Method of Road Condition Information Based on Time Decay Adaptive Fitting

With the development of urban traffic and the massive increase in data collection, there is a problem in the transit time prediction process of the intelligent transportation system, that is, the real-time traffic information contained in the new sample data causes the original prediction model failed. Therefore, the article implements adaptive update of traffic collection data based on time decay factor, thus ensuring effective prediction of short-term transit time. First, data collection is carried out on urban road conditions, and the missing data of traffic flow is complemented by a combination of time and space. Subsequently, the road condition is divided according to the traffic state index, and the semi-supervised k-Nearest Neighbor Means (k-NNM) clustering algorithm is used to obtain the typical similar road segments, for the to-be-predicted samples and the historical collection samples. Finally, based on the time decay factor, the weighted value of the historical typical similar road segment sample data is weighted, so that the validity of the data value is measured by the time distance, and the transit time prediction model is satisfied to meet the requirements of the road condition update. Experiments show the effectiveness and applicability of the method, so as to provide real-time updated road condition data for the prediction of road transit time.