Urban traffic model construction method based on big data

Abstract

The invention discloses an urban traffic model construction method based on big data. The method comprises a travel generation stage, a travel distribution stage, a mode division stage and a traffic distribution stage which are executed in sequence, in the mode division stage, a non-aggregated model is adopted; after the traffic distribution stage is executed once, the obtained motor vehicle flowof the road section is substituted into a travel distribution stage as background flow to restart calculation; and the relative error of the motor vehicle flows of the two road sections is calculated,and if the requirement of the relative error is not met, the new motor vehicle flow of the road section is substituted into the travel distribution stage again to restart calculation until the relative error meets the requirement. By adopting the non-aggregated model in the mode division stage, the model precision is improved, and meanwhile, excessive calculated amount is not increased. By meansof loop iteration of the motor vehicle flow of the road section, feedback is provided for the model building process, it is guaranteed that errors are within the precision requirement, and therefore the precision is higher, and the traffic requirement can be predicted more accurately.

Description

technical field [0001] The invention relates to the field of traffic big data, in particular to a method for constructing an urban traffic model based on big data. Background technique [0002] The research on the theory and method of urban traffic demand forecasting in our country started relatively late, and the research and compilation of traffic management planning in some cities began in the 1980s. Research work on the theory and method of demand forecasting. [0003] At present, with the compilation of urban comprehensive transportation planning, most cities have established traffic demand forecasting models, but the transportation demand forecasting models of most cities are only used to provide quantitative support for the planning compiled this time, and have not been able to perform well. maintenance, and cannot provide quantitative analysis for other traffic decisions. At the same time, because the new model only uses the traffic travel data at that time, it can...

AI Technical Summary

Problems solved by technology

The non-aggregate model of demand forecast takes the individual who actually produces traffic activities as the unit, and the data obtained from the survey are not processed according to the statistics of the traffic area, but are directly used to establish the model. Therefore, the non-aggregate model includes the selection behavior of people. Analysis, has better time transfer and space transfer, and higher precision, but due to its application of more variables, the calculation is more complicated

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