Accurate demand forecasting for online ride-hailing contributes to balancing traffic supply and demand, and improving the service level of ride-hailing platforms. In contrast to previous studies, which have primarily focused on the inflow or outflow demands of each zone, this study proposes a Conditional Generative Adversarial Network with a Wasserstein divergence objective (CWGAN-div) to predict ride-hailing origin-destination (OD) demand matrices. Residual blocks and refined loss functions help to enhance the stability of model training. Interpretable conditional information is employed to capture external spatiotemporal dependencies and guide the model towards generating more precise results. Empirical analysis using ride-hailing data from Manhattan, New York City, demonstrates that our proposed CWGAN-div model can effectively predict the network-wide OD matrix and exhibits strong convergence performance. Comparative experiments also show that the CWGAN-div outperforms other benchmarking methods. Consequently, the proposed model displays potential for network-wide ride-hailing OD demand prediction.
{"title":"Ride-hailing origin-destination demand prediction with spatiotemporal information fusion","authors":"Ning Wang, Liang Zheng, Huitao Shen, Shukai Li","doi":"10.1093/tse/tdad026","DOIUrl":"https://doi.org/10.1093/tse/tdad026","url":null,"abstract":"\u0000 Accurate demand forecasting for online ride-hailing contributes to balancing traffic supply and demand, and improving the service level of ride-hailing platforms. In contrast to previous studies, which have primarily focused on the inflow or outflow demands of each zone, this study proposes a Conditional Generative Adversarial Network with a Wasserstein divergence objective (CWGAN-div) to predict ride-hailing origin-destination (OD) demand matrices. Residual blocks and refined loss functions help to enhance the stability of model training. Interpretable conditional information is employed to capture external spatiotemporal dependencies and guide the model towards generating more precise results. Empirical analysis using ride-hailing data from Manhattan, New York City, demonstrates that our proposed CWGAN-div model can effectively predict the network-wide OD matrix and exhibits strong convergence performance. Comparative experiments also show that the CWGAN-div outperforms other benchmarking methods. Consequently, the proposed model displays potential for network-wide ride-hailing OD demand prediction.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":"49 5","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41307604","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}
Fang Wang, Mingliang Wang, Lin Hu, Ke Peng, Jiajie Yin, Danqi Wang, Liangliang Shi, Zhou Zhou
Car-to-pedestrian collision (CPC) accidents occur frequently, and pedestrians often suffer serious head/brain injuries. One major cause is the primary impact with the windshield. Here, we use a numerical simulation method to study the influence of the windshield inclination angle of a passenger car on pedestrian head/brain injury due to CPC accidents. The range of the windshield inclination angle was set to 24°–50°, with an interval of 2°. The results show that the windshield angle significantly affects the pedestrian kinematics and exerts different effects on the head injury when evaluating with various head injury criteria. Regarding the head peak linear/rotational acceleration and acceleration-based criteria head injury criterion (HIC)/rotational injury criterion (RIC), the predictions at the secondary impact stage have no clear relationship with the windshield angle (R2 = 0.04, 0.07, 0.03 and 0.26, respectively), and their distributions are scattered. In the primary impact, the peak linear acceleration and HIC show a weak trend of first decreasing and then increasing with the increasing of the windshield angle, and the rotational acceleration and RIC tend to remain relatively constant. Regarding the cumulative strain damage measure (CSDM) criterion, the predictions at the primary impact are slightly lower than those at the secondary impact, and the trend of first decreasing and then increasing with the increase in the windshield angle is observed at both impact stages. When the windshield inclination angle is approximately 32°–40°, the head injury severity in both impact phases is generally lower than that predicted at other windshield angles.
{"title":"Effects of the windshield inclination angle on head/brain injuries in car-to-pedestrian collisions using computational biomechanics models","authors":"Fang Wang, Mingliang Wang, Lin Hu, Ke Peng, Jiajie Yin, Danqi Wang, Liangliang Shi, Zhou Zhou","doi":"10.1093/tse/tdad016","DOIUrl":"https://doi.org/10.1093/tse/tdad016","url":null,"abstract":"\u0000 Car-to-pedestrian collision (CPC) accidents occur frequently, and pedestrians often suffer serious head/brain injuries. One major cause is the primary impact with the windshield. Here, we use a numerical simulation method to study the influence of the windshield inclination angle of a passenger car on pedestrian head/brain injury due to CPC accidents. The range of the windshield inclination angle was set to 24°–50°, with an interval of 2°. The results show that the windshield angle significantly affects the pedestrian kinematics and exerts different effects on the head injury when evaluating with various head injury criteria. Regarding the head peak linear/rotational acceleration and acceleration-based criteria head injury criterion (HIC)/rotational injury criterion (RIC), the predictions at the secondary impact stage have no clear relationship with the windshield angle (R2 = 0.04, 0.07, 0.03 and 0.26, respectively), and their distributions are scattered. In the primary impact, the peak linear acceleration and HIC show a weak trend of first decreasing and then increasing with the increasing of the windshield angle, and the rotational acceleration and RIC tend to remain relatively constant. Regarding the cumulative strain damage measure (CSDM) criterion, the predictions at the primary impact are slightly lower than those at the secondary impact, and the trend of first decreasing and then increasing with the increase in the windshield angle is observed at both impact stages. When the windshield inclination angle is approximately 32°–40°, the head injury severity in both impact phases is generally lower than that predicted at other windshield angles.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43056437","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}
Chaoqun Xiang, Ziyin Fan, Songyang Jiang, Xinan Zhang, Shu Cheng
Four-level active neutral point clamped (ANPC) inverter becomes increasingly widely used in the renewable energy industry since it offers one more voltage level without increasing the total number of active switches compared to the three-level ANPC inverter. The model predictive current control (MPCC) is a promising control method for multi-level inverters. However, the conventional MPCC suffers from high computational complexity and tedious weighting factor tuning in multi-level inverter applications. A low complexity MPCC without weighting factors for four-level ANPC inverter is proposed in this paper. The computational burden and voltage vector candidate set are reduced according to the relationship between voltage vector and neutral point voltage balance. The proposed MPCC shows excellent steady-state and dynamics performances while ensuring the neutral point voltage balancing. The efficacy of the proposed MPCC is verified by simulation and experimental results.
{"title":"Low complexity model predictive control of four-level active neutral point clamped inverter without weighting factors","authors":"Chaoqun Xiang, Ziyin Fan, Songyang Jiang, Xinan Zhang, Shu Cheng","doi":"10.1093/tse/tdad023","DOIUrl":"https://doi.org/10.1093/tse/tdad023","url":null,"abstract":"\u0000 Four-level active neutral point clamped (ANPC) inverter becomes increasingly widely used in the renewable energy industry since it offers one more voltage level without increasing the total number of active switches compared to the three-level ANPC inverter. The model predictive current control (MPCC) is a promising control method for multi-level inverters. However, the conventional MPCC suffers from high computational complexity and tedious weighting factor tuning in multi-level inverter applications. A low complexity MPCC without weighting factors for four-level ANPC inverter is proposed in this paper. The computational burden and voltage vector candidate set are reduced according to the relationship between voltage vector and neutral point voltage balance. The proposed MPCC shows excellent steady-state and dynamics performances while ensuring the neutral point voltage balancing. The efficacy of the proposed MPCC is verified by simulation and experimental results.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43258365","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}
As a sustainable mode of travel, carpooling is an effective solution to environmental and energy problems, but carpooling has not been fully utilized. The research on carpooling attitude, especially for college students, still needs to be improved. In response to this problem, this paper aims to investigate the effect of personal attributes, travel attributes, attitude attributes, and other attributes of students on carpooling attitudes in China. To collect the data, an online questionnaire survey was implemented at Shenzhen University, and 514 students participated. Based on the samples, the multinomial logit model is used to explore the contribution of all variables. The model results showed that personal attributes such as gender and age had no significant effect on the attitude of college students toward carpooling. The safety of carpooling and the high cost of carpooling make college students have a neutral attitude towards carpooling. The most concerning factor for college students in carpooling is the comfort of carpooling. These findings can provide valuable suggestions for measures to be taken in response to different attributes affecting students’ attitudes and are particularly important for the university and government to make optimal decisions for motivating students to select carpooling.
{"title":"Factors affecting college students’ attitudes toward carpooling","authors":"Linchao Li, Huali Zhang, Zuoxian Gan","doi":"10.1093/tse/tdad025","DOIUrl":"https://doi.org/10.1093/tse/tdad025","url":null,"abstract":"\u0000 As a sustainable mode of travel, carpooling is an effective solution to environmental and energy problems, but carpooling has not been fully utilized. The research on carpooling attitude, especially for college students, still needs to be improved. In response to this problem, this paper aims to investigate the effect of personal attributes, travel attributes, attitude attributes, and other attributes of students on carpooling attitudes in China. To collect the data, an online questionnaire survey was implemented at Shenzhen University, and 514 students participated. Based on the samples, the multinomial logit model is used to explore the contribution of all variables. The model results showed that personal attributes such as gender and age had no significant effect on the attitude of college students toward carpooling. The safety of carpooling and the high cost of carpooling make college students have a neutral attitude towards carpooling. The most concerning factor for college students in carpooling is the comfort of carpooling. These findings can provide valuable suggestions for measures to be taken in response to different attributes affecting students’ attitudes and are particularly important for the university and government to make optimal decisions for motivating students to select carpooling.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43638393","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}
As an important task of multi-floor localization, floor detection has elicited great attention. Wireless infrastructures like Wi-Fi and Bluetooth low-energy play important roles in floor detection. However, most floor detection research studies tend to focus on data modeling but pay little attention to the data collection system, which is the basis of wireless infrastructure-based floor detection. In fact, the floor detection task can be greatly simplified with proper data collection system design. In this paper, a floor detection solution is developed in a multi-floor life science automation lab. A data collection system consisting of BLE beacons, receiver node, and IoT cloud is provided. The features of the BLE beacon under different settings are evaluated in detail. A mean filter is designed to deal with the fluctuation of the RSSI data. A simple floor detection method without a training process was implemented and evaluated in more than 100 floor detection tests. The time delay and floor detection accuracy under different settings are discussed. Finally, floor detection is evaluated on the H20 multi-floor transportation robot. Two sensor nodes are installed on the robot at different heights. The floor detection performance with different installation heights is discussed. The experimental results indicate that the proposed floor detection method provides floor detection accuracy of 0.9877 to 1 with a time delay of 5 s.
{"title":"BLE Beacon-based floor detection for mobile robots in a multi-floor automation Laboratory","authors":"Haiping Wu, Hui Liu, T. Roddelkopf, K. Thurow","doi":"10.1093/tse/tdad024","DOIUrl":"https://doi.org/10.1093/tse/tdad024","url":null,"abstract":"\u0000 As an important task of multi-floor localization, floor detection has elicited great attention. Wireless infrastructures like Wi-Fi and Bluetooth low-energy play important roles in floor detection. However, most floor detection research studies tend to focus on data modeling but pay little attention to the data collection system, which is the basis of wireless infrastructure-based floor detection. In fact, the floor detection task can be greatly simplified with proper data collection system design. In this paper, a floor detection solution is developed in a multi-floor life science automation lab. A data collection system consisting of BLE beacons, receiver node, and IoT cloud is provided. The features of the BLE beacon under different settings are evaluated in detail. A mean filter is designed to deal with the fluctuation of the RSSI data. A simple floor detection method without a training process was implemented and evaluated in more than 100 floor detection tests. The time delay and floor detection accuracy under different settings are discussed. Finally, floor detection is evaluated on the H20 multi-floor transportation robot. Two sensor nodes are installed on the robot at different heights. The floor detection performance with different installation heights is discussed. The experimental results indicate that the proposed floor detection method provides floor detection accuracy of 0.9877 to 1 with a time delay of 5 s.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42728982","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 increasing use of mobile robots in laboratory settings has led to a higher degree of laboratory automation. However, when mobile robots move in laboratory environments, mechanical errors, environmental disturbances, and signal interruptions are inevitable. This can compromise the accuracy of the robot's localization, which is crucial for the safety of staff, robots, and the laboratory. A novel time-series predicting model based on the data processing method is proposed to handle the unexpected localization measurement of mobile robots in laboratory environments. The proposed model serves as an auxiliary localization system that can accurately correct unexpected localization errors by relying solely on the historical data of mobile robots. The experimental results demonstrate the effectiveness of this proposed method.
{"title":"Correcting of Unexpected Localization Measurement for Indoor Automatic Mobile Robot Transportation Based on neural network","authors":"Jiahao Huang, S. Junginger, Hui Liu, K. Thurow","doi":"10.1093/tse/tdad019","DOIUrl":"https://doi.org/10.1093/tse/tdad019","url":null,"abstract":"\u0000 The increasing use of mobile robots in laboratory settings has led to a higher degree of laboratory automation. However, when mobile robots move in laboratory environments, mechanical errors, environmental disturbances, and signal interruptions are inevitable. This can compromise the accuracy of the robot's localization, which is crucial for the safety of staff, robots, and the laboratory. A novel time-series predicting model based on the data processing method is proposed to handle the unexpected localization measurement of mobile robots in laboratory environments. The proposed model serves as an auxiliary localization system that can accurately correct unexpected localization errors by relying solely on the historical data of mobile robots. The experimental results demonstrate the effectiveness of this proposed method.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44357201","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}
Tunnels on freeways, as one of the critical bottlenecks, frequently cause severe congestion and passenger delay. To solve the tunnel bottleneck problem, most of the existing research can be divided into two types. One is to adopt Variable Speed Limits (VSL) to regulate a predetermined speed for vehicles to get through a bottleneck smoothly. The other is to adopt High-Occupancy Vehicle (HOV) lane management. In HOV lane management strategies, all traffic is divided into HOVs and Low-occupancy Vehicles (LOV). HOVs are vehicles with a driver and one or more passengers. LOVs are vehicles just with a driver. This kind of research can grant priority to HOVs by providing a dedicated HOV lane. However, the existing research cannot both mitigate congestion and maximize passenger-oriented benefits. To address the research gap, this paper leverages Connected and Automated Vehicle (CAV) technologies on intelligent freeways and develops a tunnel bottleneck management strategy with a Dynamic HOV Lane (DHL). The strategy bears the following features: 1) enable tunnel bottleneck management at a microscopic level; 2) maximize passenger-oriented benefits; 3) grant priority to HOVs even when the HOV lane is open to LOVs; 4) allocate right-of-way segments for HOVs and LOVs in real time; 5) perform well in a mixed traffic environment. The proposed strategy is evaluated through comparison against the non-control baseline and a VSL strategy. Sensitivity analysis is conducted under different congestion levels and penetration rates. The results demonstrate that the proposed strategy outperforms in terms of passenger-oriented delay reduction and HOVs'priority level improvement.
{"title":"Tunnel bottleneck management with high-occupancy vehicles priority on intelligent freeways","authors":"Jinyong Gao, Juncheng Zeng, Xinyuan Wang, Cheng Zhou, Hailin Zhang, Jintao Lai","doi":"10.1093/tse/tdad022","DOIUrl":"https://doi.org/10.1093/tse/tdad022","url":null,"abstract":"\u0000 Tunnels on freeways, as one of the critical bottlenecks, frequently cause severe congestion and passenger delay. To solve the tunnel bottleneck problem, most of the existing research can be divided into two types. One is to adopt Variable Speed Limits (VSL) to regulate a predetermined speed for vehicles to get through a bottleneck smoothly. The other is to adopt High-Occupancy Vehicle (HOV) lane management. In HOV lane management strategies, all traffic is divided into HOVs and Low-occupancy Vehicles (LOV). HOVs are vehicles with a driver and one or more passengers. LOVs are vehicles just with a driver. This kind of research can grant priority to HOVs by providing a dedicated HOV lane. However, the existing research cannot both mitigate congestion and maximize passenger-oriented benefits. To address the research gap, this paper leverages Connected and Automated Vehicle (CAV) technologies on intelligent freeways and develops a tunnel bottleneck management strategy with a Dynamic HOV Lane (DHL). The strategy bears the following features: 1) enable tunnel bottleneck management at a microscopic level; 2) maximize passenger-oriented benefits; 3) grant priority to HOVs even when the HOV lane is open to LOVs; 4) allocate right-of-way segments for HOVs and LOVs in real time; 5) perform well in a mixed traffic environment. The proposed strategy is evaluated through comparison against the non-control baseline and a VSL strategy. Sensitivity analysis is conducted under different congestion levels and penetration rates. The results demonstrate that the proposed strategy outperforms in terms of passenger-oriented delay reduction and HOVs'priority level improvement.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42424135","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}
Remaining useful life (RUL) prediction for bearing is a significant part of the maintenance of urban rail transit trains. Bearing RUL is closely linked to the reliability and safety of train running, but the current prediction accuracy is difficult to meet the requirements of high reliability operation. Aiming at the problem, a prediction model based on improved long short-term memory(ILSTM) network is proposed. Firstly, the variational mode decomposition is used to process the signal, and the intrinsic mode function with stronger representation ability is determined according to energy entropy, and the degradation feature data is constructed combined with the time domain characteristics. Then, to improve learning ability, rectified linear unit (ReLU) is applied to activate a fully connected layer lying after LSTM, the hidden state outputs of the layer are weighted by attention mechanism. Harris hawks optimization algorithm is introduced to adaptively set the hyperparameters to improve the performance of LSTM. Finally, the ILSTM is applied to predict bearing RUL. Through experimental cases, the better performance in bearing RUL prediction and the effectiveness of each improving measures of the model are validated, and its superiority of hyperparameters setting is demonstrated.
{"title":"Remaining useful life prediction for train bearing based on ILSTM network with adaptive hyperparameter optimization","authors":"Deqiang He, Jingren Yan, Zhenzhen Jin, Xueyan Zou, S. Shan, Zaiyu Xiang, Jian Miao","doi":"10.1093/tse/tdad021","DOIUrl":"https://doi.org/10.1093/tse/tdad021","url":null,"abstract":"\u0000 Remaining useful life (RUL) prediction for bearing is a significant part of the maintenance of urban rail transit trains. Bearing RUL is closely linked to the reliability and safety of train running, but the current prediction accuracy is difficult to meet the requirements of high reliability operation. Aiming at the problem, a prediction model based on improved long short-term memory(ILSTM) network is proposed. Firstly, the variational mode decomposition is used to process the signal, and the intrinsic mode function with stronger representation ability is determined according to energy entropy, and the degradation feature data is constructed combined with the time domain characteristics. Then, to improve learning ability, rectified linear unit (ReLU) is applied to activate a fully connected layer lying after LSTM, the hidden state outputs of the layer are weighted by attention mechanism. Harris hawks optimization algorithm is introduced to adaptively set the hyperparameters to improve the performance of LSTM. Finally, the ILSTM is applied to predict bearing RUL. Through experimental cases, the better performance in bearing RUL prediction and the effectiveness of each improving measures of the model are validated, and its superiority of hyperparameters setting is demonstrated.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":"41 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"61099120","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}
Yang Zeyun, Xu Gang, Wu Fan, Zhang Lei, Du Jian, D. Vainchtein
The purpose of this study is to establish the correlation between the boundary layer over the subgrade and the aerodynamic loads acting on the train model in conventional wind tunnel tests. Firstly, flow characteristics around the subgrade with different leading-edge angles (15◦, 30◦, and 45◦) are investigated through PIV experimental test method. Then, wind tunnel tests of the aerodynamic performance of a high-speed train are carried out. The results are compared with previous experimental data obtained by moving model tests. Results show that, due to the presence of boundary layer, the pressure acting on the lower part of the train head decreases, while on other location is not significantly affected. This is the reason for the reduction of the aerodynamic drag and lift on the train. In addition, the reduction effects become more obviously when the thickness of boundary layer increasing. The experimental results obtained could serve as a calibration of aerodynamic forces for wind tunnel tests on high-speed trains.
{"title":"Influence of leading-edge angle of subgrade on aerodynamic loads of high-speed train in wind tunnel","authors":"Yang Zeyun, Xu Gang, Wu Fan, Zhang Lei, Du Jian, D. Vainchtein","doi":"10.1093/tse/tdad020","DOIUrl":"https://doi.org/10.1093/tse/tdad020","url":null,"abstract":"\u0000 The purpose of this study is to establish the correlation between the boundary layer over the subgrade and the aerodynamic loads acting on the train model in conventional wind tunnel tests. Firstly, flow characteristics around the subgrade with different leading-edge angles (15◦, 30◦, and 45◦) are investigated through PIV experimental test method. Then, wind tunnel tests of the aerodynamic performance of a high-speed train are carried out. The results are compared with previous experimental data obtained by moving model tests. Results show that, due to the presence of boundary layer, the pressure acting on the lower part of the train head decreases, while on other location is not significantly affected. This is the reason for the reduction of the aerodynamic drag and lift on the train. In addition, the reduction effects become more obviously when the thickness of boundary layer increasing. The experimental results obtained could serve as a calibration of aerodynamic forces for wind tunnel tests on high-speed trains.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43989656","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}
Chengyong Liu, Shijie Li, Shuzhe Chen, Qifan Chen, Kang Liu
Due to the flammability and explosive nature of liquefied natural gas (LNG), an extremely strict process is followed for the transportation of LNG carriers in China. Particularly, no LNG carriers are operating in inland rivers within the country. Therefore, to ensure the future navigation safety of LNG carriers entering the Yangtze River, the risk sources of LNG carriers' navigation safety must be identified and evaluated. Based on the Delphi and expert experience method, this paper analyzes and discusses the navigation risk factors of LNG carriers in the lower reaches of the Yangtze River from four aspects (human, ship, environment, and management), and identifies 12 risk indicators affecting the navigation of LNG carriers, and establishes a risk evaluation index system. Further, an entropy weight fuzzy model is utilized to reduce the influence of subjective judgment on the index weight as well as to conduct a segmented and overall evaluation of LNG navigation risks in the Baimaosha Channel. Finally, the cloud model is applied to validate the consistent feasibility of the entropy weight fuzzy model. The research results indicate that the method provides effective technical support for further study on the navigation security of LNG carriers in inland rivers.
{"title":"Research on the navigational risk of liquefied natural gas carriers in an inland river based on entropy: a cloud evaluation model","authors":"Chengyong Liu, Shijie Li, Shuzhe Chen, Qifan Chen, Kang Liu","doi":"10.1093/tse/tdad018","DOIUrl":"https://doi.org/10.1093/tse/tdad018","url":null,"abstract":"\u0000 Due to the flammability and explosive nature of liquefied natural gas (LNG), an extremely strict process is followed for the transportation of LNG carriers in China. Particularly, no LNG carriers are operating in inland rivers within the country. Therefore, to ensure the future navigation safety of LNG carriers entering the Yangtze River, the risk sources of LNG carriers' navigation safety must be identified and evaluated. Based on the Delphi and expert experience method, this paper analyzes and discusses the navigation risk factors of LNG carriers in the lower reaches of the Yangtze River from four aspects (human, ship, environment, and management), and identifies 12 risk indicators affecting the navigation of LNG carriers, and establishes a risk evaluation index system. Further, an entropy weight fuzzy model is utilized to reduce the influence of subjective judgment on the index weight as well as to conduct a segmented and overall evaluation of LNG navigation risks in the Baimaosha Channel. Finally, the cloud model is applied to validate the consistent feasibility of the entropy weight fuzzy model. The research results indicate that the method provides effective technical support for further study on the navigation security of LNG carriers in inland rivers.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48077048","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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