Multi-factor Comprehensive Prediction of Delay Time through Congested Road Sections

Article history Received: 3 September 2020 Revised: 10 September 2020 Accepted: 9 October 2020 Published Online: 16 October 2020 The navigation software uses the positioning system to determine the traffic conditions of the road sections in advance, so as to predict the travel time of the road sections. However, in the case of traffic congestion, the accuracy of its prediction time is low. After empirical analysis, this paper establishes a multi-factor synthesis by studying 7 factors: traffic flow, number of stops, traffic light duration, road network density, average speed, road area, and number of intersections the prediction function achieves the purpose of accurately predicting the transit time of congested road sections. The gray correlation coefficients of the seven factors obtained from the gray correlation analysis are: 0.9827, 0.9679, 0.6747, 0.8030, 0.9445, 0.8759, 0.4328. The correlation coefficients of traffic volume, number of stops, average speed, and road congestion delay time were all about 95%, which were the main influencing factors of the study. The prediction needs to be based on functions. This paper fits the main influencing factors to the delay time of congested roads. It is found that the delay time varies parabolically with the traffic flow and the number of stops, and linearly with the average speed. Because the three impact factors have different weights on the delay time of congested roads, demand takes the weight of each factor. Therefore, the gray correlation coefficients occupied by the main influencing factors are normalized to obtain the weights of three of 0.340, 0.334, and 0.326. The weighted fitting function is subjected to nonlinear summation processing to obtain a multi-factor comprehensive prediction function. By comparing the original data with the fitting data and calculating the accuracy of the fitting function, it is found that the accuracy of each fitting function is close to 0, the residual error, the relative error is small, and the accuracy is high.