A field investigation of red-light-running in Shanghai, China

Red-Light-Running (RLR) is the major cause of severe injury crashes at signalized intersections for both China and the US. As several studies have been conducted to identify the influencing factors of RLR behavior in the US, no similar studies exist in China. To fill this gap, this study was conducted to identify the key factors that affect RLR and compare the contributing factors between US and China. Data were collected through field observations and video recordings; four intersections in Shanghai were selected as the study sites. Both RLR drivers and comparison drivers, who had the opportunity to run the light but did not, were identified. Based on the collected data, preliminary analyses were firstly conducted to identify the features of the RLR and comparison groups. It was determined that: around 57% of RLR crossed the stop line during the 0–0.4 second time interval after red-light onset, and the numbers of red light violators decreased as the time increased; among the RLR vehicles, 38% turned left and 62% went straight; and at the onset of red, about 88% of RLR vehicles were in the middle of a vehicle platoon. Furthermore, in order to compare the RLR group and non-RLR group, two types of logistic regression models were developed. The ordinary logistic regression model was developed to identify the significant variables from the aspects of driver characteristics, driving conditions, and vehicle types. It was concluded that RLR drivers are more likely to be male, have local license plates, and are driving passenger vehicles but without passengers. Large traffic volume also increased the likelihood of RLR. However, the ordinary logistic regression model only considers influencing factors at the vehicle level: different intersection design and signal settings may also have impact on RLR behaviors. Therefore, in order to account for unobserved heterogeneity among different types of intersections, a random effects logistic regression model was adopted. Through the model comparisons, it has been identified that the model goodness-of-fit was substantially improved through considering the heterogeneity effects at intersections. Finally, benefits of this study and the analysis results were discussed.

[1]  Xuedong Yan,et al.  Classification analysis of driver's stop/go decision and red-light running violation. , 2010, Accident; analysis and prevention.

[2]  Bryan E. Porter,et al.  Predicting Red-Light Running Behavior: A Traffic Safety Study in Three Urban Settings , 2000 .

[3]  Allan F. Williams,et al.  Characteristics of red light violators: Results of a field investigation , 1996 .

[4]  Kangwon Shin,et al.  The impact of red light cameras on safety in Arizona. , 2007, Accident; analysis and prevention.

[5]  Myung-Soon Chang,et al.  TIMING TRAFFIC SIGNAL CHANGE INTERVALS BASED ON DRIVER BEHAVIOR , 1985 .

[6]  Andrew Thomas,et al.  WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility , 2000, Stat. Comput..

[7]  B E Porter,et al.  A nationwide survey of self-reported red light running: measuring prevalence, predictors, and perceived consequences. , 2001, Accident; analysis and prevention.

[8]  Hoong Chor Chin,et al.  Severity of driver injury and vehicle damage in traffic crashes at intersections: a Bayesian hierarchical analysis. , 2008, Accident; analysis and prevention.

[9]  Mohamed Abdel-Aty,et al.  Multi-level Bayesian analyses for single- and multi-vehicle freeway crashes. , 2013, Accident; analysis and prevention.

[10]  Wassim G Najm,et al.  Examining driver behavior using data gathered from red light photo enforcement cameras. , 2007, Journal of safety research.

[11]  J Bonneson,et al.  ENGINEERING COUNTERMEASURES TO RED-LIGHT-RUNNING , 2002 .

[12]  Xuesong Wang,et al.  Utilizing Microscopic Traffic and Weather Data to Analyze Real-Time Crash Patterns in the Context of Active Traffic Management , 2014, IEEE Transactions on Intelligent Transportation Systems.

[13]  J Bonneson,et al.  ENGINEERING COUNTERMEASURES TO REDUCE RED-LIGHT-RUNNING , 2002 .

[14]  J Bonneson,et al.  REVIEW AND EVALUATION OF FACTORS THAT AFFECT THE FREQUENCY OF RED-LIGHT-RUNNING , 2001 .

[15]  Bryan E Porter,et al.  Characterizing red light runners following implementation of a photo enforcement program. , 2006, Accident; analysis and prevention.

[16]  A. Williams,et al.  Prevalence and characteristics of red light running crashes in the United States. , 1999, Accident; analysis and prevention.

[17]  C. Farmer,et al.  Reducing red light running through longer yellow signal timing and red light camera enforcement: results of a field investigation. , 2008, Accident; analysis and prevention.