Influence prediction and management and control method for urban expressway emergencies

Abstract

The invention relates to an influence prediction and management and control method for urban expressway emergencies, which gathers four types of data of a traffic police own class, an internal sharing class, an external perception class and an internet acquisition class. Models based on event perception and mainline traffic efficiency prediction, congestion prediction based on an LSTM time sequence, signal real-time adaptive control, self-learning optimization and the like are used, and through repeated training and learning, optimization of traffic is finally realized, and the influence of emergencies is reduced.

Description

technical field [0001] The present invention relates to the field of intelligent transportation, and more specifically, to a method for predicting and controlling the impact of urban expressway emergencies based on multi-dimensional perception data. Background technique [0002] With the socio-economic development and the acceleration of urbanization, residential and commercial complexes around urban expressways are gradually increasing, and the road traffic volume is increasing year by year, and the carrying capacity is also increasing. Taking Changzhou as an example, 60% of the vehicles in the city's morning and evening peaks pass on elevated roads. In 2019, the total flow of elevated roads was 228 million vehicles, with a monthly average of 18.632 million vehicles and a daily average of 620,100 vehicles. Urban expressways dominated by elevated roads It has become the first choice for citizens traveling across regions. In management practice, due to the lack of scientific...

AI Technical Summary

Problems solved by technology

In management practice, due to the lack of scientific decision-making basis, expressway congestion caused by traffic accidents, rainy and snowy weather, etc. will cause short-term and long-distance vehicles to queue up on expressways, but the surface roads or decommissioning roads are not fully utilized. Road resources are not fully utilized, which in turn affects the operation of the entire urban road network

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