Prediction method and visualization method of traffic jam

Abstract

The invention discloses a prediction method and visualization method of traffic jam. The methods comprise the following steps: GPS data sent back by a taxicab is utilized for being associated to roads in an electronic map through the map matching method; the road section speed calculated in terms of the matched data is utilized for judging the traffic state of road sections; the evolution rule of the traffic jam, including formation and dissipation of the traffic jam, is extracted with the utilization of the historical data; the traffic jam prediction is performed with the utilization of the sliding time window scheme after a real-time traffic information database is associated; the congestion intensity and the influence range of congested road sections can be presented by a thermodynamic chart after the congestion intensity of the congested road sections is calculated based on the prediction result. The prediction method provided by the invention can realize highly accurate traffic jam prediction, and the visualization method of the thermodynamic chart enables the traffic jam to be visualized and understandable, so that the location and the influence range of the traffic jam can be discerned conveniently.

Description

technical field [0001] The invention relates to the traffic field, and more specifically, to a traffic jam prediction method and a visualization method. Background technique [0002] Traffic congestion is a key factor affecting people's daily travel quality and travel costs. Traffic congestion prediction is the core content of traffic congestion management research. Accurate and real-time traffic congestion prediction can alleviate traffic congestion and improve road network capacity and road speed. [0003] Scholars at home and abroad have done a lot of research on traffic congestion prediction algorithms. Some of these scholars select traffic volume, occupancy rate, traffic speed and traffic density as parameters, and use a learning algorithm based on Bayesian rule to calculate the possibility of traffic congestion. Some scholars also consider parameters such as flow rate, speed, occupancy rate, and average lane flow rate, and use maximum entropy model training to obtain...

AI Technical Summary

Problems solved by technology

However, the timing diagram can only reflect the time-varying law of congestion intensity in a certain area and a certain road, and cannot reflect the influence of this area (the road) on the adjacent area (adjacent roads).

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