Road congestion prediction method and system for emergencies

Abstract

The invention provides a road jam prediction method for an emergent incident and a system. The method comprises steps of dividing a road into multiple sections; acquiring history traffic flow data of each section and the section length from a section start to a section termination, and acquiring communication relations between sections; in case of an emergent event, acquiring an incident section where the emergent incident happens, the ratio of quantity of occupied lanes to quantity of total lanes of the emergent incident, time when the emergent incident happens and the predicted jam duration; calculating a predicted theoretical jam length of the incident section according to a jam length calculation rule; and by combining the section conditions, the history traffic flow data of the incident section, and the current data, precisely predicting jam conditions. Thus, disadvantages in the prior art are overcome.

Description

technical field [0001] The invention relates to the technical field of traffic information processing, in particular to a road congestion prediction method and system for emergencies. Background technique [0002] With the gradual increase of traffic flow on the current road, the traffic pressure is getting heavier and heavier, and road congestion becomes inevitable. Traffic managers use the prediction of congested roads to guide traffic, and travelers use congestion prediction to choose roads for travel. The demand for predicting roads affected by congestion is increasing. The traditional prediction of future road conditions is based on artificial intuition, or a slightly more informatized way is to directly use historical congestion length data to obtain the average value of historical congestion length. This kind of prediction is simple but the accuracy rate is not high. Accidents, roadside parking, temporary occupation of lanes, etc.) are even more unpredictable and imp...

AI Technical Summary

Problems solved by technology

The traditional prediction of future road conditions is based on artificial intuition, or a slightly more informatized way is to directly use historical congestion length data to obtain the average value of historical congestion length. This kind of prediction is simple but the accuracy rate is not high. Accidents, roadside parking, temporary occupation of lanes, etc.) are unpredictable and difficult to start, and of course it is impossible to support road guidance

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