scholarly journals Development of a red-light running violation index model for signalized intersections

2011 ◽  
Author(s):  
S. A. Arhin ◽  
E. C. Noel ◽  
L. Williams ◽  
M. Lakew
Keyword(s):  
Red Light ◽  
Signalized Intersections ◽  
Red Light Running ◽  
Index Model

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.

scholarly journals Discrimination of Effects between Directional and Nondirectional Information of Auditory Warning on Driving Behavior

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Yuting Zhang ◽  
Xuedong Yan ◽  
Zhuo Yang
Keyword(s):  
Reference Group ◽  
Red Light ◽  
Warning System ◽  
Driving Behavior ◽  
Signalized Intersections ◽  
Directional Information ◽  
Red Light Running ◽  
Collision Warning ◽  
The Relationship ◽  
Auditory Warning

This study examines the impacts of directional and nondirectional auditory warning information in a collision warning system (CWS) on driving behavior. The data on driving behavior is collected through experiment, with scenarios containing unexpected hazard events that include different warning content. As drivers approached the collision event, either a CWS auditory warning was given or no warning was given for a reference group. Discriminant analysis was used to investigate the relationship between directional auditory warning information and driving behavior. In the experiment, the CWS warnings significantly reduced brake reaction time and prompted drivers to press the brake pedal more heavily, demonstrating the effectiveness of CWS warnings in alerting drivers to avoid red-light running (RLR) vehicles when approaching a signalized intersection. Providing a clear warning with directional information about an urgent hazard event could give drivers adequate time to prepare for the potential collision. In terms of deceleration, a directional information warning was shown to greatly help drivers react to critical events at signalized intersections with more moderate braking. From these results, requirements can be derived for the design of effective warning strategies for critical intersections.

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.

Red Light Running Behavior of Electric Bicycles at Signalized Intersections in China

2014 ◽  
Vol 2468 (1) ◽  
pp. 28-37 ◽  
Author(s):  
Yanyong Guo ◽  
Pan Liu ◽  
Lu Bai ◽  
Chengcheng Xu ◽  
Jun Chen
Keyword(s):  
Red Light ◽  
Signalized Intersections ◽  
Red Light Running ◽  
Electric Bicycles

Connected vehicle-based red-light running prediction for adaptive signalized intersections

2016 ◽  
Vol 22 (3) ◽  
pp. 229-243 ◽  
Author(s):  
Meng Li ◽  
Xiqun (Michael) Chen ◽  
Xi Lin ◽  
Dingyuan Xu ◽  
Yinhai Wang
Keyword(s):  
Red Light ◽  
Signalized Intersections ◽  
Connected Vehicle ◽  
Red Light Running

Innovative countermeasures for red light running prevention at signalized intersections: A driving simulator study

2020 ◽  
Vol 134 ◽  
pp. 105349 ◽  
Author(s):  
Qinaat Hussain ◽  
Wael K.M. Alhajyaseen ◽  
Kris Brijs ◽  
Ali Pirdavani ◽  
Tom Brijs
Keyword(s):  
Driving Simulator ◽  
Red Light ◽  
Signalized Intersections ◽  
Red Light Running ◽  
Simulator Study

scholarly journals Modeling the Frequency of Cyclists’ Red-Light Running Behavior Using Bayesian PG Model and PLN Model

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Yao Wu ◽  
Jian Lu ◽  
Hong Chen ◽  
Qian Wan
Keyword(s):  
Bayesian Statistics ◽  
Traffic Flow ◽  
Red Light ◽  
Primary Objective ◽  
Signalized Intersections ◽  
Signalized Intersection ◽  
Vehicle Speed ◽  
Red Light Running ◽  
Traffic Conflicts ◽  
Signal Type

Red-light running behaviors of bicycles at signalized intersection lead to a large number of traffic conflicts and high collision potentials. The primary objective of this study is to model the cyclists’ red-light running frequency within the framework of Bayesian statistics. Data was collected at twenty-five approaches at seventeen signalized intersections. The Poisson-gamma (PG) and Poisson-lognormal (PLN) model were developed and compared. The models were validated using Bayesianpvalues based on posterior predictive checking indicators. It was found that the two models have a good fit of the observed cyclists’ red-light running frequency. Furthermore, the PLN model outperformed the PG model. The model estimated results showed that the amount of cyclists’ red-light running is significantly influenced by bicycle flow, conflict traffic flow, pedestrian signal type, vehicle speed, and e-bike rate. The validation result demonstrated the reliability of the PLN model. The research results can help transportation professionals to predict the expected amount of the cyclists’ red-light running and develop effective guidelines or policies to reduce red-light running frequency of bicycles at signalized intersections.

Bayesian network for red-light-running prediction at signalized intersections

2018 ◽  
Vol 23 (2) ◽  
pp. 120-132 ◽  
Author(s):  
Xiqun (Michael) Chen ◽  
Lingxiao Zhou ◽  
Li Li
Keyword(s):  
Bayesian Network ◽  
Red Light ◽  
Signalized Intersections ◽  
Red Light Running
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