This study investigates pedestrian safety perception in Ho Chi Minh City under mixed traffic conditions by evaluating comfort,crash risk,and injury risk perceptions in two scenarios:walking along and crossing multilan...This study investigates pedestrian safety perception in Ho Chi Minh City under mixed traffic conditions by evaluating comfort,crash risk,and injury risk perceptions in two scenarios:walking along and crossing multilane roads.Using visual experiments with 510 participants,the study identifies how sidewalk quality,obstructions,crossing infrastructure,and traffic conditions shape pedestrian experiences.Statistical modeling reveals that protected sidewalks and comprehensive crossing features significantly enhance perceived safety and comfort.Findings emphasize the need for improved pedestrian infrastructure and traffic calming in dense urban settings to support safer,more inclusive mobility under mixed traffic conditions like Vietnam.展开更多
Front-of-package(FOP)nutrition labelling schemes were developed to improve consumer’s comprehension about the food nutrients associated with non-communicable diseases(NCDs).Several FOPs have already been developed,an...Front-of-package(FOP)nutrition labelling schemes were developed to improve consumer’s comprehension about the food nutrients associated with non-communicable diseases(NCDs).Several FOPs have already been developed,and Brazil is in the process of evaluating a scheme to introduce in the products.The aim of this study was to investigate the impact of TLS,the scheme proposed by the food industry,on the product healthfulness perception.A study with 141 participants was carried out.A conjoint task was designed considering three categories and levels:types of dairy products(light yogurt,prato cheese,and chocolate flavoured milk),Traffic Light System(yes vs.no)and brand(well-known vs.unknown).The effect of TLS on perceived healthfulness was evaluated using a 9-point scales(1:not healthy;9:very healthy).Results showed that the inclusion of TLS did not influence the perceived healthfulness of the products by consumers,even among consumers with higher interest in healthy eating.These findings suggest that the proposal supported by the food industry does not seem to be the most appropriate,being recommended the development of further studies to compare the efficacy of TLS and other FOP schemes.展开更多
Studying the spatial structure of ancient villages is helpful to grasp the spatial development of ancient villages and provides scientific methods for the protection and inheritance of villages.Taking Yuliang ancient ...Studying the spatial structure of ancient villages is helpful to grasp the spatial development of ancient villages and provides scientific methods for the protection and inheritance of villages.Taking Yuliang ancient village as an example,the paper analyzed the overall village space,street space and residents’perception of node space through field visits,questionnaire survey,space syntax and space perception.It was found that the spatial perception of Yuliang ancient village focused on the core traffic space,important ancient buildings and other spatial elements and;the residents’perception degree was positively related to the function,economic value and convenience degree of spatial elements.Finally,the methods of spatial form inheritance of ancient villages,such as transforming spatial functions,reserving spatial carriers and increasing spatial connections,are put forward.展开更多
Traffic characterization(e.g.,chat,video)and application identifi-cation(e.g.,FTP,Facebook)are two of the more crucial jobs in encrypted network traffic classification.These two activities are typically carried out se...Traffic characterization(e.g.,chat,video)and application identifi-cation(e.g.,FTP,Facebook)are two of the more crucial jobs in encrypted network traffic classification.These two activities are typically carried out separately by existing systems using separate models,significantly adding to the difficulty of network administration.Convolutional Neural Network(CNN)and Transformer are deep learning-based approaches for network traf-fic classification.CNN is good at extracting local features while ignoring long-distance information from the network traffic sequence,and Transformer can capture long-distance feature dependencies while ignoring local details.Based on these characteristics,a multi-task learning model that combines Transformer and 1D-CNN for encrypted traffic classification is proposed(MTC).In order to make up for the Transformer’s lack of local detail feature extraction capability and the 1D-CNN’s shortcoming of ignoring long-distance correlation information when processing traffic sequences,the model uses a parallel structure to fuse the features generated by the Transformer block and the 1D-CNN block with each other using a feature fusion block.This structure improved the representation of traffic features by both blocks and allows the model to perform well with both long and short length sequences.The model simultaneously handles multiple tasks,which lowers the cost of training.Experiments reveal that on the ISCX VPN-nonVPN dataset,the model achieves an average F1 score of 98.25%and an average recall of 98.30%for the task of identifying applications,and an average F1 score of 97.94%,and an average recall of 97.54%for the task of traffic characterization.When advanced models on the same dataset are chosen for comparison,the model produces the best results.To prove the generalization,we applied MTC to CICIDS2017 dataset,and our model also achieved good results.展开更多
Spatio-temporal cellular network traffic prediction at wide-area level plays an important role in resource reconfiguration,traffic scheduling and intrusion detection,thus potentially supporting connected intelligence ...Spatio-temporal cellular network traffic prediction at wide-area level plays an important role in resource reconfiguration,traffic scheduling and intrusion detection,thus potentially supporting connected intelligence of the sixth generation of mobile communications technology(6G).However,the existing studies just focus on the spatio-temporal modeling of traffic data of single network service,such as short message,call,or Internet.It is not conducive to accurate prediction of traffic data,characterised by diverse network service,spatio-temporality and supersize volume.To address this issue,a novel multi-task deep learning framework is developed for citywide cellular network traffic prediction.Functionally,this framework mainly consists of a dual modular feature sharing layer and a multi-task learning layer(DMFS-MT).The former aims at mining long-term spatio-temporal dependencies and local spatio-temporal fluctuation trends in data,respectively,via a new combination of convolutional gated recurrent unit(ConvGRU)and 3-dimensional convolutional neural network(3D-CNN).For the latter,each task is performed for predicting service-specific traffic data based on a fully connected network.On the real-world Telecom Italia dataset,simulation results demonstrate the effectiveness of our proposal through prediction performance measure,spatial pattern comparison and statistical distribution verification.展开更多
The previous research (Danno & Taniguchi, 2015) showed that near-miss incident experience was basically reduced by the Empathy Quotient (EQ) and was disturbed by the Systemizing Quotient (SQ) when the Empathy Quot...The previous research (Danno & Taniguchi, 2015) showed that near-miss incident experience was basically reduced by the Empathy Quotient (EQ) and was disturbed by the Systemizing Quotient (SQ) when the Empathy Quotient was low, based on the Empathizing and Systemizing (E-S) model using a web survey [1]. It means that drivers whose EQ was low and SQ was high had more near-miss incident experience. It suggested that drivers who have a stronger Empathizing function may have stronger hazard perception ability although the Systemizing function may weaken hazard perception ability when Empathizing is weak. And, then, it was revealed that the D score (standard SQ (T) score minus standard EQ (T) score) had a significant effect on the near-miss incident experience. Those results implied that a D score, which is used to classify “E-S types”, should have a relationship with near-miss incident experience, i.e. , hazard perception ability. The EQ and SQ scores were supposed to relate to the cognitive ability to estimate other road users’ mental situations and predict their behavior or to recognize stable laws in traffic situations. The aim of this research was to investigate the relationship between a driver’s visual attention ability (gaze movement) and hazard (near-miss incident) perception ability of different EQ and SQ scores. Drivers’ Real-time Useful Field of View (rUFOV) [2] was measured under normal and hasty driving conditions in a driving simulator which had six scenarios of traffic situation. The result from seven participants who had different EQ and SQ scores showed that a driver’s visual attention ability (gaze movement) corresponds to their scores. This pilot test research revealed a possibility that the individual difference in cognitive trait with E-S model could be a promising tool to understand the mechanism of hazard perception since a D score is used to classify “E-S types”.展开更多
The aim of this study is to determine the level to which the public is aware about ITS(intelligent transportation systems)technologies and how they perceive the potential advantages and inhibitors of ITS in Michigan.A...The aim of this study is to determine the level to which the public is aware about ITS(intelligent transportation systems)technologies and how they perceive the potential advantages and inhibitors of ITS in Michigan.A survey was performed with 200 participants living in Michigan,in urban,suburban and rural areas.Questions covered in the survey included how often and how bad traffic congestion occurred,how familiar travelers were with ITS technologies(adaptive traffic signals,real time monitoring of the traffic)and how much support travelers would provide for ITS initiatives.Results reveal that there is a high degree of traffic congestion awareness,there is low public awareness of ITS technologies.While respondents who were aware of ITS solutions had positive views about deploying them,especially in urban areas,they were less supportive of ITS solutions than they were among those who did not know much about these.Factors including area of residence,commute time and age were perceived to influence ITS along with more positive attitudes to ITS amongst urban dwellers and younger respondents.Analysis of key barriers to ITS implementation reflected high initial costs,challenges with technical integration and users’concerns surrounding privacy.展开更多
Urban traffic control is a multifaceted and demanding task that necessitates extensive decision-making to ensure the safety and efficiency of urban transportation systems.Traditional approaches require traffic signal ...Urban traffic control is a multifaceted and demanding task that necessitates extensive decision-making to ensure the safety and efficiency of urban transportation systems.Traditional approaches require traffic signal professionals to manually intervene on traffic control devices at the intersection level,utilizing their knowledge and expertise.However,this process is cumbersome,labor-intensive,and cannot be applied on a large network scale.Recent studies have begun to explore the applicability of recommendation system for urban traffic control,which offer increased control efficiency and scalability.Such a decision recommendation system is complex,with various interdependent components,but a systematic literature review has not yet been conducted.In this work,we present an up-to-date survey that elucidates all the detailed components of a recommendation system for urban traffic control,demonstrates the utility and efficacy of such a system in the real world using data and knowledgedriven approaches,and discusses the current challenges and potential future directions of this field.展开更多
Limited research has explored roadside advertisements’ effects on road user behavior in Middle Eastern countries. This study aims to understand distraction perceptions, including advertisements, their impact on behav...Limited research has explored roadside advertisements’ effects on road user behavior in Middle Eastern countries. This study aims to understand distraction perceptions, including advertisements, their impact on behavior, and potential influences from advertisement type, in-vehicle distraction, and country factors (reflecting culture and environment). A standardized questionnaire was administered in Jordan and Kuwait, complemented by observations across three segment types: roadside advertisements, commercial signs, and road signs as control segments. Descriptive and inferential statistics were used. Results indicated a significant disparity in distraction perception between the two groups. Advertisement presence influenced Kuwaiti but not Jordanian behavior. Behavior varied by advertisement type in Jordan, not Kuwait, and in-vehicle distraction did not impact driver behavior. The study suggests explicitly revising advertising policies with a traffic safety focus. Overall, the study contributes insights into road user perceptions and behaviors, highlighting the complex interplay of distractions and advertising on road safety. Further research is required to validate these findings and shape road safety regulations.展开更多
车辆轨迹数据在智能交通系统中有着诸多应用,但其实际应用效果常常受数据缺失问题影响。雷达和视频融合感知技术的迅速发展虽然实现了车辆轨迹数据的全天候采集,但在交叉口场景中仍然面临雷达对排队静止目标不敏感,大型车辆遮挡等原因...车辆轨迹数据在智能交通系统中有着诸多应用,但其实际应用效果常常受数据缺失问题影响。雷达和视频融合感知技术的迅速发展虽然实现了车辆轨迹数据的全天候采集,但在交叉口场景中仍然面临雷达对排队静止目标不敏感,大型车辆遮挡等原因导致数据缺失问题。针对交叉口车辆轨迹数据缺失,本文提出一种基于物理信息深度学习的补全算法(Transformer-Full-Velocity-Difference, TF-FVD),将FVD跟驰模型的监督信号引入到Transformer模型的训练过程中,并增加信号灯状态编码模块以考虑交通信号约束。基于雷视轨迹数据集的实验结果表明:FVD模型监督信号和信号灯状态编码模块的引入分别带来了11.6%和15.6%的精度提升;在SinD(Signalized INtersection Dataset)公开数据集中,本文提出的TF-FVD模型较纯数据驱动SOTA(State of the Art)算法精度提升了25.3%;基于补全轨迹计算的车辆延误时间分布误差降低了9.14%,体现了其在实际应用中的价值。展开更多
文摘This study investigates pedestrian safety perception in Ho Chi Minh City under mixed traffic conditions by evaluating comfort,crash risk,and injury risk perceptions in two scenarios:walking along and crossing multilane roads.Using visual experiments with 510 participants,the study identifies how sidewalk quality,obstructions,crossing infrastructure,and traffic conditions shape pedestrian experiences.Statistical modeling reveals that protected sidewalks and comprehensive crossing features significantly enhance perceived safety and comfort.Findings emphasize the need for improved pedestrian infrastructure and traffic calming in dense urban settings to support safer,more inclusive mobility under mixed traffic conditions like Vietnam.
基金Coordenacao Aperfeicaoamento de Pessoal de Nível Superior(CAPES,Brazil),Fundacaode AmparoàPesquisa do Estado do Rio de Janeiro(FAPERJ,Brazil)for financial support.
文摘Front-of-package(FOP)nutrition labelling schemes were developed to improve consumer’s comprehension about the food nutrients associated with non-communicable diseases(NCDs).Several FOPs have already been developed,and Brazil is in the process of evaluating a scheme to introduce in the products.The aim of this study was to investigate the impact of TLS,the scheme proposed by the food industry,on the product healthfulness perception.A study with 141 participants was carried out.A conjoint task was designed considering three categories and levels:types of dairy products(light yogurt,prato cheese,and chocolate flavoured milk),Traffic Light System(yes vs.no)and brand(well-known vs.unknown).The effect of TLS on perceived healthfulness was evaluated using a 9-point scales(1:not healthy;9:very healthy).Results showed that the inclusion of TLS did not influence the perceived healthfulness of the products by consumers,even among consumers with higher interest in healthy eating.These findings suggest that the proposal supported by the food industry does not seem to be the most appropriate,being recommended the development of further studies to compare the efficacy of TLS and other FOP schemes.
文摘Studying the spatial structure of ancient villages is helpful to grasp the spatial development of ancient villages and provides scientific methods for the protection and inheritance of villages.Taking Yuliang ancient village as an example,the paper analyzed the overall village space,street space and residents’perception of node space through field visits,questionnaire survey,space syntax and space perception.It was found that the spatial perception of Yuliang ancient village focused on the core traffic space,important ancient buildings and other spatial elements and;the residents’perception degree was positively related to the function,economic value and convenience degree of spatial elements.Finally,the methods of spatial form inheritance of ancient villages,such as transforming spatial functions,reserving spatial carriers and increasing spatial connections,are put forward.
基金supported by the People’s Public Security University of China central basic scientific research business program(No.2021JKF206).
文摘Traffic characterization(e.g.,chat,video)and application identifi-cation(e.g.,FTP,Facebook)are two of the more crucial jobs in encrypted network traffic classification.These two activities are typically carried out separately by existing systems using separate models,significantly adding to the difficulty of network administration.Convolutional Neural Network(CNN)and Transformer are deep learning-based approaches for network traf-fic classification.CNN is good at extracting local features while ignoring long-distance information from the network traffic sequence,and Transformer can capture long-distance feature dependencies while ignoring local details.Based on these characteristics,a multi-task learning model that combines Transformer and 1D-CNN for encrypted traffic classification is proposed(MTC).In order to make up for the Transformer’s lack of local detail feature extraction capability and the 1D-CNN’s shortcoming of ignoring long-distance correlation information when processing traffic sequences,the model uses a parallel structure to fuse the features generated by the Transformer block and the 1D-CNN block with each other using a feature fusion block.This structure improved the representation of traffic features by both blocks and allows the model to perform well with both long and short length sequences.The model simultaneously handles multiple tasks,which lowers the cost of training.Experiments reveal that on the ISCX VPN-nonVPN dataset,the model achieves an average F1 score of 98.25%and an average recall of 98.30%for the task of identifying applications,and an average F1 score of 97.94%,and an average recall of 97.54%for the task of traffic characterization.When advanced models on the same dataset are chosen for comparison,the model produces the best results.To prove the generalization,we applied MTC to CICIDS2017 dataset,and our model also achieved good results.
基金supported in part by the Science and Technology Project of Hebei Education Department(No.ZD2021088)in part by the S&T Major Project of the Science and Technology Ministry of China(No.2017YFE0135700)。
文摘Spatio-temporal cellular network traffic prediction at wide-area level plays an important role in resource reconfiguration,traffic scheduling and intrusion detection,thus potentially supporting connected intelligence of the sixth generation of mobile communications technology(6G).However,the existing studies just focus on the spatio-temporal modeling of traffic data of single network service,such as short message,call,or Internet.It is not conducive to accurate prediction of traffic data,characterised by diverse network service,spatio-temporality and supersize volume.To address this issue,a novel multi-task deep learning framework is developed for citywide cellular network traffic prediction.Functionally,this framework mainly consists of a dual modular feature sharing layer and a multi-task learning layer(DMFS-MT).The former aims at mining long-term spatio-temporal dependencies and local spatio-temporal fluctuation trends in data,respectively,via a new combination of convolutional gated recurrent unit(ConvGRU)and 3-dimensional convolutional neural network(3D-CNN).For the latter,each task is performed for predicting service-specific traffic data based on a fully connected network.On the real-world Telecom Italia dataset,simulation results demonstrate the effectiveness of our proposal through prediction performance measure,spatial pattern comparison and statistical distribution verification.
文摘The previous research (Danno & Taniguchi, 2015) showed that near-miss incident experience was basically reduced by the Empathy Quotient (EQ) and was disturbed by the Systemizing Quotient (SQ) when the Empathy Quotient was low, based on the Empathizing and Systemizing (E-S) model using a web survey [1]. It means that drivers whose EQ was low and SQ was high had more near-miss incident experience. It suggested that drivers who have a stronger Empathizing function may have stronger hazard perception ability although the Systemizing function may weaken hazard perception ability when Empathizing is weak. And, then, it was revealed that the D score (standard SQ (T) score minus standard EQ (T) score) had a significant effect on the near-miss incident experience. Those results implied that a D score, which is used to classify “E-S types”, should have a relationship with near-miss incident experience, i.e. , hazard perception ability. The EQ and SQ scores were supposed to relate to the cognitive ability to estimate other road users’ mental situations and predict their behavior or to recognize stable laws in traffic situations. The aim of this research was to investigate the relationship between a driver’s visual attention ability (gaze movement) and hazard (near-miss incident) perception ability of different EQ and SQ scores. Drivers’ Real-time Useful Field of View (rUFOV) [2] was measured under normal and hasty driving conditions in a driving simulator which had six scenarios of traffic situation. The result from seven participants who had different EQ and SQ scores showed that a driver’s visual attention ability (gaze movement) corresponds to their scores. This pilot test research revealed a possibility that the individual difference in cognitive trait with E-S model could be a promising tool to understand the mechanism of hazard perception since a D score is used to classify “E-S types”.
文摘The aim of this study is to determine the level to which the public is aware about ITS(intelligent transportation systems)technologies and how they perceive the potential advantages and inhibitors of ITS in Michigan.A survey was performed with 200 participants living in Michigan,in urban,suburban and rural areas.Questions covered in the survey included how often and how bad traffic congestion occurred,how familiar travelers were with ITS technologies(adaptive traffic signals,real time monitoring of the traffic)and how much support travelers would provide for ITS initiatives.Results reveal that there is a high degree of traffic congestion awareness,there is low public awareness of ITS technologies.While respondents who were aware of ITS solutions had positive views about deploying them,especially in urban areas,they were less supportive of ITS solutions than they were among those who did not know much about these.Factors including area of residence,commute time and age were perceived to influence ITS along with more positive attitudes to ITS amongst urban dwellers and younger respondents.Analysis of key barriers to ITS implementation reflected high initial costs,challenges with technical integration and users’concerns surrounding privacy.
基金supported by the National Key Research and Development Program of China(2021YFB2900200)the Key Research and Development Program of Science and Technology Department of Zhejiang Province(2022C01121)Zhejiang Provincial Department of Transport Research Project(ZJXL-JTT-202223).
文摘Urban traffic control is a multifaceted and demanding task that necessitates extensive decision-making to ensure the safety and efficiency of urban transportation systems.Traditional approaches require traffic signal professionals to manually intervene on traffic control devices at the intersection level,utilizing their knowledge and expertise.However,this process is cumbersome,labor-intensive,and cannot be applied on a large network scale.Recent studies have begun to explore the applicability of recommendation system for urban traffic control,which offer increased control efficiency and scalability.Such a decision recommendation system is complex,with various interdependent components,but a systematic literature review has not yet been conducted.In this work,we present an up-to-date survey that elucidates all the detailed components of a recommendation system for urban traffic control,demonstrates the utility and efficacy of such a system in the real world using data and knowledgedriven approaches,and discusses the current challenges and potential future directions of this field.
文摘Limited research has explored roadside advertisements’ effects on road user behavior in Middle Eastern countries. This study aims to understand distraction perceptions, including advertisements, their impact on behavior, and potential influences from advertisement type, in-vehicle distraction, and country factors (reflecting culture and environment). A standardized questionnaire was administered in Jordan and Kuwait, complemented by observations across three segment types: roadside advertisements, commercial signs, and road signs as control segments. Descriptive and inferential statistics were used. Results indicated a significant disparity in distraction perception between the two groups. Advertisement presence influenced Kuwaiti but not Jordanian behavior. Behavior varied by advertisement type in Jordan, not Kuwait, and in-vehicle distraction did not impact driver behavior. The study suggests explicitly revising advertising policies with a traffic safety focus. Overall, the study contributes insights into road user perceptions and behaviors, highlighting the complex interplay of distractions and advertising on road safety. Further research is required to validate these findings and shape road safety regulations.
文摘车辆轨迹数据在智能交通系统中有着诸多应用,但其实际应用效果常常受数据缺失问题影响。雷达和视频融合感知技术的迅速发展虽然实现了车辆轨迹数据的全天候采集,但在交叉口场景中仍然面临雷达对排队静止目标不敏感,大型车辆遮挡等原因导致数据缺失问题。针对交叉口车辆轨迹数据缺失,本文提出一种基于物理信息深度学习的补全算法(Transformer-Full-Velocity-Difference, TF-FVD),将FVD跟驰模型的监督信号引入到Transformer模型的训练过程中,并增加信号灯状态编码模块以考虑交通信号约束。基于雷视轨迹数据集的实验结果表明:FVD模型监督信号和信号灯状态编码模块的引入分别带来了11.6%和15.6%的精度提升;在SinD(Signalized INtersection Dataset)公开数据集中,本文提出的TF-FVD模型较纯数据驱动SOTA(State of the Art)算法精度提升了25.3%;基于补全轨迹计算的车辆延误时间分布误差降低了9.14%,体现了其在实际应用中的价值。
