Road traffic jam early warning method and system

Abstract

The invention discloses a road traffic jam early warning method and system. The method comprises steps of carrying out feature classification according to obtained multi-source traffic data, constructing a corresponding feature membership function, and obtaining a first fuzzy weight; constructing an artificial membership function for the multi-source data by adopting an expert evaluation method, and calculating a second fuzzy weight; according to a fusion fuzzy weight obtained after the first fuzzy weight and the second fuzzy weight are fused, performing fuzzy weighted average of the feature membership function, performing ambiguity resolution of the obtained weighted average membership function of different feature quantities, and obtaining multi-source fusion traffic data; constructing aroad traffic jam model by adopting a kernel extreme learning machine group algorithm, and calculating an optimal road traffic jam index; and obtaining current multi-source traffic data, predicting acurrent congestion index, and carrying out early warning on whether a current road is congested or not by comparing the current congestion index with the optimal road traffic congestion index; and constructing a man-machine hybrid enhanced intelligent multi-source data fusion system for exerting the group intelligence of road participants.

Description

technical field [0001] The present disclosure relates to the technical field of intelligent transportation, in particular to a road traffic congestion early warning method and system. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] In recent years, with the increase of vehicles on the road, the driving environment on the road is more complicated, the probability of traffic congestion is greatly increased, the driving safety of vehicles on the road is greatly threatened, and the speed of driving and the time to reach the destination are also decreasing. will increase; in the road operating environment, the driving state of the vehicle is affected by many factors, such as the state of the vehicle, the weather environment, the behavior of pedestrians or drivers, etc.; and the inventor found that there are some defects in the traditional road...

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Problems solved by technology

[0003] In recent years, with the increase of vehicles on the road, the driving environment on the road is more complicated, the probability of traffic congestion is greatly increased, the driving safety of vehicles on the road is greatly threatened, and the speed of driving and the time to reach the destination are also decreasing. will increase; in the road operating environment, the driving state of the vehicle is affected by many factors, such as the state of the vehicle, the weather environment, the behavior of pedestrians or drivers, etc.; and the inventor found that there are some defects in the traditional road traffic early warning platform, The traditional road congestion early warning platform mostly uses a single sensor device as the source of data collection, only considering the influence of road conditions or the vehicle itself, and the data collected by the data collection end is not perfect; the evaluation index of road congestion degree is too simple, and does not consider human The important role of perception and group experience in urban road congestion ignores the management experience of traffic managers to improve traffic control rules; the complexity of the traffic system and the human presence in the traffic system are not considered in the construction of the urban road traffic early warning platform. The important role in the congestion warning for drivers has not formed a complete congestion warning network

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