Travel impedance model calibration and checking method based on multi-source GPS data

Abstract

The invention provides a travel impedance model calibration and checking method based on multi-source GPS data. The travel impedance model calibration and checking method comprises the following steps: acquiring multi-source GPS data of a vehicle on a road network; calibrating a flow delay function of a road section of the road network according to the multi-source GPS data; obtaining geographic space data of the road network, and determining travel sample data of the vehicle according to the multi-source GPS data and the geographic space data; according to the flow delay function and the travel itineraries, predicting first predicted travel time spent by the vehicle for adopting each travel itinerary in the travel sample data; checking goodness of fit between the first predicted travel time and the actual travel time; when the goodness of fit meets a preset condition, the flow delay function meeting a precision requirement; and when the goodness of fit does not meet the preset condition, repeating the steps until the goodness of fit meets the preset condition. According to the method, the travel impedance model is circularly calibrated and checked through the multi-source GPS data, so that the comprehensiveness, reliability and accuracy of the finally obtained travel impedance model are ensured.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation, in particular to a method for calibrating and checking travel impedance models based on multi-source GPS data. Background technique [0002] As a sub-model of the traffic demand forecasting model, the travel impedance model is mainly used to describe the comprehensive cost that road users spend on the road during travel, and its accuracy directly affects the results of traffic demand forecasting. However, the current travel impedance model calibration generally collects corresponding traffic data from a single source, resulting in a certain one-sidedness in the final travel impedance model; Certain random errors inevitably exist in demand forecasting, which leads to low accuracy of traffic demand forecasting results. Contents of the invention [0003] The problem solved by the invention is: how to accurately reflect the corresponding traffic data through the model, and reduc...

AI Technical Summary

Problems solved by technology

However, the current travel impedance model calibration generally collects corresponding traffic data from a single source, resulting in a certain one-sidedness in the final travel impedance model; There will inevitably be some random errors in demand forecasting, resulting in low accuracy of traffic demand forecasting results

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