The Proceedings of the 11th International Conference on Traffic and Transportation Studies

With the vigorous development of the current e-commerce industry, China’s urban logistics business volume has achieved rapid growth and has gradually become the country with the largest express delivery industry. However, the parcel distribution problem caused by the surge in express logistics has gradually become one of the main bottlenecks restricting the sustainable development of urban transportation logistics. The metro has the characteristics of strong timeliness, low pollution, and large transportation volume. If the accessibility and excess transportation capacity of the existing metro network can be fully utilized, cooperating transportation of passengers and freight in express logistics is an important direction for the future development of urban logistics. Based on the existing stations, this paper proposes a freight transportation scheme of metro stations with a high degree of automation, and builds a simulation model to analyze freight efficiency and passenger- freight interference under the automatic scheme. The results show that the design scheme has little influence on passenger travel behavior in the station, and can promote the level of freight transportation service.

In addressing the challenge of uneven passenger flow distribution within urban rail transit networks, this study introduces an advanced multi-path passenger flow assignment model grounded in regret theory. The model meticulously constructs a multi-routing space-time topological network and incorporates regret theory to accurately depict passengers’ decision-making processes. By taking into account key factors such as perceived travel time, the number of transfers, and train congestion levels, the model aims to allocate passenger flow based on the principle of minimizing passengers’ anticipated regret. This innovative approach not only enhances the balance between supply and demand but also significantly improves operational efficiency. The findings of this study provide robust theoretical support and practical solutions for optimizing urban rail transit network management, offering a novel perspective on achieving more efficient and passenger-friendly transit operations.

As an efficient logistics mode, multimodal transport can effectively integrate the advantages of various transportation modes and reduce energy consumption and carbon emissions. Aiming at the low-carbon multimodal transport path optimization problem, this paper provides a hybrid optimization method combining genetic algorithm and particle swarm optimization algorithm, establishes a multi-objective optimization model considering transport time, cost and carbon emission, and designs a PSO-GA hybrid algorithm to realize the comprehensive optimization of multimodal transport path. Finally, a numerical example is given to verify the effectiveness and feasibility of the proposed method. The results show that the PSO-GA hybrid algorithm can effectively reduce the transportation cost and carbon emissions of multimodal transportation while ensuring transportation efficiency, and provide theoretical support and practical guidance for the low-carbon development of China’s transportation industry, which will help China set a model of low-carbon development in the field of global transportation and make positive contributions to the global response to climate change.

In recent years the cold chain logistics of fresh agricultural products has gradually become a research hotspot, and there are many problems in cold chain logistics, such as high cost, backward cold chain logistics information statistics, and many factors affecting demand forecasting. In this paper, the output of cold chain agricultural products is used as the predictor index to establish an index system of influencing factors of cold chain logistics demand for agricultural products, and the cold chain logistics demand of agricultural products in China from 2024 to 2026 is predicted by using the combination of grey correlation analysis method and BP neural network model. The prediction results show that the prediction accuracy of the proposed model is high and the fitting is good, and the predicted data can accurately reflect the growth trend of China’s agricultural product cold chain logistics demand in the next three years, which provides a theoretical basis for logistics planning of government departments and related logistics enterprises.

Achieving precise prediction of short-term inbound and outbound passenger flows in urban rail transit is of great significance for improving train transportation efficiency and enhancing passenger travel quality. This paper aims to analyze the influencing factors of short-term passenger flows and utilize Spearman correlation analysis to screen characteristic variables. Based on this, a short-term inbound passenger flow prediction model for urban rail transit is constructed, combining an improved Particle Swarm Optimization (PSO) algorithm with a Long Short-Term Memory (LSTM) network. Furthermore, to address complex and ever-changing scenarios, this paper introduces a digital twin perception function that can dynamically optimize the key parameters of the prediction model, ensuring its robustness and adaptability, and improving the accuracy of prediction results. Experimental results demonstrate that, compared to traditional prediction methods, the model proposed in this paper exhibits superior performance in terms of average absolute error and goodness-of-fit value, showcasing stronger generalization capabilities and adaptability to various conditions.

To strengthen the implementation of the “road-to-rail” policy, railway transport enterprises must accurately capture the sensitivity of freight demand characteristics to pricing policies, develop a more comprehensive freight pricing system, and effectively identify the demand elasticity of different freight products. This study collects and analyzes actual transport data, using freight volume weighting to construct a discrete choice model based on freight demand elasticity model within the context of road competition. Results show high accuracy in predicting freight transport demand and reveal sensitivity to cost and transport time. The elasticity calculations show that for coal, the cost elasticity of railway transport is significantly lower than its cross elasticity, indicating that higher road prices boost railway freight volume more than reduced railway prices. For food and beverages, the time elasticity of railway is significantly higher than that of road transport, suggesting that improving railway service quality will promote the shift of road freight to railway transport. Additionally, the rail market share rates for both types of goods are highly sensitive to transport time, indicating that railway transport enterprises should focus on improving service quality, transport speed, and timeliness in future operations to meet market demand and promote an increase in freight volume.

With the improvement of people’s living standards, the development of frozen and chilled goods transportation has also ushered in greater challenges. Therefore, accurate prediction of frozen and chilled goods transportation demand for fresh agricultural products holds significant practical importance. This investigation utilizes the GM (1,1) model to predict the transportation needs for frozen and chilled products in Shandong Province, starting with an analysis of the present scenario and projected trends in this sector. The GM (1,1) model was then engaged to estimate future demand for the transportation of frozen and chilled goods in Shandong Province from 2023 to 2032. The high prediction accuracy of the model was verified through a development coefficient test, residual test, and posterior error test. It effectively predicts the changing trend of frozen and chilled goods transportation demand and provides a source for subsequent planning of frozen and chilled goods transportation facilities, construction of cold storage, and transportation equipment in Shandong Province.

This study utilizing an advanced ultra-efficiency slacks-based measure model derived from data envelopment analysis, an evaluation index system has been developed to systematically assess the social and economic benefits of the Beijing-Shanghai high-speed railway express in order to delve into the operational dynamics of Beijing-Shanghai high-speed railway express services. The evaluation involves a comprehensive assessment of efficiency values, input redundancy rates, and output insufficiency rates, providing a comprehensive review of the service’s operational effectiveness. The results indicate that railway firms have the opportunity to make use of the unused capacity of high-speed railway in order to actively participate in the market for transporting small parcels. Additionally, it promotes the expansion of the positive impacts of high-speed railway, making it easier to provide secure, top-notch, affordable, and effective express services.

This study investigates the potential of employing autonomous vehicles (AVs) for catering waste collection, aiming to enhance customer satisfaction and facilitate route learning for AVs. We formulate the problem as a coherent multi-trip vehicle routing problem, accounting for both arrival time consistency and route consistency. We develop an advanced column generation algorithm tailored for consistency and multi-trip routing constraints. The effectiveness of algorithm is validated across diverse instances, demonstrating superior computational efficiency compared to GUROBI. Moreover, sensitivity analysis of the two consistency types reveals that a marginal cost increase of up to 1.5% can significantly improve both arrival time and route consistency. Furthermore, our findings indicate a mutually reinforcing relationship between time consistency and route consistency, offering valuable management insights for waste collection companies. This study highlights AVs’ potential to efficiently manage catering waste collection, ensuring reliability and customer satisfaction with minimal cost impact.

Improving the accuracy of the airport aircraft’s CO2 inventory is an important issue for the development of green civil aviation. Firstly, this study uses the flight operation data and meteorological data of Guangzhou Baiyun International Airport in 2019 as the driving force to improve key parameters (i.e., engine fuel flow rate, mixing layer height, and flight operation stage duration). Secondly, the CO2 emission inventories are established and compared based on the standard and improved models. Finally, the emission characteristics are explored from three dimensions: aircraft types, temporal, and spatial. The results show that the annual CO2 emissions obtained by the data-driven model are 31.6% lower than the ICAO standard model. The B738 and A320 have lower emission intensities, which are more in line with the need for carbon reduction. The peak emissions of CO2 occur from 8:00 to 9:00 in the morning, while the off-peak emission times are from 1:00 to 2:00 in the early morning. CO2 emissions increase in November and December when the mixing layer height increases. The emissions are concentrated in the directions corresponding to LMN, YIN, and VIBOS departure route points.

Traditionally Electric Multiple Unit (EMU) requirements for High-Speed Railway (HSR) are determined through a rigorous sequential process manually that can lead to sub-optimal EMU circulation plans. In response to the inherent complexities and inefficiencies of this sequential approach, this paper presents a coordinated approach that integrates timetable and EMU circulation planning through feedback links. The proposed approach explores application of adjustable departure time windows in the initial timetable to minimize the number of EMUs needed for the entire timetable. The study considers two types of EMU usage methods i.e. fixed routing and flexible routing. An Integer Linear Programming (ILP) model incorporating train operation constraints is devised and solved over CPLEX commercial solver. To solve the NP-Hard problem efficiently, an iterative local search algorithm inspired by TABU search principles is introduced. Comparative analysis between ILP and the algorithm is carried out, validating their applicability. The model and algorithm are validated against the real case data of Beijing-Tianjin HSR. The findings demonstrate the efficacy of the proposed approach in addressing optimization challenges in coordinated circulation planning. The proposed algorithm is demonstrated to be capable of returning high-quality solutions swiftly, especially in scenarios involving time windows, where exact solutions by commercial solvers are time-consuming.

To improve the operational service of urban rail transit Y-type lines, a method for optimizing the train schedule under the flexible train composition mode with online coupling/decoupling is proposed. Initially, a unique flexible train composition scenario specifically designed for Y-shaped lines is outlined, detailing the coupling/decoupling operations at stations utilizing side track and its impact on the mainline capacity. A train schedule optimization model for the flexible train composition mode with online coupling/decoupling is proposed subsequently, which objective is to minimize enterprise operation cost and passenger travel cost. To implement this model effectively, an algorithm is designed and a case study is conducted. The results demonstrate that proposed method not only improves passenger service quality but also offers cost-saving benefits compared to the traditional modes of trunk/branch operation or branch independent operation. By using flexible train composition mode with online coupling/decoupling, the transportation organization of Y-type lines is enriched, provides a promising solution for achieving a more sustainable and efficient urban rail transit system.

Subway station waterlogging disaster is a major challenge currently faced in the field of operational safety. In response to subway waterlogging disasters, this paper constructs a subway station waterlogging risk classification model based on triangular fuzzy Analytic Hierarchy Process (AHP). Firstly, the paper systematically analyzes the influencing factors of subway waterlogging using case analysis and literature review methods. Nine quantitative risk indicators were proposed, including natural factors, geographic information, and flood control capabilities;. Utilizing the “1–9 degree scale” expert system analysis method, the impact of each indicator is quantified. By applying triangular fuzzy numbers, a waterlogging risk assessment matrix is constructed, and the weights of each indicator are determined. Combining the different risk levels of the indicators and based on the triangular fuzzy AHP, the paper puts forward risk grading thresholds. The research results provide technical support for enhancing the safety risk prevention and control capability of urban rail transit operations and ensuring the safety of the public during people's travel.

Environmental vibration and noise issues caused by subways have become significant concerns in urban development. To mitigate the impact on residents, various vibration-reducing track structures have been implemented in newly constructed subway lines. However, the performance differences of these structures in practical applications remain substantial. This study analyzes the vibration transmission characteristics and reduction effects of typical vibration-reducing track structures through field measurements. Four track structures were selected on a newly constructed subway line: normal slab track, slab track with elastic fastening, floating slab with rubber pads, and floating slab with steel spring. Sensors were installed on the rails, track slabs, and tunnel walls to monitor structural vibrations induced by passing trains. Time-domain and frequency-domain analyses, along with insertion loss analysis, were performed on the measured data to reveal the vibration transmission characteristics and vibration reduction performance of the different track structures. Results show that all three vibration-reducing measures can effectively reduce environmental vibration. The slab track with elastic fastening achieves 5–30 dB vibration reduction at the track slab, while the floating slab with steel spring achieves 25–50 dB reduction at the tunnel wall. The floating slab with rubber pads achieves a stable 5–30 dB reduction across different train conditions. By analyzing the measured data, a more accurate assessment of the vibration reduction performance of different track structures can be achieved, significantly contributing to understanding the relationship between subway track structures and environmental vibration.

Based on WOS and CNKl databases and relying on CiteSpace visual bibliometric tool, this paper constructs the knowledge graph of dry port research progress. In this paper, the development of dry port at home and abroad is analyzed, and the research progress of dry port is reviewed and prospected. Research findings: In recent years, the research on dry ports at home and abroad has developed rapidly, the number of research papers is increasingly rich, and the research hotspots are constantly updated. The current research focuses and hot spots are mainly reflected in the connotation of dry port the linkage mechanism of port and city under the background of dry port construction, etc. The core of the research is in the design and implementation stage of dry port. Future research should focus on the research of new issues such as enriching the new connotation of the concept of dry port, building the combined transport network of dry port, expanding the symbiosis and evolution of port industry and city, so as to deepen the research of dry port with the development of practice.

With the rapid development of global trade, multimodal transport as an efficient mode of transport, its importance in the field of logistics has become increasingly prominent. However, the traditional multimodal transport mode has information island, low efficiency of document circulation, high trust cost and imperfect coordination mechanism, which leads to the problem of difficult coordination between entities in the implementation process of “one single system”. This paper aims to explore how to use blockchain technology to build a new type of “one single system” mode of multimodal transport. Combining with emerging technologies such as cloud computing, big data and artificial intelligence, it proposes the construction strategy of “one single system” of multimodal transport in China. By constructing the data framework of “one single system” of multimodal transport online and implementing the “one single system” of multimodal transport online step by step, logistics efficiency is improved. Reduce operating costs and increase transparency and safety in the transportation process.

With the rapid urbanization, urban mobility issues have become increasingly prominent. To create customized travel packages that meet the individual needs of travellers, Mobility as a Service (MaaS) is regarded as one of the most promising solutions. This paper first summarizes the application form practice of MaaS platform in different cities in China through case analysis, and analyzes the current status of public transportation in Shenzhen. Then, a questionnaire was designed for users in Shenzhen based on the Kano model and a functional expectation survey was carried out. The collected questionnaire results were then compared with the Kano quality type evaluation table, and the attributes of each demand were statistically classified. Finally, based on the analysis of questionnaire data, the function of the Shenzhen MaaS platform has been established.

It is necessary to carry out effective traffic organization optimization design of the connecting section of urban expressway off-ramp and its downstream intersection in order to avoid the adverse impact on the capacity and safety of connecting section caused by weaving movements. This paper summarizes the traffic organization optimization method of the connecting section, introduces an improved Webster timing signal timing algorithm, and proposes a combination optimization scheme of signal timing and traffic organization according to the conditions. The evaluation index system considering the traffic ability and safety of the connecting section and the intersection is established, and the comprehensive evaluation model is constructed by using the matter-element analysis method. VISSIM is used to simulate and analyze the combination optimization scheme for a typical case and its field survey data. The simulation result is evaluated by the comprehensive evaluation model. The simulation results show that the optimization method can optimize the connecting section and intersection in a number of evaluation indicators, and improve the traffic operation.

The merging area is a typical traffic congestion point and accident-prone zone. As automated vehicles (AVs) improve and become more commercially available, they will coexist with human-driven vehicles (HDVs) for a long time. However, current methods struggle to provide convincing evaluation results for different AVs penetration rates in merging areas. To address this, a mixed traffic simulation framework was designed to evaluate the impact of varying AVs penetration rates on traffic safety and efficiency in merging areas. This framework incorporates the intelligent driver model (IDM) to describe HDVs longitudinal behavior and a mandatory lane-changing model to characterize HDVs lane-changing behavior under pressure of different positions. For AVs, adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC) models were used to simulate car-following behavior. Polynomial curve-based models were employed to plan AVs’ lane-changing trajectories. A merging zone scenario with different flow rates and AVs penetration rates was developed using SUMO. Results indicate that AVs can significantly improve traffic safety and efficiency in merging areas.

Train blocking plan and shipment path (TBSP) are key parts of China’s freight operation plan, and the integrated optimization problem of them has attracted more scholars’ attention in recent years. In China’s existing practice, shipment path and train blocking plan are determined in order. In this paper, a reverse-order two-stage solution method is proposed to solve the TBSP problem, which is opposite to the existing decision order. The first stage determines train blocking plan, that is, which stations to build blocks and the distribution of shipments to blocks. The second stage determines the physical path of each block, and then shipment path is determined. The corresponding two-stage integer programming model is established. A series of numerical experiments show that reverse order method balances solution quality and computational efficiency, and has advantages over both the existing positive order method and the method of establishing an integrated model.

Given the current issues of packaging recycling difficulties and significant resource depletion, the closed-loop supply chain has developed as an environmentally friendly supply chain. To study the impact of government regulation measures on the recycling volume, overall efficiency, and total social welfare in a closed-loop dual-channel recycling supply chain, a Stackelberg game leader-follower model is established, with packaging manufacturers as the leaders, express delivery enterprises as the followers, and the government regulating different supply chain entities separately. The research shows that introducing a government reward and punishment mechanism into the closed-loop supply chain of express packaging can significantly change the current situation of difficulties in express packaging recycling, motivate express packaging manufacturers to actively recycle and reuse express packaging, and play a role in guiding express delivery enterprises to increase recycling prices, thus forming an efficient closed-loop supply chain for express packaging.

This paper examines the synergistic relationship between port and urban resilience within the Guangxi port city cluster along the Pinglu Canal. Employing a hybrid methodology integrating factor analysis and the Decision Making Trial and Evaluation Laboratory - Analytic Network Process, the study assesses resilience across economic, social, ecological, and infrastructure dimensions for 11 key port cities. The analysis indicates varying resilience levels, with Nanning, Qinzhou, and Beihai demonstrating higher resilience, while Baise and Hechi require enhancement. The research highlights the significant improvement in port resilience for Nanning due to its strategic role as a transportation hub. The coupling coordination degree model reveals the coordinated development between urban and port resilience, particularly in central cities like Qinzhou and Nanning. The study concludes with strategic insights for bolstering port operational capabilities, enhancing port-city interaction, and establishing comprehensive risk management systems to foster resilience and competitiveness. Limitations are acknowledged, including data timeliness and model assumptions, suggesting avenues for future research on policy interventions and coordinated development strategies.

With the rapid development of the tourism industry, passenger demand is experiencing significant growth and increasing diversity. The gradual establishment of the “Eight Vertical and Eight Horizontal” high-speed railway network framework presents a prime opportunity for integrating “railway + tourism”. This integration requires railway operators to design line plans based on the spatial and temporal distribution characteristics of passenger demand. Furthermore, these plans must be adjusted according to tourism products to provide appropriate transportation services. In response to the passenger demand of high-speed railway, we propose an integer linear programming model for the line plan of multi-day tourism products. We select the high-speed railway in Jiangxi Province as a real-world case to validate the effectiveness of our proposed model.

The start-up operation of high-speed trains is an important component of train operation simulation, playing a crucial role in railway line design, vehicle selection, power system design, and train timetable formulation. By considering driver manipulation characteristics, a simulation method for high-speed train startup operations has been designed. Using the CRH380A high-speed train's departures from Zhengzhou East and Xinxiang East stations as examples, simulation tests were conducted. Comparative analysis between the proposed method and traditional simulation methods that do not consider driver manipulation characteristics shows that the simulation method designed in this paper aligns more closely with the actual train operation process.

The development of new detection equipment and vehicle networking technology has enabled the acquisition of a significant quantity of accurate realtime vehicle trajectory data on urban roads, which is being utilized in the field of urban transportation research. However, due to the existence of diverse driving scenarios and random driving behavior on real roads, simple mathematical and theoretical models are insufficient to simulate complex real driving scenarios. Therefore, in this paper we present a data-driven model for predicting vehicle trajectories by means of Long Short-Term Memory (LSTM) cells with attention mechanism. The model is trained and tested using high-precision trajectory data coming from intersections of Beijing, China. The results illustrate that the improved Long Short-Term Memory model exhibits a lower prediction error in comparison to the previous model and other machine learning methods. It is capable of accurately reflecting the trajectory trend in terms of longitude prediction. The results of this research offer a theoretical basis for the enhancement of assisted driving systems and the creation of vehicle warning systems.

This study presents a framework designed to enhance the location and allocation of relief supplies to earthquake-affected areas. Initially, the framework incorporates various types of relief supplies, delivery modes, multiple periods, and addresses the uncertainties of supply-demand scenarios following an earthquake. This leads to the development of a stochastic programming model that aims to minimize total costs while determining the optimal locations and distribution strategies. Further, the model undergoes a transformation to consider uncertainties under the framework of chance constraints, and the Gurobi solver is applied to solve the model. An illustrative example using the context of Mianyang City post-Wenchuan earthquake validates the model's adaptability. It demonstrates effectiveness amidst dynamic changes in multi-period road network damage and fluctuating supply-demand scenarios. The study also compares the performance of the stochastic planning model with a deterministic planning model through comprehensive numerical results. The evaluation reveals that in practical scenarios, where quantifying the total emergency supplies or demand from individual disaster-affected areas is challenging, our stochastic model outperforms deterministic models. It provides effective solutions for emergency facility locations and multi-material allocations.

In static identification based on measured deflection, given the current research typically employs the displacement influence matrix principle for linear fitting, this paper proposes a method using nonlinear fitting to identify prestress. Through finite element analysis and polynomial ridge regression, a correlation is established between long-term bridge deflection and prestress loss in bridges. Utilizing the measured bridge deflection, a hyperstatic equation system is constructed to solve for the residual prestress in operational bridges. An inversion validation was conducted on a prestressed concrete continuous rigid-frame box girder, showing that when prestress loss does not exceed 40%, the inversion error of this method is less than 3.53%, indicating high accuracy. This study provides a reference for the identification of prestress in similar types of bridges using measured deflection in the future.

The “one-bill coverage” of China-Laos railway multimodal transport is an effective way to promote the growth of bilateral trade between China and Laos railways, and it is an inevitable requirement for the construction of a modern comprehensive transport system. In order to enhance the efficiency and economic benefits of cargo transport on China-Laos Railway, this paper combines the current situation of freight transport on China-Laos Railway and divides the life cycle of “one-bill coverage” of China-Laos Railway multimodal transport into five phases: order submission, domestic consolidation, cross-border transport, foreign evacuation and order signing, etc., and builds a “one-bill coverage” covering all the links, such as loading, transport, transshipment, warehousing, connecting, and customs clearance. This paper can provide a reference for the development of “one-bill coverage” transport service of China-Laos Railway multimodal transport.

Effective response to emergencies is crucial for minimizing loss of life and property. Urban rail transit operators rely on emergency drills to enhance their emergency response capabilities. These drills help train employees in emergency operation skills, test the effectiveness of emergency plans, and evaluate the availability of emergency supplies. Currently, there is a significant gap in the emergency response capacity of urban rail transit in China, characterized by limited opportunities for emergency drills, deficiencies in systematic emergency response training, and inadequate emergency equipment. To address these challenges and improve emergency response capabilities in the urban rail transit industry, the State Council and the Ministry of Transport have proposed the establishment of a national urban rail transit emergency drill center. This paper outlines the functional positioning of the national emergency drill center, compare and analyze the characteristics and requirements of two key supporting units - operating units and scientific research institutions, and establish an evaluation index system for the national emergency drill center, draws on the experiences of national power emergency training drill bases and other industries to provide recommendations for expediting the construction of the national emergency drill center.

The social logistics sector in China has witnessed exponential growth, outpacing the United States in 2016 to claim the title of the world’s largest logistics market. By December 2023, China’s express delivery volume reached an all-time high, breaking through the 120 billion pieces milestone for the first time. The surge in cross-border e-commerce has further fueled the expansion of the logistics industry. This study constructs a conceptual model based on an extensive literature review, underscoring the pivotal role of Information Technology (IT) in shaping the competitive edge of the industry. Utilizing the Concentration Ratio (CR) with a focus on a brand concentration index specific to express and parcel services, key express companies were identified for analysis. An examination of digital transformation trends within listed companies since 2019 reveals that digitalization is deeply integrated into every phase of express delivery operations. The research suggests that a unified, comprehensive, and multi-channel platform has emerged as the norm, seamlessly integrating digital products with a diverse array of applications to form a cohesive ecosystem. These insights offer businesses and researchers a clearer understanding of IT-driven trends in Chinese express companies, and they provide a roadmap for future investigative directions.

Throughout the protracted three-year COVID-19 pandemic, China’s express logistics industry has exhibited remarkable resilience and robust growth, thereby ensuring the seamless continuity of package delivery services. This paper aims to assess the resilience of Chinese express companies’ Elastic Logistics Platforms (ELPs). The study selects a sample set based on the Concentration Ratio (CR) of prominent express and parcel service brands. Drawing on annual reports from 2019 onwards, the research employs an information technology-driven analytical model to examine the innovative adaptability of ELPs across diverse scenarios. The express products are systematically categorized into three types: (1) Domestic standard and expedited services, complemented by premium specialized products, which underscore the industry’s commitment to product diversification and market competition, while simultaneously integrating with the agricultural and manufacturing sectors. (2) Local and on-demand delivery services, which focus on consolidating distribution terminals and innovating last-mile logistics, thereby bridging real-time consumer demand with efficient logistics operations. (3) International services that bolster the “dual circulation” strategy and contribute to a cohesive domestic market approach. The findings of this analysis are set to guide the development of multi-channel marketing strategies and policy choices, aiming to enhance the express logistics industry’s prosperity and sustainability.

In order to achieve accurate prediction of short-term spatial and temporal distribution of crowd density in each area after the crowd enters the station space, and to solve the problems of risk warning and operational safety in railway station crowd management. This paper proposes a local density prediction model for pedestrians in public space areas based on graph embedding. By applying graph structure data, train schedule data and density distribution time series data. Pedestrian density prediction in spatial areas is achieved by attention Bi-directional LSTM model. The results show that the proposed method outperforms traditional prediction models such as ARIMA, RNN, LSTM and their ablation models. The RMSE error and MAE error are reduced from 12.048 to 1.639 and 11.592 to 1.148, respectively, which can be used as a reference for optimising the spatial layout of functional areas according to the pedestrian distribution and improving the spatial risk prevention ability in future railway station.

With the acceleration of urbanization and the increasing demand for public transportation, the problem of large passenger flow congestion in urban subway stations during holidays has become increasingly prominent, which brings great challenges to passenger travel and subway operation. In order to solve the problem of poor passenger travel experience caused by large passenger flow congestion and paralysis of passenger flow organization in urban subway stations during holidays, taking Nanjing East Road subway station as an example, the station is simulated and analyzed by MassMotion software. It is identified that the station mainly faces three problems during the holidays: bottleneck facilities and equipment hinder passenger flow passage, serious passenger flow conflict and insufficient passenger flow carrying capacity of the station. Based on this, a targeted optimization scheme is proposed, and the optimization effect is verified from the perspective of passenger travel experience. The simulation evaluation shows that after optimization, the average travel time of passengers decreases from 344 s to 206 s, the average moving speed increases from 0.696 m/s to 1.001 m/s, the average passenger flow density of the station decreases obviously, and the duration of service level F decreases from 158.55 s to 15.18 s. The congestion and delay problems are effectively alleviated, and the passenger travel experience is significantly improved. The research is helpful to improve the operation efficiency of subway stations and passenger travel experience during holidays.

This paper aims to propose a method for assigning lane functions at entry approaches to achieve balanced saturation across lanes. With this design method, the traffic efficiency of $$n$$ n -leg, $$N$$ N -lane unsignalized roundabouts can be improved without the need for traffic signals. Firstly, the root arrays and degree arrays are defined to describe the results of entrance lane-use assignment, and the problem of lane-use assignment is transformed into the problem of solving the optimal values of integer variables. Then, the integer planning model is constructed with the objective function of minimizing the equilibrium saturation of each lane, and the solution method is given. Finally, the proposed method is applied to a given four-leg, three-lane unsignalized roundabout under 111 different directional volume ratios and the VISSIM software is used to simulate the roundabout's performance under high and low load situations before and after applying the lane-use assignment method. The simulation results show that under high load situation, after implementing the lane-use assignment at entry approaches, the average vehicle delay decreased for 109 volume ratios, and the level of service improved for 34 volume ratios. Under low load situation, the average vehicle delay decreased for 95 volume ratios, and the level of service improved for 27 volume ratios. The analysis results indicate that the method of lane-use assignment at entry approaches based on saturation equalization is an effective design approach for improving the operational efficiency of unsignalized roundabouts.

The prevailing disparity in the availability and demand for medical services necessitates an in-depth analysis and projection of the travel behavior patterns of medical travelers. First, this paper screens and processes data on travel to healthcare facilities from the Fifth Tokyo Metropolitan Area Resident Travel Survey. Second, the preliminary descriptive statistics reflected that the medical and health travelers in this dataset are elderly individuals, from smaller households, exhibiting high travel frequency, with a pronounced preference for morning travel. This paper used multinomial logit model, decision tree and random forest model to predict travelers’ choices of public transportation, private transportation, non-motorized vehicles and walking in daily healthcare travel scenarios. The models demonstrate robust fit, achieving commendable predictive accuracies of 63.3%, 65.41%, and 65.54%, respectively.

After an earthquake, the urgency of material needs at affected sites varies across different post-earthquake emergency stages. To maximize the temporal utility value of emergency supplies and ensure their effective use in rescue operations despite limited post-earthquake transportation capacity, this paper proposes a multi-stage post-earthquake emergency material urgency evaluation method. This study examines the changes in materials during the emergency response, divides the emergency response stages, and comprehensively considers indicators such as material durability, life hazard degree, irreplaceability, demand volume, and demand gap rate to construct a more suitable urgency evaluation system reflecting post-earthquake characteristics. Additionally, the improved TOPSIS-CRITIC evaluation method is introduced to scientifically quantify the urgency of material needs at affected sites during different emergency stages. An empirical study of the Wenchuan earthquake demonstrates that this method can effectively quantify the urgency of different types of materials at various stages and achieve priority ranking for material dispatch across different stages, thereby improving the efficiency of emergency rescue operations.

This paper makes an in-depth analysis of the characteristics of overseas cargo on the China-Laos Railway and predicts the future freight trend based on historical data, so as to provide decision-making support for enhancing the influence of the China-Laos Railway freight market and promoting the quality and increment of freight transportation. Firstly, the current situation of cargo transportation were analyzed. Then, by using the K-means cluster analysis method, the cluster analysis of the freight volume of major stations outside the China-Laos Railway was carried out, and the characteristics of freight categories between stations were discussed. Finally, based on the method of time series analysis, the total amount of freight and the total amount of major goods are predicted, so as to reveal the development trend of China-Laos railway freight transportation and provide decision-making support for relevant departments and enterprises to formulate industrial development strategies.

Ballasted track, which is widely used worldwide, is particularly susceptible to earthquake-induced damage and exhibit complex dynamic responses under seismic loading. Therefore, a comprehensive investigation of their seismic response from both micro and macro perspectives was conducted using the discrete element method. It was found that earthquakes disrupted the original force transmission mechanisms of the ballasted track, causing a redistribution of forces along the trajectory. With increasing peak ground acceleration (PGA), the number of force chains decreased. The displacement amplitude of the ballasted track decreased gradually with elevation, with the slope surface experiencing greater displacement than the center of the track bed. Additionally, the lateral ballast resistance of the ballast bed decreased approximately linearly with increasing PGA.

Urban rail transit hub is an important intersection of multiple traffic flows. The relatively closed operating environment and complex passenger flow situation make the hub vulnerable to unexpected factor interference and reduce operational efficiency. Resilience evaluation is an important prerequisite for the planning and operational management of the urban rail transit hub. Firstly, this paper constructs a secondary evaluation indicator system of urban rail transit hub resilience from three aspects of resistance, absorption and recovery, and gives the indicator calculation methods. Secondly, this paper adopts the analytic hierarchy procedure to quantify and comprehensively evaluate the urban rail transit hub resilience. Finally, the Lize hub is used as an example for instance verification. The result indicates that the overall resilience index of Lize hub is 3.00, and the capacity of staircase group is the most important factor affecting the resilience of the hub. Based on the proposed urban rail transit hub resilience evaluation method, the manager can assess the weak points of the hub in the face of disturbances and improve the service level of urban rail transit hub.

When exceptional length goods are transported by railway, the protruding part of the goods will lead to an increase in the vertical dynamic load, which is not conducive to the safety. Considering the future application needs, the allowable load of 80t railway flat wagons for loading exceptional length goods is studied. Based on SIMPACK to establish the NX80 flat wagon model and the reliability of the model is verified through actual experiments conducted by the original Ministry of Railways. After determining the worst running conditions of the vehicle, the vertical load generated by exceptional length goods in different weights and lengths is obtained. Based on this, the allowable load of NX80 flat wagons for transporting exceptional length goods of different lengths is proposed, providing technical support for the safe use of 80t railway freight cars in the future.

Since 2015, bus ridership in China’s megacities and their satellite cities has exhibited continuous decline. In contrast, there has been an observable increase in bus ridership interchange with metro services. This trend signifies a fundamental transformation in the role of buses within the urban transport system of satellite cities, highlighting an urgent need for a comprehensive study of the underlying laws and mechanisms driving these changes. This research focuses on Jiading, a satellite city of Shanghai, employing the classical Ordinary Least Squares (OLS) model and the theory of demand elasticity to elucidate the determinants affecting total bus ridership and bus ridership interchange with metro services. The findings reveal that the primary factor contributing to the reduction in total bus ridership is the deterioration of bus service levels in Jiading’s high development intensity areas. Conversely, it is intriguing to note that urban development in regions with lower development intensity fosters an increase in bus ridership interchange with metro services. In conclusion, this study proposes a near-term development strategy for Jiading district, tailored to various levels of land use, based on the mechanisms elucidated by the model.

In order to realize the efficient utilization of space-time resource in port station yard, the distribution trains organization scheme is studied. The paper considers sea-rail combined transport operation links after ships arrive at the port. When the arrival time of ships is definite, the optimization model of distribution trains organization scheme is established. The model with the goal of minimizing the product of total container storage quantity and storage time in the port station yard. The model considers constraints such as the loading capacity of distribution trains, the storage capacity of the port station yard, the shortest departure time interval in the port station and so on. On this basis, another optimization model is established that the arrival time of ships is fuzzy by using chance constraint theory. Finally, a case is designed to verify the effectiveness of the model. The sensitivity analysis of the average loading time of a single container in the port station is carried out. The results show that shortening the parameter affects the organization scheme, which can reduce the value of objective function. The method provides support for the compilation of the distribution trains organization scheme in the port station.

Autonomous vehicles are rapidly evolving, but public acceptance remains challenging. Research on mixed traffic flow, where human-driven and self-driving cars coexist, is crucial. Most studies focus on the impact of a single variable on route selection, overlooking the combined influence of multiple factors and varying driver cognition. This study delves into traffic efficiency, safety, and the overall traffic environment to scrutinize the network equilibrium of route selections in a mixed traffic environment. By incorporating the heterogeneity of comprehensive cost cognition into a route choice model, the analysis of Hong Kong’s road network yields several pivotal insights. Firstly, autonomous vehicles exhibit considerable variations in route preferences under diverse scenarios for the same origin-destination pair, highlighting their sensitivity to cost performance. Secondly, the comprehensive travel costs in a mixed traffic environment are significantly higher compared to those in single traffic flow scenarios, revealing a greater heterogeneity in cost cognition. Thirdly, Mixed traffic flow assignment is more balanced. This study provides insights for path selection in mixed traffic, offering valuable guidance for vehicle-road collaboration and promoting green travel.

The traditional coordinated control of arterial road only considers the green band width of the main road traffic, but the arterial road traffic in the green band will exist when the driving distance is too long, which brings a series of safety and efficiency problems. Based on the trajectory data, this paper solves the basic signal control scheme with the objective of delay minimization, and on the basis of this scheme, the concept of traffic storage area is introduced. According to the characteristics of the traffic dispersion phenomenon, three constraints of speed, normalized speed dispersion coefficient, and intersection importance are set to set up the storage area in the arterial road, and the signal updating scheme based on this storage area and the subsequent intersection signal updating scheme are proposed. SUMO software is utilized to build a simulation platform, and six consecutive intersections on Hong Kong Road in Jiaozhou City are used as cases for simulation experiments. The experimental results show that, compared with the algorithmic solution of the basic scheme, the inclusion of the storage area will improve the intersection delay to a certain extent, but can greatly reduce the dispersion of the arterial traffic flow and improve the intersection efficiency.

During unexpected operational disruptions, addressing how to develop targeted guidance information for affected passengers in urban rail transit systems to ensure their safety and maintain network operations is a pressing issue. This thesis focuses on passengers present in the network during disruptions, categorizing them and disseminating guided paths through station broadcasts. This thesis primarily minimizes the total travel time of passengers by formulating an optimized passenger flow guidance information release model based on station broadcasts, solved using a genetic algorithm to determine the optimal paths release strategy between affected origin-destination pairs during disruptions. A case thesis is conducted on the regional rail network of Guangzhou. The induced path release strategy calculated by the model results in an average passenger travel time of 21.31 min. The release strategy with the maximum target value generated during the optimization process yielded an average travel time of 24.79 min, an increase of 16.34%. In contrast, the strategy of universally releasing the shortest path information to passengers resulted in an average travel time of 23.25 min, an increase of 9.11%. The computational results indicate that the model effectively generates reasonable solutions that significantly reduce passenger travel costs, providing theoretical support for management departments in organizing passenger flow during disruptions.

Understanding the charging behavior of electric vehicle (EV) is crucial for both supply planning and demand management of EV charging. The present work investigated the influencing factors of private EV owners’ charging behavior based on Stated Preference (SP) survey data. Specifically, charging behavior preference of private EV owners were collected through SP survey, considering cognitive ability, risk attitude, inertia, environmental and emotional factors. Then a structural equation model (SEM) was established to explore the relationships between various factors and EV charging behavior. The results revealed that inertia is influenced by the other four factors, charging risk attitude is affected by cognitive ability and emotional factors, charging emotion is influenced by cognitive ability and environmental factors, and cognitive ability is positively correlated with environmental factors. These conclusions provide deeper understanding about private EV owners' charging behavior and important insights for better charging behavior management.

The minimum tracking interval distance (TID) of trains is a crucial operational element in the scheduling of Medium-Speed Maglev (MSM) train timetables. In the scenario of mixed passenger and freight operations, the differences in traction performance between MSM passenger trains and freight trains result in challenges for calculating the tracking interval. Therefore, this paper proposes a method for calculating the minimum TID by considering the different parameters and operational strategies of passenger and freight trains in mixed transport scenarios. First, this paper constructs a simulation model for the operation of MSM trains. Next, based on safety braking principles and the MSM train operation simulation approach, a dynamic tracking simulation model for MSM trains is established. Finally, this paper simulates the operating speed curves of passenger and freight trains sequentially. The minimum dispatch interval distance is calculated by evaluating the safety constraints during the operation of MSM trains and employing an iterative optimization method. To validate the proposed method, simulation tests were conducted using constructed MSM lines and data from passenger and freight trains. Two cases of mutual tracking between passenger and freight trains were specifically used to verify the method for calculating the TID of MSM trains in the scenario of mixed passenger and freight operations. The simulation results indicate that the method can calculate the minimum TID for passenger and freight trains in mixed operations.

This paper addresses the issue of delays in high-speed rail trains, applying the Markov chain model to analyze the lateral propagation mechanism of delays and based on this, constructs an integrated CNN-LSTM-Attention model. The model incorporates the Maximum Information Coefficient-based MIC-BP feature selection algorithm to optimize the input feature set, effectively enhancing the model’s performance in practical applications. In the case study using Guangzhou-Shenzhen high-speed rail data, the model achieved a prediction accuracy of 97.71% within an allowable error margin of one minute, demonstrating its effectiveness in handling complex datasets, providing a basis for the adjustment of high-speed railway transportation organizations.

Geological and meteorological disasters such as landslides, debris flows, avalanches, and extreme weather events have significant impacts on human society. These disasters not only threaten lives and property but also damage transportation infrastructure, affecting the delivery of relief supplies and evacuation of personnel. In disaster-prone areas, road networks are often sparse due to geological and climatic constraints, characterized by long distances between road segments, few traffic nodes, and limited alternative routes. Constructing an effective dual-layer topological model for sparse road networks in such regions is crucial. This study proposes a dual-layer topological model comprising a backbone layer and a local layer. Using directed graphs to represent the topology of sparse road networks, combined with complex graph theory and dynamic attribute data, the model accurately describes the state of the road network and its changes under disaster conditions. The model aids in disaster risk management and emergency route planning, providing robust tools and perspectives for sustainable development and safety in disaster-prone areas. Case studies validate the model’s effectiveness in managing and responding to geological and meteorological disasters.

High-speed rail is a successful example of China’s independent innovation, and also an important embodiment of new quality productivity. By constructing game model of the R&D cost allocation and intellectual property sharing of China High-Speed EMUs between China State Railway Group and China Railway Rolling Stock Corporation, this paper discusses the earnings change and decision choice under different ratio of R&D cost allocation and intellectual property sharing, and further analyzes the influence factors on the strategy choice through the simulation analysis of game model. The results show that the collaborative innovation mode is the best strategy between the two parties, which should further strengthen cooperation, reduce R&D costs, set the reasonably ratio of R&D cost allocation and intellectual property sharing, promote patent transformation, and maximize the positive synergistic effect.

When subgrade settlement occurs, especially if it is significant, the structural integrity and functionality of the track interlayer may be significantly and negatively affected. There may be discontinuous contact (void) between track and subgrade, which is particularly difficult to monitor. In this research, a damage identification method based on one-dimensional convolutional neural (1D-CNN) network and vehicle-track coupled dynamics is proposed. Frist, a large-scale vehicle-track-subgrade coupled dynamic model is established to simulate the impact of settlement-induced track voids on the train’s vertical vibration acceleration Then a 1D-CNN network capable of automatically extracting features is built to identify the track voids from the vehicle acceleration signals. The efficiency and accuracy of the method is improved through training, optimization, and validation adjustments. The results show that the identification rate of this method reaches 98%.

To accurately calculate the capacity of non-motorized vehicles at urban intersections, and determine the service level of non-motorized lanes in mixed traffic environments, this study analyzed the difference in overtaking behavior between electric bicycles and traditional bicycles when passing intersections. It also explored the expansion characteristics of two types of non-motorized vehicles at mixed intersections and analyzed the driving characteristics of non-motorized vehicles arriving at intersections under different scenarios. Additionally, the study proposed a calculation method for the electric bicycle conversion coefficient at intersections. Based on two drone videos at signal intersections in Xi’an, it was found that the conversion coefficient of electric bicycles at the intersection to traditional bicycles is 1.447. The conversion coefficients were verified by Vissim simulation. The relative errors associated with capacity, including linear speed, right turn speed, starting acceleration, and arrival acceleration are all within acceptable ranges and closely align with practical conditions. Therefore, this conversion factor model can provide a theoretical basis for accurately evaluating the mixed state of non-motor vehicles at intersections.

Various safety surrogate indicators have been used to identify conflicts within intersections, however most of them are limited to assess the conflict risk without the consideration of conflict consequences. At the mixed intersection, the conflict consequences are critical measurements for risk assessment due to the heterogeneity of traffic participants. Therefore, we proposed a Traffic Conflict Criticality Index (TCCI) to identify conflict risk considering heterogeneity of traffic participants. The coulomb force theory is applied to the field of traffic safety, viewing different traffic participants as particles with varying charges and identifying conflicts by analyzing the forces between them. In this model, energy and a heterogeneity index are used to identify and quantify the conflict risk between different traffic participants. The velocities and accelerations of different traffic participants are used as input features and thresholds for different conflict levels are determined through the k-means clustering algorithm. The results of a case study from InD dataset, demonstrate that the proposed TCCI model has high accuracy in identifying traffic conflicts, avoiding misjudgments, especially when considering the heterogeneity of traffic participants, accurately identifying 495 serious conflicts, comparing to the traditional TTC model. The model proposed in this paper has a high conflict prediction capability, effectively identifying and differentiating the conflict risks of different traffic participants at intersections. The application of this model provides an alternative tool to enhance traffic safety management at urban intersections, with particular emphasis on the safety of vulnerable groups.

The study focuses on the valuation method of railway data assets, aiming to address the lack of pricing methods for data assets in the railway industry. Firstly, a basic value assessment model is constructed that includes total cost and quality factors of data assets. Subsequently, market supply and demand, value realization risk, and data monopoly coefficient factors are incorporated into the value assessment model to establish an internal market pricing based on cost price as well as three external market pricing methods including comparable transaction pricing, buyer utility pricing, and bargaining pricing. Finally, the work uses a case study to calculate the final price range of the data assets to be 59,323.75 to 84,000.00 Yuan RMB.

Green transportation is the object of transportation in the future, and reducing energy consumed in transportation is of great significance to sustainable development of the society. This paper mainly studies the impact of line slope between stations on the energy consumed by the train. The paper starts with description of typical line conditions between stations. Three control modes: traction, coasting and braking are considered in the process of train running. Based on these, the basic dynamic model of the train running energy consumption, and the corresponding energy-saving operation algorithm are set. Then, in the presumption that the distance between stations of urban rail transit is 3 km, energy consumption curve of the train and the corresponding velocity curve distribution of different slope and slope length are analyzed, and the optimal combination between station section line slope and train energy-saving operation was obtained. This provides a reference for the design of station section lines based on train energy-saving operation in urban rail transit.

Train timetable plays a crucial role in railway transportation operations, outlining the arrival and departure times for each train. The precision of the timetable is vital in ensuring the efficiency, stability, and safety of train operations. In this paper, we introduce a precision-oriented approach to generating train timetables, leveraging a space-time network framework. An integer programming model is formulated to capture the complexities of timetable generation, and an efficient alternating direction method of multipliers (ADMM) decomposition algorithm is designed to solve the model. Utilizing the lower bound solution obtained through the ADMM-based decomposition algorithm, we further propose a topological order-based algorithm to generate an upper bound solution, resulting in a feasible timetable with second-level granularity. Finally, we validate the effectiveness and efficiency of our proposed model and algorithm through a real-world experiment involving the Beijing-Shanghai high-speed railway.

Combining refined passenger demand optimization with intercity train timetables holds significant practical value in promoting the public transport operation of intercity trains. Under specified refined passenger demand conditions, the model takes into account key constraints related to train operations, passenger boarding and alighting processes, as well as the statistical data on passengers boarding and alighting. This comprehensive approach ensures the safety and efficiency of train operations. Moreover, the study introduces a system cost calculation formula that incorporates both the total cost of the railway department and the total cost of passenger travel. The goal is to minimize the system cost while constructing a non-linear optimization planning model that considers intercity train cross-station operations and rational adjustments of train departure intervals. The solution to this model involves the utilization of the simulated annealing algorithm. To validate the efficacy of the model and algorithm, a case study is conducted on a specific intercity line during a weekday morning peak period. The results demonstrate that the optimized timetable leads to a reduction of 11.5% in system costs, a 7.5% decrease in the total cost incurred by the railway department, and a 13.45% decrease in the total cost of passenger travel. This highlights the potential of strategies such as flexible train stops and rational adjustment of train departure intervals as valuable references for achieving a public transport operation of intercity trains, especially under refined passenger demand conditions.

The photovoltaic-energy storage-charging station (PV-ES-CS) is an important approach of promoting the transition from fossil energy consumption to low-carbon energy use. However, the integrated charging station is underdeveloped. One of the key reasons for this is that there lacks the evaluation of its economic and environmental benefits. This study proposes an evaluation framework to assess the utilization potential of PV-ES-CS in the senior of highway service areas. Based on the data of highway infrastructure and roadside energy use in Chongqing, this paper analyses the technical, economic and environmental benefits of integrated charging station.

This paper investigates the optimization of multistage dynamic pricing for high-speed railway (HSR) considering flexible pre-sale period division scheme. To address the variability of daily demand during the booking horizon, an elastic demand function for each day is developed. Considering various constraints, including train capacity constraints, passenger demand constraints, price-related constraints, a non-linear mixed integer optimization model is formulated for the multistage dynamic pricing optimization problem to maximize railway revenue. The complicated capacity-sharing relationship for HSR and flexible pre-sale period division increase the problem’s scale. Thus, a comprehensive optimization approach is proposed based on decomposing the optimization problem into two subproblems. Subproblem 1 solves multistage dynamic pricing and ticket allocation based on a known period division scheme generated by subproblem 2, while subproblem 2 adjusts the boundaries between two consecutive periods to generate a period division neighborhood scheme. Subproblem 1 is solved by formulating as a bi-level programming problem. The numerical examples are conducted to evaluate the proposed model and solution methods, providing valuable decision support for railway operators.

Reinforcement learning (RL) has recently been applied to solve railway timetabling problems. In this paper, we provide a detailed overview of the state-of-the-art research for RL in railway timetabling. Specifically, we categorize RL into basic RL and deep reinforcement learning (DRL), and further divide the research of railway timetabling into scheduling and rescheduling for exhaustively review and discussion. The present research on RL in railway timetabling is still in the primary stage and the scale of problems that can be solved is still limited. However, the applications of RL shows great promise and excitement, with significant potential for addressing various challenges in railway planning and management in the future.

High-speed railways are vital infrastructure projects that significantly enhance regional connectivity and collaboration. This study presents an advanced data-driven predictive control methodology to optimize the precision of high-speed train operations. By analyzing operational data across various phases—startup, constant speed, and braking—this study identifies critical patterns and performance variations under different conditions, revealing optimization opportunities. The proposed predictive control system, based on a Random Forest algorithm model, incorporates key processes such as error quantification, predictive control, internal modeling, disturbance compensation, and feedback loops. The internal model utilizes Random Forest algorithms, involving detailed steps like training data analysis, random regression forest development, regression tree calculations, and aggregation of tree estimates to forecast future train states. Simulation and validation results demonstrate that integrating multiple parameters as predictive feature sets in the Random Forest model significantly improves forecast accuracy. Future research could further enhance this machine learning-based control method by incorporating more diverse data sources, thereby improving the accuracy and reliability of train predictions, advancing automation and intelligence in high-speed railways, and promoting sustainability and efficiency.

Bahrain is a small island country in the Arabian Gulf that relies heavily on imports for its agricultural products. Manama Central Market (MCM) is the largest central market in Bahrain, supplying agricultural products to the entire country. Its fruits and vegetables must be of high quality and safe to eat. This study examines the current state of the cold chain of fruits and vegetables in the MCM using questionnaires, interviews, and observation, identifies problems in the process of storage and sale of fruits and vegetables, and investigates the key factors affecting the freshness of fruits and vegetables. The findings revealed that the storage temperature of fruits and vegetables, their own durability, professional knowledge, and professional training all had varying degrees of impact on fruit and vegetable freshness. Finally, it is suggested that MCM and merchants work together to improve temperature control and environmental sanitation, as well as strengthen professional training, in order to improve the quality of fruits and vegetables in the MCM.