Dynamic fusion type travel time predicting method with multi-source and isomorphic data adopted

Abstract

The invention discloses a real-time fusion type travel time predicting method with multi-source and isomorphic data adopted. The real-time fusion type travel time predicting method with the multi-source isomorphic data adopted comprises the steps that on the basis that multi-source isomorphic continuous travel time data sequences at equal time intervals are obtained, a multi-source travel time D-S evidence inference model recognition framework is constructed; the real-time prediction mean value and dynamic variance of each kind of single travel time data source are calculated respectively by means of a time sequence model which enables the prediction mean value and the dynamic variance to be learnt; with a dynamic variance prediction result serving as input data, an elementary probability distribution function and a basic trust distribution function of a D-S evidence inference model are obtained through calculation, and the dynamic fusion weight of the multi-source travel time data is calculated according to the evidence synthesis rule; a travel time fusion result is calculated through the prediction mean values of the single data sources and the weight sum of the dynamic fusion weights. According to the real-time fusion type travel time predicting method with the multi-source isomorphic data adopted, the road travel time description or prediction uncertainty caused when a single data source is used for describing or predicting the road travel time is lowered, the travel time prediction accuracy and the travel time prediction reliability are improved, and the operability is high.

Description

technical field [0001] The invention relates to the field of traffic prediction, in particular to a method for predicting road travel time by using multi-source isomorphic traffic information. Background technique [0002] Travel time is an important reference index for traffic management and operation departments to implement traffic control measures, and it is also an intuitive basis for traffic travelers to judge the actual traffic status of the road. Accurate and reliable real-time prediction technology of travel time has become an important research content of intelligent transportation systems such as urban road active traffic control system, traffic guidance system and traffic information service system. [0003] At present, a lot of research has been carried out on the short-term prediction technology of road travel time at home and abroad, and prediction methods based on statistical models and artificial intelligence technologies have been continuously proposed, and...

AI Technical Summary

Problems solved by technology

However, my country's urban road traffic conditions are complex, and there are many uncertainties in the travel time prediction process due to factors such as intersection delays and mixed traffic.

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