A multidimensional examination of performances of HSR (High-Speed Rail) systems

This paper deals with a multidimensional examination of the infrastructural, technical/technological, operational, economic, social, and environmental performances of high-speed rail （HSR） systems, including their overview, analysis of some real-life cases, and limited （analytical） modeling. The infrastructural performances reflect design and geometrical characteristics of the HSR lines and stations. The technical/technological performances relate to the characteristics of rolling stock, i.e., high-speed trains, and supportive facilities and equipment, i.e., the power supply, signaling, and traffic control and management system（s）. The operational performances include the capacity and productivity of HSR lines and rolling stock, and quality of services. The economic per- formances refer to the HSR systems＇ costs, revenues, and their relationship. The social performances relate to the impacts of HSR systems on the society such as congestion, noise, and safety, and their externalities, and the effects in terms of contribution to the local and global/country social- economic development. Finally, the environmental performances of the HSR systems reflect their energy consumption and related emissions of green house gases, land use, and corresponding externalities.

Regression model for daily passenger volume of high-speed railway line under capacity constraint

A non-linear regression model is proposed to forecast the aggregated passenger volume of Beijing-Shanghai high-speed railway(HSR) line in China. Train services and temporal features of passenger volume are studied to have a prior knowledge about this high-speed railway line. Then, based on a theoretical curve that depicts the relationship among passenger demand, transportation capacity and passenger volume, a non-linear regression model is established with consideration of the effect of capacity constraint. Through experiments, it is found that the proposed model can perform better in both forecasting accuracy and stability compared with linear regression models and back-propagation neural networks. In addition to the forecasting ability, with a definite formation, the proposed model can be further used to forecast the effects of train planning policies.

Decision-making method for high-speed rail early warning system in complex earthquake situations

To address the shortcomings in decision-making methods for ground motion threshold warning models in high-speed rail earthquake early warning systems(HSREEWs),we propose a dual judgement method and corresponding early warning process for earthquake early warning decisions based on joint peak ground acceleration(PGA)and complex earthquake environmental risk evaluation(ERE)values.First,we analyse the characteristics of four complex earthquake environments based on the characteristics of high-speed rail(HSR)operating environments.Second,we establish an earthquake environmental risk evaluation index system and propose an adversarial interpretive structure modelling method-based complex earthquake situation evaluation model(AISM-based ESEM).The AISM method firstly evaluates the proximity by the TOPSIS(technique for order preference by similarity to an ideal solution)method,then effectively rank targets with fuzzy attributes through opposite hierarchical extraction rules without sacrificing system functionality.Since PGA can reflect the current size of earthquake energy,combining PGA thresholds with ESEM-derived values of ERE can effectively determine the risk status of each train and make decisions on the most appropriate alarm form and control measures for that status.Finally,case analysis results under the background of Wenchuan Earthquake show that the new early warning decisionmaking method accurately assesses environmental risks in affected areas and provides corresponding warning levels as a supplement to existing HSREEWs warning models.

Theoretical analysis of high-speed rail seismic imaging

We do theoretical research on using high-speed rail(HSR)as an active source to perform reverse time migration(RTM)and analyze the influence of the interferometric field on the seismic imaging results.When a train runs on a rail viaduct,the evenly spaced piers of the viaduct generate a nearly spherical interferometric wavefield with radically travelling waves in frequency-determined directions.We find that the directions span stationary areas of the interference phases,of which cross-talks deteriorating HSR seismic imaging can be well suppressed by stacking.Accordingly,we propose a method for performing RTM by employing HSR data.Numerical tests primarily verify the proposed method by use of 2 D and 3 D acoustic wave equations.Subsequently,we execute least square RTM to suppress crosstalk artifacts,further improving the imaging quality.At last,we stack images derived from trains with different velocities,which extends the frequency band,effectively overcoming the limit from the discrete spectrum of the source wavelet.

高铁提速情景下出行者“空铁抉择”的动态演化博弈

为了探究高铁提速情况下用户出行选择高铁和民航动态适应过程,基于效用理论构建出行方式博弈收益矩阵,建立基于Logit动态的交通方式选择演化博弈模型,并对模型的平衡点和演化稳定性以及参数取值进行分析。以京沪起讫点(corigin-destination,OD)点对间的高铁、民航行程时耗与票价作为参照基准,将收益函数进行时间均一化转换,设定两种高铁提速情形,并对其价值收益进行对比分析。基于数值仿真探讨出行者“空铁抉择”的动态演化过程以及提速背景下竞争区间的变化。结果表明:高铁提速会吸引更多出行者,在400 km/h和600 km/h两种提速情形下其分担率上升至58.31%和72.73%,同时高铁运行速度提高会使空铁激烈竞争区间向更远的出行距离扩展。

CO_(2) emissions reduction performance of China’s HSR based on substitution effect and demand effect

As an important transportation infrastructure and transportation backbone in China,high-speed rail(HSR)plays a critical role in promoting the development of green and low-carbon transportation.Calculating the CO_(2) emissions reduction performance of HSR will be conduci v e to pr omote the CO_(2) emissions r eduction w ork of the r ail w ay.Based on the Dalkic HSR CO_(2) emissions r eduction performance model,by adjusting the HSR CO_(2) emission factor(CEF HSR),the annual times of departures(T)and other parameters,this study develops a Chinese HSR CO_(2) emissions reduction performance model.Taking the Beijing-Shanghai HSR as the resear c h object,this study conducts a questionnaire survey to explore the substitution effect and demand effect of HSR on different transportation modes;collects data such as passenger v olume,av era ge electricity use and annual times of departures of the Beijing-Shanghai HSR in 2019;and calculates the CO_(2) emissions reduction performance of the Beijing-Shanghai HSR.This study has two main results:(1)It builds a Chinese HSR CO_(2) emissions reduction performance model based on substitution effect and demand effect.(2)In 2019,the CO_(2) emissions of the Beijing-Shanghai HSR are 2898233.62 t,the CO_(2) emissions reduction performance of the Beijing-Shanghai HSR is 17999482.8 t,the annual CO_(2) emissions of the Beijing-Shanghai line in‘No HSR’case are 7.2 times as in the’HSR’case and the PKT of the HSR is 10.2 g/pkm.Based on the research results,this study proposes three CO_(2) emissions reduction policy suggestions.This study would be helpful for further HSR CO_(2) emissions reduction resear c h and departments related to railway transportation management to make CO_(2) emissions reduction policies.