Minqing Zhu, Bo Zhao, Hongjun Cui, Sheng Yao, Feng Xu
The imbalance of rural parking supply and demand has a great impact on traffic congestion and environmental pollution, which has attracted the attention of many scholars as well as policymakers. However, most of the current research on parking choice mainly focuses on urban business and residential areas, lacks research on rural parking choice behavior, and focuses on the analysis of observable factors, ignoring the internal relationship with potential variables. Based on this, this study considers the heterogeneity of individuals and uses the random forest algorithm to construct a model of rural residents’ willingness to choose parking with both latent and explicit variables, to explore how much and in what ways individual characteristics and parking characteristics affect rural residents’ parking choices, and to explore parking planning programs and strategies that are truly applicable to rural areas. The results of the study suggest that safety and convenience of the parking environment are key factors influencing the parking choice behavior of rural residents, and can greatly improve the predictive accuracy of the parking willingness model. Upon comparison, it is found that the application of the random forest algorithm is also significantly better than the logit model in terms of prediction effect, indicating that there is a nonlinear effect among the factors influencing the parking choice behavior of rural residents and that the random forest model with the addition of latent variables provides a better explanatory ability for the study of the parking choice behavior of rural residents.
{"title":"Parking choice behavior analysis of rural residents based on latent variable random forest model","authors":"Minqing Zhu, Bo Zhao, Hongjun Cui, Sheng Yao, Feng Xu","doi":"10.1093/tse/tdad045","DOIUrl":"https://doi.org/10.1093/tse/tdad045","url":null,"abstract":"\u0000 The imbalance of rural parking supply and demand has a great impact on traffic congestion and environmental pollution, which has attracted the attention of many scholars as well as policymakers. However, most of the current research on parking choice mainly focuses on urban business and residential areas, lacks research on rural parking choice behavior, and focuses on the analysis of observable factors, ignoring the internal relationship with potential variables. Based on this, this study considers the heterogeneity of individuals and uses the random forest algorithm to construct a model of rural residents’ willingness to choose parking with both latent and explicit variables, to explore how much and in what ways individual characteristics and parking characteristics affect rural residents’ parking choices, and to explore parking planning programs and strategies that are truly applicable to rural areas. The results of the study suggest that safety and convenience of the parking environment are key factors influencing the parking choice behavior of rural residents, and can greatly improve the predictive accuracy of the parking willingness model. Upon comparison, it is found that the application of the random forest algorithm is also significantly better than the logit model in terms of prediction effect, indicating that there is a nonlinear effect among the factors influencing the parking choice behavior of rural residents and that the random forest model with the addition of latent variables provides a better explanatory ability for the study of the parking choice behavior of rural residents.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":"23 4","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138943568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wildlife-vehicle collisions (WVCs) with large animals are estimated to cost the United States over ${$}$8 billion in property damage, tens of thousands of human injuries, and nearly 200 fatalities each year. Most WVCs occur on rural roads and are not collected evenly among road segments, leading to imbalanced data. There are a disproportionate number of analysis units that have zero WVC cases when investigating large geographic areas for collision risk. Analysis units with zero WVCs can reduce prediction accuracy and weaken the coefficient estimates of statistical learning models. This study demonstrates that the use of the synthetic minority over-sampling technique (SMOTE) to handle imbalanced WVC data in combination with statistical and machine learning models improves the ability to determine seasonal WVC risk across the rural highway network in Montana, USA. An array of regularized variables describing landscape, road, and traffic were used to develop negative binomial and random forest models to infer WVC rates per 100 million vehicle-miles traveled. The RF model is found to work particularly well with SMOTE-augmented data to improve prediction accuracy of seasonal WVC risk. SMOTE-augmented data are found to improve the accuracy to predict crash risk across fine-grained grids while retaining the characteristics of the original dataset. The analyses suggest that SMOTE augmentation mitigates data imbalance that is encountered in seasonally divided WVC data. This research provides the basis for future risk-mapping models and can potentially be used to address the low rates of WVCs and other crash types along rural roads.
{"title":"Risk Mapping of Wildlife-Vehicle Collisions across the State of Montana, U.S.A.: A Machine Learning Approach for Imbalanced Data along Rural Roads","authors":"Matthew Bell, Yiyi Wang, Rob Ament","doi":"10.1093/tse/tdad043","DOIUrl":"https://doi.org/10.1093/tse/tdad043","url":null,"abstract":"Wildlife-vehicle collisions (WVCs) with large animals are estimated to cost the United States over ${$}$8 billion in property damage, tens of thousands of human injuries, and nearly 200 fatalities each year. Most WVCs occur on rural roads and are not collected evenly among road segments, leading to imbalanced data. There are a disproportionate number of analysis units that have zero WVC cases when investigating large geographic areas for collision risk. Analysis units with zero WVCs can reduce prediction accuracy and weaken the coefficient estimates of statistical learning models. This study demonstrates that the use of the synthetic minority over-sampling technique (SMOTE) to handle imbalanced WVC data in combination with statistical and machine learning models improves the ability to determine seasonal WVC risk across the rural highway network in Montana, USA. An array of regularized variables describing landscape, road, and traffic were used to develop negative binomial and random forest models to infer WVC rates per 100 million vehicle-miles traveled. The RF model is found to work particularly well with SMOTE-augmented data to improve prediction accuracy of seasonal WVC risk. SMOTE-augmented data are found to improve the accuracy to predict crash risk across fine-grained grids while retaining the characteristics of the original dataset. The analyses suggest that SMOTE augmentation mitigates data imbalance that is encountered in seasonally divided WVC data. This research provides the basis for future risk-mapping models and can potentially be used to address the low rates of WVCs and other crash types along rural roads.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":"35 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139207777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The imbalance between supply and demand in urban settings poses a significant barrier to the sustainable advancement of urban transportation. Shared parking serves as a viable solution to mitigate these challenges. Nevertheless, for its sustained growth, a regulatory mechanism enforced by the government is imperative. To promote shared parking market diffusion, we construct an evolutionary game model that incorporates the government, enterprises, and parking demanders. It explores stabilisation strategies for these stakeholders and identifies multiple equilibrium states under different parameter conditions. The results show that the rate and stability of these evolutionary strategies are constrained by the mutual benefits derived by the three parties. Furthermore, such stakeholders are reciprocally influenced by their willingness to engage in shared parking to varying degrees. Government subsidies serve as a determining factor for the strategic choices made by both enterprises and demanders, albeit at different evolutionary rates. Demanders who place a higher value of time demonstrate a preference for on-street parking, thereby influencing enterprise strategies. To foster the long-term growth of the shared parking market, the government must enact appropriate subsidy policies, maintain consistent regulations, and advocate for increased subsidies for parking demanders to reduce the effect of temporal heterogeneity on parking behavioural choices.
{"title":"Evolutionary game analysis of the shared parking market promotion under government management","authors":"Qingqi Wei, Guomei Xiao","doi":"10.1093/tse/tdad041","DOIUrl":"https://doi.org/10.1093/tse/tdad041","url":null,"abstract":"The imbalance between supply and demand in urban settings poses a significant barrier to the sustainable advancement of urban transportation. Shared parking serves as a viable solution to mitigate these challenges. Nevertheless, for its sustained growth, a regulatory mechanism enforced by the government is imperative. To promote shared parking market diffusion, we construct an evolutionary game model that incorporates the government, enterprises, and parking demanders. It explores stabilisation strategies for these stakeholders and identifies multiple equilibrium states under different parameter conditions. The results show that the rate and stability of these evolutionary strategies are constrained by the mutual benefits derived by the three parties. Furthermore, such stakeholders are reciprocally influenced by their willingness to engage in shared parking to varying degrees. Government subsidies serve as a determining factor for the strategic choices made by both enterprises and demanders, albeit at different evolutionary rates. Demanders who place a higher value of time demonstrate a preference for on-street parking, thereby influencing enterprise strategies. To foster the long-term growth of the shared parking market, the government must enact appropriate subsidy policies, maintain consistent regulations, and advocate for increased subsidies for parking demanders to reduce the effect of temporal heterogeneity on parking behavioural choices.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":"31 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139228636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lane changing behavior is a more complex driving behavior among driving behaviors. The lane changing behavior of drivers may exacerbate congestion, however, driver behavioral characteristics are difficult to be accurately acquired and quantified, and thus tend to be simplified or ignored in existing lane changing models. In this paper, the Bik-means clustering algorithm is used to analyze the urban road congestion state discrimination method. Then, simulated driving scenarios under different traffic congestion conditions for simulated driving tests. Through the force feedback system and infrared camera, the data of driver lane-changing behaviors at different traffic congestion levels are obtained separately, and the definitions of the starting and ending points of a vehicle changing lanes are determined. Furthermore, statistical analysis and discussion of key feature parameters including driver lane-changing behavior data and visual data under different levels of traffic congestion were conducted. It is found that the average lane change intention times in each congestion state are 2s, 4s, 6s and 7s, while the turn signal duration and the number of rearview mirror observations have similar patterns of change to the data on lane-changing intention duration. Moreover, drivers’ pupil diameters become smaller during the lane-changing intention phase, and then relatively enlarge during lane-changing, the range of pupil variation is roughly 3.5-4 mm. The frequency of observing the vehicle in front of the target lane increased as the level of congestion increased, and the frequency of observation in the driver's mirrors while changing lanes approximately doubled compared to driving straight ahead, and this ratio increased as the level of congestion increased.
{"title":"The Characteristics of Driver Lane-Changing Behavior in Congested Road Environments","authors":"Wanqi Wang, Guozhu Cheng","doi":"10.1093/tse/tdad039","DOIUrl":"https://doi.org/10.1093/tse/tdad039","url":null,"abstract":"Lane changing behavior is a more complex driving behavior among driving behaviors. The lane changing behavior of drivers may exacerbate congestion, however, driver behavioral characteristics are difficult to be accurately acquired and quantified, and thus tend to be simplified or ignored in existing lane changing models. In this paper, the Bik-means clustering algorithm is used to analyze the urban road congestion state discrimination method. Then, simulated driving scenarios under different traffic congestion conditions for simulated driving tests. Through the force feedback system and infrared camera, the data of driver lane-changing behaviors at different traffic congestion levels are obtained separately, and the definitions of the starting and ending points of a vehicle changing lanes are determined. Furthermore, statistical analysis and discussion of key feature parameters including driver lane-changing behavior data and visual data under different levels of traffic congestion were conducted. It is found that the average lane change intention times in each congestion state are 2s, 4s, 6s and 7s, while the turn signal duration and the number of rearview mirror observations have similar patterns of change to the data on lane-changing intention duration. Moreover, drivers’ pupil diameters become smaller during the lane-changing intention phase, and then relatively enlarge during lane-changing, the range of pupil variation is roughly 3.5-4 mm. The frequency of observing the vehicle in front of the target lane increased as the level of congestion increased, and the frequency of observation in the driver's mirrors while changing lanes approximately doubled compared to driving straight ahead, and this ratio increased as the level of congestion increased.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":"36 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139252721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Objectives Electric bikes (e-bikes) are widely used for commuting and delivery in China. With the rapid increase in e-bikes on the road, related accidents have become crucial issues threatening the public. This research aims to explore the protection effect of helmet wearing regulation and investigate some factors influencing head injury with reference to two case cities in China, obtaining enlightment to protect e-bike riders. Methods The traffic police-reported crash data cover the periods before and after the implementation of helmet wearing regulations in Taizhou (with data from 2017 to 2019) and Nanning (with data in 2020) of China. Preliminary statistical analysis, logistic regression and chi-square test with a Bonferroni correction were applied in the research. Results Lack of helmets was common among victims in the context of high helmet wearing rate among general e-bike riders in Taizhou, indicating that fatality could be avoided to some extent by wearing helmets. Specifically, helmet wearing could reduce the probability of suffering fatal head injury by 6.4%. After the regulation implementation in Taizhou, the fraction of fatal head injury decreased from 89% to 79%, remaining at a high level, which indicates that other measures in addition to helmet wearing regulation should be taken. According to the results of Nanning, mandatory regulation worked more effectively than encouraged regulation in terms of reducing head injury. Conclusions Mandatory helmet wearing regulation is highly recommended to policymakers to reduce head injury among e-bike riders. Measures in addition to helmet wearing regulation, e.g., enhancing law-abiding awareness and improving road infrastructure, should be considered to further protect e-bike riders.
{"title":"Effect of helmet wearing regulation on electric bike riders: a case study of two cities in China","authors":"Wenxin Ma, Zhiyong Liu, Ruimin Li","doi":"10.1093/tse/tdad038","DOIUrl":"https://doi.org/10.1093/tse/tdad038","url":null,"abstract":"Abstract Objectives Electric bikes (e-bikes) are widely used for commuting and delivery in China. With the rapid increase in e-bikes on the road, related accidents have become crucial issues threatening the public. This research aims to explore the protection effect of helmet wearing regulation and investigate some factors influencing head injury with reference to two case cities in China, obtaining enlightment to protect e-bike riders. Methods The traffic police-reported crash data cover the periods before and after the implementation of helmet wearing regulations in Taizhou (with data from 2017 to 2019) and Nanning (with data in 2020) of China. Preliminary statistical analysis, logistic regression and chi-square test with a Bonferroni correction were applied in the research. Results Lack of helmets was common among victims in the context of high helmet wearing rate among general e-bike riders in Taizhou, indicating that fatality could be avoided to some extent by wearing helmets. Specifically, helmet wearing could reduce the probability of suffering fatal head injury by 6.4%. After the regulation implementation in Taizhou, the fraction of fatal head injury decreased from 89% to 79%, remaining at a high level, which indicates that other measures in addition to helmet wearing regulation should be taken. According to the results of Nanning, mandatory regulation worked more effectively than encouraged regulation in terms of reducing head injury. Conclusions Mandatory helmet wearing regulation is highly recommended to policymakers to reduce head injury among e-bike riders. Measures in addition to helmet wearing regulation, e.g., enhancing law-abiding awareness and improving road infrastructure, should be considered to further protect e-bike riders.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":"31 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134907558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract As the latest research direction of train-to-train communication between trains, virtual coupling train formation technology has attracted the attention of many scholars. This paper studies the influence of time delay on the formation of virtual coupled trains. And then we proposed a distributed PID controller, which makes the trains can still form a stable fleet operation under the influence of communication delay and control delay. After modeling and analysis, the research uses Matlab to conduct simulation, involving two sets of experiments, including the speed, acceleration, position, position error, expected distance between adjacent trains and the actual distance between adjacent trains are simulated. The results demonstrate that the distributed PID controller can effectively control the impact of time delay on the virtual coupling fleet.
{"title":"Research on the stability of virtual coupling train formation based on PID controller under the influence of time delay","authors":"Sixuan Qiu, Ningzhou Li, Xiaojuan Wei, Gaosong Li","doi":"10.1093/tse/tdad037","DOIUrl":"https://doi.org/10.1093/tse/tdad037","url":null,"abstract":"Abstract As the latest research direction of train-to-train communication between trains, virtual coupling train formation technology has attracted the attention of many scholars. This paper studies the influence of time delay on the formation of virtual coupled trains. And then we proposed a distributed PID controller, which makes the trains can still form a stable fleet operation under the influence of communication delay and control delay. After modeling and analysis, the research uses Matlab to conduct simulation, involving two sets of experiments, including the speed, acceleration, position, position error, expected distance between adjacent trains and the actual distance between adjacent trains are simulated. The results demonstrate that the distributed PID controller can effectively control the impact of time delay on the virtual coupling fleet.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136292630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Haiping Wu, Steffen Junginger, Thomas Roddelkopf, Hui Liu, Kerstin Thurow
Abstract Estimation of the sample position is essential for working process monitoring and management in the life science automation laboratory. Bluetooth low-energy (BLE) beacons have the advantage of low price, small size, and low-energy consumption, which make them a promising solution for sample position estimation in the automated laboratory. Several fingerprinting models have been proposed to achieve indoor localization with the received signal strength (RSS) data. However, most of the research depends on intensive beacon installation. Proximity estimation, which depends entirely on one beacon, is more suitable for sample position estimation in large automated laboratories. The complexity of the life science automation laboratory environment brings challenges to the traditional path loss model (PLM), which is a widely used radio wave propagation model-based proximity estimation method. In this paper, BLE sensing devices for sample position estimation are proposed. The BLE beacon-based proximity estimation is discussed in the framework of machine learning, in which the support vector regression (SVR) is utilized to model the nonlinear relationship between the RSS data and distance, and the Kalman filter is utilized to decrease the RSS data deviation. The experimental results over different environments indicate that the SVR outperforms the PLM significantly, and provides 1 m absolute errors for more than 95% of the testing samples. The Kalman filter brings benefits to stable distance predictions. Apart from proximity-based sample position estimation, the proposed framework turned out to be effective in position estimation between parallel workbenches and position estimation on an automated workstation.
{"title":"BLE Beacons for Sample Position Estimation in A Life Science Automation Laboratory","authors":"Haiping Wu, Steffen Junginger, Thomas Roddelkopf, Hui Liu, Kerstin Thurow","doi":"10.1093/tse/tdad033","DOIUrl":"https://doi.org/10.1093/tse/tdad033","url":null,"abstract":"Abstract Estimation of the sample position is essential for working process monitoring and management in the life science automation laboratory. Bluetooth low-energy (BLE) beacons have the advantage of low price, small size, and low-energy consumption, which make them a promising solution for sample position estimation in the automated laboratory. Several fingerprinting models have been proposed to achieve indoor localization with the received signal strength (RSS) data. However, most of the research depends on intensive beacon installation. Proximity estimation, which depends entirely on one beacon, is more suitable for sample position estimation in large automated laboratories. The complexity of the life science automation laboratory environment brings challenges to the traditional path loss model (PLM), which is a widely used radio wave propagation model-based proximity estimation method. In this paper, BLE sensing devices for sample position estimation are proposed. The BLE beacon-based proximity estimation is discussed in the framework of machine learning, in which the support vector regression (SVR) is utilized to model the nonlinear relationship between the RSS data and distance, and the Kalman filter is utilized to decrease the RSS data deviation. The experimental results over different environments indicate that the SVR outperforms the PLM significantly, and provides 1 m absolute errors for more than 95% of the testing samples. The Kalman filter brings benefits to stable distance predictions. Apart from proximity-based sample position estimation, the proposed framework turned out to be effective in position estimation between parallel workbenches and position estimation on an automated workstation.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":"278 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135045580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Bogie is a pivotal system and plays a critical part in the safety and reliability management of high speed rail. However, the available bogie system reliability analysis methods lack the consideration of multi-state characteristics, and the common multi-state reliability analysis methods, being an NP-hard problem, lead to a low efficiency. In order to overcome the mentioned drawbacks, this paper proposes a novel multi-state rail train bogie system reliability analysis approach based on the extended d-MC model. Three different function interactions within the bogie system are considered to build the multi-state bogie system flow network model. Meanwhile, an extended d-MC model is established to remove unnecessary d-MC candidates and duplicates, which greatly enhances the computing efficiency. The bogie system reliability is calculated, and the examples are provided. Numerical experiments are carried out for the different operational conditions of the bogie system and are used to testify the practicability of the method projected in this article, and is is found that the proposed method outperforms a newly developed method in solving the multi-state reliability problems.
{"title":"A Novel Multi-state Bogie System Reliability Evaluation Approach by Extended <i>d</i>-MC Model for High Speed Train","authors":"Yong Fu, Ruoqu Liu, Jisheng Dai, Wenlong Zhu, Xin Liu, Yong Qin","doi":"10.1093/tse/tdad036","DOIUrl":"https://doi.org/10.1093/tse/tdad036","url":null,"abstract":"Abstract Bogie is a pivotal system and plays a critical part in the safety and reliability management of high speed rail. However, the available bogie system reliability analysis methods lack the consideration of multi-state characteristics, and the common multi-state reliability analysis methods, being an NP-hard problem, lead to a low efficiency. In order to overcome the mentioned drawbacks, this paper proposes a novel multi-state rail train bogie system reliability analysis approach based on the extended d-MC model. Three different function interactions within the bogie system are considered to build the multi-state bogie system flow network model. Meanwhile, an extended d-MC model is established to remove unnecessary d-MC candidates and duplicates, which greatly enhances the computing efficiency. The bogie system reliability is calculated, and the examples are provided. Numerical experiments are carried out for the different operational conditions of the bogie system and are used to testify the practicability of the method projected in this article, and is is found that the proposed method outperforms a newly developed method in solving the multi-state reliability problems.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":"336 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136061784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract To address the shortcomings in decision-making methods for ground motion threshold warning models in High-speed rail (HSR) earthquake early warning systems (HSREEWs), we propose a dual judgment method and corresponding early warning process for earthquake early warning decisions based on joint peak ground acceleration (PGA) and complex earthquake environmental risk evaluation (ERE) values. First, we analyze the characteristics of four complex earthquake environments based on the characteristics of HSR operating environments. Second, we establish an earthquake environmental risk evaluation index system and propose an adversarial interpretive structure modeling method-based complex earthquake situation evaluation model (AISM-based ESEM). The AISM method firstly evaluates the proximity by the TOPSIS method, then effectively rank targets with fuzzy attributes through opposite hierarchical extraction rules without sacrificing system functionality. Since PGA can reflect the current size of earthquake energy, combining PGA thresholds with ESEM-derived values of ERE can effectively determine the risk status of each train and make decisions on the most appropriate alarm form and control measures for that status. Finally, case analysis results under the background of Wenchuan Earthquake show that the new early warning decision-making method accurately assesses environmental risks in affected areas and provides corresponding warning levels as a supplement to existing HSREEWs warning models.
{"title":"Decision-Making Method for High-speed Rail Early Warning System in Complex Earthquake Situations","authors":"Minjia Tan, Qizhou Hu, Yikai Wu, Xin Fang","doi":"10.1093/tse/tdad034","DOIUrl":"https://doi.org/10.1093/tse/tdad034","url":null,"abstract":"Abstract To address the shortcomings in decision-making methods for ground motion threshold warning models in High-speed rail (HSR) earthquake early warning systems (HSREEWs), we propose a dual judgment method and corresponding early warning process for earthquake early warning decisions based on joint peak ground acceleration (PGA) and complex earthquake environmental risk evaluation (ERE) values. First, we analyze the characteristics of four complex earthquake environments based on the characteristics of HSR operating environments. Second, we establish an earthquake environmental risk evaluation index system and propose an adversarial interpretive structure modeling method-based complex earthquake situation evaluation model (AISM-based ESEM). The AISM method firstly evaluates the proximity by the TOPSIS method, then effectively rank targets with fuzzy attributes through opposite hierarchical extraction rules without sacrificing system functionality. Since PGA can reflect the current size of earthquake energy, combining PGA thresholds with ESEM-derived values of ERE can effectively determine the risk status of each train and make decisions on the most appropriate alarm form and control measures for that status. Finally, case analysis results under the background of Wenchuan Earthquake show that the new early warning decision-making method accurately assesses environmental risks in affected areas and provides corresponding warning levels as a supplement to existing HSREEWs warning models.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135395301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Noise mapping is an effective tool to measure noise. By noise mapping one can represent noise graphically. Noise mapping was carried out for Ahmedabad city. Data was collected at 633 different locations across the city. Latitude, longitude and real-time noise levels were noted at each location. ArcGIS software was used for creating noise maps and colour noise maps. The percentage of the city covered by the respective value of contour of noise was also found using the software, as well as the number of people in the city suffering from the respective levels of noise. Considering all the results, a multi-linear regression model was developed to predict noise, using SPSS statistics software. The developed model was analysed using an R2 test as well as a paired t-test.
{"title":"GPS-based noise mapping of Ahmedabad city","authors":"Akshay Vinaychandra Vora, R. Vasani","doi":"10.1093/tse/tdac033","DOIUrl":"https://doi.org/10.1093/tse/tdac033","url":null,"abstract":"Noise mapping is an effective tool to measure noise. By noise mapping one can represent noise graphically. Noise mapping was carried out for Ahmedabad city. Data was collected at 633 different locations across the city. Latitude, longitude and real-time noise levels were noted at each location. ArcGIS software was used for creating noise maps and colour noise maps. The percentage of the city covered by the respective value of contour of noise was also found using the software, as well as the number of people in the city suffering from the respective levels of noise. Considering all the results, a multi-linear regression model was developed to predict noise, using SPSS statistics software. The developed model was analysed using an R2 test as well as a paired t-test.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":"35 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139343415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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