Scale Forecast Of P&R Based On Passengers’ Trip Utility Function

Problems such as traffic congestion, parking difficulties and air pollution keep popping up in metropolis nationwide during morning and evening peak hours. The problems has been significantly relieved according to the experience of foreign countries by introducing park and ride system (Park and Ride, P&R). The National Development and Reform Commission has addressed that by the time the construction of P&R facilities speeding up, the parking demand and investment scale should be rationally considered. Research on P&R scale both at home and abroad is analyzed, based on which the best-suited prediction model of P&R scale is concluded.Starting from the planning of P&R parking lot construction, the P&R construction scale and influence factors are analyzed, exploring the advantage in P&R micro forecast model by using category analysis. To begin with, the most prominent factors, time and cost, which affect the passengers choice on P&R are pointed out. Based on that, the value utility function for passenger travel is established. After the comparative analysis of utility function of driving along for the entire trip and transferring at a P&R parking lot, it is concluded that the P&R appealing range for car users can be calculated and the boundary of the range can be expressed in hyperbolic. Then, the principle of kernel density function was employed to study P&R passengers’travel willingness (attraction intensity). The attraction intensity is simulated by using SPSS software and it appears to be exponential distribution. Finally, a complete P&R scale prediction model is established considering the distribution function of population, the rate of ownership of automobile, the coverage of rail transit network, and the accessibility of P&R sites.The Xi’erqi P&R site is used as a case study. After investigating a large amount of data, characteristics of passengers is summarized and the parameters of the model is calibrated, verifying the validity of the P&R scale prediction model. Conclusions are as follows:1.Passengers who are 2 to 5 km away from the P&R facilities accounted for about half of P&R users. The reasonable P&R berth for Xi’erqi P&R site is about 614.The relationship between station distance, fares, and prediction scale is further studied. The further the P&R site is away from the destination, the larger attractive scope it will contain, with a higher attractive intensity and a lower population density. All these phenomenon will eventually lead to a result that the P&R predicting scale will become smaller. Every time the distance between the P&R facilities and the destination increases by 1 km, the demanding parking space will be reduced by 27correspondingly. When we raise the P&R fares, the attracting scope and attracting strength will become smaller, and the total forecast will change; each yuan added to the parking fee will cause the decrease of about 31 a parking space. Additionally, the model can be widely used in other Beijing P&R sites.In this paper, a complete P&R scale prediction model is established, in which the calibration method of the parameters in the theoretical model are all given. It also explains reasonable form of the P&R sites attracting range and the type of strength distribution function. At the end of the paper, the model is successfully applied to the forecast of Beijing P&R scale planning, which will provide instructive guidance for related Department.