Urban Travel Mode Split Optimization based on Travel Costs

With the rapid development of economy and accelerated pace of urbanization in China, the trip share of private cars has been increasing continually. This study investigates the optimal mode-split for a developing megacity and optimizes the weighted generalized travel cost per capita for one trip on an urban transport network. The main urban area of Beijing is taken as the study area of this research and the revealed preference survey method is utilized to get the trip survey data. Based on a nest-logit model, an optimization model is developed for the minimal weighted generalized travel cost per capita for one trip. The phase estimation method with the Newton-Raphson algorithm and the genetic algorithm are used to solve the optimization model. In addition, different cases are studied to assess the effect of different transport policies for the improvement of urban transport in Beijing. These policies are concerned with parking fee, taxi average fare, bus priority and rail transfer time. It is found that the bus priority policy for reducing the in-vehicle time of a bus trip has the greatest weighted generalized travel cost per capita for one trip in Beijing. Moreover, successful rail transfer time reduction is more beneficial to travellers in comparison to the effect of increasing parking fees of private cars or increasing the average fare of taxi utilization. In the future research, more comprehensive policy packages are worthy of studies in a further.