Modeling and analysis method for joint selection of travel mode and departure time

Abstract

The invention discloses a modeling and analysis method for joint selection of a travel mode and the departure time, and belongs to the field of urban travel analysis. The method includes the steps of firstly, constructing a model based on the principle of cross-nested logit; secondly, pre-processing a resident travel survey database and urban point of information data and selecting an influence factor variable; thirdly, estimating parameters based on maximum likelihood estimation and evaluating the model; and fourthly, performing emulation using a Monte Carlo method, simulating travel joint selection results under different cases such as congestion pricing, and evaluating the performance of travel demand management measures in reducing peak travel and promoting public transportation. The modeling and analysis method of the invention can be used for analyzing a joint selection behavior of the travel mode and departure time of a commuter and revealing the factors affecting travel behaviors of the commuter, can be applied to help analyze a travel joint selection behavior mechanism of the commuter and know the pattern of travel behaviors from a deeper level, and provides theoretical and technical support for making transportation policies.

Description

technical field [0001] The invention relates to a modeling and analysis method for joint selection of traffic travel mode and departure time, belonging to the field of urban traffic travel analysis. This method can reveal the behavior mechanism and influencing factors of commuters when making decisions about travel mode and departure time, and simulate scenarios under different traffic policies to provide new theoretical and technical support for the formulation of traffic policies. Background technique [0002] With the rapid development of urban economy, the urban transportation system is becoming more and more perfect, and the coordinated operation of various transportation modes has improved the efficiency of traffic operation, but the accompanying traffic problems have become increasingly prominent and need to be resolved urgently. Studying the travel characteristics of residents and digging out the mechanism of travel mode and departure time selection are the prerequis...

AI Technical Summary

Problems solved by technology

The invention starts from the CNL model, which not only overcomes the shortcomings of the existing multiple joint selection modeling methods, but also fully considers various factors that affect commuters' travel modes and departure time selection decisions, such as built environment information, and provides a basis for traffic travel analysis. It is of great significance to reveal the spatial and temporal distribution of residents' travel

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