Modeling Level of Urban Taxi Services Using Neural Network

Abstract

This paper is concerned with the modeling of the complex demand-supply relationship in urban taxi services. A neural network model is developed, based on a taxi service situation observed in the urban area of Hong Kong. The input consists of several exogenous variables including number of licensed taxis, incremental charge of taxi fare, average occupied taxi journey time, average disposable income, and population and customer price index; the output consists of a set of endogenous variables including daily taxi passenger demand, passenger waiting time, vacant taxi headway, average percentage of occupied taxis, taxi utilization, and average taxi waiting time. Comparisons of the estimation accuracy are made between the neural network model and the simultaneous equations model. The results show that the neutral network-based macro taxi model can obtain much more accurate information of the taxi services than the simultaneous equations model does. Although the data set used for training the neural network is small, the results obtained thus far are very encouraging. The neural network model can be used as a policy tool by regulator to assist with the decisions concerning the restriction over the number of taxi licenses and the fixing of the taxi fare structure as well as a range of service quality control.