, for weekday and weekend data used. Moreover, the prediction performance based on weekday is better than that of weekend. The research results lay a decision-making basis for managers in determining the key parts of road network to develop future improvement measures.
Highlights
In recent decades, the increasing demand for road transportation has negatively affected the stability and reliability of traffic operation which caused a series of drawbacks, such as extensive waste of travel time, decreasing of environmental quality, and aggravated vehicle wear and tear
A two-phase model of multistep prediction is designed based on wavelet neural network optimized by particle swarm algorithm
The lower-phase model based on wavelet neural network optimized by particle swarm algorithm begins to converge at
Summary
The increasing demand for road transportation has negatively affected the stability and reliability of traffic operation which caused a series of drawbacks, such as extensive waste of travel time, decreasing of environmental quality, and aggravated vehicle wear and tear. Zheng and Su [5] develop a novel algorithm based on compressed sensing theory to recover traffic data with Gaussian measurement noise, missing partial data, and corrupted noise using information recovered from noisy traffic data and traffic state estimation. Bezuglov and Comert [11] have studied the possible applications and accuracy levels of three gray system theory models for short-term traffic speed and travel time predictions. Chen and Rakha [13] develop an agent-based modeling approach to predict multistep ahead experienced travel time using real-time and historical spatiotemporal traffic data. The results show that the agent-based modeling approach produced the least prediction errors compared with other state-of-the-practice and state-of-the-art methods (such as instantaneous travel time, historical average, and k-nearest neighbor).
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