Commuter drivers' parking choice behavior in central business district

Abstract

Commuter travel is the most basic and the most important travel in most metropolises, and it is also one of the most important factors to cause traffic congestion during morning and evening peak hours. To improve parking management of commuter drivers is an essential method to ease urban traffic congestion in transportation demand management. Through RP and SP surveys to commuter drivers in a central business district of Shenzhen, socio-economics attributes and traffic characteristics of commuter drivers about parking behavior are analyzed. Random utility theory is applied to establish a multinomial Logit (ML) model of commuter drivers' parking choice, and parameters in the model are calibrated and tested. Route choice behavior and parking choice behavior of commuter drivers are integrated in parking assignment model to reflect coordination between static and dynamic traffic further, and a case is studied to examine errors of the prediction results.