scholarly journals Investigating Different Types of Red-Light Running Behaviors among Urban e-Bike Rider Mixed Groups

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Fan Zhang ◽  
Chenchen Kuai ◽  
Huitao Lv ◽  
Wenhao Li
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Unobserved Heterogeneity ◽  
Red Light ◽  
Multinomial Logit Model ◽  
Time Of Day ◽  
Signalized Intersections ◽  
Traffic Light ◽  
Safety Level ◽  
Red Light Running

The red-light running (RLR) behaviors of urban mixed e-bike groups (delivery and ordinary e-bike) have become the main cause of traffic accidents at signalized intersections. The primary purpose of this study is to identify influencing factors of e-bike riders’ RLR behaviors, focusing on the role of delivery e-bike riders in mixed e-bike rider groups. Crossing behaviors of 4,180 e-bike samples (2006 delivery e-bikes and 2174 ordinary e-bikes) at signalized intersections are observed in Xi’an, China. The random parameter multinomial logit model is employed to capture the unobserved heterogeneous effects, and the effects of interaction terms are also considered. The results indicate that delivery e-bike riders are more likely to run red lights than ordinary e-bike riders. E-bike type, riders’ age, waiting position, traffic volume, traffic light type, and time of day are associated with crossing behaviors in urban mixed e-bike groups. In addition, the variable of traffic light status is found to account for unobserved heterogeneity. Findings are indicative to the development of effective implications in improving e-bikes’ traffic safety level at signalized intersections.

scholarly journals Selection and assessment of the relevant data for reducing the number of red-light running

Transport ◽  
2016 ◽  
Vol 33 (1) ◽  
pp. 268-279
Author(s):  
Milan Vujanić ◽  
Dalibor Pešić ◽  
Boris Antić ◽  
Nenad Marković
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Statistical Significance ◽  
Red Light ◽  
Risk Indicator ◽  
Traffic Light ◽  
Anova Analysis ◽  
Red Light Running ◽  
The City ◽  
Selection And Assessment

Although traffic light controlled intersections separate, the traffic flows by time and space, road traffic accidents still occur, usually due to Red-Light Running (RLR). In order to define countermeasures to solve this problem, it is necessary to collect and analyze certain data that will indicate type of measures, which should be applied. In this paper, it was done on the example of one 3-leg and one 4-leg intersection where citizens provided information about frequent RLR to the City Administration of Belgrade (Serbia). The statistical significance of differences between the collected data was tested by ANOVA analysis and by PostHoc Tukey test, which showed that forecasting of second of RLR after red-light onset could effectively be conducted by Cubic distribution. In order to define the so-called RLR risk indicator for the intersection, the use of the Danger Degree (DD) indicator, that presents the rate between the number of dangerous situations caused by RLR and the total number of RLR, was proposed.

scholarly journals Application of traffic conflict technique for traffic safety evaluation at intersection based on image processing

2021 ◽  
Vol 7 (3A) ◽  
pp. 134-142
Author(s):  
Ngoc Dung Bui ◽  
Dinh Tran Ngoc Huy ◽  
Tuan Thanh Nguyen
Keyword(s):  
Image Processing ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Economic Damage ◽  
Traffic Light ◽  
Safety Level

Traffic accidents occur frequently at intersections area because of conflicts among vehicles. Many researchers have been assessing traffic safety, however most of them are based on accident data that happened and caused injury as well as economic damage. Recently, conflict techniques provide general views and solutions to prevent early collisions. This paper proposes a method of conflict analysis using image processing technique and fuzzy comprehensive evaluation. Based on vehicles detection, the conflict parameters collected from two intersections in Hanoi, Vietnam were processed and evaluated by fuzzy comprehensive evaluation to give out the safety level of conflict points. The experimental results show that the proposed methods have successfully shown the distribution and location of dangerous conflict points according to the actual situation of the two intersections. Based on our results, authorities can consider reorganizing traffic light pattern at these intersections to reduce possible collisions.

scholarly journals Evaluation of Automated Enforcement Program in Amman

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.

scholarly journals Safety evaluation of left-turn priority phasing at Toronto intersections

2021 ◽  
Author(s):  
Anwarul Haq Dogar
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Safety Evaluation ◽  
Vital Role ◽  
Signalized Intersections ◽  
Side Impact ◽  
Empirical Bayesian ◽  
Left Turn ◽  
Safety Performance Functions ◽  
The City

Traffic accidents cause a huge loss to the society. According to statistics, 50% of all accidents occur at urban intersections and 47% of these are due to left-turn collisions. Countermeasure Implementation at these locations therefore can play a vital role in the improvement of traffic safety. This study illustrates a methodology for evaluation of urban 4-legged signalized intersections treated with left-turn priority phasing. The methodology is applied to three important collisions types: those due to left-turn collisions; those due to left-turn side impact collisions; and all impact types combined collisions. Data used in this analysis were obtained from the City of Toronto. Safety Performance Functions for left-turn and all impact types combined collisions which were developed by the City of Toronto, were calibrated and used in an empirical Bayesian methodology that was employed to estimate the expected frequency of accidents occurring at each intersection in order to evaluate the effectiveness of left-turn priority phasing in reducing this frequency. The results revealed that left-turn priority phasing can be an effective treatment for addressing and reducing the number of collision at signalized intersections. Flashing advance green phasing is more effective in improving safety for two of three types; all left-turn and all impact types combined collisions. Left-turn green arrow (protected/permissive) phasing is more effective for left-turn side impact collisions. By implementing this type of treatment, the number of crashes and the associated monetary loss to society could be significantly reduced.

scholarly journals Safety evaluation of left-turn priority phasing at Toronto intersections

2021 ◽  
Author(s):  
Anwarul Haq Dogar
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Safety Evaluation ◽  
Vital Role ◽  
Signalized Intersections ◽  
Side Impact ◽  
Empirical Bayesian ◽  
Left Turn ◽  
Safety Performance Functions ◽  
The City

Traffic accidents cause a huge loss to the society. According to statistics, 50% of all accidents occur at urban intersections and 47% of these are due to left-turn collisions. Countermeasure Implementation at these locations therefore can play a vital role in the improvement of traffic safety. This study illustrates a methodology for evaluation of urban 4-legged signalized intersections treated with left-turn priority phasing. The methodology is applied to three important collisions types: those due to left-turn collisions; those due to left-turn side impact collisions; and all impact types combined collisions. Data used in this analysis were obtained from the City of Toronto. Safety Performance Functions for left-turn and all impact types combined collisions which were developed by the City of Toronto, were calibrated and used in an empirical Bayesian methodology that was employed to estimate the expected frequency of accidents occurring at each intersection in order to evaluate the effectiveness of left-turn priority phasing in reducing this frequency. The results revealed that left-turn priority phasing can be an effective treatment for addressing and reducing the number of collision at signalized intersections. Flashing advance green phasing is more effective in improving safety for two of three types; all left-turn and all impact types combined collisions. Left-turn green arrow (protected/permissive) phasing is more effective for left-turn side impact collisions. By implementing this type of treatment, the number of crashes and the associated monetary loss to society could be significantly reduced.

Deep Reinforcement Learning Agent with Varying Actions Strategy for Solving the Eco-Approach and Departure Problem at Signalized Intersections

2020 ◽  
Vol 2674 (8) ◽  
pp. 119-131
Author(s):  
Saleh R. Mousa ◽  
Sherif Ishak ◽  
Ragab M. Mousa ◽  
Julius Codjoe ◽  
Mohammed Elhenawy
Keyword(s):  
Reinforcement Learning ◽  
Control Problem ◽  
Fuel Consumption ◽  
Red Light ◽  
Signalized Intersections ◽  
Red Light Running ◽  
Fuel Savings ◽  
Learning Agent ◽  
Target Network ◽  
Experience Replay

Eco-approach and departure is a complex control problem wherein a driver’s actions are guided over a period of time or distance so as to optimize fuel consumption. Reinforcement learning (RL) is a machine learning paradigm that mimics human learning behavior, in which an agent attempts to solve a given control problem by interacting with the environment and developing an optimal policy. Unlike the methods implemented in previous studies for solving the eco-driving problem, RL does not require prior knowledge of the environment to be learned and processed. This paper develops a deep reinforcement learning (DRL) agent for solving the eco-approach and departure problem in the vicinity of signalized intersections for minimization of fuel consumption. The DRL algorithm utilizes a deep neural network for the RL. Novel strategies such as varying actions, prioritized experience replay, target network, and double learning were implemented to overcome the expected instabilities during the training process. The results revealed the significance of the DRL algorithm in reducing fuel consumption. Interestingly, the DRL algorithm was able to successfully learn the environment and guide vehicles through the intersection without red light running violation. On average, the DRL provided fuel savings of about 13.02% with no red light running violations.

Using Focus Groups to Investigate Issues of Red Light Running

2000 ◽  
Vol 1734 (1) ◽  
pp. 38-45 ◽  
Author(s):  
Leanne M. Wissinger ◽  
Joseph E. Hummer ◽  
Joseph S. Milazzo
Keyword(s):  
Law Enforcement ◽  
Focus Groups ◽  
Traffic Safety ◽  
Traffic Engineering ◽  
Red Light ◽  
The United States ◽  
Red Light Running ◽  
Grace Period ◽  
The Public ◽  
Red Light Cameras

Red light running (RLR) has been an important issue among transportation officials seeking to make intersections safer for drivers and pedestrians. Many cities in the United States have started programs aimed at reducing the number of red light violations, and many of these programs include the use of automated enforcement utilizing a camera to record violations. Previous research on such enforcement has quantified the rate of its public acceptance through surveys; however, little research has been performed probing the reactions and concerns of the public toward red light cameras. For this study, focus groups were used to investigate the attitudes, beliefs, and perceptions of the public toward RLR and red light cameras. Fifteen focus groups were held throughout North Carolina with representatives from organizations interested in and knowledgeable about traffic safety, traffic engineering, and traffic law enforcement, as well as with people not professionally involved in law enforcement or traffic engineering. Some of the focus group discussions involved such issues as determining an appropriate RLR grace period, developing an educational campaign, addressing financial issues, and determining appropriate penalties for RLR violations. Participants voiced their opinions on both sides of the issues; for instance, many participants said they strongly believed there should be some sort of grace period with automated enforcement, whereas others said they felt a zero-tolerance policy should be used. Also, many participants voiced their unequivocal support for automated enforcement, whereas others expressed concerns.

scholarly journals Exploring the Application of the Linear Poisson Autoregressive Model for Analyzing the Dynamic Impact of Traffic Laws on Fatal Traffic Accident Frequency

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Yue Zhang ◽  
Yajie Zou ◽  
Lingtao Wu ◽  
Jinjun Tang ◽  
Malik Muneeb Abid
Keyword(s):  
Traffic Safety ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Negative Binomial ◽  
Moving Average ◽  
Red Light ◽  
Dynamic Impact ◽  
Temperature And Precipitation ◽  
Accident Data ◽  
Poisson Autoregressive Model

Annual fatal traffic accident data often demonstrate time series characteristics. The existing traffic safety analysis approaches (e.g., negative binomial (NB) model) often cannot accommodate the dynamic impact of factors in fatal traffic accident data and may result in biased parameter estimation results. Thus, a linear Poisson autoregressive (PAR) model is proposed in this study. The objective of this study is to apply the PAR model to analyze the dynamic impact of traffic laws and climate on the frequency of fatal traffic accidents occurred in a large time span (from 1975 to 2016) in Illinois. Besides, the NB model, NB with a time trend, and autoregressive integrated moving average model with exogenous input variables (ARIMAX) are also developed to compare their performances. The important conclusions from the modelling results can be summarized as follows. (1) The PAR model is more appropriate for analyzing the dynamic impacts of traffic laws on annual fatal traffic accidents, especially the instantaneous impacts. (2) The law that allows motorcycles and bicycles to proceed on a red light following the rules applicable after a “reasonable period of time” leads to an increase in the frequency of annual fatal traffic accidents by 14.98% in the short term and 30.69% in the long term. The climate factors such as average temperature and precipitation concentration period have insignificant impacts on annual fatal traffic accidents in Illinois. Thus, the modelling results suggest that the PAR model is more appropriate for annual fatal traffic accident data and has an advantage in estimating the dynamic impact of traffic laws.

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