OD calibration method using mobile phone signaling big data and dynamic traffic assignment

Abstract

The invention relates to a resident trip OD matrix calibration method using mobile phone signaling big data and dynamic traffic assignment. The method comprises main steps: an urban road simulation network model is built, microwaves and bayonet data are used for calibrating simulation parameters of each road section in the built urban road network, a microwave detector is used for acquiring the real traffic flow in the actual road network, the mobile phone signaling data are used for acquiring the initial OD matrix traffic flow, the initial traffic flow is dynamically adjusted through a dynamic traffic assignment method to enable the initial traffic flow to be close to the real traffic flow in the road network, errors between the simulation and estimation traffic flow and the real traffic flow are calculated, the calibration is ended if a threshold is met, or otherwise, simulation-based dynamic assignment is continued until the calibration is ended. The mobile phone signaling big data are adopted to calibrate the resident trip dynamic OD matrix, and the method has the advantages of stable and objective data information source, large sample amount, wide coverage range, high data precision, strong dynamics and the like.

Description

Technical field [0001] The invention relates to the field of OD calibration methods for residents' travel, in particular to calibrate the residents' travel OD matrix based on mobile phone signaling big data and adopting a dynamic traffic allocation method. Background technique [0002] The continuous economic and social development has not only improved the outlook of urban life, but also put increasing pressure on urban traffic. At present, the process of domestic urbanization and urban traffic motorization is becoming faster and faster. Under the combined effect of urbanization and motorization, traffic congestion has become one of the main problems that restrict urban development and affect the quality of life of residents. In order to effectively solve the problem of traffic congestion under the existing road network conditions, the establishment of an intelligent traffic information system has become the key to solving the problem of urban traffic congestion, and the establi...

AI Technical Summary

Problems solved by technology

It overcomes the shortcomings of low data sample size and poor data accuracy obtained by traditional resident survey methods, and makes up for the shortcomings of static traffic allocation methods that cannot consider the time-varying characteristics of traffic demand

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