Disclosed are a road traffic jam early warning method and system, comprising: performing feature classification according to obtained multi-source traffic data, constructing corresponding feature membership functions, and obtaining a first fuzzy weight; constructing an artificial membership function for the multi-source data by using an expert evaluation method, and calculating a second fuzzy weight; according to a fusion fuzzy weight obtained by fusing the first fuzzy weight and the second fuzzy weight, performing fuzzy weighted averaging on the feature membership functions, performing defuzzification on obtained weighted average membership functions of different feature quantities, and obtaining multi-source fusion traffic data; constructing a road traffic jam model by using a kernel extreme learning machine group algorithm, and calculating an optimal road traffic jam index; and obtaining current multi-source traffic data, predicting a current jam index, and comparing the current jam index with the optimal road traffic jam index to perform early warning on whether a current road is congested. A man-machine hybrid enhanced smart multi-source data fusion system for exerting the group intelligence of road participants is constructed.
ROAD TRAFFIC JAM EARLY WARNING METHOD AND SYSTEM