Logit model for travel mode choice with traffic capacity constraints
In order to improve the accuracy of traffic demand forecasting for passenger transport corridors,addressing the shortcoming that the traffic capacity constraints are not adequately considered in the traditional passengers'mode choice models,the mechanism of passengers'mode choice and constraints from traffic capacity were deeply analyzed,and a penalty factor was introduced through the utility function optimization of the classical MNL model,to characterize the constraints of traffic capacity on passengers'mode choice,a constrained MNL model was proposed,and the corresponding algorithm was designed to forecast the share of each transportation mode.The XiBao(Xi'an to Baoji)passenger corridor was taken as an example to verify the superior performance of the constrained MNL model by comparative analyzing the forecasting results of the two models.The results show that in the process of passengers'mode choice,the constraints of traffic capacity are universal,which is an important factor that cannot be ignored for effective traffic demand forecasting,the constrained MNL model takes into account the impact of traffic capacity on passengers'mode choice,which is more in line with the decision-making process of passengers'mode choice,and provides a reliable guarantee for improving the accuracy of traffic demand forecasting from the mechanism.The penalty factor reflects the impact of traffic capacity constraints on passengers'mode choice,and represents the decline in transportation service quality and the loss of passengers'utility.Through the reasonable assignment of the penalty factor,it can redistribute the passengers'choice probability,effectively simulate the shift of passengers'mode choice and control the probability of passengers'mode choice.Compare with the traditional MNL model,the constrained MNL model shows better performance,which can always control the forecasting results within the upper limit of the share determined by the traffic capacity,and the results are realistic,scientific and effective,which can provide reliable data support for the optimization of the network layout and the design of the transportation organization.2 tabs,6 figs,30 refs.
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