Revenue management is widely practiced in the airline industry yet rarely applied to high-speed rail (HSR). In the history of the rail and HSR industry, passenger ticket assignment across all stations has often been employed to regulate the supply of seats and demand from rail passengers and thus increase profitability. This study proposes a systematic revenue management approach for HSR passenger ticket assignment with dynamic adjustments. A major advantage of the proposed approach is the integration of dynamic ticket assignment and rigorous short-term demand forecasting; this method can effectively avoid the situation in which passenger tickets are not sufficient at some stations while certain seats remain empty on the train. This novel methodology has three main components. First, it develops a short-term passenger flow forecasting method for dynamic travel demand. Second, it builds a passenger ticket assignment model to allocate passenger tickets during presale periods. Third, it incorporates a dynamic ticket adjustment mechanism to adjust previous passenger ticket assignments. For demonstration purposes, the proposed approach is applied to the HSR system in China. Results show that revenue increases by 13.48% when the existing ticket assignment method is improved by this enhanced revenue management method.

Energy consumption in transportation industry is increasing. Transportation has become one of the fastest energy consumption industries. Transportation energy consumption variation and the main influencing factors of decomposition contribute to reduce transportation energy consumption and realize the sustainable development of transportation industry. This paper puts forwards an improved decomposition model according to the factors of change direction on the basis of the existing index decomposition methods. Transportation energy consumption influencing factors are quantitatively decomposed according to the transportation energy consumption decomposition model. The contribution of transportation turnover, transportation structure and transportation energy consumption intensity changes to transportation energy consumption variation is quantitatively calculated. Results show that there exists great energy-conservation potential about transportation structure adjustment, and transportation energy intensity is the main factor of energy conservation. The research achievements enrich the relevant theory of transportation energy consumption, and help to make the transportation energy development planning and carry out related policies.

Accurate prediction of airport passenger throughput can provide a scientific basis for airport construction, aircraft procurement, and route planning. Based on the characteristics of airport passenger throughput, the support vector regression machine prediction model based on particle swarm optimization (PSO-SVR) is proposed. First, the conditional attributes and decision attributes are selected in the historical data of airport passenger throughput. Secondly, the particle swarm optimization algorithm is embedded to optimize the kernel function parameters and penalty factors. Finally, the support vector regression machine is used to predict the airport passenger throughput in the next year. Beijing Capital, Shanghai Pudong, Guangzhou Baiyun and Chengdu Shuangliu were selected as examples to verify the validity of the model. And compared with the moving average method, exponential smoothing method, ARIMA precision. The results show that the PSO-SVR model improves the prediction accuracy, and the average absolute percentage errors for the four airport throughput forecasts are 3.21%, 4.37%, 2.38, and 4.43%, respectively.

Rail ineffective transport refers to meaningless and unvalued transportation in the rail freight system, which including convective transportation, roundabout transportation, and unprocessed transportation. Ineffective transport does not only result in a strain on transportation capacity and increase costs but also imposes significant external pollution on low-carbon development. This paper attempts to study ineffective transport among rail stations. Base on defining the concept of ineffective transportation, it analyzes the ineffective transport volume and ineffective transport propagation on each station, which helps to seek more efficiently the core ineffective transport stations in the rail freight network. To better understand the mechanism of ineffective transport propagation effect at the rail freight transport systemlevel, an ineffective transport causality network (ITCN) was built based on the Granger causality test. Through the topology model of ineffective transport at 50 stations in China from 2013 to 2016, the results show that the ineffective transport of each station affects approximately 12 stations and also affected by 12 stations on average. Large-sized stations are affected by more stations than downstream. Small-sized and medium-sized stations are opposed. There are four core stations in this ITCN, and optimization of the ineffective transport at these four stations will save nearly fifty million kilogram of transport volume per year.