Travel/activity behavior selection model parameter calibration method based on least square method

Abstract

The invention discloses a travel / activity behavior selection model parameter calibration method based on a least square method, which is used for solving the problem of model parameter calibration inurban traffic planning. The method comprises the following steps: (1) defining a data type and carrying out preprocessing; (2) taking a least square method as a core, designing an upper-layer optimization model, and solving parameter optimal estimation of a travel / activity behavior selection model; (3) taking traffic distribution as a core, designing a lower-layer optimization model, and solving flow distribution in the traffic network; (4) solving the double-layer optimization model by adopting a simulated annealing method to obtain estimated values of parameters; and (5) smoothing the estimated value of the parameter by using a Kalman filter, and outputting a final calibration value of the parameter. By applying the method provided by the invention, parameter calibration can be effectively carried out on a travel / activity behavior selection model, the method has wide applicability, and the input data volume and the accuracy of the model can be adjusted as required.

Description

technical field [0001] The invention belongs to the field of travel behavior modeling, and relates to a method for calibrating parameters of a travel / activity behavior selection model based on a least square method. Background technique [0002] Modeling and analysis of residents' travel behavior choices can help to accurately estimate the traffic demand in different areas of the city at different times, and provide a theoretical basis for the management department to plan and manage the traffic system. Commonly used behavior choice models can be divided into two categories, one is the behavior choice model based on the travel chain, the representative method is the "four-stage" analysis method, based on the interaction between land use and transportation systems; the other is based on The behavioral choice model of activities, the model believes that travel is activity-oriented, and the value of activity behaviors affects the decision-making process of people's travel behav...

AI Technical Summary

Problems solved by technology

However, residents’ travel / activity behavior choices are usually obtained from traffic survey data, which is limited by manpower and material resources. The amount of data samples obtained from traffic surveys is small, and the maximum likelihood estimation method is not supported.

At the same time, due to the complexity of the traffic network, even with a sufficient number of samples, it is impossible to calculate the convergence value of the maximum likelihood estimation in a short period of time with an ordinary computer

In summary, maximum likelihood estimation is not suitable for parameter estimation of residents' travel / activity behavior choice models

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