Comparison of Red-Light Running (RLR) and Yellow-Light Running (YLR) traffic violations in the cities of Enna and Thessaloniki

This study aims, in a first attempt, to evaluate the effectiveness of using the Automated Enforcement Program (AEP) to improve traffic safety in Amman, Jordan. The evaluation of the program on crashes and violations was examined based on a “before-and-after” study using the paired t-test at 95 percent confidence level. Twenty one locations including signalized intersections monitored by red light cameras and arterial roads monitored by excessive speed cameras were selected. Nine locations were used to study the effectiveness of the program on violations, and twelve locations were used to determine the effectiveness of the program on frequency and severity of crashes. Data on number and severity of crashes were taken from Jordan Traffic Institution. Among the general findings, it was found that the AEP was generally associated with positive impact on crashes. Crash frequency was significantly reduced by up to 63%. Crash severities were reduced by up to 62.5%. Also, traffic violations were significantly reduced by up to 66%. Finally, drivers’ opinion and attitude on the program was also analyzed using a questionnaire survey. The questionnaire survey revealed that 35.5% of drivers are unaware of AEP in Amman, 63.9% of drivers don’t know the camera locations, most drivers knew about excessive speed and red light running penalties, most drivers reduce their speed at camera locations, 44.4% of drivers think that the program satisfies its objective in improving traffic safety and 52% of drivers encourage increasing the number of camera devices in Amman.

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Evaluation of a Public Technology-Based Traffic Enforcement Program

Sustainability ◽

10.3390/su132111966 ◽

2021 ◽

Vol 13 (21) ◽

pp. 11966

Author(s):

Gila Albert ◽

Dimitry Bukchin ◽

Tomer Toledo

Keyword(s):

Red Light ◽

Evaluation Process ◽

Traffic Enforcement ◽

Unique Type ◽

Red Light Running ◽

Rigorous Evaluation ◽

Video Recordings ◽

Traffic Violations ◽

Police Enforcement ◽

Simple Technology

While police enforcement is a well-known means of reducing traffic violations, it is also recognized that other agents should be involved in creating sustainable deterrence. This paper describes and evaluates the Israeli Road Guards program, a new and unique type of traffic enforcement, which enables simple technology-based enforcement of traffic violations by citizens. In its 24 months of operation, more than 3400 volunteers who submitted over 64,000 violation reports were involved in this program. Each report went through a rigorous evaluation process. More than 80% of the submitted reports were rejected in the various stages of the procedure. In 13.7% of the cases a notice letter was sent, and in 4.3% of cases (reflecting the most severe offenses) a citation was issued by the police. The monthly rate of report submission by the volunteers was at its highest initially, then decreased and stabilized after about six months at 1.4 reports per month. The proportion of active volunteers also decreased over time to a level of 0.26 at the end of the study period. The violation types reported within the program differed substantially from those captured by police enforcement. These differences are likely due to the manner in which each mode of enforcement was performed. The most common violations reported by volunteers were lane deviations, red light running and driving on the roads’ shoulders, which are easily documented by means of video recordings. They are also associated with higher crash risks. Thus, the results show that such public technology-based traffic enforcement, which can be carried out during regular daily driving and does not require anyone to make extra trips, may efficiently complement traditional police enforcement.

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Assessing Drivers’ Compliance with Restrictive Yellow Traffic Lights in a Developing Country

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120973659 ◽

2020 ◽

pp. 036119812097365

Author(s):

Abdoul-Ahad Choupani

Keyword(s):

Developing Country ◽

Red Light ◽

Yellow Light ◽

Red Light Running ◽

Traffic Lights ◽

Light Passing ◽

Yellow Lights ◽

Behavioral Issue ◽

Stop Line ◽

Restrictive Policy

Driving rules adopt permissive or restrictive policies concerning yellow light running (YLR). In a restrictive policy, vehicles behind the stop line are not allowed to enter the intersection on yellow no matter how close they are to the stop line. YLR policy affects driving risks, safety, and operation. There is limited knowledge about the restrictive policy and drivers’ compliance with this rule. Previous studies on YLR are limited in scope since they tended to use binary stop/go decision models without considering red light running decisions. This potentially results in the loss of information about drivers’ conformity to red signals. This paper examines whether drivers are only non-compliant with yellow lights or whether non-conformity to any prohibitive yellow/red signal emerges as a wider behavioral issue. This study develops regression choice models to predict drivers’ illegal yellow-light passing decisions in a developing country with a poor safety record and explores reasons for drivers’ non-compliance. The results obtained show that the restrictive policy is ineffective in relation to driver compliance, especially in cases where drivers’ non-conformity to any restrictive rule emerges as a behavioral issue of concern. Drivers make their stop/go decisions according to the time needed to cross the intersection, and they consider the yellow light as an opportunity for crossing. Yellow (red) light running rates were 101 (31) per 1,000 vehicles per hour (vph) for the restrictive policy, whereas these rates for the U.S.A., with a permissive policy, were at most 29 (6) per 1,000 vph.

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Red-light running traffic violations: A novel time-based method for determining a fine structure

Transportation Research Part A Policy and Practice ◽

10.1016/j.tra.2016.08.015 ◽

2016 ◽

Vol 93 ◽

pp. 55-65 ◽

Cited By ~ 5

Author(s):

Fatemeh Baratian-Ghorghi ◽

Huaguo Zhou ◽

Wesley C. Zech

Keyword(s):

Fine Structure ◽

Red Light ◽

Red Light Running ◽

Traffic Violations

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Autonomous Vehicles and Vulnerable Road-Users—Important Considerations and Requirements Based on Crash Data from Two Countries

Behavioral Sciences ◽

10.3390/bs11070101 ◽

2021 ◽

Vol 11 (7) ◽

pp. 101

Author(s):

Andrew Paul Morris ◽

Narelle Haworth ◽

Ashleigh Filtness ◽

Daryl-Palma Asongu Nguatem ◽

Laurie Brown ◽

...

Keyword(s):

Autonomous Vehicles ◽

Red Light ◽

Road User ◽

Red Light Running ◽

Crash Data ◽

Passenger Vehicles ◽

Road Users ◽

Vulnerable Road User ◽

Vulnerable Road Users ◽

Road Intersections

(1) Background: Passenger vehicles equipped with advanced driver-assistance system (ADAS) functionalities are becoming more prevalent within vehicle fleets. However, the full effects of offering such systems, which may allow for drivers to become less than 100% engaged with the task of driving, may have detrimental impacts on other road-users, particularly vulnerable road-users, for a variety of reasons. (2) Crash data were analysed in two countries (Great Britain and Australia) to examine some challenging traffic scenarios that are prevalent in both countries and represent scenarios in which future connected and autonomous vehicles may be challenged in terms of safe manoeuvring. (3) Road intersections are currently very common locations for vulnerable road-user accidents; traffic flows and road-user behaviours at intersections can be unpredictable, with many vehicles behaving inconsistently (e.g., red-light running and failure to stop or give way), and many vulnerable road-users taking unforeseen risks. (4) Conclusions: The challenges of unpredictable vulnerable road-user behaviour at intersections (including road-users violating traffic or safe-crossing signals, or taking other risks) combined with the lack of knowledge of CAV responses to intersection rules, could be problematic. This could be further compounded by changes to nonverbal communication that currently exist between road-users, which could become more challenging once CAVs become more widespread.

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Real-Time Prediction of Vehicle Trajectories for Proactively Identifying Risky Driving Behaviors at High-Speed Intersections

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118797211 ◽

2018 ◽

Vol 2672 (38) ◽

pp. 233-244 ◽

Cited By ~ 1

Author(s):

Chaopeng Tan ◽

Nan Zhou ◽

Fen Wang ◽

Keshuang Tang ◽

Yangbeibei Ji

Keyword(s):

Phase Transition ◽

High Speed ◽

Red Light ◽

Risky Behaviors ◽

Risky Driving ◽

Red Light Running ◽

Driving Behaviors ◽

Vehicle Trajectories ◽

Single Vehicle ◽

Risky Driving Behaviors

At high-speed intersections in many Chinese cities, a traffic-light warning sequence at the end of the green phase—three seconds of flashing green followed by three seconds of yellow—is commonly implemented. Such a long phase transition time leads to heterogeneous decision-making by approaching drivers as to whether to pass the signal or stop. Therefore, risky driving behaviors such as red-light running, abrupt stop, and aggressive pass are more likely to occur at these intersections. Proactive identification of risky behaviors can facilitate mitigation of the dilemma zone and development of on-board safety altering strategies. In this study, a real-time vehicle trajectory prediction method is proposed to help identify risky behaviors during the signal phase transition. Two cases are considered and treated differently in the proposed method: a single vehicle case and a following vehicle case. The adaptive Kalman filter (KF) model and the K-nearest neighbor model are integrated to predict vehicle trajectories. The adaptive KF model and intelligent driver model are fused to predict the following vehicles’ trajectories. The proposed models are calibrated and validated using 1,281 vehicle trajectories collected at three high-speed intersections in Shanghai. Results indicate that the root mean square error between the predicted trajectories and the actual trajectories is 5.02 m for single vehicles and 2.33 m for following vehicles. The proposed method is further applied to predict risky behaviors, including red-light running, abrupt stop, aggressive pass, speeding pass, and aggressive following. The overall prediction accuracy is 95.1% for the single vehicle case and 96.2% for the following vehicle case.

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