Engineering Countermeasures to Reducing Red-Light Running

Qian, Hongbo; Dong, Yu-pu

doi:10.1109/case.2009.22

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Drivers may also commit a red light running (RLR) violation due to misjudgment at the onset of yellow at a signalised intersection. Qian and Dong [7] defined the RLR behaviour as the act of a vehicle encroaching the intersection once a red signal has appeared. Combating the RLR violation is a major safety concern due to the associated high crash risk and the crash severity outcomes resulting from such unsafe driving behaviour [8,9].…”

Section: A Backgroundmentioning

confidence: 99%

A Review on Drivers’ Red Light Running Behavior Predictions and Technology Based Countermeasures

Komol

Pinnow²,

Elhenawy

et al. 2022

IEEE Access

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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 technologybased 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. INDEX TERMSRed-light running; Stop-go at yellow onset; Dilemma zone; Intersection; Behaviour prediction; Statistical and Machine learning models, Countermeasures.

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Section: A Backgroundmentioning

confidence: 99%

A Review on Drivers’ Red Light Running Behavior Predictions and Technology Based Countermeasures

Komol

Pinnow²,

Elhenawy

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…At the signalised intersection, the red signal is symbolised to stop the vehicle when activated. If the vehicle invades the intersection after the activation of the red signal, the behaviour is classified as Red-light running (Qian & Dong, 2009). Even the drivers often collide with other vehicles and vulnerable road users during turning at the intersection, and the frequency of such collision is significant (Fujinami, 2016).…”

Section: Historymentioning

confidence: 99%

Prediction of Red-Light Running on Basis of Inductive-Loop Detectors for Dynamic All-Red Extension

Wang

Zhang

Zhou

et al. 2012

Transportation Research Record: Journal of the Transportation R

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Driver's behaviour prediction has been an unceasing concern for transportation safety divisions all over the world. A massive amount of lives and properties losses due to the adversities at intersections and pedestrian crossings. Especially for countries with poor road safety technologies, this toll knows no bounds. A myriad of research and studies have been mastered for technological evaluation and model representation over this issue. Instead, little comprehensive review has been made on the driver's behaviour prediction at signalised intersections on red-light running and turning. This Paper aims at incorporating previous researches on driver's behaviour prediction and the prediction parameters leading to traffic violation like red-light running and turning at intersection and pedestrian crossing. The review also covers the probable crash scenarios by red-light running and turning and analyses the innovation of counter-crash technologies with future research directions.

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Prediction of Drivers’ Red-Light Running Behaviour in Connected Vehicle Environments Using Deep Recurrent Neural Networks

Komol,

Elhenawy,

Pinnow

et al. 2024

MAKE

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Red-light running at signalised intersections poses a significant safety risk, necessitating advanced predictive technologies to predict red-light violation behaviour, especially for advanced red-light warning (ARLW) systems. This research leverages Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to forecast the red-light running and stopping behaviours of drivers in connected vehicles. We utilised data from the Ipswich Connected Vehicle Pilot (ICVP) in Queensland, Australia, which gathered naturalistic driving data from 355 connected vehicles at 29 signalised intersections. These vehicles broadcast Cooperative Awareness Messages (CAM) within the Cooperative Intelligent Transport Systems (C-ITS), providing kinematic inputs such as vehicle speed, speed limits, longitudinal and lateral accelerations, and yaw rate. These variables were monitored at 100-millisecond intervals for durations from 1 to 4 s before reaching various distances from the stop line. Our results indicate that the LSTM model outperforms the GRU in predicting both red-light running and stopping behaviours with high accuracy. However, the pre-trained GRU model performs better in predicting red-light running specifically, making it valuable in applications requiring early violation prediction. Implementing these models can enhance red-light violation countermeasures, such as dynamic all-red extension (DARE), decreasing the likelihood of severe collisions and enhancing road users’ safety.

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Engineering Countermeasures to Reducing Red-Light Running

Cited by 3 publications

References 7 publications

A Review on Drivers’ Red Light Running Behavior Predictions and Technology Based Countermeasures

A Review on Drivers’ Red Light Running Behavior Predictions and Technology Based Countermeasures

Prediction of Red-Light Running on Basis of Inductive-Loop Detectors for Dynamic All-Red Extension

Prediction of Drivers’ Red-Light Running Behaviour in Connected Vehicle Environments Using Deep Recurrent Neural Networks

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