Safety Impacts of Red Light Cameras at Signalized Intersections Based on Cellular Automata Models

Chai, Chunlei; Wong, Yiik Diew; Lum, Kit Meng

doi:10.1080/15389588.2014.942418

Cited by 10 publications

(4 citation statements)

References 20 publications

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“…Ahmed and Abdel-Aty [21] analyzed traffic accidents at intersections and found that traffic violation monitoring would reduce left-turn traffic accidents but increase traffic accidents in other directions. Chai et al [22] believed that traffic violation monitoring has different effects on different types of traffic accidents. Traffic violation monitoring reduces the occurrence of collision accidents, but the probability of rear-end accidents increases.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Impact of Traffic Violation Monitoring on the Vehicle Speeds of Urban Main Road: Taking China as an Example

Pan

Yang

Zhang

et al. 2020

Journal of Advanced Transportation

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In recent years, there are more and more applications of traffic violation monitoring in some countries. The present work aims to analyze the vehicle speeds nearby road traffic violation monitoring area on urban main roads and find out the impact of road traffic violation monitoring on the vehicle speeds. A representative urban main road section was selected and the traffic flow was recorded by camera method. The vehicle speeds before, within, and after the road traffic violation monitoring area were obtained by the calculation method. The speed data was classified and processed by SPSS software and mathematical method to establish the vehicle speed probability density models before, within, and after the road traffic violation monitoring area. The results show that the average speed and maximum speed within the traffic violation monitoring area are significantly slower than those before and after the traffic violation monitoring area. 70.1% of the vehicles before the road traffic violation monitoring area were speeding, and 80.2% of the vehicles after the road traffic violation monitoring area were speeding, while within the road traffic violation monitoring area, the speeding vehicles were reduced to 15.9%. When vehicles pass through the road traffic violation monitoring area, the vehicle speeds tend to first decrease and subsequently increase. In its active area, road traffic violation monitoring can effectively regulate driving behaviors and reduce speeding, but this effect is limited to the vicinity of the traffic violation monitoring. The distribution of vehicle speeds can be calculated from vehicle speed probability density models.

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Section: Introductionmentioning

confidence: 99%

Analysis of the Impact of Traffic Violation Monitoring on the Vehicle Speeds of Urban Main Road: Taking China as an Example

Pan

Yang

Zhang

et al. 2020

Journal of Advanced Transportation

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show abstract

“…Model was calibrated and validated with field data. Model was able to predict delays but saturation flows were predicted with 30% variabilitylane and direction[37]2✓✓✗✗open✓simulation of heterogeneous traffic at simple X and T‐intersectionsmodel was validated with data of Dublin[61]1✗✗✗✗periodic✗besides free flow, jamming and platoon phases in fundamental diagram, a fourth phase is visible and vanishes when probability of priority is 0.5[62]1✗✗✗✗periodic✗modified NS model, simulated urban two‐way traffic having one lane both sides, added additional movement rule and grid‐lock prevention rulefundamental diagrams were validated[38, 63]1✗✗✗✗open✓simulated right‐turn vehicular interactions for Singapore. Model was validated with field data.…”

Section: Literature Review Of the Ca Model At The Intersectionmentioning

confidence: 99%

Cellular automata models for signalised and unsignalised intersections with special attention to mixed traffic flow: a review

Singh

Rao

2020

IET intell. transp. syst

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“…A straightforward way is to use RLR cameras to detect RLR behaviors, and the traffic police will give a corresponding penalty related to the severity of the RLR behavior, and much research has shown that RLR cameras significantly reduce RLR frequency [10,11]. However, some researches argue that RLR cameras may increase rear-end crashes [12]. Hence, it is not enough to only use enforcement methods for RLR reduction.…”

Section: Introductionmentioning

confidence: 99%

Reducing Red Light Running (RLR) with Adaptive Signal Control: A Case Study

Chang

2023

Electronics

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Traffic accidents are a leading cause of premature death for citizens, with millions of injuries and fatalities occurring annually. Due to the fact that a large proportion of accidents are caused by red light running, reduction of the frequency of red light running (RLR) has been extensively researched in recent years. However, most of the previous studies have focused on reducing RLR frequency through driver education or warning sign design, with little attention paid to the relationship between RLR behavior and traffic signal control. Considering RLR is significantly affected by the number of vehicles arriving during yellow, it is possible to identify RLR behaviors in advance by analyzing data on yellow-arriving vehicles. Meanwhile, based on the strong correlation between yellow arriving and RLR frequency, it is possible to reduce RLR by traffic signal control. In this paper, we propose a quantitative model of correlation between RLR frequency and yellow light arrival based on high-resolution traffic and signal event data from Twin Cities, Minnesota. On this basis, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is implemented to find trade-offs between minimizing the RLR frequency and the traffic delay. A case study of a 6-intersection arterial road reveals that in unsaturation, saturation, and supersaturation flow, our approach can converge to a Pareto optimal front in 30–50 iterations, which shows that is possible to simultaneously reduce RLR frequency and enhance traffic efficiency safety, which is conducive to ensuring the life safety of traffic participants.

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Safety Impacts of Red Light Cameras at Signalized Intersections Based on Cellular Automata Models

Abstract: RLCs are found to have a mixed effect on accident risk at signalized intersections: crossing collisions are reduced, whereas rear-end collisions may increase. The specially developed CA model is found to be a feasible safety assessment tool.

Cited by 10 publications

References 20 publications

Analysis of the Impact of Traffic Violation Monitoring on the Vehicle Speeds of Urban Main Road: Taking China as an Example

Analysis of the Impact of Traffic Violation Monitoring on the Vehicle Speeds of Urban Main Road: Taking China as an Example

Cellular automata models for signalised and unsignalised intersections with special attention to mixed traffic flow: a review

Reducing Red Light Running (RLR) with Adaptive Signal Control: A Case Study

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