Applying machine learning and google street view to explore effects of drivers’ visual environment on traffic safety
出版年份 2021 全文链接
标题
Applying machine learning and google street view to explore effects of drivers’ visual environment on traffic safety
作者
关键词
Drivers’ visual environment, Google street view, Coordinate transformation, Speeding crashes, Deep learning, Explainable machine learning, Computer vision
出版物
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
Volume 135, Issue -, Pages 103541
出版商
Elsevier BV
发表日期
2021-12-29
DOI
10.1016/j.trc.2021.103541
参考文献
相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。- Factors Contributing to Operating Speeds on Arterial Roads by Context Classifications
- (2021) Nada Mahmoud et al. Journal of Transportation Engineering Part A-Systems
- Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions
- (2021) Shih-Yuan Yu et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Real-time crash prediction on expressways using deep generative models
- (2020) Qing Cai et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Applying fractional split model to examine the effects of roadway geometric and traffic characteristics on speeding behavior
- (2019) Amir Pooyan Afghari et al. Traffic Injury Prevention
- An ensemble machine learning‐based modeling framework for analysis of traffic crash frequency
- (2019) Xiang Zhang et al. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
- Mining automatically extracted vehicle trajectory data for proactive safety analytics
- (2019) Kun Xie et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- A novel method for predicting and mapping the occurrence of sun glare using Google Street View
- (2019) Xiaojiang Li et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Effects of visual complexity of in-vehicle information display: Age-related differences in visual search task in the driving context
- (2019) Seul Chan Lee et al. APPLIED ERGONOMICS
- Mapping sky, tree, and building view factors of street canyons in a high-density urban environment
- (2018) Fang-Ying Gong et al. BUILDING AND ENVIRONMENT
- A deep learning approach for detecting traffic accidents from social media data
- (2018) Zhenhua Zhang et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Urban form and composition of street canyons: A human-centric big data and deep learning approach
- (2018) Ariane Middel et al. LANDSCAPE AND URBAN PLANNING
- Use of Google Street View to Assess Environmental Contributions to Pedestrian Injury
- (2016) Stephen J. Mooney et al. AMERICAN JOURNAL OF PUBLIC HEALTH
- Does Roadside Vegetation Affect Driving Performance?
- (2015) Alessandro Calvi TRANSPORTATION RESEARCH RECORD
- Assessing street-level urban greenery using Google Street View and a modified green view index
- (2015) Xiaojiang Li et al. URBAN FORESTRY & URBAN GREENING
- Camera calibration and vehicle tracking: Highway traffic video analytics
- (2014) Yiwen Wan et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Make3D: Learning 3D Scene Structure from a Single Still Image
- (2009) A. Saxena et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Accurate and efficient processor performance prediction via regression tree based modeling
- (2009) Bin Li et al. JOURNAL OF SYSTEMS ARCHITECTURE
- Can you see green? Assessing the visibility of urban forests in cities
- (2009) Jun Yang et al. LANDSCAPE AND URBAN PLANNING
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started