Analysis On The Parking Behavior Of Central Business Districts

With the rising ownership of cars, more and more people would like to choose self-driving and the parking problem has become worse and worse at the same time. Parking garages are woefully inadequate in Central Business District, especially during peak periods in parking on the weekend. The parking conflict is even obvious which resulting in invalid traffic and traffic congestion. So parking behaviors are need to be studied for establishing parking-demand management strategy, only have a good understanding of parking behaviors, can we develop the practical parking management strategy. Parking policy is an important component of contemporary travel demand management policies. The effectiveness of many parking policy measures depends on influencing parking type choice, so that understanding the factors affecting these choices is of considerable practical importance. Yet, academic interest in this issue has been, at best, intermittent.This thesis selected a famous business district in Chengdu——Chun xi Road Commercial Area as a research object., a survey questionnaire was designed according to factors which influence the behavior of drivers. The face to face surveys were conducted in the parking lot of Chun xi Road Commercial Area at the weekend, and the online survey were also conducted in the meantime. Then data were collected through questionnaire survey and sorted out, and corresponding analysis methods were then used on survey results. Built parking choice model based on random utility theory, the MNL model is to select from four different kind of parking lots which are underground parking, curb parking, off-street parking and multistory parking. Combined with the survey data, analysis software--Biogeme was utilized to calibrate and check the parking choice model.The analyzing result of model shows that trip purpose, walking distance from parking place to destination, parking time and parking experience are the significant factors and all of these have obvious effect on parking behaviors. Then corresponding elastic analysis of these significant factors were conducted, and quantitative analysis was applied to describe these significant factors’ influence on the choice of parking.