Time Prediction Through A Congested Road Section

• Published in: Modern Electronic Technology Vol. 4; no. 1; p. 25
• Main Authors: Cheng, Ziyi, Wei, Ziqi, Huang, Xinhao, Li, Ying
• Format: Journal Article
• Language: English
• Published: 27.05.2020
• ISSN: 2591-7110; 2591-7129
• DOI: 10.26549/met.v4i1.3249

First, the cellular automaton was used to simulate a "T" junction, and the correlation analysis was performed by combining the traffic pattern and the corresponding data to obtain the reason for the inaccurate prediction time of the navigation software. The collected data is preprocessed to obtain the driving time of multiple road vehicles in a week, and this is used as the influencing factor. Reuse the collected information: the length of the intersection, the average speed of real-time vehicles at the intersection, and the length of the intersection. The first two processes of the three pre-processing processes are considered together to obtain a time-dependent factor. The correlation factors and the duration of the intersections are used to predict the results of neural network training. Based on the analysis and prediction of the data, the causes of urban traffic congestion are analyzed, and measures to reduce urban congestion are proposed.