Attitudes toward Applying Facial Recognition Technology for Red-Light Running by E-Bikers: A Case Study in Fuzhou, China

The application of facial recognition technology (FRT) can effectively reduce the red-light running behavior of e-bikers. However, the privacy issues involved in FRT have also attracted widespread attention from society. This research aims to explore the public and traffic police’s attitudes toward FRT to optimize the use and implementation of FRT. A structured questionnaire survey of 270 people and 94 traffic police in Fuzhou, China, was used. In the research, we use several methods to analyze the investigation data, including Mann–Whitney U test, Kruskal–Wallis test, and multiple correspondence analysis. The survey results indicate that the application of FRT has a significant effect on reducing red-light running behavior. The public’s educational level and driving license status are the most influential factors related to their attitudes to FRT (p < 0.001). Public members with these attributes show more supportive attitudes to FRT and more concerns about privacy invasion. There are significant differences between the public and traffic police in attitudes toward FRT (p < 0.001). Compared with the public, traffic police officers showed more supportive attitudes to FRT. This research contributes to promoting the application of FRT legitimately and alleviating people’s concerns about the technology.

Evaluation of a Public Technology-Based Traffic Enforcement Program

While police enforcement is a well-known means of reducing traffic violations, it is also recognized that other agents should be involved in creating sustainable deterrence. This paper describes and evaluates the Israeli Road Guards program, a new and unique type of traffic enforcement, which enables simple technology-based enforcement of traffic violations by citizens. In its 24 months of operation, more than 3400 volunteers who submitted over 64,000 violation reports were involved in this program. Each report went through a rigorous evaluation process. More than 80% of the submitted reports were rejected in the various stages of the procedure. In 13.7% of the cases a notice letter was sent, and in 4.3% of cases (reflecting the most severe offenses) a citation was issued by the police. The monthly rate of report submission by the volunteers was at its highest initially, then decreased and stabilized after about six months at 1.4 reports per month. The proportion of active volunteers also decreased over time to a level of 0.26 at the end of the study period. The violation types reported within the program differed substantially from those captured by police enforcement. These differences are likely due to the manner in which each mode of enforcement was performed. The most common violations reported by volunteers were lane deviations, red light running and driving on the roads’ shoulders, which are easily documented by means of video recordings. They are also associated with higher crash risks. Thus, the results show that such public technology-based traffic enforcement, which can be carried out during regular daily driving and does not require anyone to make extra trips, may efficiently complement traditional police enforcement.

Exploring factors associated with red-light running: a case study of Hanoi city

Red-light running (RLR) is the most significant factor involved in traffic crashes and injuries at signalized intersections. In Vietnam, little knowledge of factors affecting RLR has been found. This paper applied an ordered probit model to investigate factors associated with RLR using questionnaire data collected in Hanoi. Generally, this paper found that males and motorcyclists have a higher likelihood of RLR than females and car drivers. In addition, the younger and lower-income road users and the ones who are businessmen and who have a commuting trip in off-peak hours are more likely to run the red light. By contrast, the road users who go to school and the people who understand traffic law are less likely to violate the red light. In the future, it is necessary to collect data in different cities to generalize the results. In addition, may need to apply a more powerful method such as the latent class model, which can discover hidden facts among respondents. In the new model, other factors such as weather, waiting time, and countdown signal will be considered to investigate their effects on RLR.

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

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.

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

(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.