Short-term traffic flow forecasting is one of the key issues in the field of dynamic traffic control and management. Because of the uncertainty and nonlinearity, short-term traffic flow forecasting remains a challenging task. In order to improve the accuracy of short-term traffic flow forecasting, a short-term traffic flow forecasting method based on LSSVM model optimized by GA-PSO hybrid algorithm is put forward. Firstly, the LSSVM model is constructed with combined kernel function. Then the GA-PSO hybrid optimization algorithm is designed to optimize the kernel function parameters efficiently and effectively. Finally, case validation is carried out using inductive loop data collected from the north-south viaduct in Shanghai. The experimental results demonstrate that the proposed GA-PSO-LSSVM model is superior to comparative method.

In order to improve the effect of estimating travel time and provide more precise and reliable traffic information to traffic management department and travelers, we proposed an arterial travel time estimation method using Sydney Coordinated Adaptive Traffic System traffic data based on K-nearest neighbor-least squares support vector regression model. First, the virtual time series is constructed by analyzing the characteristics of the inconsistent time intervals of Sydney Coordinated Adaptive Traffic System traffic data. Second, the K-nearest neighbor method was used to search the K similarity patterns matching the current traffic pattern and obtain K travel time data. Then, the least squares support vector regression model was used to perform travel time estimation. Finally, case validation is carried out using the measured data of Sydney Coordinated Adaptive Traffic System traffic control system. The estimation results demonstrate that the travel time estimation accuracy of proposed method outperforms the other two methods.

The objective of this paper is to study the toll standard of regional expressway network so as to optimizing traffic assignment and consequently reducing the holistic cost of the network. Based on Wardrop's principles and expense analyses, a function called generalized expense model is brought forward to calculate the optimal toll rate. Genetic algorithm (GA) is applied to solving the model according to its characteristics. The paper takes a network planning as an example for analysis. The traffic distribution without toll controlling is depicted by VISUM traffic simulation. Contrast analysis, which is made between the model solution and the simulation results, demonstrates that the model is valid and practicable.

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