Transportation Research Part F-Traffic Psychology and Behaviour最新文献

In conditional automated driving, the takeover response ability threshold is necessary for driver qualification assessment and liability division of automated vehicle accidents. The primary objective of this study is to establish a clear and quantifiable threshold for drivers’ takeover response ability in conditional automated driving scenarios. This threshold aims to serve as a benchmark for evaluating drivers’ readiness and developing safety regulations in automated driving. We designed 18 takeover events and invited 42 drivers to participate in the driving simulation experiment, and obtained their takeover time data. First, we analyze the differences of takeover time among drivers with different attributes (gender, age, driving year). Second, based on the Peaks Over Threshold and the generalized Pareto distribution model, we use the graphic method to calculate the range of takeover time threshold for drivers with different attributes. The result shows that the difference in the threshold range of takeover time between male and female drivers is relatively tiny. There are differences in the threshold range of takeover time for different age drivers, and the threshold is negatively correlated with age. Drivers with high driving experience within a safe range are allowed to have longer takeover times. Finally, the rationality of the takeover time threshold for drivers with different attributes has been verified. The return level curves are approximately linear (R² > 0.77), indicating that the GPD model can capture the overall trend of the return level, which is changing with the probability level. This proves that the takeover time threshold is reasonable. This study uses TTC_min to calibrate takeover safety, and the takeover time threshold has a good classification performance for takeover safety (accuracy > 85 %). The above content proves the rationality of the takeover time threshold. The contribution of this study is to calculate the takeover time threshold of drivers with different attributes, which can help regulatory authorities assess the driver’s takeover response ability and support the liability division of automated vehicle accidents.

Rear-end collisions in work zones, particularly those resulting from tailgating, pose a significant safety challenge that has not been adequately addressed in road safety research. Existing studies have extensively investigated speeding, but the psychological triggers and behavioral patterns leading to tailgating remain largely unexplored. Bridging this critical gap, this research introduces an anti-tailgating strategy using dynamic and fixed message signs, designed with a deep understanding of driver psychology. The methodology involved a user-centric survey assessing the effect of positively toned, non-numerical anti-tailgating messages, which favored clearer comprehension and elicited favorable psychological responses from drivers. Field experiments demonstrated that these signs effectively increased headway and decreased tailgating, without the traffic disruptions associated with pavement markings or the technological limitations of in-vehicle alerts. These encouraging results underscore the potential of strategically placed message signs to positively influence driver behavior. This research not only fills a significant void by elucidating the behavioral factors of tailgating in work zones, but also paves the way for the implementation of behaviorally informed and nonintrusive road safety interventions. The use of dynamic and fixed message signs emerges as a promising, technologically inclusive, and psychologically effective tool for improving road safety.

This paper provides preliminary insights on the challenges of processing Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study (NDS) videos and data, particularly those with Personally Identifiable Information (PII). Insights and lessons learned are presented from a study designed to evaluate the effectiveness of High Visibility Crosswalks (HVCs). Over a one-month period, 15,379 videos were processed in the secure data enclave of Virginia Tech Transportation Institute (VTTI). As these videos are not available outside of the secure data enclave due to PII restrictions, researchers visiting the secure data enclave for the first time may face several challenges: navigating the software interface; identifying the video views and frames of interest; and identifying and extracting information of interest from the video views, etc. These challenges, the procedures followed to address them, and the process for identifying and classifying distracted driving behaviors are discussed. Lastly, hypothesis tests are conducted to investigate distracted driving behavior, with the results revealing that HVCs have the potential to make drivers more cautious in their proximity. The information presented in this paper is expected to aid researchers who intend to utilize SHRP2 NDS or similar videos for future research, to preemptively plan for the video processing phase.

Many researchers have delved into driver aggression for decades. Studies have been carried out in countries all over the world and attempts to curb it are well under way. However, motorcycle rider aggression has seen little effort into its description and study. The purpose of this study is to generate a scale that will allow evaluation rider aggression in Spanish-speaking countries. Through a two-phase methodology, a scale that evaluates this variable was constructed. The first phase consisted of gathering content validity for the scale, and a large group of items was comprised. Then, the second phase consisted of gathering 800 riders from Mexico and Spain, which provided the data from which construct validity was attempted and the indicators here presented were generated. Through this effort, a scale that evaluated Rider Aggression is constructed and is ready for its use in Mexico and Spain, although more research is warranted, and a larger number of Spanish-speaking countries is indicated. In the end, a definition for Rider Aggression is provided by the authors, and it was interesting that it lacks the component of Risky Driving, probably because all motorcycle use is risky in itself.

China started implementing a public transport priority policy in 2004 to encourage people to use public transit, especially buses, and reduce reliance on private cars. This paper used 334 questionnaire survey data from Changzhou City, China, and explored the moderating role of bus satisfaction based on the examination of the antecedents and consequences of affective motivation for private cars through two studies. Study 1 explored the antecedents of affective motivation for private cars and the moderating effect of bus satisfaction. The empirical results revealed that instrumental motivation is the most relevant predictor of affective motivation favoring private cars, followed by symbolic motivation. The moderating effect of bus satisfaction on the relationship between symbolic motivation and affective motivation was significantly negative. Study 2 explored the consequences of affective motivation for private cars and the moderating effect of bus satisfaction. Empirical results indicate that decisions regarding private car ownership and use are significantly negatively influenced by bus satisfaction. Bus satisfaction has a significantly positive moderating effect on the relationship between affective motivation and private car ownership and use. The marginal effect of bus satisfaction on the probability of simultaneously owning and using private cars is significantly negative, and it strengthens the influence of affective motivation in promoting both ownership and use of private cars. These results demonstrate that for individuals with experience using both buses and private cars, the public transport priority policy focused on improving bus service quality can effectively reduce private car ownership and use. These results can serve as evidence to support policymakers in continuing to enhance the public transport priority policy.

There is a rising interest in using AI-powered safety analytics to predict accident outcomes. However, companies face the challenge of developing accurate safety analytics models. One possible solution is to use a pretrain-then-finetune parameter-transfer learning approach for a “target company” to utilize knowledge generated from the data of other “source companies”. However, transfer learning is uncommon in safety analytics due to reasons such as lack of publicly available large-scale pre-training data and pre-trained models, differences between the source and target companies, and lack of guidelines. To fill the above gaps, we conducted experiments to study the effectiveness of transfer learning in the context of using truck drivers’ safety climate data for predicting trucking accidents. To facilitate the experiments, we developed SafeNet, a deep neural network algorithm for classifying accident outcomes. The safety climate survey data are from seven trucking companies with different sample sizes. Three new evaluation indicators are proposed to evaluate the difference between finetuned models and models trained from scratch. The study shows that transfer learning is not effective in about 20% of the cases that used pretrained models trained on one source company’s data. Instead of sample size and data diversity, the study found that accuracy of the pretrained model is more important. The trucking industry may, thus, consider developing different pretrained models for different types of companies. To promote the adoption of transfer learning in safety analytics, we make our code and pretrained models publicly available at https://github.com/NUS-DBE/Pretrain-Finetune-safety-climate.

Transportation is a major source of climate change emissions. Providing people with better information on those emissions is one means of helping individuals make climate-friendly choices. However, not everyone is influenced by the same type of information. Previous research has demonstrated that Goal Framing Theory could help improve the influence of climate change emissions information and that different framings have different levels of influence depending on a number of socio-demographic and attitudinal characteristics. However, apart from climate change motivation, what other underlying psychological factors might help us understand why the framings vary in their influence between individuals? Moral Foundation Theory (MFT) identifies key values that influence people’s moral decisions, providing a useful framework for understanding diverse responses to information. The objective of this study is to understand whether MFT can help explain different responses by individuals and identify which framings are associated with stronger responses for different moral foundations. This study investigates the moderating effects of moral foundations on individuals’ responsiveness to different emission information framings. Utilizing data from discrete choice experiments involving 2015 Canadian drivers, we examine how different moral foundations impact the willingness-to-pay (WTP) for reducing emissions. The results reveal that the impact of emissions information framing varies significantly according to individuals’ moral foundations. Specifically, moral values associated with Authority, Fairness, and Purity play negative moderating roles on WTP for CO₂ emissions under different framings, whereas Ingroup and Harm foundations have positive moderating effects on WTP with the framings tested. Additionally, innovative communication tools like new emojis demonstrated strong positive effects on WTP, especially among those with strong Ingroup, Fairness, and Purity values. Conversely, individuals with a strong Authority value showed the lowest WTP when presented with pressure gauge visuals. Using appropriate framing based on Moral Foundation Theory can considerably change the willingness-to-pay for climate change emissions for different parts of the population, with a notable increase in WTP observed among individuals inclined to alter their behavior. Future framings should incorporate MFT in their design.

Recognizing the potential transformative impact on transportation systems, safety perceptions of Shared Automated Vehicles (SAVs) have gained significant attention from researchers in recent years. Yet, the critical factors influencing perception changes and the willingness to re-ride (WTR) have not been extensively studied despite their relevance to SAV operations. This study applied Bayesian Networks (BNs) and Text Network (TN) methodologies to analyze survey data from a shared automated passenger shuttle (SAPS) pilot program conducted between March and April 2023, at Fred G. Bond Metro Park in Cary, North Carolina. Participants in the survey provided feedback on their safety perceptions of the SAPS before and after riding, as well as their willingness to ride again. Key findings reveal that shuttle operations, especially timely arrival and drop-off, significantly affect both perceptional change and WTR. Furthermore, users who accessed the shuttle by walking, biking, or public transportation were more likely to positively change their perception and express a willingness to ride the shuttle again. Also, individuals with initial perceptions of the SAPS as very unsafe or unsafe showed a higher likelihood of perception change. Conversely, older respondents were less likely to experience safety perception changes and WTR. Text network analysis further illuminated that the primary motivations for WTR were the enjoyable experience and convenience offered by the shuttle. The study contributes to the growing body of literature on SAVs by providing practical implications for the future development and testing of SAPSs. These insights are invaluable for policymakers and planners in optimizing SAPS operations, providing a deeper understanding of user experiences and expectations.

Objective

This study explores how different visibility aids of contrasting colours could enhance the e-scooter riders’ conspicuity under daylight conditions.

Background

With the rise in popularity of shared e-scooter services, an increase in incidents involving e-scooter riders and motorized counterparts was noticed.

Method

48 licensed drivers (mean age = 24.6 years) detected the e-scooter riders in the videos and images featuring a simulated urban road environment in daylight, while performing a secondary task (n-back). The time to detect standing and moving e-scooter riders and subjective conspicuity ratings were assessed in three visibility aid conditions: Control (grey clothes and grey backpack), Patterned Jacket (jacket and backpack with checkerboard black and yellow pattern) and Wheels-Backpack (yellow stripes on the e-scooter wheels and on a dark backpack).

Results

Improved detectability was observed for e-scooter riders in the Wheels-Backpack condition, resulting in a minimum 168 ms increase in detection time compared to other two conditions. In terms of conspicuity ratings, participants gave higher scores to riders wearing Wheels-Backpack and Patterned Jacket (6.02 and 6.31, respectively, on a Likert 10-point scale), with no significant difference between them. Detection from videos or images showed similar effects. For all of these analyses, Bonferroni post-hoc tests followed a Repeated-measures ANOVA.

Conclusion

Distributing contrasting colours across the e-scooter and the rider enhanced their daylight conspicuity better than when contrasting colours were located on the riders’ upper body. However, drivers did not realise that these two visibility aids were not equal in terms of conspicuity benefits.

According to the European Environment Agency (European Environmental Agency, EEA, 2018), road transport is responsible for 72% of all transport-related greenhouse gas emissions in the European Union (EU), which accounts for 25% of total energy-related emissions (Eurostat, 2018). Thus, it is crucial to identify drivers and barriers to more sustainable transport behaviors. In this regard, the Norm Activation Model and Theory of Planned Behavior have often been used as conceptual frameworks for predicting such behaviors. The present study aimed to analyze the differential impact of both socio-psychological factors and persuasive messages sent through a Personalized Travel Plan (PTP) on Sustainable Transport Choices (STC). To reach this aim we administered a survey two times (T1: Oct./Dec. 2020; T2: March/May 2021) to 398 car users. Measures of constructs included in the Norm Activation Model and the Theory of Planned Behavior, such as behavioral intention, attitude, perceived behavioral control, beliefs, and personal and social norms, were detected. Participants were then exposed to a PTP built on feedback information regarding kilocalories, CO₂ emissions, cost, and time savings when using sustainable transport compared to driving a car. Structural Equation Modeling (SEM) analysis shows that intention to use sustainable transport in T1 is on one side directly predicted by personal norm, perceived behavioral control, and attitude, and on the other side emerged as the main predictor of sustainable travel choices in T2, together with kcal spent, whereas time was the major barrier. Implications and future developments are discussed in the light of the conceptual framework.