Transportation Safety and Environment最新文献_第7页

Predicting the severity of traffic accidents on mountain freeways with dynamic traffic and weather data 利用动态交通和天气数据预测山区高速公路交通事故的严重程度

IF 2.2 4区工程技术

Transportation Safety and Environment Pub Date : 2023-01-23 DOI: 10.1093/tse/tdad001

Juan Li, F. Guo, Yanning Zhou, Wenchen Yang, Dingan Ni

{"title":"Predicting the severity of traffic accidents on mountain freeways with dynamic traffic and weather data","authors":"Juan Li, F. Guo, Yanning Zhou, Wenchen Yang, Dingan Ni","doi":"10.1093/tse/tdad001","DOIUrl":"https://doi.org/10.1093/tse/tdad001","url":null,"abstract":"\u0000 Traffic accident severity prediction is essential for dynamic traffic safety management. To explore the factors influencing the severity of traffic accidents on mountain freeways and to predict the severity of traffic accidents, four models based on machine learning algorithms are constructed using support vector machine (SVM), decision tree classifier (DTC), Ada_SVM and Ada_DTC. In addition, random forest (RF) is used to calculate the importance degree of variables, and accident severity influences with high importance levels form the RF dataset. The results show that rainfall intensity, collision type, number of vehicles involved in the accident and road section type are important variables influencing accident severity. The RF feature selection method improves the classification performance of four machine learning algorithms, resulting in 9.3%, 5.5%, 7.2% and 3.6% improvement in prediction accuracy for SVM, DTC, Ada_SVM and Ada_DTC, respectively. The combination of Ada_SVM integrated algorithm and RF feature selection method has the best prediction performance, and it achieves 78.9% and 88.4% prediction precision and accuracy, respectively.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43895381","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}

引用次数: 1

Analyzing freeway diverging risks using high-resolution trajectory data based on conflict prediction models 基于冲突预测模型的高分辨率轨迹数据分析高速公路分流风险

IF 2.2 4区工程技术

Transportation Safety and Environment Pub Date : 2023-01-19 DOI: 10.1093/tse/tdad002

Ye Li, S. Dalhatu, Chen Yuan

{"title":"Analyzing freeway diverging risks using high-resolution trajectory data based on conflict prediction models","authors":"Ye Li, S. Dalhatu, Chen Yuan","doi":"10.1093/tse/tdad002","DOIUrl":"https://doi.org/10.1093/tse/tdad002","url":null,"abstract":"\u0000 This study aims to develop a reliable safety evaluation model for diverging vehicles and investigates the impact of the surrounding traffic environment on freeway diverging risks. High-resolution trajectory data from three sites in the Netherlands (Delft, Ter-Heide, and Zonzeel) were employed for the risk analysis. Linear regression (LR), Support vector machine (SVM), Random Forest (RF), Extreme randomize trees (ET), Adaptive boosting (Adaboost), Extreme gradient boosting (XGboost), and Multilayer perceptron (MLP), were developed for safety evaluation. The result showed that MLP outperforms the other models for diverging risk prediction over all the indicators, conflict thresholds, and locations. Pairwise matrix, Shapely addictive explanation (SHAP), and Linear regression algorithms were further adopted to interpret the influence of the surrounding environment. It indicates that an increase in traffic density, subject vehicle lateral speed, the distance of subject vehicle from ramp nose, and subject vehicle length would increase the diverging risk. At the same time, an increase in leading vehicle speed and space headway would decrease diverging risk. Finally, spatial analysis was also conducted to explore the stability of identified traffic features regarding the impact on the diverging risk across the sites.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43181425","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}

引用次数: 0

Mandatory lane-changing modelling based on a game theoretic approach in traditional and connected environments 基于博弈论方法的传统互联环境下强制变道建模

IF 2.2 4区工程技术

Transportation Safety and Environment Pub Date : 2023-01-13 DOI: 10.1093/tse/tdac035

G. Cheng, Qiuyue Sun, Y. Bie

引用次数: 1

A mandatory lane changing decision making and planning under emergency situation 紧急情况下的强制性变道决策与规划

IF 2.2 4区工程技术

Transportation Safety and Environment Pub Date : 2022-12-24 DOI: 10.1093/tse/tdac041

Yang Liu, Cong-Ling Shi

引用次数: 0

Research on headway control of guided transport system based on intersections conditions evaluation 基于交叉口条件评价的引导交通系统车头时距控制研究

IF 2.2 4区工程技术

Transportation Safety and Environment Pub Date : 2022-12-22 DOI: 10.1093/tse/tdac068

Xiao Yu, Yuan Cao, Yongkui Sun

引用次数: 0

Modified Multi-Scale Symbolic Dynamic Entropy and Fuzzy Broad Learning-Based fast fault diagnosis of Railway Point Machines 基于改进的多尺度符号动态熵和模糊广义学习的铁路转辙机故障快速诊断

IF 2.2 4区工程技术

Transportation Safety and Environment Pub Date : 2022-12-22 DOI: 10.1093/tse/tdac065

Junqi Liu, Tao Wen, Guo Xie, Yuan Cao

引用次数: 1

Research on fault diagnosis of railway point machine based on multi-entropy and support vector machine 基于多熵和支持向量机的铁路点机故障诊断研究

IF 2.2 4区工程技术

Transportation Safety and Environment Pub Date : 2022-12-22 DOI: 10.1093/tse/tdac071

Yunting Zheng, Shaohua Chen, Zhiyong Tan, Yongkui Sun

引用次数: 0

Condition monitoring and fault diagnosis strategy of railway point machines using vibration signals 基于振动信号的铁路转辙机状态监测与故障诊断策略

IF 2.2 4区工程技术

Transportation Safety and Environment Pub Date : 2022-12-22 DOI: 10.1093/tse/tdac048

Yongkui Sun, Yuan Cao, Haitao Liu, Weifeng Yang, Shuai Su

引用次数: 2

A hybrid ensemble deep reinforcement learning model for locomotive axle temperature using the deterministic and probabilistic strategy 基于确定性和概率策略的机车轴温混合集成深度强化学习模型

IF 2.2 4区工程技术

Transportation Safety and Environment Pub Date : 2022-12-21 DOI: 10.1093/tse/tdac055

Guangxi Yan, Hui Liu, Chengqing Yu, Chengming Yu, Ye Li, Zhu Duan

引用次数: 0

An approach of dynamic response analysis of nonlinear structures based on least square Volterra kernel function identification 基于最小二乘Volterra核函数辨识的非线性结构动力响应分析方法

IF 2.2 4区工程技术

Transportation Safety and Environment Pub Date : 2022-12-21 DOI: 10.1093/tse/tdac046

Zhenhao Zhang, Zhenpeng Zhao, Jun Xiong, Fuming Wang, Yi Zeng, Bingfang Zhao, Lu Ke

{"title":"An approach of dynamic response analysis of nonlinear structures based on least square Volterra kernel function identification","authors":"Zhenhao Zhang, Zhenpeng Zhao, Jun Xiong, Fuming Wang, Yi Zeng, Bingfang Zhao, Lu Ke","doi":"10.1093/tse/tdac046","DOIUrl":"https://doi.org/10.1093/tse/tdac046","url":null,"abstract":"\u0000 Analysis of the dynamic response of a complex nonlinear system is always a difficult problem. By using Volterra functional series to describe a nonlinear system, its response analysis can be similar to using Fourier/Laplace transform and linear transfer function method to analyze a linear system's response. In this paper, a dynamic response analysis method for nonlinear systems based on Volterra series is developed. Firstly, the recursive formula of the least square method is established to solve the Volterra kernel function vector, and the corresponding MATLAB program is compiled. Then, the Volterra kernel vector corresponding to the nonlinear response of a structure under seismic excitation is identified, and the accuracy and applicability of using the kernel vector to predict the response of a nonlinear structure are analyzed. The results show that the Volterra kernel function identified by the derived recursive formula can accurately describe the nonlinear response characteristics of a structure under an excitation. For a general nonlinear system, the first three order Volterra kernel function can relatively accurately express its nonlinear response characteristics. In addition, the obtained Volterra kernel function can be used to accurately predict the nonlinear response of a structure under the similar type of dynamic load.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47120327","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}

引用次数: 0