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

Drivers engage in a variety of secondary activities while driving. Research suggests that many secondary tasks interfere with driving, making performance worse as compared to single-task driving, but a recent study suggests that in simple environments (low scenery and traffic) listening to an audiobook may actually benefit driving performance. Nonetheless, these effects may vary based on both the textual complexity of the audiobook and the working memory capacity of the driver. In this study, we used a driving simulator to compare single-task driving with that when the driver was listening to an audiobook (dual-task). We manipulated the complexity of the audiobook as measured by Lexile scores (a standard index of text difficulty). Licensed drivers did two 30-minute drives on simple roads, alternating between driving while listening to an audiobook (dual-task) or single-task driving. Drivers did one drive with the simple and the other with the complex audiobook (order counterbalanced). Listening to the simple audiobook improved driving performance as compared to single-task driving: braking response times to hazards were lower, as were steering and headway variability. Conversely, listening to the complex audiobook interfered with driving; braking times to hazards and steering variability were higher when drivers were listening to the audiobook than for single-task driving. Individual differences in working memory capacity as measured by the OSPAN (Operation Span) predicted how much listening to an audiobook benefitted performance, with the highest OSPAN scorers benefitting most, though these OSPAN-related differential benefits were restricted to reduced hazard response times while listening to the simple audiobook.

Pedestrian risky behaviors are one of the contributing factors to crashes involving pedestrians. Therefore, it is crucial to comprehend the mechanisms by which pedestrians interact with many influential components in the traffic environment. This study aimed to evaluate pedestrians’ red light running intentions and related factors under different traffic flow scenarios, including straight traffic flow, right-turning traffic flow, and left-turning traffic flow. A theoretical approach based on the theory of planned behavior (TPB) and the prototype willingness model (PWM) was employed. Data were collected from an online survey of 2250 participants in Tehran, Iran. Structural equation modeling (SEM) was used to identify the significant factors that explain intentions. All models successfully explained the behavioral intention for red light running violation; however, the findings revealed that the integrated model was the best-performing model to represent violation and, thus, was selected for interpreting the results and drawing relevant conclusions. Different traffic flow scenarios had varied effects on violation intentions for individual characteristics and model constructs. Previous crash experiences and driving-related background variables emerged to impact pedestrian violation intention across three scenarios. The findings also suggested that the rational constructs (attitude, perceived behavioral control, and facilitating conditions) had a more robust impact on violation intention compared to reactive constructs (prototype similarity, prototype favorability), with facilitating conditions being the strongest predictor of the model, followed by attitudes toward violation as a significant predictor of intention for red light violation. According to the results, the mechanism of risk-taking varies depending on the direction of the traffic flow. Higher risk was associated with the violation at the intersections with straight traffic flow compared to the intersections with turning traffic flow. Based on the findings of this study, several implications, including interventions focusing on individuals’ transportation safety attitudes, countermeasures to increase the risk perception of pedestrians toward turning vehicles, and countermeasures regarding the use of mobile phones while walking for the context of this study were proposed.

Research has demonstrated the benefits of external human–machine interfaces (eHMIs) in increasing vulnerable road users’ (VRU) feeling of safety in interactions with automated vehicles (AVs). However, two key gaps exist in the literature. First, existing studies examined AV-VRU communication aspects in the context of conventional roads with traffic controls, but not for shared spaces where VRU-AV interaction is reliant on communication between the two parties. Second, limited knowledge is available on the differences between cyclists and pedestrians when interacting with AV. This paper aims to address these gaps through an online questionnaire among 254 cyclists and pedestrians in Australia and the UK. Perceived safety was measured in terms of willingness to cross in front of an AV, feeling of security, and feeling of relaxation. Results from a three-stage least square regression analysis identified differences in the factors for pedestrians and cyclists. Pedestrians that were male, over the age of 35, not regular cyclists, or residents of the UK reported lower feelings of safety, relaxation, and willingness to cross than their counterparts. Similar results were found cyclists who are older than 45 years, and UK residents compared to other cyclist participants. Both pedestrians and cyclists reported more willingness to cross and higher feelings of security and relaxation when an eHMI was present. These findings indicate that for effective use and understanding of eHMIs targeted interventions are needed to address the specific concerns of different demographic groups, as identified in this research. By increasing public understanding and acceptance of AVs – as well as eHMIs – across all demographic groups, researchers can promote a smooth integration of these technologies into shared spaces.

Evaluating drivers’ situation awareness (SA) is important in the implementation of alert prioritization. This study investigates the relationship between driving performance measures (speed, acceleration and brake usage, steering wheel and lane deviation), pedestrian interaction (location, direction and motion), and driver SA. To achieve this, a controlled study was conducted with 56 participants using a Balanced Incomplete Block Design, where each participant drove 18 out of 48 possible intersections in a driving simulator environment. The Situational Awareness Global Assessment Technique (SAGAT) method was used to assess drivers’ SA. Mixed effects logit models were developed to examine the different SA Levels (perception, comprehension, projection). The driving performance measures were aggregated across three time windows (1, 3, and 5 s). The findings show significant contributions from both driving performance measures and pedestrian interactions in predicting driver SA. More specifically, a one-second time window was useful for predicting pedestrian direction and a three-second time window was best for predicting pedestrian location and intention to cross. The results indicate the importance of considering different time windows for predicting various levels of driver SA responses. These findings offer insights into factors to be considered in driver SA predictive models.

The transition from automated to manual driving, referred as to Take-over conditions (TOC), in highly automated vehicles (e.g., SAE Level 4 or higher) is a subject of great interest to driver’s safety researchers, considering advancement of automotive technologies. While the literature has focused primarily on the post-take-over behavior of passenger car drivers, assessing different aspects of Commercial Motor Vehicle (CMV) drivers’ post-take-over behavior has received less attention, although it is anticipated that CMVs will be the first to vastly adopt highly automated technology. This paper aims to address the question of how long the effect of TOC lasts in CMV drivers and how automated operation duration before TOC, repeated TOC, and driver’s factors (i.e., age, gender, education, and driving history) affect the duration of TOC’s effect. To accomplish this, we designed a 40-minute experiment on a driving simulator and compared participants’ responses to TOC with continuous manual driving to first, assess significant changes in driving behavior indices (e.g., acceleration, velocity, and headway) in different time intervals and second, evaluate the survival patterns of unsafe behaviors (e.g., hard brakes, sharp turns, and speeding) over time. Multilevel Mixed-effect Linear Models and Multilevel Mixed-effect Parametric Survival Models are incorporated to assess the duration of TOC’s effects. Results showed that the first 10 s of TOC carries the most significant driving behavior changes while the probability of observing unsafe behaviors reduces significantly after 20 s. The results indicated that the effect of TOC lasts longer in long-automated operations, old drivers, and drivers with bad driving history, while repeated TOCs, showed positive effects on mediating the effect of this transition. The findings of this paper offer valuable insights to automotive companies and transportation planners on the nature of Take-over conditions.

Electronic fences are now used to regulate the parking behavior of bike-sharing users, but the issue of improper parking within such fenced areas has not been resolved. Based on the theories of perceived value and perceived risk, this study used online behavioral experiments to simulate a scenario of users parking shared bicycles. By considering three factors — economic incentives, punitive measures, and travel scenarios — this study examined variations in users’ willingness to standardize the parking of shared bicycles. Data from 809 valid questionnaires were collected and empirically analyzed using bootstrap and regression analyses. According to the results, both economic incentives and penalties significantly enhanced users’ willingness to standardize the parking of shared bicycles, and the impact of penalties was slightly stronger than that of incentives. Perceived value played a mediating role between economic incentives and users’ willingness to properly park shared bicycles. Perceived risk acted as a mediator between punitive measures and the regulated parking intention of users. Travel scenarios served as a moderating factor between penalties and users’ willingness to park shared bicycles in a compliant manner, with the users’ compliance willingness in non-commuting travel scenarios significantly surpassing that in commuting contexts. These findings enrich the knowledge of sustainable usage behaviors among bike-sharing users, providing insights for bike-sharing companies to manage user behavior. Based on these results, several policy recommendations aimed at guiding governments and companies in regulating electronic fences and user parking behaviors are proposed.

Detecting and predicting the stop/go decisions of drivers at grade crossings is crucial for enhancing road safety. Electroencephalography (EEG) data, which provides direct and effective physiological indicators for recognizing driver states, combined with associated machine-learning techniques, can be used to monitor driver decisions. However, the ability of EEG to predict a driver’s stop/go decisions remains unclear. To investigate this, we collected both EEG and behavioral data from drivers at a flashing-light-controlled grade crossing, where stop/go decisions are critical, using a driving simulator. Herein, we propose an EEG-based prediction framework that combines functional brain network analysis with conventional neural networks (FBN-CNNs) to predict drivers’ stop/go decisions. The functional brain network was measured using phase-lag index matrices and minimum-spanning tree techniques. We subsequently compared the obtained results of the FBN-CNN with those from traditional machine learning methods, specifically random forest (RF) and Support Vector Machines (SVM). The results indicate that when facing a flashing red light, drivers who decide to stop exhibit stronger alpha band connectivity and weaker delta and theta activity than those who run the red-light. Furthermore, the FBN-CNN model outperformed the machine learning methods (RF and SVM) in both extracting EEG features and achieving high prediction accuracy. Interestingly, the EEGs of drivers during normal driving stages could help to predict their stop-or-go behavior at the onset of a flashing red light. In the typical dilemma zone, combining EEG data from the normal driving stage with those from the pre-decision stage improved the accuracy from 76% to 90%. These findings demonstrate the efficacy of EEG and deep learning methods in driver decision monitoring.

Drivers continually adapt their information sampling behavior to changing traffic conditions for safe driving. Scientists have studied this sampling behavior for decades; however, the literature on how drivers adapt their visual information sampling in response to observed driving dynamics is still incomplete, especially concerning what might be considered safe adaptation from an external perspective. While occlusion methods are commonly employed to study drivers’ visual information sampling, the variability in self-selected occlusion times and their relationship to actual driving performance has yet to be fully understood. In a driving simulator study with 30 participants, we analyzed and compared the situational dynamics influencing visual information sampling and performance in an occluded lane-keeping task. The findings underscore the significant influence of speed, lane position, time-to-line-crossing at the start of occlusion, and steering during occlusion on spare visual capacity in lane-keeping. Although the participants were able to make slight adjustments to their visual sampling based on these variables, their occlusion time choices appeared to be stable and primarily driven by individual preferences, unrelated to their driving experience or general lateral control instability under occlusion. In contrast, drivers’ general instability in lateral control under single-occlusion driving emerged as the strongest predictor of lane crossing during continuous, intermittently occluded driving. These insights contribute to the understanding of information sampling dynamics and spare visual capacity in lateral vehicle control, potentially guiding the development of personalized and contextually intelligent driver attention monitoring and warning systems.

An important issue for mixed traffic conditions, in which autonomous vehicles (AVs) and manual vehicles (MVs) coexist, is to analyze various vehicle interactions caused by different driving behaviors. Understanding the responsive behavioral characteristics of the following MV affected by the maneuver of the leading AV is a backbone in evaluating mixed traffic performance. The purpose of this study is to characterize the driving behavior of MVs following AVs in mixed-traffic situations. To characterize vehicle interactions between AVs and MVs, this study conducts multi-agent driving simulation (MADS) experiments, which can synchronize the space and time domains on the road by connecting two driving simulators. A maneuvering control logic for AV driving, which is used for MADS, is developed in this study. The driving behavioral data of MVs following AVs obtained from MADS are used to modify the parameters associated with the intelligent driver model (IDM). The IDM is a microscopic car-following model to represent the longitudinal following behavior of vehicles. This study identifies how the MV following AV would be different from the case where the MV follows MV. The results show that the average time headway of the following MVs in the AV-MV pair increased by 13.9% compared to the MV-MV pair. However, the maximum acceleration and average deceleration decreased by 44.45% and 4.89%, respectively. The proposed IDM for MV following AV was further plugged into a microscopic traffic simulation platform. VISSIM simulations were conducted to identify the difference in driving behavior between the proposed IDM and the original IDM. The outcome of this study is expected to simulate the maneuvering behavior of MV more realistically in the mixed traffic stream.

As we move towards a future with Automated Vehicles (AVs) incorporated in the current traffic system, it is crucial to understand driver-pedestrian interaction, in order to enhance AV design and optimization. Previous research in this area, which has primarily used naturalistic observations or single-actor virtual reality simulations, has been limited by its inability to draw causal conclusions, also due to a lack of real human–human interactions. Our study addresses these limitations by employing a high-fidelity distributed simulation setup that links drivers in a motion-based simulator with pedestrians in a CAVE-based environment. This method allows for the examination of real-time and reciprocal interactions across a range of road-crossing scenarios. Using thirty-two pairs of drivers and pedestrians, we investigated how different factors, such as the presence of zebra crossings and varying time gaps of the approaching vehicle, influence driver behaviour and pedestrian crossing decisions. The effect of drivers’ control of the vehicle during such crossings (e.g., braking behaviour and lateral deviation) on pedestrians’ crossing decisions were also analysed. We found that the distribution of drivers’ average deceleration values were bimodal, where drivers either markedly yielded to pedestrians, or continued in their path, with very few instances of intermediate behaviour. We also found that pedestrian decisions were seemingly influenced by the different braking strategies adopted by the driver, with pedestrians crossing before the vehicles in response to soft and early, or late and hard braking, while late and soft braking often resulted in the vehicle passing first. We also observed a slight lateral movement of the vehicle away from pedestrians when drivers were not yielding, but more of a lateral deviation towards them when yielding. This may be because drivers subconsciously transfer their walking interaction habits to their driving behaviour, to avoid a collision with pedestrians. Finally, our results showed a stronger influence of these kinematic cues on pedestrian crossing decisions, when compared to zebra crossings. As well as highlighting the value of a novel approach for investigating vehicle–pedestrian interactions, this study illustrates how vehicle cues can assist pedestrian decisions, adding new knowledge in the development of human-like behaviour for future AVs.