P5.010 Red light running rate in the Kumasi metropolis of Ghana

(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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Prediction of Red Light Running Based on Statistics of Discrete Point Sensors

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2128-14 ◽

2009 ◽

Vol 2128 (1) ◽

pp. 132-142 ◽

Cited By ~ 23

Author(s):

Liping Zhang ◽

Kun Zhou ◽

Wei-bin Zhang ◽

James A. Misener

Keyword(s):

Red Light ◽

Discrete Point ◽

Red Light Running

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Evaluation of Automated Enforcement Program in Amman

Periodica Polytechnica Transportation Engineering ◽

10.3311/pptr.10939 ◽

2018 ◽

Author(s):

Hana Naghawi ◽

Bushra Al Qatawneh ◽

Rabab Al Louzi

Keyword(s):

Traffic Safety ◽

Questionnaire Survey ◽

Positive Impact ◽

Red Light ◽

Crash Frequency ◽

Red Light Running ◽

Red Light Cameras ◽

Percent Confidence Level ◽

Before And After ◽

Traffic Violations

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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