A Roadside Precision Monocular Measurement Technology for Vehicle-to-Everything (V2X).

Within the context of smart transportation and new infrastructure, Vehicle-to-Everything (V2X) communication has entered a new stage, introducing the concept of holographic intersection. This concept requires roadside sensors to achieve collaborative perception, collaborative decision-making, and control. To meet the high-level requirements of V2X, it is essential to obtain precise, rapid, and accurate roadside information data. This study proposes an automated vehicle distance detection and warning scheme based on camera video streams. It utilizes edge computing units for intelligent processing and employs neural network models for object recognition. Distance estimation is performed based on the principle of similar triangles, providing safety recommendations. Experimental validation shows that this scheme can achieve centimeter-level distance detection accuracy, enhancing traffic safety. This approach has the potential to become a crucial tool in the field of traffic safety, providing intersection traffic target information for intelligent connected vehicles (ICVs) and autonomous vehicles, thereby enabling V2X driving at holographic intersections.

Dense pedestrian face detection in complex environments.

To address the problem of dense crowd face detection in complex environments, this paper proposes a face detection model named Deep and Compact Face Detection (DCFD), which adopts an improved lightweight EfficientNetV2 network to replace the backbone network of RetinaFace. A large kernel attention mechanism is introduced to address the face detection task more accurately. The backbone network, an improved efficient channel attention (ECA) mechanism, is added to further improve the algorithm performance. The feature fusion module is an improved neural architecture search feature pyramid network (NAS-FPN) that significantly improves the face detection accuracy in different scenes. To balance the training process of positive and negative samples, we use the focus loss function to replace the traditional cross-entropy loss function. In different environments, the DCFD algorithm has shown efficient face detection performance. This algorithm provides not only a feasible and effective solution for solving the problem of face detection in dense groups but also an important basis for improving the accuracy of face detection models in practical applications.

How should human-driven and autonomous vehicles behave at a zebra crossing? Determining acceptable stopping distances and setting-off times.

Autonomous vehicles (AVs) offer a range of substantial safety and mobility benefits. However, successful deployment of AVs will involve interacting with other road users, such as pedestrians and other human-driven vehicles. While previous research has focused on factors that influence perceptions of AVs, less work has addressed how specific interactions with other road users influence acceptability from multiple perspectives. Accordingly, we conducted six studies examining how vehicles, either human-driven or autonomous, should behave at a zebra crossing in terms of stopping distance from the crossing, how long a vehicle should wait before setting off, and the influence of traffic context. Using computer-generated videos we found that: the optimal stopping distance was just before the stop line; participants were generally accepting of a vehicle that waited until a pedestrian had fully cleared the crossing before setting off, and sometime earlier; the presence of other vehicles, context and observer viewpoint can affect judgements of vehicle behaviour; autonomous vehicles were judged more harshly than human drivers with learner drivers judged less harshly in some circumstances, and that vehicle size appeared to have little influence over the acceptability of vehicle behaviours. The results are important for informing the design of autonomous vehicle manoeuvres from the viewpoints of vehicle occupants and other road users.

Pedestrian behavior among Chinese seniors: Insights from factor analysis and structural equation modeling.

Pedestrians contribute significantly to the total number of road fatalities and injuries, with their behavior playing a pivotal role in traffic mishaps. Despite this, a limited body of research has delved into the walking behaviors of Chinese seniors. Given this gap, our study rigorously examines the patterns of seniors' walking behaviors and their influencing factors. We employed exploratory factor analysis to decipher the intrinsic component structures of seniors' walking patterns in China. Subsequently, structural equation modeling was utilized to analyze the impact of demographic attributes and personality characteristics on these behaviors. The findings revealed a four-dimensional structure for senior walking behaviors: transgression, inattention, aggression, and positive behaviors. Introducing personality traits as variables notably enhanced the explanatory power of our model. Specifically, anger, altruism, and normlessness significantly influenced certain dimensions of walking behaviors, while sensation-seeking did not exhibit any notable effect. This study not only highlights the complexity and diversity of elderly walking behaviors but also underscores the importance of tailored interventions to improve walking safety and quality of life for seniors.

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.

Dataset on factors influencing pedestrian crosswalk usage behavior in high-density urban areas of a developing country.

The dataset consists of survey data on pedestrian crosswalk usage behavior in high-density urban areas of a developing country, specifically collected from Dhaka, the capital city of Bangladesh. Data were gathered through a questionnaire survey conducted at twelve key locations, covering eight attributes related to crosswalk behavior and the demographic details of respondents. The survey yielded 682 valid responses, focusing on factors such as the suitability of crosswalk locations, guard rails, and lighting. The dataset is structured to support analyses using supervised machine learning techniques, facilitating reproducibility, secondary analysis, and policy development for pedestrian safety improvements. Furthermore, the dataset can be reused for cross-validation of future studies, comparison with pedestrian behavior in similar urban settings, and the development of predictive models to enhance pedestrian infrastructure in other developing regions.

Synchronous End-to-End Vehicle Pedestrian Detection Algorithm Based on Improved YOLOv8 in Complex Scenarios.

In modern urban traffic, vehicles and pedestrians are fundamental elements in the study of traffic dynamics. Vehicle and pedestrian detection have significant practical value in fields like autonomous driving, traffic management, and public security. However, traditional detection methods struggle in complex environments due to challenges such as varying scales, target occlusion, and high computational costs, leading to lower detection accuracy and slower performance. To address these challenges, this paper proposes an improved vehicle and pedestrian detection algorithm based on YOLOv8, with the aim of enhancing detection in complex traffic scenes. The motivation behind our design is twofold: first, to address the limitations of traditional methods in handling targets of different scales and severe occlusions, and second, to improve the efficiency and accuracy of real-time detection. The new generation of dense pedestrian detection technology requires higher accuracy, less computing overhead, faster detection speed, and more convenient deployment. Based on the above background, this paper proposes a synchronous end-to-end vehicle pedestrian detection algorithm based on improved YOLOv8, aiming to solve the detection problem in complex scenes. First of all, we have improved YOLOv8 by designing a deformable convolutional improved backbone network and attention mechanism, optimized the network structure, and improved the detection accuracy and speed. Secondly, we introduced an end-to-end target search algorithm to make the algorithm more stable and accurate in vehicle and pedestrian detection. The experimental results show that, using the algorithm designed in this paper, our model achieves an 11.76% increase in precision and a 6.27% boost in mAP. In addition, the model maintains a real-time detection speed of 41.46 FPS, ensuring robust performance even in complex scenarios. These optimizations significantly enhance both the efficiency and robustness of vehicle and pedestrian detection, particularly in crowded urban environments. We further apply our improved YOLOv8 model for real-time detection in intelligent transportation systems and achieve exceptional performance with a mAP of 95.23%, outperforming state-of-the-art models like YOLOv5, YOLOv7, and Faster R-CNN.

A singular, broadly-applicable model for estimating on- and off-path walking travel rates using airborne lidar data.

Accurate prediction of walking travel rates is central to wide-ranging applications, including modeling historical travel networks, simulating evacuation from hazards, evaluating military ground troop movements, and assessing risk to wildland firefighters. Most of the existing functions for estimating travel rates have focused on slope as the sole landscape impediment, while some have gone a step further in applying a limited set of multiplicative factors to account for broadly defined surface types (e.g., "on-path" vs. "off-path"). In this study, we introduce the Simulating Travel Rates In Diverse Environments (STRIDE) model, which accurately predicts travel rates using a suite of airborne lidar-derived metrics (slope, vegetation density, and surface roughness) that encompass a continuous spectrum of landscape structure. STRIDE enables the accurate prediction of both on- and off-path travel rates using a single function that can be applied across wide-ranging environmental settings. The model explained more than 80% of the variance in the mean travel rates from three separate field experiments, with an average predictive error less than 16%. We demonstrate the use of STRIDE to map least-cost paths, highlighting its propensity for selecting logically consistent routes and producing more accurate yet considerably greater total travel time estimates than a slope-only model.

The relationships between the campus built environment and walking activity.

Despite the gradual development of students' sedentary habits and associated health problems, only a few studies have extensively and systematically measured campus built environments (CBE) and their impact on street walking activity. This study explores the association between CBEs and pedestrian volume (PV). Comprehensive questionnaires, field audits, and GIS were used to measure the CBE variables and PV of 892 street segments on eight Chinese campuses in Tianjin. We used negative binomial regression models without spatial autocorrelations to investigate the relationship between the CBEs and PV. The findings indicated that campus Walk Score, facility and residential land ratio, campus design qualities, sidewalk conditions, street amenities, and other streetscape features were positively associated with PV. This study presents implications for campus research and planning practices in designing a pedestrian-friendly, sustainable, and healthy campus.

Effect of A-Pillar Blind Spots on a Driver's Pedestrian Visibility during Vehicle Turns at an Intersection.

This study aims to elucidate the impact of A-pillar blind spots on drivers' visibility of pedestrians during left and right turns at an intersection. An experiment was conducted using a sedan and a truck, with a professional test driver participating. The driver was instructed to maintain sole focus on a designated pedestrian model from the moment it was first sighted during each drive. The experimental results revealed how the blind spots caused by A-pillars occur and clarified the relationship between the pedestrian visible trajectory distance and specific vehicle windows. The results indicated that the shortest trajectory distance over which a pedestrian remained visible in the sedan was 17.6 m for a far-side pedestrian model during a right turn, where visibility was exclusively through the windshield. For the truck, this distance was 20.9 m for a near-side pedestrian model during a left turn, with visibility through the windshield of 9.5 m (45.5% of 20.9 m) and through the passenger-side window of 11.4 m (54.5% of 20.9 m). Additionally, we quantified the trajectory distances where pedestrians became invisible when the driver's view was obstructed by A-pillars. The sedan exhibited the highest invisibility rate at 46.1% for a far-side pedestrian model during a right turn, followed by the truck at 17.8% for the same model. These findings will be instrumental in developing new driving support systems aimed at enhancing visibility in situations where pedestrians are obscured by A-pillars.

The association between passenger-vehicle front-end profiles and pedestrian injury severity in motor vehicle crashes.

INTRODUCTION: Vehicles play an important role in pedestrian injury risk in crashes. This study examined the association between vehicle front-end geometry and the risk of fatal pedestrian injuries in motor vehicle crashes. METHOD: A total of 17,897 police-reported crashes involving a single passenger vehicle and a single pedestrian in seven states were used in the analysis. Front-end profile parameters of vehicles (2,958 vehicle makes, series, and model years) involved in these crashes were measured from vehicle profile photos, including hood leading edge height, bumper lead angle, hood length, hood angle, and windshield angle. We defined a front-end-shape indicator based on the hood leading edge height and bumper lead angle. Logistic regression analysis evaluated the effects of these parameters on the risk that a pedestrian was fatally injured in a single-vehicle crash. RESULTS: Vehicles with tall and blunt, tall and sloped, and medium-height and blunt front ends were associated with significant increases of 43.6%, 45.4%, and 25.6% in pedestrian fatality risk, respectively, when compared with low and sloped front ends. There was a significant 25.1% increase in the risk if a hood was relatively flat as defined in this study. A relatively long hood and a relatively large windshield angle were associated with 5.9% and 10.7% increases in the risk, respectively, but the increases were not significant. CONCLUSIONS: Vehicle front-end profiles that were significantly associated with increased pedestrian fatal injury risk were identified. PRACTICAL APPLICATIONS: Automakers can make vehicles more pedestrian friendly by designing vehicle front ends that are lower and more sloped. The National Highway Traffic Safety Administration (NHTSA) can consider evaluations that account for the growing hood heights and blunt front ends of the vehicle fleet in the New Car Assessment Program or regulation.

How state mindfulness affects mobile phones usage while walking: A daily diary study in China.

INTRODUCTION: Pedestrians are a particularly vulnerable group of road users. Mobile phone usage while walking (MPUWW) is a significant contributor to pedestrians' involvement in road crashes and associated injuries. The current study aims to explore the effect of state mindfulness on daily MPUWW via phone dependence (at the within-person level), and the moderating role of risk perception (at the between-person level) in the phone dependence-MPUWW relationship. METHOD: We utilized a fine-grained method, the daily diary methodology (DDM) to explore the aforementioned model. A total of 88 Chinese college students participated in a consecutive 12-day study, yielding 632 daily data. Unconflated multilevel modeling was used to analyze the data. RESULTS: After trait mindfulness being controlled, state mindfulness has a negative impact on MPUWW via phone dependence at the daily level. Furthermore, risk perception as an individual difference variable moderates the relationship between phone dependence and MPUWW, in which a weaker effect observed in individuals with higher levels of risk perception. CONCLUSIONS: State mindfulness can decrease the frequency of daily MPUWW by reducing phone dependence, and risk perception is a crucial factor in mitigating the negative effects of phone dependence on MPUWW. PRACTICAL APPLICATIONS: To lower MPUWW and thereby minimize the risk of road crashes and associated injuries, it is beneficial to foster present-moment awareness of individuals, encourage individuals to use mobile phones in a balanced and sensible manner, and integrate the enhancement of risk perception into road safety education.

Analysis of pedestrian second crossing behavior based on physics-informed neural networks.

Pedestrian two-stage crossings are common at large, busy signalized intersections with long crosswalks and high traffic volumes. This design aims to address pedestrian operation and safety by allowing navigation in two stages, negotiating each traffic direction separately. Understanding crosswalk behavior, especially during bidirectional interactions, is essential. This paper presents a two-stage pedestrian crossing model based on Physics-Informed Neural Networks (PINNs), incorporating fluid dynamics equations to determine characteristics such as speed, density, acceleration, and Reynolds number during crossings. The study shows that PINNs outperform traditional deep learning methods in calculating and predicting pedestrian fluid properties, achieving a mean squared error as low as 10-8. The model effectively captures dynamic pedestrian flow characteristics and provides insights into pedestrian behavior impacts. The results are significant for designing pedestrian facilities to ensure comfort and optimizing signal timing to enhance mobility and safety. Additionally, these findings can aid autonomous vehicles in better understanding pedestrian intentions in intelligent transportation systems.

Distinguishing injury patterns in fatal falls from heights versus pedestrian impacts: an autopsy study for differential diagnosis in ambiguous cases.

This study investigated the injury patterns associated with fatal falls from heights compared to individuals struck by cars, aiming to enhance the differential diagnosis in ambiguous cases, where it is unclear whether the body fell from nearby building or was left on the street following a road traffic incident. A retrospective review of comprehensive forensic reports from the Institute of Legal Medicine of the University of Rome "Tor Vergata" between 2012 and 2023 was conducted. The analysis included 232 cases, gathering data on internal organ injuries, skeletal fractures, external skin injuries, as well as pleural, peritoneal, and pericardial effusions. Bilateral lung injuries were significantly more common in falls from height (33.3%) compared to pedestrians (13.6%, p < 0.001). Liver injuries also occurred more frequently in fall victims (49.6%) than in pedestrians (28.2%, p < 0.001). Skull fractures were more frequent in falls from height (68.2%) versus individuals struck by cars (55.3%, p = 0.044), while unilateral leg fractures were more common in pedestrians (28.2%) compared to fall victims (16.3%, p = 0.029). External injuries, notably to the head and legs, were more frequent in pedestrians. The "Total Injured Skin Area" analysis revealed a significant discriminative power with an optimal cut-off of 84.2 cm², suggesting that injuries exceeding this threshold may be indicative of a pedestrian road fatality.

Inherent uncertainty in pedestrian collision reconstruction: How evidence variability affects head kinematics and injury prediction.

Reconstructing individual cases from real-world collision data is used as a tool to better understand injury biomechanics and determine injury thresholds. However, real-world data tends to have inherent uncertainty within parameters, such as ranges of impact speed, pre-impact pedestrian stance or pedestrian anthropometric characteristics. The implications of this input parameter uncertainty on the conclusions made from case reconstruction about injury biomechanics and risk is not well investigated, with a 'best-fit' approach more frequently adopted, leaving uncertainty unexplored. This study explores the implications of uncertain parameters in real-world data on the biomechanical kinematic metrics related to head injury risk in reconstructed real-world pedestrian-car collisions. We selected six pedestrian-car cases involving seven pedestrians from the highly detailed GB Road Accident In-Depth Studies (RAIDS) database. The collisions were reconstructed from the images, damage measurements and dynamics available in RAIDS. For each case, we varied input parameters within uncertain ranges and report the range of head kinematic metrics from each case. This includes variations of reconstructed collision scenarios that fits within the constraints of the available evidence. We used a combination of multibody and finite element modelling in Madymo to test whether the effect of input data uncertainty is the same on the initial head-vehicle and latter head-ground impact phase. Finally, we assessed whether the predicted range of head kinematics correctly predicted the injuries sustained by the pedestrian. Varying the inputs resulted in a range of output head kinematic parameters. Real-world evidence such as CCTV footage enabled predicted simulated values to be further constrained, by ruling out unrealistic scenarios which do not fit the available evidence. We found that input data uncertainty had different implications for the initial head-vehicle and latter head-ground impact phase. There was a narrower distribution of kinematics associated with the head-vehicle impact (initial 400 ms of the collision) than in the latter head-ground impact. The mean head-vehicle kinematics were able to correctly predict the presence or absence of both subdural haematoma (using peak rotational acceleration) and skull vault fracture (using peak contact force) in all pedestrians presented. This study helps increase our understanding of the effects of uncertain parameters on head kinematics in pedestrian-car collision reconstructions. Extending this work to a broad range of pedestrian-vehicle collision reconstructions spanning broad population demographics will improve our understanding of injury mechanisms and risk, leading to more robust design of injury prevention measures.

Feasibility of using electrodermal activity responses to assess level of crowdedness of pedestrian paths.

As urban populations grow, it's imperative to evaluate and enhance the quality of pedestrian paths from the user's perspective. Crowdedness, associated with discomfort and safety, is crucial in determining the overall walking quality and user experience. Previously utilized methods for measuring crowdedness, such as travel diaries and floating population surveys, were limited to collecting perceptual data from sporadic surveys with restricted spatial coverage. Similarly, methods based on CCTV or mobile service data have been used but present issues with blind spots and fail to consider pedestrian perspectives. Against this background, this study explores the feasibility of assessing crowdedness levels by measuring subjects' physiological responses in a laboratory setting based on visual images of real and virtual environments. This study hypothesizes that the amount of people or vehicles passing by affects the electrodermal activity (EDA) of pedestrians, indicating the comfort level of using the environment. Experimental EDA data were measured using a wearable device while the subjects were watching videos showing different pedestrian traffic flows. Representative EDA signal features (e.g., skin conductance responses) were extracted after data pre-processing. Noticeable changes in EDA responses are observed when subjects countered specific environmental variations, such as differing volumes of passing people, on pedestrian paths. The findings suggest that EDA data can be instrumental in differentiating crowdedness levels on pedestrian paths. This study contributes to the body of knowledge by demonstrating the potential of EDA data to characterize the crowdedness experienced by pedestrians. This aids in the development of a novel, quantitative method to gauge pedestrian path crowdedness and to discern contributing factors, such as path width.

Assessing head injury risks in electric scooter accidents: A multi-body simulation study with insights into sex differences.

E-scooters have become increasingly popular for short-distance travel in urban areas, but this rise in usage also brings about an increased risk of accidents. Studies have shown that approximately 40% of electric scooter accident victims admitted to hospitals suffer head injuries. Therefore, it is crucial to implement safety measures and improve safety systems and equipment to mitigate these risks. One approach to gaining insights into the injuries users face is through simulations using the multi-body method. This method allows for the reconstruction of accidents by modeling and analyzing the dynamic behavior of interconnected bodies. This study aims to assess the impacts on the user's head and the injuries they may sustain in electric scooter accidents using numerical methods. Initially, a reference scenario was established based on a YouTube video, with the assumption that the user was an average-height man. Simulations were conducted for various percentiles, including both males and females. Different velocities were simulated to determine the threshold velocity at which survival becomes practically impossible. Two scenarios were considered: one where the car braked for 0.333 s and another where the distance between the start the braking task and the collision was kept constant. The location of the first head impact on the vehicle was also examined. Injury assessment was conducted using two criteria: Head Injury Criterion (HIC) and Brain Injury Criterion (BrIC). The study found that smaller individuals are more vulnerable to severe injuries, and higher car velocities correlate with more severe user injuries. Furthermore, the location of the first impact varies between genders, with women more likely to experience impacts in the lower part of the windshield, while men tend to experience impacts in the central zone. This study highlights the importance of considering user characteristics and accident dynamics in assessing injury risks associated with e-scooters.

A comparison of factors influencing the safety of pedestrians accessing bus stops in countries of differing income levels.

Pedestrian fatalities comprise a quarter of all traffic deaths in Low-and-Middle-Income Countries (LMICs). The use of safer modes of transport such as buses can reduce road trauma as well as air pollution and traffic congestion. Although travelling by bus is safer than most other modes, accessing bus stops can be risky for pedestrians. This paper systematically reviews factors contributing to the safety of pedestrians near bus stops in countries of differing income levels. The review included forty-one studies from high (20), upper-middle (13) and lower-middle income countries (8) during the last two decades. The earliest research was conducted in high-income countries (HICs), but research has spread in the last decade. The factors influencing pedestrian safety fell into three groups: (a) characteristics of road users, (b) characteristics of bus stops and (c) characteristics of the road traffic environment. Pedestrians near bus stops are frequently exposed to a high risk of collisions and fatalities due to factors such as unsafe pedestrian behaviours (e.g., hurrying to cross the road), lack of bus stop amenities such as safe footpaths, high traffic speeds and traffic volumes, multiple lanes, and roadside hazards (e.g., parked cars obscuring pedestrians). Road crash statistics are commonly used to identify unsafe bus stops in HICs but the unavailability and unreliability of data have prevented more widespread use in LMICs. Future research is recommended to focus on surrogate safety measures to identify hazardous bus stops for pedestrians.

Pedestrian flow-environmental pollutants interactions and health risks to residents in high-occupancy public areas of apartment buildings.

The current interaction of pedestrian flow and environmental pollutants in high-occupancy public areas of apartment and the risks of residents being exposed to environmental pollutants are issues that are often overlooked but urgently need to be addressed. In this study, we provide a comprehensive of pedestrian flow-environmental pollutants interactions and health risks to residents in first-floor public areas of apartment with high-occupancy. The main findings indicate that under closed management conditions, there is a significant increase in TVOC and noise levels during the peak periods of nighttime pedestrian flow. In the correlation analysis, the significant impact of time granularity selection in clarifying the correlation between pedestrian flow and environmental pollutants has been highlighted, with larger time granularities generally showing stronger correlations, while finer time granularities may help identify specific risks in areas directly connected to the external environment. There is a significant correlation exists between pedestrian flow and environmental pollutants (TVOC, ozone, and noise), with higher concentrations of these pollutants observed during peak pedestrian flow periods, thereby increasing the risk of residents being exposed to adverse environmental conditions. To mitigate the risks associated with TVOC pollution and noise exposure, it is crucial to maintain proper ventilation, avoid conducting cleaning or maintenance activities during peak hours, and implement noise-reducing measures, such as distancing noise sources from residential areas or installing soundproofing barriers. Additionally, the study identifies total volatile organic compounds originating from property maintenance activities and clarifies their dispersion patterns, emphasizing the importance of developing robust, standardized maintenance protocols for indoor environmental quality assurance. This research can improve the environmental sustainability of apartment buildings and provide a theoretical basis for the development of environmental health strategies for high-occupancy public areas of apartment buildings.

Children's ability to estimate approaching vehicle time-to-arrival following training in a virtual pedestrian environment.

OBJECTIVES: Child pedestrian injuries represent a significant public health challenge. Understanding the most complex cognitive skills required to cross streets helps us understand, improve, and protect children in traffic, as underdeveloped cognitive skill likely impacts children's pedestrian safety. One complex component of street-crossing is the cognitive-perceptual task of judging time-to-arrival of oncoming traffic. We examined capacity of 7- and 8-year-olds to judge time-to-arrival for vehicles approaching from varying distances and speeds, as well as improvement in those judgments following intensive street-crossing training in a virtual reality (VR) pedestrian simulator. METHODS: 500 seven- and eight-year-olds participated in a randomized trial evaluating use of a large kiosk VR versus smartphone-based VR headset to teach street-crossing skills. Prior to randomization into VR training condition and also prior to initiation of any training, children engaged in a video-based vehicle approach estimation task to assess ability to judge traffic time-to-arrival. They then engaged in multiple VR-based pedestrian safety training sessions in their randomly assigned condition until achieving adult functioning. Soon after training and again 6 months later, children repeated the vehicle estimation task. RESULTS: Prior to randomization or training, children were more accurate judging time to arrival for closer versus farther traffic, and rapidly-moving versus slower-moving traffic, but those results were subsumed by a speed x distance interaction. The interaction suggested distance cues were used more prominently than speed cues, and speed had varying effects at different distances. Training group had minimal effect on learning and all children became significantly better at judging vehicle arrival times following training. CONCLUSIONS: Children tend to underestimate vehicle arrival times. Distance cues are more impactful on time-to-arrival judgments than speed cues, but children's estimations based both on manipulations of vehicle speed and manipulations of vehicle distance improved post-training. Improvements were retained six months later. This finding is consistent with psychophysics research suggesting vehicle approach judgments rely on optical size and looming, which are impacted both by vehicle speeds and distances. Implementation of VR-based training for child pedestrian safety is recommended, as it may improve children's judgment of vehicle time-to-arrival, but it must be conducted cautiously to avoid iatrogenic effects.