The Proceedings of the 11th International Conference on Traffic and Transportation Studies
Volume I
- 2025
- Book
- Editors
- Lingyun Meng
- Yongsheng Qian
- Yun Bai
- Bin Lv
- Yuanjie Tang
- Book Series
- Lecture Notes in Civil Engineering
- Publisher
- Springer Nature Singapore
About this book
This book reflects the latest research trends, methods and experimental results in the field of traffic and transport, covering a wealth of state-of-the-art research theories and ideas. As a vital field of research, highly relevant to current developments in a number of technological areas, the topics covered include traffic data analysis, transport planning, multimodal and integrated transport, modelling and analysis of passenger behaviour, etc. The aim of the proceedings is to provide a major interdisciplinary forum for researchers, engineers, academics and industry professionals to present the most innovative research and development in the field of traffic and transportation. Engineers and researchers from academia, industry and government will also explore the solutions that combine ideas from different disciplines in the field. The volumes serve as an excellent reference for researchers and graduate students working in the field of traffic and transportation.
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Table of Contents
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Frontmatter
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Disposition-Friendly Parking Planning for Urban Train Operating Companies
Christian Liebchen, Bennett BussekThe chapter explores the challenge of efficiently parking trains during off-peak hours in urban railway systems. It highlights the need to balance the costs of empty kilometers with the operational benefits of keeping sidings available for disruptions. The authors present two procedures: a simple priority-based method and a more sophisticated monetarized approach. The simple method relies on the track layout to identify critical sidings, while the monetarized approach considers the potential loss of service during disruptions. The monetarized procedure involves computing the expected annual monetary loss if a siding is not available for disruption management. The chapter concludes with a real-world example from S-Bahn Berlin GmbH, illustrating the practical application of these procedures. The text offers valuable insights and tools for train operating companies seeking to optimize their vehicle scheduling and maintain high service quality during unplanned incidents.AI Generated
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AbstractOutside peak hours, urban train companies usually have to park a considerable amount of their rolling stock. In many urban rail networks, such parking takes also place on sidings outside any depot. Obviously, if according to the vehicle schedule a siding is occupied by a parked train, it is no longer available for disposition purposes. In particular, in the event of some unplanned track blockage, depending on the track topology, if some siding constitutes the only option to change from one track to the opposite track in order to continue as return trip, then such an available siding is of high operational value.We propose a procedure to assess significant parts of the operational value of an available siding. In particular, for each siding, we identify an amount of scheduled empty kilometers to park trains in stations that are farther away, that are likely to pay off during operations. This procedure is already in daily use in the planning department of the Berlin fast train company S-Bahn Berlin GmbH. -
Calibration of Car-Following Behavior Based on Monocular Camera
Yufei Liu, Chong WeiThe chapter delves into the calibration of car-following behavior models based on monocular camera data, highlighting the use of the YOLOv5 algorithm for vehicle detection. It introduces a homography matrix for establishing a ranging model, enabling accurate measurement of vehicle distances. The study validates the ranging model through empirical data and employs factor analysis to select key influencing factors for car-following behavior. The chapter concludes with the calibration of linear dynamic car-following models and ACC models, demonstrating the practical application of these models in diverse traffic conditions. The research emphasizes the need for localized models to accommodate China’s unique traffic environment and infrastructure, setting the stage for future advancements in traffic flow analysis and automated driving systems.AI Generated
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AbstractIn the past, most of the research on car-following models in China focused on physical statistics, with relatively little collection and analysis of car-following data. Therefore, it is essential to establish a car-following model suitable for analyzing the car-following characteristics of Chinese drivers based on real driving data. This paper utilizes a vehicle-mounted monocular camera to capture traffic flow images on the highway and uses the YOLOv5 algorithm to detect vehicles. Through the homography transformation between the image plane in the front view and the spatial plane in the top view, the pixel coordinates are converted to 3D spatial coordinates. Then, the ranging model is established, and its effectiveness is verified using the length of the lane dividing lines. The factor analysis is used to determine the main influencing factors of car-following behavior. The appropriate linear car-following models are selected and its parameters are calibrated using multiple linear regression methods. This paper realizes the collection and subsequent processing of car-following behavior data under natural driving conditions, and transforms generalized car-following models into specific research theories for application in practical situations. This method of collecting car-following driving data has strong practicality and can provide effective data support for studying car-following models that accurately reflect the operation conditions of road traffic. -
Optimal Design of Fixed-Route Transit and Point-to-Point Transit Network Considering Layout of Expressway Network
Tian Zeng, Sida LuoThe chapter delves into the optimal design of fixed-route transit (FRT) and point-to-point transit (PPT) networks in grid cities, taking into account the layout of expressway networks. It introduces a bimodal transit system where PPT connects long-distance hubs via expressways without intermediate stops, while FRT operates in a grid pattern with higher frequency in city centers. The study formulates an optimization model using a continuum approximation method to explore the efficiency of PPT services and the optimal allocation of resources between FRT and PPT. Numerical experiments based on data from Suzhou, China, compare the bimodal system with a traditional FRT system, revealing that the bimodal system is more effective under high and heterogeneous demand. The chapter highlights the significant reduction in in-vehicle travel time by PPT, despite increases in walking time, waiting time, and transfer penalties. It also provides insights into the optimal decision variables under varying demand conditions, showing that PPT is particularly effective in cities with high radial demand. The study concludes with practical implications for designing key parameters such as network density, service area, and fleet size for FRT and PPT under different demand scenarios.AI Generated
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AbstractWith the expansion of urban scale, traditional public transit struggles to fulfill long-distance travel demands (Rail transit is costly and fixed-route transit is time-consuming). This paper proposes a bimodal transit system with point-to-point transit (PPT) services and fixed-route transit (FRT) services. PPT is designed for passengers traveling long distances, efficiently linking two major transport hubs with few intermediate stops. We formulate the joint design problem as a mixed-integer program through a parsimonious continuum model. By minimizing the system cost, the sum of agency and user costs, we determine optimal design parameters using MATLAB built-in genetic algorithm. Through numerical experiments, we analyze how the performance of the bimodal transit system varies with demand, city size, and other factors. Our results demonstrate that the proposed system is more cost-efficient than the regular system in cities with high travel demand. The proposed model can be used for the sketchy design of such bimodal transit system and provide a theoretical foundation for the adjustment of public transit networks. -
Identification and Explanatory Analyses of Driving Risk Factors for Freeway Driven by Trajectory Data
Bo Xu, Chunjiao Dong, Xiaoya Wang, Penghui Li, Xuedong Yan, Kun XieThe chapter delves into the critical issue of freeway safety, leveraging trajectory data to identify and explain driving risk factors. It systematically extracts variables related to human, vehicle, road, and environmental factors, employing the XGBoost algorithm for high-accuracy risk assessment. The use of SHAP values offers interpretability, allowing for a detailed understanding of contributing factors and their interactions. The study concludes with a call for enhanced data quality to further improve driving safety assessments.AI Generated
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AbstractTo explore the micro-level influencing factors of driving risks for private cars on freeway sections, the study integrates private car trajectory data matched with traffic accidents in both temporal and spatial dimensions. It systematically extracts indicators for driving risk assessment from four aspects: personnel, vehicle, road, and environment. The spatial–temporal range of 5 km upstream and downstream within 5 min before and after the occurrence of accidents was selected for data screening. An XGBoost-based driving risk identification algorithm was established to classify the data, and SHAP values were used for interpretability analysis of the indicators. The results indicate that the developed XGBoost algorithm achieved accuracy rate of 92.5% in identifying driving risks, with certain indicators showing strong interactions and inhibitory effects. The findings provide theoretical support and insights for traffic safety management and risk control. -
Optimizing the Timetable of Cross-Lines Urban Rail Transit for Shared Passenger and Freight Transport
Yue Gao, Yixiang YueThe chapter delves into the optimization of urban rail transit timetables for shared passenger and freight transport, with a focus on cross-line operations. It introduces a mixed-integer programming model that balances supply and demand while minimizing passenger waiting time and train operation costs. The model addresses the spatial imbalance between supply and demand in the postal industry of first-tier cities by integrating small parcels from logistics transfer bases into standardized cargo units for subway transportation. The chapter presents a case study on the Fangshan Line and Line 9 of Beijing Metro, demonstrating the effectiveness of the model in reducing passenger waiting time, fully utilizing redundant capacity, and lowering train operation costs. The research highlights the practicality of operating cross-line trains during off-peak hours to enhance the efficiency of urban rail transit systems.AI Generated
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AbstractTo alleviate urban traffic pressure and reduce congestion costs, this paper constructs a multi-objective mixed integer programming model for optimizing the timetable and allocation of passenger and freight wagons in the case of operating cross-line trains. The model aims to minimize passenger waiting time and train operation costs, thereby optimizing the timetable and distribution of passenger and freight wagons to enhance the transport capacity. The paper conducts data experiments using the Beijing Subway Line Fangshan and Line 9 as a case study, and utilizes the optimization solver Gurobi for solution. Experimental results demonstrate that the timetable determined by the model can better meet the requirements and constraints. Compared with the scenario of not operating cross-line trains, operating cross-line trains on subway lines can significantly reduce average passenger waiting time, fully utilize redundant transport capacity, and reduce train operation costs. -
Reliability-Centered Maintenance Scheduling Optimization for High-Speed Railway Facilities with Multi-level Tasks
Gehui Liu, Ling Liu, Qing Li, Qi Wang, Huiru ZhangThe chapter delves into the optimization of maintenance scheduling for high-speed railway facilities, focusing on reliability-centered approaches. It introduces a model that incorporates multi-level maintenance tasks such as preventive replacement, preventive maintenance, and corrective maintenance, alongside a reliability model based on multi-stage deterioration processes. The model aims to minimize maintenance costs while meeting reliability and availability constraints. The study compares the proposed model with baseline strategies, demonstrating significant cost savings and improved facility lifespan. The chapter also explores the impact of imperfect maintenance on the optimization process, highlighting the crucial role of effective preventive maintenance in reducing costs and extending the renewal cycle of facilities.AI Generated
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AbstractThe high-speed railway provides a safe, punctual, and comfortable mode of transportation underpinned by stable infrastructure, fast trains, and dependable control systems. Implementing an appropriate maintenance schedule is essential for ensuring operational status and reducing the malfunction events of these systems. This study proposes a reliability-centered maintenance (RCM) strategy for high-speed railway facilities maintenance scheduling optimization. A reliability model is developed to characterize the multi-stage deterioration process of facilities, and a maintenance optimization model is put forward to minimize the maintenance cost by incorporating reasonable critical reliability with multi-level maintenance tasks. Two baseline models that typify current maintenance strategies are also formulated to illustrate the enhanced effectiveness of the RCM approach. The numerical example demonstrates that the RCM can significantly reduce maintenance schedule costs while simultaneously prolonging the lifespan of the facilities, all within the bounds of the same reliability constraints. -
Simulation for Train Group Tracking Operation of Urban Rail Transit
Miaomiao Zhong, Dejie Xu, Liang Gong, Changwu HuiThe chapter delves into the challenges of urban rail transit operations, such as congestion during peak hours and capacity reduction. It introduces a high-precision train operation simulation model to optimize train tracking under different scenarios. The model analyzes train dynamics performance, including acceleration and deceleration, and evaluates the impact of parameter settings on simulation results. Notably, the study simulates energy-saving operation strategies and optimal control problems during train movement, aiming to enhance the efficiency and punctuality of train operations. The chapter also explores the relationship between maximum operating speed, station spacing, and energy consumption, offering insights into system optimization and energy efficiency.AI Generated
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AbstractTrain operation efficiency is the basis for formulating and adjusting the train operation plan. Constructing an accurate train tracking operation model is the key to solving the low operation efficiency during the peak period. According to the actual operation scenario of the train, a cellular automata model based on spatial discretization and time continuity is constructed by using sparse matrix to store cellular state. The simulation results show that the train traction and braking performance parameters meet the requirements of the train speed limit. The simulation results of the train group operation curve under different departure intervals are consistent with the actual operation, which verifies the reliability and simulation accuracy of the model in this paper. On the lines with low speed limit and long station spacing, the adaptability of train speed control is stronger, and the increase of traction energy consumption with speed is smaller. The research conclusions can provide reference for urban rail transit optimization system and adjustment of operation strategy. -
Research on Critical Node Identification and Resilience Optimization Strategy of Urban Rail Transit Network
Yangyang Yang, Liang Gong, Dejie Xu, Yuning Zeng, Chenhao HuThe chapter delves into the identification of critical nodes in urban rail transit (URT) networks and the optimization of network resilience. It introduces an entropy-based TOPSIS technique (E-T) for evaluating node importance, considering both local and global centrality measures. The study highlights the effectiveness of the E-T method in accurately identifying key nodes, especially under deliberate attacks. Additionally, it proposes a resilience optimization model based on edge augmentation, which aims to maximize network resilience while minimizing construction costs. The research is validated through a case study of the Beijing subway network, demonstrating significant improvements in network resilience under various attack scenarios. The chapter offers valuable insights into enhancing the stability and efficiency of urban rail transit systems.AI Generated
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AbstractStudying the identification and resilience optimization of critical nodes in urban rail transit networks can prevent network interruptions and enhance the network’s ability to resist risks. This article adopts a TOPSIS model based on entropy weighting method to design a node importance evaluation approach that considers both local and global network structure information. Furthermore, under the constraint of financial budget and considering the comprehensive interests of different stakeholders in the network, a network resilience optimization model is constructed. Using Beijing’s subway as a case, when the network is subjected to sequential-deliberate attacks until collapse, the node coupling importance evaluation method proposed in this article reduces the number of attacked nodes by 4.39%, 2.59%, and 0.52% respectively, compared to Degree Centrality, Closeness Centrality, and Betweenness Centrality. This approach helps identify and maintain critical network nodes in different areas, ensuring the stability and safe operation of urban rail transit networks. Compared to the existing network, the optimized network exhibits a 3.36% improvement in flexibility, with an increase in the number of attacked nodes by 0.26% and 14.21% under random and sequential-deliberate attacks until collapse, demonstrating that the proposed elastic optimization method effectively improves the risk resilience of urban rail transit networks. -
Analysis and Evaluation of Design Parameters of Long-Span Steel Plate Composite Girder Bridge Based on Stability
Qiheng Nie, Wei Hou, Yang Lu, Shuanhai HeThe chapter delves into the critical design parameters of long-span steel plate composite girder bridges, emphasizing stability as a key factor. It begins with an overview of existing design guidelines and previous research, highlighting the need for further optimization. The study then focuses on a specific bridge model, analyzing the impact of cross beam spacing, height-span ratio, web height-thickness ratio, and flange width-thickness ratio on structural stability. Through single-factor and multi-factor analyses, the chapter identifies the most influential design parameters and provides recommendations for optimizing bridge safety and material usage. The use of finite element modeling and orthogonal test methods offers a comprehensive approach to bridge design, making this chapter a valuable resource for engineers seeking to enhance the stability and efficiency of long-span composite girder bridges.AI Generated
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AbstractThe stability of steel plate composite girder bridge is an important factor that must be considered in design. In this paper, the finite element method is used to analyze the overall stability and local stability of long-span steel plate composite girder bridge under single factor by selecting the cross beam spacing, height-span ratio, web height-thickness ratio and flange width-thickness ratio as the key design parameters. The orthogonal experimental method was used to analyze the multi factor combination of the bridge, and the importance of each design parameter was evaluated from the aspects of structural safety and economy. The results show that the safety factor of stability of steel plate girder bridge is positively correlated with the height-span ratio, and negatively correlated with the cross beam spacing, web height-thickness ratio and the flange width-thickness ratio. As far as the whole bridge is concerned, the cross beam spacing has the greatest influence on safety factor of stability and its value should be less than 15 m; the web height-to-thickness ratio has the greatest influence on the amount of steel, and its value should be between 100 and 120. The results of this paper will make this type of bridge structure more reliable and less costly. -
An Expressway Short-Term Traffic Flow Prediction Model Based on Attention Mechanism
Jiaxin Liu, Xianyu WuThe chapter introduces an innovative deep learning model for short-term traffic flow prediction on expressways, combining Conv-LSTM for spatial-temporal feature extraction, an attention mechanism for enhancing prediction accuracy, and an error correction module for further refining predictions. The model is evaluated through extensive experiments, showcasing its superior performance over existing models in both expressway and urban scenarios. The integration of these advanced techniques offers a promising solution for intelligent traffic management systems.AI Generated
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AbstractUpgrades are continuously made to expressway traffic perception technology as part of the ongoing development of intelligent transportation, resulting in a large amount of data, including vehicle position, passing time, and speed. This real-time, accurate, and efficient source data is used to estimate short-term traffic flow on expressways. Aiming to enhance predictive accuracy and more accurately capture the intricate spatial and temporal features of traffic flow, this paper presents a convolutional long short-term memory neural network model based on attention mechanism to predict traffic flow. To extract spatial-temporal features rather than just single temporal features, convolutional long short-term memory neural networks combine the advantages of long short-term memory neural networks to extract temporal features and convolutional neural networks to extract spatial features. Attention mechanism is added to capture the influence of past feature states of time series data on traffic flow. On this basis, the error correction mechanism is introduced to further enhance the prediction accuracy of the model. The experimental results indicate that the proposed model outperforms other current techniques in terms of prediction accuracy. -
Research on Train Working Diagram Optimization Technology of Intercity Railway Based on Improved Sarsa Algorithm
Xiaohuan Liu, Jiaming Fan, Peiyu Zhou, Bo Li, Junren Wei, Angyang ChenThe chapter delves into the optimization of train working diagrams for intercity railways, highlighting the need for efficient scheduling to meet increasing passenger demands. It introduces an improved Sarsa algorithm to address the complexities of balancing train departures, minimizing operational conflicts, and reducing both costs and waiting times. The method is particularly innovative in its segmented approach, differentiating optimization strategies for peak and off-peak hours. Experimental results demonstrate the effectiveness of the proposed algorithm in significantly reducing train operation costs and passenger waiting times compared to existing methods.AI Generated
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AbstractAt present, the increase of intercity railway trains has made it more convenient for passengers to travel. However, in the face of constantly adding trains, the work of traditional manual preparation of train working diagram has become very difficult. The rapid development of artificial intelligence technology makes it possible to optimize the train working diagram by intelligent means. In this paper, based on the existing intelligent adjustment strategy using the Sarsa algorithm, an optimization method for intercity railway train working diagram using the improved Sarsa algorithm is proposed. The proposed method adopts a differentiated optimization strategy for peak and off-peak hours to solve the tradeoff problem between train evenness and operation costs. The experimental results show that the proposed strategy can reduce passenger waiting time and train operation costs by 20.00% and 67.80%, respectively. Moreover, compared with the Sarsa algorithm for improving balance and reducing conflict severity in previous studies, the proposed strategy can not only reduce the train operation costs, but also reduce the waiting time of passengers by 12.12%. -
Research on the Carbon Footprint Measurement of Corrugated Boxes in Whole Life Cycle
Junzhe Zhang, Ling YangThe chapter delves into the carbon footprint measurement of corrugated boxes, a crucial component in the logistics industry's transition to green packaging. Utilizing the Life Cycle Assessment (LCA) method, the study analyzes the entire life cycle of corrugated boxes, from production to disposal, with a particular focus on the significant carbon emissions generated during manufacturing. The research highlights the importance of reducing manufacturing power consumption and increasing recycling rates to minimize the overall carbon footprint. By quantifying the carbon emissions at each stage, the chapter offers valuable insights for companies to formulate emission reduction strategies and improve the environmental performance of their products.AI Generated
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AbstractAs an important transportation packaging container, corrugated boxes cause pollution to the environment by the carbon emissions generated during their whole life cycle, and analyzing and calculating the carbon footprint of corrugated boxes has high economic value and social significance. This paper takes corrugated boxes as an example to study the carbon footprint. Taking a 5-ton van as a functional unit, based on the life cycle evaluation method, we focus on analyzing the carbon footprints of its production, storage, transportation, and waste recycling, and measure the carbon footprints of its whole life cycle using Gabi software. The study shows that, in the whole life cycle, the corrugated carton production and manufacturing stage has the largest carbon emissions, with a contribution rate of 64.55%, followed by the waste recycling stage accounting for 32.28% of the total carbon emissions. The warehousing and transportation stage accounts for a small proportion. This shows that measures should be taken to strengthen supervision and reduce carbon emissions in the production and recycling of corrugated boxes. -
Pedestrian Evacuation Modeling in Highway Tunnel Fire: A Review
Zhongxin Guo, Wenjie Yang, Yanlong Zhang, Mingwei Hu, Daochu Wang, Xiaofeng XieThis chapter offers a detailed review of pedestrian evacuation modeling in highway tunnel fires, a critical safety concern given China's extensive tunnel network. It begins by outlining the state of fire numerical simulation, including methods, models, and software like FDS and Pyrosim. The chapter then delves into the complex mechanisms influencing crowd evacuation in tunnel fires, considering both objective factors (tunnel structure, fire source location) and subjective factors (psychological state, decision-making ability). It also explores current evacuation strategies and modeling techniques, emphasizing the need for real-time simulation and decision support systems. The chapter concludes by highlighting future research directions, such as the integration of AI and advanced data integration, to enhance the safety and efficiency of tunnel evacuations.AI Generated
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AbstractTunnel fires represent a paramount threat within tunnels, encompassing multifaceted dangers beyond the immediate fire hazard. These threats extend to potential traffic congestion and associated casualties. A rapid and coordinated pedestrian evacuation strategy is pivotal in mitigating both human and property losses. However, conventional emergency plans often grapple with the complexities inherent in ever-evolving tunnel fire scenarios. This underscores the compelling need to introduce tunnel fire evacuation simulations as a proactive approach. Such simulations can authentically replicate fire scenarios and human behaviors, thereby providing a basis for informed emergency decision-making. This paper aims to comprehensively review the current state of tunnel emergency management simulation research, encompassing the evolution of fire numerical simulation techniques and pedestrian evacuation simulation technologies, as well as prospective avenues for future development. This endeavor seeks to enhance the capability to effectively address the challenges posed by tunnel fires. -
Cross-Line Operation Plan for Urban Rail Transit Trains
Linghao Xu, Haozhe Xu, Jiazheng Liu, Shaokuan ChenThis chapter delves into the critical issue of optimizing urban rail transit train operation plans under a cross-line operation scenario. It begins by highlighting the benefits of interconnected train operations, such as reducing transfer passenger flow and alleviating pressure on transfer stations. The existing research is predominantly focused on single-line operations, prompting the need for a more sophisticated model that accounts for the complexities of cross-line operations. The chapter introduces a mixed integer programming model that takes into account the minimum total cost of passenger travel and enterprise operation. It divides passenger flow into various categories and considers factors such as waiting time, on-board time, and transfer time. The model is solved using an enumeration method and is validated through a case study of a local urban rail transit network. The results demonstrate significant improvements in passenger travel time, direct travel rates, and overall network transfer coefficients, making a compelling case for the implementation of cross-line train operations.AI Generated
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AbstractThis study divides the passenger travel path and constructs a mixed integer programming model for solving the cross-line operation plan of urban rail transit train. The optimization goal of model is to minimize the sum of passenger travel cost and enterprise operating cost. The model takes into account the constraints of passenger flow demand, train driving conditions and passenger service level, and is solved by using the enumeration method in short computation times. Through the analysis on the local urban rail transit network in a city, the model results are evaluated by the number of cross-line passenger flow, proportion of cross-line passenger flow, average transfer time of passengers, through rate for cross-line passengers and transfer coefficient. The results show that the operation of cross-line trains among different lines can effectively reduce the number of transfer and the travel time for cross-line passengers, and reduce the operating costs of enterprises. -
Machine Learning-Based Prediction of Effective Prestress Values Under Anchorage for Post-tensioned Small Box Girders
Pengfei Lv, Yuan LiThe chapter delves into the crucial role of effective prestress values in the design and maintenance of small box girders. It highlights the limitations of current calculation methods and introduces machine learning algorithms as a more accurate predictive tool. The study compares four algorithms—multiple linear regression, adaptive boosting regression, random forest, and extreme gradient boosting—to determine the most effective model. The extreme gradient boosting model is found to be the most accurate, with a high goodness of fit and low prediction error. The analysis also identifies key factors influencing prestress values, with the thickness of the end web being the most significant. This research offers valuable insights and a practical solution for predicting prestress values in post-tensioned small box girders, contributing to the safety and efficiency of bridge construction.AI Generated
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AbstractEffective prestress under the anchorage refers to the prestress force retained by the prestress tendons under the anchorage opening of the working anchorage after the prestress tendons are tensioned and anchored. In order to improve the accuracy of the selection of the effective prestress under anchorage value of post-tensioned method small box girder, based on the measured data of effective prestress under anchorage of small box girder of a highway in Guangdong, four machine learning models, namely, multiple linear regression (MLR), adaptive boosting regression (AdaBoost), random forest (RF) and extreme gradient boosting (XGBoost), are used to comprehensively analyze the factors affecting the value. The results show that the mean square error (MSE) of the extreme gradient boosting model is 0.11, the mean absolute error (MAE) is 0.08, the Standard Deviation of the Residuals (SDR) is 0.33, and the goodness of fit (R-Squared) is 0.99. It means that the model predicts accurately, so it can be applied to the problem of accurately fetching the value of the effective prestress force under the anchorages of post-tensioned small box girders under certain conditions. In the XGBoost model, the degree of contribution of each factor to the model value is evaluated by introducing the Gini impurity (Gini) value, and the results show that the thickness of end web is the most important factor affecting the value of effective prestress under anchorages, accounting for 21.65%. -
Influence of Joint Rigidity on Mechanical Properties of Main Truss of Double-Deck Steel Truss Girder-Arch Bridge
Xixi Li, Yuan LiThe chapter delves into the significance of joint rigidity in the design of steel truss girder bridges, focusing on a 202m simply supported double-deck truss-arch system bridge. It compares the mechanical properties of the main truss under different joint models—hinged, rigid, and rigid zone—using finite element analysis. The study evaluates the impact of joint rigidity on axial forces, shear forces, bending moments, combined stresses, and joint deflections, offering valuable insights into the structural performance and optimization of steel truss bridges.AI Generated
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AbstractIn order to study the influence of the mechanical properties of the steel truss bridge in the rigid area, the 202 m simply supported through double-deck truss-arch system bridge is taken as the research object. Three finite element space models of joint hinged model, joint rigid model and rigid zone of joint are established. The influence of three kinds of joint rigidity on the internal force, combined stress and deflection of steel truss bridge is analyzed. The research shows that the rigidity of the joint has little effect on the axial force of the bar, but has a great influence on the shear force, bending moment and combined stress. The increase of the rigidity of the joint will cause the connecting bar to produce larger secondary bending moment and secondary stress. At the same time, the influence of joint rigidity on the deflection of member joints cannot be ignored. There-fore, in the design and construction of double-deck steel truss girder-arch system bridge, the influence of joint rigidity on the mechanical performance of the structure should be fully considered. -
Research on Resilience Evaluation Method for Urban Rail Transit Lines Under Severe Weather Based on Bayesian Network
Huiru Zhang, Fei Dou, Jie Liu, Kai Lu, Jing Qiu, Gehui LiuThe chapter delves into the critical issue of urban rail transit (URT) system resilience under severe weather conditions, highlighting the need for robust evaluation methods. It defines URT system resilience in severe weather conditions and categorizes resilience indicators into basic and state resilience indicators. The research constructs a hierarchical resilience indicator system, analyzes the risk of each subsystem under severe weather, and classifies severe weather types. The study employs Bayesian Networks to quantitatively assess resilience, enabling targeted emergency maintenance and enhancing the overall safety and efficiency of URT systems.AI Generated
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AbstractSevere weather events constitute a significant safety hazard for urban rail transit systems, emphasizing the importance of risk management. This paper introduces a resilience evaluation method for urban rail transit lines during adverse weather conditions, leveraging Bayesian networks. The proposed method encompasses a comprehensive resilience indicator system tailored to severe weather scenarios, encompassing topological characteristics, passenger organization, and equipment factors. A risk analysis framework utilizing a risk matrix is then applied to evaluate subsystem vulnerabilities under various weather conditions. Furthermore, a Bayesian network-based resilience evaluation approach is designed to integrate and analyze these diverse factors. To demonstrate the effectiveness of this approach, a case study is conducted using real-world data from the Changping Line of Beijing Subway, resulting in resilience scores for critical components like elevators, 400V, traction power supply, and on-board equipment, as well as an overall resilience value for the entire line. The results indicate that, on rainy days, the resilience levels of Changping Line's key indicators and the entire line are precisely predicted by the proposed method. Furthermore, the key indicators and the overall line are generally anticipated to exhibit high resilience, which closely aligns with actual operational performance. These findings not only establish the accuracy of the resilience evaluation method but also reinforce its practical relevance and applicability in real-world settings.
- Title
- The Proceedings of the 11th International Conference on Traffic and Transportation Studies
- Editors
-
Lingyun Meng
Yongsheng Qian
Yun Bai
Bin Lv
Yuanjie Tang
- Copyright Year
- 2025
- Publisher
- Springer Nature Singapore
- Electronic ISBN
- 978-981-9796-44-1
- Print ISBN
- 978-981-9796-43-4
- DOI
- https://doi.org/10.1007/978-981-97-9644-1
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