Numerous functions, especially the Gompertz function, have been predetermined to analyze the growth in vehicle ownership. This study utilizes the data-driven symbolic regression to automatically find a generalized function, named as new equation by symbolic regression (NE-SR), for passenger car ownership in six representative countries including Japan, England, USA, Finland, Poland and Australia. Then the new proposed function is applied for forecasting the passenger car ownership in China up to the year 2060. The experimental results indicate that the NE-SR, as an extension of the Gompertz function, fits better than the classical Gompertz function for car ownership growth. In NE-SR function, three scenarios can be realized by the variation of parameter signs, which are represented by the patterns of Japan, USA and Australia, respectively. The predicted results based on the NE-SR also show that the Chinese car ownership still has a potential to increase after 2060 in the pattern of Japan and Australia, but grows until around 2057 in the pattern of USA. The results can be used to further predict the energy demand and carbon emissions of passenger cars, which can provide a basis for the policymaker to propose transportation and environmental strategies.

Transportation infrastructure investment (TII) has significant influence on regional economic development. This paper aims to measure the potential economic impact of TII and to help policymakers to evaluate different alternatives and strategies at the regional level through a system dynamics (SD) approach. First, an SD model is established to simulate the impact of TII on the economy and employment. The relationship between transportation investments and economic development is examined and the relevant employment and economic indicators are identified. Second, the variables, flow diagrams, and structural equations of the SD model are defined. Third, the program Vensim is employed to establish the SD model with economy, transportation, and employment subsystems. It is then calibrated based on the historical statistics data in Liaoning Province, China. Finally, scenario analysis of different transportation investment plans is presented. The results will help investors, policymakers, and government agencies to estimate the potential outcomes of proposed transportation investment plans and to further develop optimal policies for transportation investment.

Bus rapid transit (BRT) is characterized by higher speed, higher comfort level, and larger capacity than conventional bus service. Although many cities worldwide have adopted BRT in recent years, there is an absence of scientific and quantitative approach for BRT network planning. The problem of BRT planning in an existing transportation network is very complex with constraints of road geometrics, regulations, right of way, travel demand, vehicle operations, and so on. This paper focuses on developing an optimization model for BRT network planning, where an integer programing model is established to identify station locations and route layout with the objective of maximizing the number of trips served by the network, subjected to constraints including distance between stations, cost of construction, road geometrics, etc. The detour factor of the BRT route, which is an important indicator but widely ignored in previous studies, is also taken as a constraint. A heuristic method is applied to generate optimal solutions to the integer programming model, followed by a case study using the transportation network and travel demand data in Luoyang, China. The numerical results show that the method is valid and can therefore be applied to improve BRT network planning and the sustainable transportation system development.

This paper discusses the results of stated preferences and choice models in express industry research. We established a binary logit model with the three most influential factors of express service: (1) transit time; (2) delivery price; and (3) distance between the demand point and express service point. Our analysis, based on the survey data, showed that different express choice behaviors in China are influenced not only by the three express service factors mentioned above, but also by a wide variety of customer personal factors, including: gender, age, income, delivery frequency each month and goods type. Finally, the model is suggested to estimate express market shares in China.