Applying machine learning and google street view to explore effects of drivers’ visual environment on traffic safety
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Applying machine learning and google street view to explore effects of drivers’ visual environment on traffic safety
Authors
Keywords
Drivers’ visual environment, Google street view, Coordinate transformation, Speeding crashes, Deep learning, Explainable machine learning, Computer vision
Journal
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
Volume 135, Issue -, Pages 103541
Publisher
Elsevier BV
Online
2021-12-29
DOI
10.1016/j.trc.2021.103541
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- 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
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started