A Study of Pricing on Parking Space Reservation System

Under considerations of economy growth and the car-oriented transport policy, the number of vehicles increases rapidly in recent years. This leads to the problem of lacking parking space, which becomes more and more serious. In large cities, this problem is even worse, where it causes inconvenience to the public. Since the price is important for the transport model choice, parking operators can control the demand of vehicle parking efficiently by increasing parking fee. On the other hand, because most of pricing policies related to parking are inflexible, the revenue of parking lot is capped. According to these reasons, the pricing schemes of parking should be improved. In order to solve the above problems, a dynamical scheme was proposed for the parking, that is based on the concept of revenue management. During rush hours, the price is higher. The parking lot operators can make more profits, although the number of customers is suppressed. During off-peak hour, the price is lower in order to attract customers. The parking lot operators may encounter lots of problems while implementing the dynamical pricing policy, such as the troubles in notifying customer prices. Therefore, we construct a dynamical pricing model which is based on reservation system. This thesis assumes that number of customers can be controlled by adjusting parking price. The research is mainly made of two parts, which are the dynamical pricing model and the forecast model. A data preprocessing module was used to remove the outliers and perform missing data interpolation. The forecast model based on ARIMA and Fourier transform is capable of predicting the number of customers. In order to predict the number closer to the actual situation, Kalman filter and k-NN algorithm are employed when updating prediction. The preferred price can then be set by the dynamical pricing model according to the prediction.