Modeling Pedestrians Red Light Running Violation at Signalized Intersection: Accounting for Unobserved Heterogeneity

CICTP 2018 ◽  
2018 ◽  
Author(s):  
Yao Wu ◽  
Yanyong Guo ◽  
Jian Lu
Keyword(s):  
Unobserved Heterogeneity ◽  
Red Light ◽  
Signalized Intersection ◽  
Red Light Running

scholarly journals Red light running motorcyclists at signalized intersection in Malaysia: An empirical study

2019 ◽  
Vol 512 ◽  
pp. 012019
Author(s):  
Muhammad Marizwan Abdul Manan ◽  
Syed Tajul Malik Syed Tajul Arif ◽  
Ho Jen Sim ◽  
Norfaizah Mohamad Khaidir ◽  
Hawa Mohamed Jamil ◽  
...  
Keyword(s):  
Empirical Study ◽  
Red Light ◽  
Signalized Intersection ◽  
Red Light Running

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 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.

Field Evaluation of the Dilemma Zone Protection System at Suburban Intersections

2018 ◽  
Vol 2672 (21) ◽  
pp. 51-62 ◽  
Author(s):  
Sung Yoon Park ◽  
Chien-Lun Lan ◽  
Ranteg S. Rao ◽  
Gang-Len Chang
Keyword(s):  
Red Light ◽  
Field Evaluation ◽  
Protection System ◽  
Signalized Intersection ◽  
Red Light Running ◽  
Dilemma Zone ◽  
Effective Strategies ◽  
The Past ◽  
Intersection Safety ◽  
Arterial Traffic

Despite the fact that both traffic researchers and highway agencies have devoted considerable efforts over the past few decades to improving intersection safety, development of effective strategies to contend with this vital issue remains a challenging task. This research presents the field evaluation results of a Dilemma Zone Protection System (DZPS) implemented at two hazardous intersections in Maryland, U.S.A. The deployed DZPS can offer both proactive and reactive protections to drivers approaching a signalized intersection during a yellow phase. Field evaluations conducted at two intersections with DZPS deployed confirmed a 100% detection rate for red-light-running vehicles, timely activation of the all-red extension to prevent right-angle crashes, and effectively discouraging drivers from taking aggressive “pass” decisions during the yellow or all-red phases. The potential extension of the DZPS for speed harmonization on arterial traffic flows is also discussed.

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

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.

Effects of adaptive and cooperative adaptive cruise control on the fuel consumption and emissions at the signalized intersection

2018 ◽  
Vol 32 (32) ◽  
pp. 1850396 ◽  
Author(s):  
Hongjun Cui ◽  
Jiangke Xing ◽  
Xia Li ◽  
Minqing Zhu
Keyword(s):  
Fuel Consumption ◽  
Red Light ◽  
Signalized Intersection ◽  
Environmental Benefit ◽  
Cruise Control ◽  
Car Following ◽  
Vehicle Type ◽  
Cooperative Adaptive Cruise Control ◽  
Car Following Model ◽  
Fuel Consumption And Emissions

In this paper, the HDM car-following model, the IIDM car-following model and the IDM car-following model with a constant-acceleration heuristic is utilized to explore the effects of ACC/CACC on the fuel consumption and emissionsat the signalized intersection. Two simulation experiments are studied: (i) one with free road ahead and (ii) the second with a red light 300 m downstream at the second intersection. The numerical results show that CACC vehicle is the best vehicle type among the three vehicle types from the perspective of vehicle’s cumulative fuel consumptions and cumulative exhaust emissions. The results of this paper also suggest a very high environmental benefit of ACC/CACC at little or no cost in infrastructure.

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

2018 ◽  
Vol 2672 (38) ◽  
pp. 233-244 ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document