基于多指标模糊综合评价的交通拥堵预测与评估

In view of the different impacts of different traffic periods on traffic congestion and single factor fail to accurately characterize the traffic congestion state, this paper proposed a multi-index fuzzy comprehensive evaluation method for traffic congestion state evaluation. The method used the particle swarm optimization algorithm to optimize the support vector regression to predict the average road speed and traffic flow, obtained the predicted values of the average speed v, traffic flow density D, and road saturation S, and input the three factor indexes to Multi-index fuzzy comprehensive evaluation model that establish the set of factors and evaluation (level) for traffic congestion. It determined the weight coefficients of three factors under the morning peak and the evening peak and other periods by the entropy method, then determined the degree of membership of each index in each period by the trapezoidal membership function. Finally, it dividedhe traffic congestion state into six levels. The results of predictive evaluation at the traffic data of the I405 highway in PeMS show that the traffic congestion state evaluated by the proposed method is basically consistent with the actual state and has a high prediction accuracy, the correct rate can reach 94.79%.