Adolescents’ Mobile Phone Use While Crossing the Road

Distracted Walking: Does it impact pedestrian-vehicle interaction behavior?

Several studies have developed pedestrian-vehicle interaction models. However, these studies failed to consider pedestrian distraction, which considerably influences the safety of these interactions. Utilizing data from two intersections in Vancouver, Canada, this research uses the Multi-agent Adversarial Inverse Reinforcement Learning (MA-AIRL) framework to make inferences about the behavioral dynamics of distracted and non-distracted pedestrians while interacting with vehicles. Results showed that distracted pedestrians maintained closer proximity to vehicles, moved at reduced speeds, and rarely yielded to oncoming vehicles. In addition, they rarely changed their interaction angles regardless of lateral proximity to vehicles, indicating that they mostly remain unaware of the surrounding environment and have decreased navigational efficiency. Conversely, non-distracted pedestrians executed safer maneuvers, kept greater distances from vehicles, yielded more frequently, and adjusted their speeds accordingly. For example, non-distracted pedestrian-vehicle interactions showed a 46.5% decrease in traffic conflicts severity (as measured by the average Time-to-Collision (TTC) values) and an average 30.2% increase in minimum distances when compared to distracted pedestrian-vehicle interactions. Vehicle drivers also demonstrated different behaviors in response to distracted pedestrians. They often opted to decelerate around distracted pedestrians, indicating recognition of potential risks. Furthermore, the MA-AIRL framework provided different results depending on the type of interactions. The performance of the distracted vehicle-pedestrian model was lower than the non-distracted model, suggesting that predicting non-distracted behavior might be relatively easier. These findings emphasize the importance of refining pedestrian simulation models to include the unique behavioral patterns from pedestrian distractions. This should assist in further examining the safety impacts of pedestrian distraction on the road environment.

Adolescent Aggressive Riding Behavior: An Application of the Theory of Planned Behavior and the Prototype Willingness Model

Cycling has always been popular in China, especially during the years when the government encouraged green travel. Many people participate in rides to ease traffic congestion and increase transfer convenience. Due to the disorganized and tidal nature of cycling, cyclists create many conflicts with other groups. Adolescents are vulnerable road users with a strong curiosity and risk-taking mindset. Identifying the factors influencing adolescents’ aggressive riding behavior can assist in developing strategies to prevent this behavior. An online questionnaire was used to collect data on bicycling among students in a middle school in Guangzhou, China. The theory of planned behavior (TPB) and prototype willingness model (PWM) have been applied to study travel behavior and adolescent risk behavior. To investigate the impact of psychological variables on adolescent aggressive behavior, we used TPB, PWM, TPB + PWM, and an integrated model. Behavioral intentions are greatly influenced by attitudes, subjective norms, and perceived behavioral control. Both descriptive and moral norms played a role in behavioral willingness. The integrated model explained 18.3% more behavioral variance than the TPB model. The social reactive pathway explained more variance in behavior than the rational path.

Prevalence and dynamics of distracted pedestrian behaviour at railway level crossings: Emerging issues

Recent increases in pedestrian collisions have led to several studies investigating the effects of distraction on pedestrian behaviour at road intersections. Although distraction has been identified as a contributing factor to pedestrian crashes at railway crossings, only limited research is available regarding the prevalence of this behaviour occurring at railway level crossings. It is, therefore, essential to better understand distraction prevalence at railway crossings to support the use of countermeasures to improve safety outcomes. We conducted field observations at a railway crossing in Brisbane, Australia and its adjacent road intersection to gauge the prevalence of distracted pedestrians. Overall, 585 pedestrians were observed and video recorded during the daytime. The video recordings were coded to estimate the prevalence of distraction behaviour that road users engaged in, factors that affected these proportions, and dynamic changes in behaviour. Compliance with signals was also analysed. We found distraction behaviours such as talking and looking at the mobile screen (41.9%) while walking to be prevalent and affected by age. Highly distractive tasks were found to be less commonplace at the railway crossing, accounting for 3% of the observations. Still, pedestrians at the railway crossing engaged in these highly distractive tasks on their phones for a much longer period of time. While most non-compliances (with traffic lights) occurred among attentive pedestrians and are likely to be intentional, non-compliances by distracted pedestrians were also observed, highlighting that distraction can lead to unsafe decisions or lack of decisions that result in unsafe behaviours. Finally, distraction was found to be a dynamic phenomenon as a few pedestrians stopped engaging in distractive tasks once they reached the crossing, while others engaged in more distractive tasks once they were on the road or crossing. Our study shows that pedestrian distraction is a prevalent issue at railway crossings and future research is required to further understand and mitigate this changing behaviour.

Road Injuries Associated With Cellular Phone Use While Walking or Riding a Bicycle or an Electric Bicycle: A Case-Crossover Study

Vulnerable road users (pedestrians, bicyclists, and motorcyclists) account for an increasing proportion of traffic injuries. We used a case-crossover study design to examine the association between cell-phone usage and traffic injuries among pedestrians, bicyclists, and electric bicycle riders during the course of their travel. We studied 643 pedestrians, bike riders, and electric bike riders aged 10-35 years who were involved in a road injury, visited the emergency department in one of the 3 hospitals in Shanghai, China, in 2019, and owned a cell phone. Half of the participants (n = 323; 50.2%) had used a cell phone within 1 minute before the injury happened. A pedestrian's or rider's use of a mobile phone up to 1 minute before a road injury was associated with a 3-fold increase in the likelihood of injury (odds ratio = 3.00, 95% confidence interval: 2.04, 4.42; P < 0.001). The finding was consistent across subgroups by sex, occupation, reason for travel, mode of transportation, and location of injury. Use of a cell phone when walking or riding was associated with an increased risk of road injury. Measures should be taken to make people aware of this detrimental impact on the risk of road injury.

The adaptability and challenges of autonomous vehicles to pedestrians in urban China

China is the world's largest automotive market and is ambitious for autonomous vehicles (AVs) development. As one of the key goals of AVs, pedestrian safety is an important issue in China. Despite the rapid development of driverless technologies in recent years, there is a lack of researches on the adaptability of AVs to pedestrians. To fill the gap, this study would discuss the adaptability of current driverless technologies to China urban pedestrians by reviewing the latest researches. The paper firstly analyzed typical Chinese pedestrian behaviors and summarized the safety demands of pedestrians for AVs through articles and open database data, which are worked as the evaluation criteria. Then, corresponding driverless technologies are carefully reviewed. Finally, the adaptability would be given combining the above analyses. Our review found that autonomous vehicles have trouble in the occluded pedestrian environment and Chinese pedestrians do not accept AVs well. And more explorations should be conducted on standard human-machine interaction, interaction information overload avoidance, occluded pedestrians detection and nation-based receptivity research. The conclusions are very useful for motor corporations and driverless car researchers to place more attention on the complexity of the Chinese pedestrian environment, for transportation experts to protect pedestrian safety in the context of AVs, and for governors to think about making new pedestrians policies to welcome the upcoming driverless cars.