Transportation Letters-The International Journal of Transportation Research最新文献_第10页

Real-time Vulnerability Analysis of Urban Expressway 城市快速路实时脆弱性分析

IF 3.3 3区工程技术

Transportation Letters-The International Journal of Transportation Research Pub Date : 2024-10-20 DOI: 10.1080/19427867.2023.2260972

引用次数: 0

Analysis of the factors affecting the time spent on leisure activities by using an ordered logit model 利用有序对数模型分析影响休闲活动时间的因素

IF 3.3 3区工程技术

Transportation Letters-The International Journal of Transportation Research Pub Date : 2024-10-20 DOI: 10.1080/19427867.2023.2266189

引用次数: 0

Spatio-temporal graph attention networks for traffic prediction 用于交通预测的时空图注意力网络

IF 3.3 3区工程技术

Transportation Letters-The International Journal of Transportation Research Pub Date : 2024-10-20 DOI: 10.1080/19427867.2023.2261706

{"title":"Spatio-temporal graph attention networks for traffic prediction","authors":"","doi":"10.1080/19427867.2023.2261706","DOIUrl":"10.1080/19427867.2023.2261706","url":null,"abstract":"<div><div>The constraints of road network topology and dynamically changing traffic states over time make the task of traffic flow prediction extremely challenging. Most existing methods use CNNs or GCNs to capture spatial correlation. However, convolution operator-based methods are far from optimal in their ability to fuse node features and topology to adequately model spatial correlation. In order to model the spatio-temporal features of traffic flow more effectively, this paper proposes a traffic flow prediction model, the Spatio-Temporal Graph Attention Network (STGAN), which is based on graph attention mechanisms and residually connected gated recurrent units. Specifically, a graph attention mechanism and a random wandering mechanism are used to extract spatial features of the traffic network, and gated recurrent units with residual connections are used to extract temporal features. Experimental results on real-world public transportation datasets show that our approach not only yields state-of-the-art performance, but also exhibits competitive computational efficiency and improves the accuracy of traffic flow prediction.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"16 9","pages":"Pages 978-988"},"PeriodicalIF":3.3,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136279553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Shared travel demand forecasting and multi-phase vehicle relocation optimization for electric carsharing systems 电动汽车共享系统的共享出行需求预测和多阶段车辆迁移优化

IF 3.3 3区工程技术

Transportation Letters-The International Journal of Transportation Research Pub Date : 2024-10-20 DOI: 10.1080/19427867.2023.2262205

引用次数: 0

Psychological antecedents of electric vehicle adoption in the West Bank 约旦河西岸采用电动汽车的心理诱因

IF 3.3 3区工程技术

Transportation Letters-The International Journal of Transportation Research Pub Date : 2024-10-20 DOI: 10.1080/19427867.2023.2266184

引用次数: 0

Can taxi data inform bus route improvement? A case study in Shanghai 出租车数据能否为公交线路改进提供参考？上海案例研究

IF 3.3 3区工程技术

Transportation Letters-The International Journal of Transportation Research Pub Date : 2024-10-20 DOI: 10.1080/19427867.2023.2262207

引用次数: 0

Multi-objective optimization for through train service integrating train operation plan and type selection 结合列车运行计划和类型选择的直通列车服务多目标优化

IF 3.3 3区工程技术

Transportation Letters-The International Journal of Transportation Research Pub Date : 2024-10-20 DOI: 10.1080/19427867.2023.2264046

引用次数: 0

Experimental study on the departure time choice behavior during the morning rush hours under different road capacity conditions 不同道路通行能力条件下早高峰时段出发时间选择行为的实验研究

IF 3.3 3区工程技术

Transportation Letters-The International Journal of Transportation Research Pub Date : 2024-10-20 DOI: 10.1080/19427867.2023.2259143

引用次数: 0

Impact assessment of professional drivers’ speed compliance and speed adaptation with posted speed limits in different driving environments and driving conditions 职业驾驶员在不同驾驶环境和驾驶条件下遵守速度规定和速度适应的影响评估

IF 3.3 3区工程技术

Transportation Letters-The International Journal of Transportation Research Pub Date : 2024-09-13 DOI: 10.1080/19427867.2023.2252222

{"title":"Impact assessment of professional drivers’ speed compliance and speed adaptation with posted speed limits in different driving environments and driving conditions","authors":"","doi":"10.1080/19427867.2023.2252222","DOIUrl":"10.1080/19427867.2023.2252222","url":null,"abstract":"<div><div>This study analyzed the impact of driving environments (real-world and simulated world) and driving conditions (no time pressure and time pressure) on speed compliance and speed adaptation. Professional car drivers were recruited, and the data was collected in real-world and simulated world under no time pressure and time pressure driving conditions. The comparison results using Wilcoxon-signed rank test showed that speed compliance and speed adaptation were not consistently significant and were not in the same direction highlighting the influence of various factors like road features and driver characteristics. The generalized linear mixed model results showed that speed compliance was relatively better in simulated world (by 3.98 kmph) than real-world. Further, speed adaptation under time pressure was about 5.86 kmph lower during real-world as compared to simulated world. The findings from this study can provide new insights on road safety strategies and policy implications for limiting speeding-related crash risks.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"16 8","pages":"Pages 872-882"},"PeriodicalIF":3.3,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42001120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Vehicle detection in diverse traffic using an ensemble convolutional neural backbone via feature concatenation 基于特征拼接的集成卷积神经主干在不同交通条件下的车辆检测

IF 3.3 3区工程技术

Transportation Letters-The International Journal of Transportation Research Pub Date : 2024-09-13 DOI: 10.1080/19427867.2023.2250622

{"title":"Vehicle detection in diverse traffic using an ensemble convolutional neural backbone via feature concatenation","authors":"","doi":"10.1080/19427867.2023.2250622","DOIUrl":"10.1080/19427867.2023.2250622","url":null,"abstract":"<div><div>Nowadays, deploying an intelligent vehicle detection system (IVDS) in diverse traffic is a work priority. It provides real-time traffic information with vehicle counts and types of vehicles. IVDS deployment in diverse traffic is challenging because different vehicle classes occlude each other on the road. In recent years, convolutional neural network (CNN) based deep learning (DL) methods have attained incredible progress in implementing IVDS. However, most CNN-based DL methods do not include diverse traffic conditions in Asian countries. Also, due to existing feature extraction backbones, they cannot accurately detect multi-scale vehicles. This work proposes an advanced visual computing deep learning (AVCDL) method with a vast labeled vehicle dataset to detect vehicles in diverse traffic. It includes an ensemble backbone and an improved multi-stage vehicle detection head (MSVDH). An ensemble CNN backbone extracts the vehicle features and combines them on a single channel via a feature concatenation. The final detection is carried out by an improved MSVDH that classifies the target vehicles. The proposed method is examined, tested, and evaluated using traffic statistics. It is contrasted with current cutting-edge vehicle detection techniques. It achieves 86.32% mean average precision (mAP) on self-collected diverse traffic labeled dataset (DTLD) and 86.17% mAP on KITTI. Moreover, the real-time performance is validated with NVIDIA Jetson Tx2 and Nano boards. It achieves 15 frames per second (FPS) on Jetson Tx2 and 7 FPS on Jetson Nano.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"16 8","pages":"Pages 838-856"},"PeriodicalIF":3.3,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45326643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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