A Review on Drivers Red Light Running Behavior Predictions and Technology Based Countermeasures
August 15, 2020 Β· The Cartographer Β· π IEEE Access
"No code URL or promise found in abstract"
"Title-pattern auto-detect: A Review on Drivers Red Light Running Behavior Predictions and Technology Based Countermeasures"
Evidence collected by the PWNC Scanner
Authors
Md Mostafizur Rahman Komol, Jack Pinnow, Mohammed Elhenawy, Shamsunnahar Yasmin, Mahmoud Masoud, Sebastien Glaser, Andry Rakotonirainy
arXiv ID
2008.06727
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.CY,
cs.NE
Citations
11
Venue
IEEE Access
Last Checked
3 days ago
Abstract
Red light running at signalised intersections is a growing road safety issue worldwide, leading to the rapid development of advanced intelligent transportation technologies and countermeasures. However, existing studies have yet to summarise and present the effect of these technology based innovations in improving safety. This paper represents a comprehensive review of red light running behaviour prediction methodologies and technology-based countermeasures. Specifically, the major focus of this study is to provide a comprehensive review on two streams of literature targeting red light running and stop and go behaviour at signalised intersection (1) studies focusing on modelling and predicting the red light running and stop and go related driver behaviour and (2) studies focusing on the effectiveness of different technology based countermeasures which combat such unsafe behaviour. The study provides a systematic guide to assist researchers and stakeholders in understanding how to best identify red light running and stop and go associated driving behaviour and subsequently implement countermeasures to combat such risky behaviour and improve the associated safety.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Artificial Intelligence
π
π
The Cartographer
R.I.P.
π»
Ghosted
Explanation in Artificial Intelligence: Insights from the Social Sciences
R.I.P.
π»
Ghosted
Federated Machine Learning: Concept and Applications
R.I.P.
π»
Ghosted
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
R.I.P.
π»
Ghosted
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
R.I.P.
π»
Ghosted