Rail transit airport lines, as fast passages connecting urban areas and airports, play an important role in facilitating residents' travel and optimizing urban spatial layout. This study aims to explore the passenger flow characteristics of airport landside modes under three kinds of rail transit access form: dedicated, general, and mixed. On the basis of analyzing the access features of different airport lines, a three-level nested logit (TLNL) model is proposed to explore the decision-making mechanism of passengers' landside mode choice in two stages. Then, taking the rail transit of Beijing Daxing International Airport as an example, scenario application is carried out to discuss the change of the landside mode sharing rate after the airport line is extended. The results show that the access form of rail transit has no essential impact on the landside traffic structure. When the airport line extends to the urban area, its sharing rate increases significantly, the passenger attraction effect of the dedicated line being significantly better than the other two forms. Extending the airport line downtown and connecting with large transport hubs can reduce the number of transfers, effectively managing the contradiction between passengers' travel convenience and comfort. In general, the TLNL model has good applicability to the analysis of how rail transit affects the sharing rate of landside modes; the results can provide reference and ideas for the selection and planning of rail airport line access forms.

A reasonable investment and financing mode is of great significance for the sustainable development of railways’ “Go Global” projects. This study discussed the main investment and financing characteristics and key issues of railways’ “Go Global” projects and formulated the experience of investment and financing operation through the comparison and analysis of typical railways’ “Go Global” projects. In addition, it took the strategic project of China –Pakistan Railways as an example to study the investment and financing mode and made the mode recommendation. The results show that commercial “Go Global” projects aim at direct economic benefits, the PPP mode can be used as the main investment and financing mode. Strategic projects take the quality and benefit of railway construction as the target orientation, the EPC mode is dominant. For demonstration projects, multi-mode combinations can be adopted. For foreign aid projects, the BT mode or the BOT mode can be adopted to obtain financing. The results can provide a reference for the formulation of investment and financing strategies for railways’ “Go Global” projects.

A well-designed layout of a logistics park can improve logistics efficiency and reduce associated costs. This study proposes an optimization method for logistics park layout, taking into account the impact of carbon emissions. The method combines the Stochastic Impacts by Regression on Population, Affluence, and Technology（STIRPAT） model with the Systematic Layout Planning（SLP）method. To select and decompose factors, the Kaya extended identity and Logarithmic Mean Divisia Index（LMDI） method are used based on the functional layout requirements of the logistics park. The STIRPAT carbon emission model is constructed, and regression analysis is carried out to explore the influencing mechanism of carbon emissions in logistics parks. Factors considered in this analysis include employee size, industrial structure, energy structure, and energy intensity. Then, based on the improved SLP method, the gray forecast model is used to divide the freight demand status, the forecast result is corrected based on the Markov chain. With the optimization objectives of minimizing freight transportation cost, maximizing comprehensive correlation, and reducing carbon emissions, a multi-population genetic algorithm is used to solve the layout optimization scheme. The case study results show that when optimizing the layout of the logistics park, the industrial structure and energy intensity factors related to park operation and transportation organization should be considered in the non-logistics relationship and comprehensive correlation analysis. Moreover, comparison and analysis with the Non-dominated Sorting Genetic Algorithm-Ⅱ（NSGA-Ⅱ） show that the layout scheme obtained by the STIRPAT-SLP method is more in line with the flow relationship of logistics operations. Additionally, it has relative superiority in terms of solution convergence speed, compatibility of optimization objectives, and rationality of actual layout. This method is more suitable for the actual needs of functional area layout of logistics parks.

Aiming at the layout planning of electric vehicle (EV) charging facilities on highways, this study builds a multi-objective optimization model with the minimum construction cost of charging facilities, minimum access cost to the grid, minimum operation and maintenance cost, and maximum carbon emission reduction benefit by combining the state of charge (SOC) variation characteristics and charging demand characteristics of EVs. A chaos cat swarm simulated annealing (CCSSA) algorithm is proposed. In this algorithm, chaotic logistic mapping is introduced into the cat swarm optimization (CSO) algorithm to satisfy the planning demand of EV charging facilities. The location information of the cat swarm is changed during iteration, the search mode and tracking mode are improved accordingly. The simulated annealing method is adopted for global optimization search to balance the whole swarm in terms of local and global search ability, thus obtaining the optimal distribution strategy of charging facilities. The case of the Xi’an highway network in Shanxi Province, China, shows that the optimization model considering carbon emission reduction benefits can minimize the comprehensive cost and balance economic and environmental benefits. The facility spacing of the obtained layout scheme can meet the daily charging demand of the target road network area.

This study focuses on the economic benefits of railway transportation from the aspect of data mining using convolution neural network (CNN), long short-term memory (LSTM), and multiple linear regression (MLRA) models. Improved CNN and LSTM (CNN-LSTM), and CNN and bidirectional LSTM (CNN-Bi-LSTM) models have been developed to enhance learning. The case data sets include operating mileage, and passenger and freight turnover for four transportation modes (railway, highway, aviation, and water transportation) from 1952 to 2020 in China; various evaluation indexes are used to verify model effectiveness. The CNN and LSTM model prediction error rates are 29% and 22%, respectively, verifying the role of an integrated transportation system in railway transportation and the time effect as a national economic benefit of railway transportation, respectively. The CNN-LSTM model prediction error rate is 12%, indicating that the economic benefits of railway transportation depend on the structure of various transportation modes and the time stage of transportation resource allocation. The prediction error rate of the CNN-Bi-LSTM model is 14%, suggesting the irreversibility of the impact of railway transportation resources on economic benefits. The MLRA model error rate is lower than those of the CNN and LSTM models, at 19%, but higher than those of the other models. The CNN-LSTM model is recommended to quantify the economic benefits of railway transportation. This study illustrates the systematic nature, periodicity, time lag, and irreversibility of railway transportation and economic development, providing a theoretical basis for the formulation of transportation development and investment plans.

"一带一路"作为新时期中国对外经贸关系的顶层设计,依托中国与沿线国家的双边和多边机制,借助既有合作平台,积极发展与沿线国家的经济伙伴关系.在合作平台上运营的中欧班列是欧亚大陆货物运输新通道,是基础设施互联互通的重要举措.中欧班列已成为"一带一路"建设的标志性运输合作平台,是中国铁路部门与国际社会的重要衔接.中国铁路"走出去"不同于传统的劳务输出和工艺品输出,而是意味着中国与其他国家在高科技领域的合作,把中国制造变成中国创造.尽管中国铁路"走出去"的成效和国际竞争力不断增强,但国际政治经济格局和国外社会环境的演变,对铁路"走出去"项目的可持续发展产生了重大影响,面临着巨大的风险与挑战,其风险控制任务还很艰巨.

针对铁路"走出去"公私合营(PPP)项目的风险评估决策需求,提出一种基于变权可拓物元模型与累积前景理论的风险决策方法.在构建风险评价指标体系的基础上,通过经典域、节域矩阵及待评物元等级量化值综合确定各风险指标的组合权重.考虑决策者损失规避、收益偏好等心理特征,结合变权理论与贴近度准则,从损益角度综合确定风险决策参考点,基于蒙特卡洛模拟构建风险决策价值函数与概率权重函数,通过敏感性分析对不同风险指标取值下累积前景值的相应变化进行探讨,并以实际铁路"走出去"PPP项目为例进行风险评估决策.相应分析结果表明,所提出的风险决策方法能够有效识别铁路"走出去"PPP项目建设运营过程中面临的关键风险,判别项目风险等级贴近度,并为铁路企业与政府机构进行风险决策提供参考.

This study aims to propose a comprehensive evaluation index for the economy and integrated transportation system of metropolitan areas to analyze the interactive correlations between them and, thus, determine their developmental trends and characteristics, as well as the developed and developing metropolitan areas in China from 1998 to 2017. Moreover, a combination of a time-domain analysis and frequency-domain analysis was adopted to explore the interactive evolution correlation and spatial differences between both methods for 1998-2017. The sustainable developments of both metropolitan areas and China's integrated transportation exhibited a steady upward trend, and the gap between the developed and the developing metropolitan areas showed an expanding trend. While long-term equilibrium interactions were noted between both, some variances were present in different regions at different times. While China and its developed metropolitan areas were of a pulling type, the developing metropolitan areas were of a promoting type. Besides, the impact of integrated transportation development on the economy of the metropolitan areas was more significant, and the degree of impact exhibited a high trend in the developed metropolitan areas but a common trend in the developing metropolitan areas.

Traffic status recognition and classification is an important prerequisite for traffic management and control. Based on the idea of weight optimal, a weighted fuzzy c-means clustering method for improving the accuracy of traffic classification is proposed in this study to ease traffic congestion. First, since there are many indexes that affect the traffic flow state classification, three commonly used indexes namely, volume, speed and occupancy are chosen as the main parameters for the traffic flow state classification in this paper. Second, in order to quantitatively analyze the influence degree of different traffic flow parameters on traffic flow state division, based on the principle of weight optimization, the objective function of weight optimization is established. Then the weight of each attribute index is obtained by using the branch and bound algorithm. Finally, since the traditional fuzzy c-means clustering method will not consider the influence of different traffic flow parameter weights on the traffic flow state classification results, the classification effect needs to be further improved. A fuzzy weighted c-means classification method which uses weighted Euclidean distance instead of Euclidean distance is proposed to classify the traffic flow states. Based on the same traffic flow data sample on the same road section, the traffic state classification results with different methods show that it is helpful to improve the traffic flow state classification accuracy by weighting the clustering index. Because the influence of different parameters on the traffic flow state classification is considered in the process of clustering, it is more conducive to improve the classification accuracy. Moreover, it can provide more accurate classification information for traffic control and decision making.

As people’s lives get better and better, more and more people choose to travel and with that comes the demand for more transportation. For now, traditional transportation hubs can temporarily meet people’s travel needs. If driven by big data concepts and methods, the various capabilities of high-speed rail transportation hubs will be sublimated, and the regional economy will be in line with the prosperity of this place. Proportionally, railway hubs are extremely attractive to the rapid growth of the regional economy. This paper takes the high-speed railway hub construction model under big data as the research object and verifies the reliability of the research model and the development of economic regions based on the high-speed railway data in recent years as reference parameters. This article selects the panel data of railway transportation and regional economy in China’s provinces for 10 consecutive years from 2011 to 2020. Among them, seven indicators were selected for railway transportation: passenger volume, freight volume, passenger turnover, cargo turnover, number of railway employees, railway transportation industry fixed asset investment and construction scale, and per capita railway network density. In terms of regional economy, six indicators were selected: regional GDP, per capita GDP, per capita investment in fixed assets, per capita total retail sales of consumer goods, per capita investment in imports and exports, and the proportion of the added value of the tertiary industry in GDP. The experimental results prove that each sample is tested in pairs, the standard error level of the mean is 0.002, which is less than 0.05, and high-speed railway construction can finally achieve economic integration. By improving the development of high-speed railways, continuously shortening the distance between time and space, breaking regional trade barriers, and reducing the cost of commodity circulation, industrial interaction and coordinated development between different regions can be effectively promoted.

Advanced technology has ushered in the urge to enhance the travel experience. Besides the consistent desire to travel faster and more comfortably, the need to ensure transportation sustainability has remained constant. Smart cities employ top-grade technological applications to facilitate operations. Intelligent transportation systems involve the use of advanced transportation technologies. Through the integration of the Internet of Vehicles, cars in traffic can send and receive data between themselves and other vehicles and the environment. This data is processed to ensure efficient transportation by controlling traffic flows and preventing accidents. In this study, a literature review is conducted on how intelligent transportation systems contribute to environmental sustainability in smart cities. With technologies such as electricity-driven cars and autonomous vehicles, the systems minimize the emission of toxic substances to the environment while enhancing the interaction of the car with its surroundings to avoid accidents.

知识的传授与能力的培养是高等教育改革进程中人才培养质量的两个关键环节.通过借鉴国际知名院校教育教学改革思路与经验,从课程体系构成、学科交叉融合等方面分析了知识传授与能力培养之间的博弈,并以新工科建设为背景阐述了知识与能力的五个结合:坚持问题导向与学科交叉相结合,坚持通识教育与专业教育相结合,坚持师生共进与技术革新相结合,坚持教学方式与学习方式相结合,坚持知识传授与能力培养相结合.

Abstract In this paper, the expressway traffic flow prediction model based on the Bi-LSTM is designed, and four sections of expressway are applied to the model for training and evaluation. Through training and verification, the results show that the average prediction accuracy, MAPE, RMSE, and MAE of the proposed model is 89.54%, 10.46%, 31.55, and 24.58, respectively. In addition, in order to evaluate the effect of the proposed model, this paper introduces the ARIMA model for comparison. It is found that the prediction accuracy of the Bi-LSTM is 18.30% higher than ARIMA, the RMSE is reduced by 31.85, and the MAE is reduced by 26.32. The results show that the proposed Bi-LSTM model exhibits higher prediction performance. Afterwards, this paper makes a comparative analysis of the predicted value and the original value of each road section. The results show that the proposed model has a certain lag, and the forecast value of traffic flow is low for rush hours, however, the forecast value of traffic flow is higher in the low peak hours.

With the development of the economy, the prediction of expressway project costs has gained increasing research attention. In this study, based on the convolution neural network (CNN) algorithm, the prediction of the expressway construction cost was analyzed with respect to the conceptual design stage. By summarizing the existing research results, 10 new factors related to the bridge and tunnel are creatively introduced into the cost-prediction index of the expressway conceptual design stage. In addition, the data structure of the expressway project cost prediction is defined and a CNN model is established. Finally, the project information of 415 expressways in China collected in this study is used to verify the research results. The results of the case analysis show that the 10 new indexes related to the bridge and tunnel can improve the prediction accuracy of the model. In addition, the CNN model is more suitable for solving the high-dimensional nonlinear problem of expressway cost prediction than the conventional artificial-neural-network and regression-analysis models, and it can improve the prediction accuracy. The findings of this study can be used to devise financial plans in the early stage of expressway construction and facilitate cost management at the conceptual design stage to help investors acquire project funds in advance.

Passenger flow forecasting is an important part of railway project planning and design, and is the main basis of train operation plan compilation and line economic benefit calculation. This paper takes the relationship between railway passenger transport and economic and social development as the breakthrough point, adopts the method of combining qualitative analysis with quantitative calculation, and applies data mining technology to analyze the evolution mechanism of passenger transport demand, the main influencing factors and the future development trend of passenger transport demand in the Jiao-Ji railway passage. Taking "feature analysis, mechanism research and trend judgment" as the core, a prediction model based on multi-method integration is established to predict passenger density of the Jiao-Ji railway passage. Finally, some suggestions are put forward for the train operation plan, so as to optimize the transportation capacity of the Jiao-Ji railway passage.

The prize-collecting location and routing problem (PCLRP) is a new variant of the location-routing problem (LRP), which considers the profit that can be obtained by visiting each demand node. In this research, the PCLRP is applied to the planning of rural logistics network to help the enterprise make a trade-off between the revenue and cost. The model takes the maximization of enterprise profit as the optimization objectives, in which enterprise profit equals the difference between the enterprise distribution revenue and cost. Then, a bi-level genetic algorithm with a new crossover method are proposed to solve the PCLRP. The model and algorithm are validated by the benchmark instances, and the effects of different parameter values on the target value of the model are analyzed. The results show that as the price of unit demand rises, PCLRP makes the rural logistics enterprises profitable earlier than the LRP. Meanwhile, there is a strong relationship between the size of the enterprise profits and the number of demands being satisfied. The results can provide relevant decision-making basis for government and enterprises.

Road traffic injury is currently the leading cause of death among children and young adults aged 5–29 years all over the world. Measures must be taken to avoid accidents and promote the sustainability of road safety. The current study aimed to identify risk factors that are significantly associated with the severity in crash accidents; therefore, traffic crashes could be reduced, and the sustainable safety level of roadways could be improved. The Apriori algorithm is carried out to mine the significant association rules between the severity of the crash accidents and the factors influencing the occurrence of crash accidents. Compared to previous studies, the current study included the variables more comprehensively, including environment, management, and the state of drivers and vehicles. The data for the current study comes from the Wisconsin Transportation crash database that contains information on all reported crashes in Wisconsin in the year 2016. The results indicate that male drivers aged 16–29 are more inclined to be involved in crashes on roadways with no physical separation. Additionally, fatal crashes are more likely to occur in towns while property damage crashes are more likely to occur in the city. The findings can help government to make efficient policies on road safety improvement.

Aiming at the problems of lagging development and lack of supply in rural e-commerce logistics in China, this paper uses the theory of iceberg cost for reference, regards transport cost as the main revenue of transport sector, and combines Dixit-Stiglitz model in spatial economics to independent modeling the transport industry.This method is used to analysis the causes of the problems faced by rural e-commerce logistics by revealing the dynamic changes of the transportation industry, which concealed by the traditional center-periphery model.The results show that the high fixed cost and low distribution price of rural e-commerce logistics enterprises are the main reasons that restrict their development.

选取我国主要旅游城市2007—2016年的面板数据,以客运成本为纽带建立交通基础设施建设与产业集聚的计量经济模型,以此来检验空间经济学中运输成本和产业集聚之间的变动关系.研究结果表明:交通基础设施建设在产业集聚变动及空间溢出效应中起到了重要的作用,且存在"倒U型"非线性关系;在我国现阶段的交通基础设施建设中,以高铁建设为代表的质量因素对产业集聚的影响更加明显.基于此,建议以交通基础设施建设为着力点,促进我国产业结构均衡发展;加强高速铁路建设,进一步降低我国综合运输成本,减少产业集聚阻力;提升运输服务质量,加快我国交通现代化发展进程,推动产业集聚发展.

In order to meet the development trend of world economy and improve the growth of shipping market freight, China Ocean Shipping (Group) Company and China Shipping (Group) Company merged into China COSCO Shipping Corporation Limited in the area of container transport, financial business and other business segments as the goal of enhancing the level of international management and avoiding homogeneous competition. After reorganization, COSCO Shipping Lines will be the world's fourth largest container shipping business. As the core of liner shipping enterprise, container fleet plays an important role in promoting the healthy development of liner enterprises, so it is necessary to compare the competitiveness after their reorganization. In this paper, using the relevant knowledge of decision-making theory, the problem to be studied can be considered as a multi-attribute model problem. Then this paper analyzes the factors which have influence on the competitiveness of the fleet and builds the appropriate evaluation index system and uses the TOPSIS method to carry on empirical analysis of the world's outstanding liner shipping companies with COSCO Shipping Lines. According to the result, container fleet competitiveness has significantly improved.