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

https://doi.org/10.1016/j.trf.2015.12.010Get rights and content

Highlights

  • Red-light running vehicles and non-red-light running vehicles were observed.

  • Compare characteristics for red-light running vehicles and non-red-light running vehicles.

  • Identify significant factors of driver characteristics, driving conditions, and vehicle types.

  • Adopt random effects logistic regression model to consider unobserved heterogeneity among intersections.

Abstract

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.

Introduction

Red-Light-Running (RLR) is a common traffic violation and the major cause of crashes at signalized intersections (Wang, Zhang, & Wang, 2011). In the United States, RLR is associated with about 260,000 crashes and 750 fatalities each year (Retting & Williams, 1996). In China, according to the statistics revealed by The Ministry of Public Security (2012), 4227 severe injury crashes and 789 fatalities between January and October 2012 were attributable to RLR.

Given the similar traffic safety problems caused by RLR in China and the US, there are major gaps between the two countries in the aspects of traffic regulations, enforcement procedures, and signal settings. For example, traffic regulations in China do not allow vehicles to enter the intersection during the yellow phase, whereas it is legal in the US. Another difference is that red light cameras are more frequently used in China compared to the US. Regarding the signal settings, green signal countdown displays are commonly utilized in China to make it easier for drivers to anticipate the end of the green phase, to avoid entering the intersection during the yellow phase.

Previous studies focused on RLR in the US have provided important findings regarding the characteristics of RLR behavior, which include the characteristics of drivers and corresponding driving conditions. However, RLR studies in China have only focused on the characteristics of RLR vehicles; no comparison studies that investigate factors that would separate RLR and non-RLR vehicles have been conducted. This study fills the gap through acquiring data for both RLR and non-RLR vehicles, and the development of models to identify the influencing factors for RLR events. Results from this study will be compared to studies in the US, which will further help us to understand the RLR events across different countries.

Data of RLR drivers and comparison drivers (who did not run the red lights) were collected at four intersections in the urban area of Shanghai. Drivers’ genders, safety belt use, hand-held cell phone use, and presence of passengers were manually recorded by observers at each intersection and double checked through video recordings; drivers’ vehicle operations as they approached and traveled through the intersections were recorded by video cameras. The characteristics of RLR drivers and comparison drivers were then compared through preliminary analysis with a Chi-square test and systematic modeling analysis with an ordinary logistic regression model. However, the ordinary logistic regression models only have the capability of analyzing variables at the vehicle level; factors at the intersection level (such as position of traffic signals and lane markings) may also have substantial influence on red-light running behavior. Because these variables were not included in the ordinary logistic regression model analysis due to the small sample size, a random effects logistic regression model was utilized to capture the influence of unobserved heterogeneity across the intersections.

Section snippets

Background

Previous RLR studies in the US have examined various aspects of RLR, which include RLR prevalence, frequency, antecedents (e.g., signal control, cycle length), and correlates (e.g., age, gender). For example, Retting and Williams (1996) conducted an on-site survey in Arlington County, Virginia, and observed 462 RLR drivers and 911 non-RLR drivers during 234 h of data collection. They found that 48% of RLR drivers entered the intersection 0.5–0.9 s after red onset; 34% at 1.0–1.4 s; 11% at 1.5–1.9 

Intersection selection

The following criteria were adopted to select intersections in this study:

  • (1)

    Selected intersections must be located in different areas of the city.

  • (2)

    Approach volumes must be large enough to allow sufficient RLR events to be captured.

  • (3)

    Intersection signal controls must have left-turn phases.

  • (4)

    Intersections must have signal countdown indicators.

Based on the abovementioned criteria, four intersections in Shanghai were selected. The characteristics of four selected intersections are listed in Table 1.

All

Preliminary analysis

Preliminary analyses were conducted on the following aspects: hourly traffic volume, RLR frequency and distribution, RLR traffic behavior characteristics, vehicle characteristics, and RLR driver characteristics. Comparisons between the RLR group and the comparison group were conducted; the descriptive statistical and Chi-square test results are presented below.

Modeling analysis

In addition to the preliminary analysis, logistic regression models were developed for the purpose of identifying influencing factors in RLR events. The following sections illustrate the methodology of the logistic regression models and the modeling results.

Conclusion and discussion

In this study, four intersections in Shanghai were selected for the collection of field data, through both manual observing and video recording, on red-light running. Instead of only focusing on characteristics of RLR vehicles, this study compared RLR and non-RLR groups to identify through logistic regression models the key influencing factors from the aspects of driver characteristics, vehicle types, and driving conditions. Unobserved heterogeneity across different intersections was considered

Acknowledgements

This study was jointly sponsored by the National Key Technology Support Program (2014BAG01B03) and Chinese National Science Foundation (51522810).

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