Investigating the dynamics of collective behavior among pedestrians crossing roads: A multi-user virtual reality approach.

The utility maximization theory, based on the rationality of human beings, has proven effective in modeling pedestrians' decision-making processes while crossing roads. However, there are still unexplained variations in crossing behavior, and deviations from the rational utility model frequently occur in real-life scenarios. This experimental study sheds new light on the presence of inter-individual interactions among pedestrians and the nature of collective behaviors during road crossings. The present study develops a multi-pedestrian virtual reality simulator specifically designed to investigate the impact of social interaction on pedestrians' eye-scanning patterns, perceived responses, crossing behaviors, and the associated crash risk. Our findings indicate that the collective behavior significantly influences pedestrians' behaviors by diverting their attention from essential eye-scanning patterns that reflect their cognitive processes. Pedestrians in pairs exhibit a higher tendency to fixate on each other, spend less time in the decision phase, walk at a slower pace during the crossing phase, and consequently face a higher degree of exposure to dangerous situations compared to when crossing alone. Encouraged by these findings on the effects of social interaction, we discuss preventive strategies to mitigate the negative impacts of collective behavior and foster pedestrians' safety awareness.

Autonomous vehicles and street design: Exploring the role of medians in enhancing pedestrian street crossing safety using a virtual reality experiment.

As traffic lanes and on-street parking spots can potentially be downsized with the introduction of autonomous vehicles (AVs), the possibility of additional spare road space becoming available arises in future urban streets. While discussions on converting the leftover space into pedestrian-friendly alternatives exist, allocating that limited space to which alternative is foreseen to be another practical issue shared in both urban and transportation planning. However, evidence-based guidance on the issue provided from the actual verification on whether or to what extent the proposed alternatives may have an effect seems to be absent. Therefore, with an emphasis on pedestrian safety, this study focused on the "median strip" alternative as a first example and, through a VR simulation experiment aimed at empirically examining its suggested role on enhancing street crossing safety and further exploring its possible influence on pedestrians' trust toward autonomous driving. With 99 participants, perceived safety (individual assessments of safety), performance-based safety (crossing success/abandonment and collision occurrence), and trust were either questioned or recorded for nine scenarios with varying crossing conditions. A combination of multilevel models and cross-tabulation results indicate that medians seem especially significant in ensuring the performance-based safety results of pedestrians even when AVs are driving at high speeds or with smaller gaps, thus suggesting it a win-win option for both. Insights and implications on the role and management of medians in future streets are further provided.

Examining the causal relationship between bike-share and public transit in response to the COVID-19 pandemic.

As urban transportation systems often face disruptive events, including natural and man-made disasters, the importance of resilience in the transportation sector has recently been on the rise. In particular, the worldwide spread of the COVID-19 pandemic resulted in a significant decrease in citizens' public transit use to avoid unnecessary physical contact with others. Accordingly, bike-share has been highlighted as one of the sustainable modes that can replace public transit and, thus, improve the overall resilience of the urban transportation systems in response to COVID-19. This study aims to examine the changes in causal relationships between bike-share and public transit throughout the COVID-19 pandemic in Seoul, Korea. We analyzed bike-share and public transit ridership from Jan 2018 to Dec 2020. We developed a weekly panel vector autoregressive (PVAR) model to identify the bike-transit relationships before and after the pandemic. Our results showed that COVID-19 weakens the competitive relationships between bike-share and bus transit and modal integration between bike-share and subway transit. This study also found that bus and subway transit were more competitive with each other after the outbreak of COVID-19. The study's findings suggest that bike-share can increase the overall resilience of the urban transportation system during the pandemic situation, particularly for those who rely on public transit for their mobility.

Pedestrians safety perception and crossing behaviors in narrow urban streets: An experimental study using immersive virtual reality technology.

Virtual reality (VR) technology emerges as a promising tool for investigating human perception and behavior in highly controlled, immersive, and risk-free environments. This study proposed to apply simulated VR technology to investigate the interactions between perceived crash risk and behavior patterns in a road crossing with changes in the safety-related environmental attributes. In the context of the 8-meter-wide segment in a residential block, 35 VR environments with variations of six environmental attributes were generated. Two hundred participants were recruited for the experiment. The measured behavioral outcomes were 1) waiting and reaction time in the decision phase before crossing and 2) crossing speed and gait variability in the crossing phase. Random effect regression and multi-level structural equation models were constructed to test the study hypotheses. The results demonstrated that environmental attributes, including barriers to visibility (coefficient = 0.446), geometric patterns (coefficient = -0.625), and pavement signs (coefficient = -0.502), were associated with the pedestrians' perceived risk, but the influence varied by street types. In addition, changes in the perceived threats to pedestrians were found to mediate the environment-crossing behavior relationship (coefficient of the indirect effect = 0.679). Those who perceive higher crash risk took longer to decide to start walking at a crosswalk and tended to walk in haste while crossing the road. Using VR technology, the present study addressed an inter-relationship between environmental characteristics, cognition, and crossing behavior, contributing to better knowledge on road safety interventions to reduce the risk of pedestrian-involved crashes.

Influence of Evacuation Policy on Clearance Time under Large-Scale Chemical Accident: An Agent-Based Modeling.

Large-scale chemical accidents that occur near areas with large populations can cause significant damage not only to employees in a workplace but also to residents near the accident site. Despite the increasing frequency and severity of chemical accidents, few researchers have argued for the necessity of developing scenarios and simulation models for these accidents. Combining the TRANSIMS (Transportation Analysis and Simulation System) agent-based model with the ALOHA (Areal Location of Hazardous Atmospheres) dispersion model, this study aims to develop a modeling framework for simulating emergency evacuations in response to large-scale chemical accidents. The baseline accident scenario assumed the simultaneous leakage of toxic chemicals from industrial complexes near residential areas. The ALOHA model results showed that approximately 60% of residents in the scenario's city were required to evacuate their homes. The majority of evacuees completed their evacuations within 5 h in the baseline scenario (evacuating maximum number of private vehicles without any intervention), while the distribution of the population and street network density caused geographical variability in clearance time. Clearance time can be significantly reduced by changing both the evacuees' behaviors and the evacuation policy, which suggests the necessity for proper public intervention when the mass evacuation of residents is required due to chemical accidents.

An examination of the intersection environment associated with perceived crash risk among school-aged children: using street-level imagery and computer vision.

While computer vision techniques and big data of street-level imagery are getting increasing attention, a "black-box" model of deep learning hinders the active application of these techniques to the field of traffic safety research. To address this issue, we presented a semantic scene labeling approach that leverages wide-coverage street-level imagery for the purpose of exploring the association between built environment characteristics and perceived crash risk at 533 intersections. The environmental attributes were measured at eye-level using scene segmentation and object detection algorithms, and they were classified as one of four intersection typologies using the k-means clustering method. Data on perceived crash risk were collected from a questionnaire conducted on 799 children 10 to 12 years old. Our results showed that environmental features derived from deep learning algorithms were significantly associated with perceived crash risk among school-aged children. The results have revealed that some of the intersection characteristics including the proportional area of sky and roadway were significantly associated with the perceived crash risk among school-aged children. In particular, road width had dominant influence on risk perception. The findings provide information useful to providing appropriate and proactive interventions that may reduce the risk of crashes at intersections.

Park use and physical activity among adolescent girls at two time points.

This longitudinal study described park usage and assessed the contribution of parks to moderate to vigorous physical activity (MVPA) among adolescent girls. High school girls from California (n = 131) and Minnesota (n = 134) wore a global positioning system (GPS) monitor and accelerometer for 6 consecutive days at two time points, one year apart. Park visits were classified by linking the GPS, accelerometer, and park and built environment data around home and school locations into a geographic information system. At baseline, 20% of girls visited a park at least once (mean 0.1 times/day), which was similar one year later (19%, mean 0.1 times/day). Girls lived a mean Euclidean distance of 0.2 miles to the nearest park at both times. Among all park visits, the mean Euclidean distance of the park visited was 4.1 (baseline) and 3.9 miles (follow-up). The average duration of park visits was higher at baseline (63.9 minutes) compared to follow-up (38.4 minutes). On days when a park was visited, MVPA was higher than on days when a park was not visited. On average, 1.9% (baseline) and 2.8% (follow-up) of MVPA occurred in parks. In this study, parks were an under-used resource for adolescent girls, particularly for MVPA.

Association between intersection characteristics and perceived crash risk among school-aged children.

This research examined how environmental attributes near intersections influence the perceived crash risk among school-aged children, which provides information on the potential risks of pedestrian crashes that can guide the development of proactive countermeasures. In a sample of 799 children aged 10-12 years old in Korea, the environmental attributes of intersections perceived as having a high risk of producing crashes near elementary schools were investigated using standard negative binomial and zero-inflated negative binomial models.The results showed that a higher number of student crossings, a wider road width, the presence of crosswalks, student-friendly facilities at the intersection, and four-way intersections were significant and positively associated with perceived crash risk among school-aged children. The findings related to building characteristics indicated that a higher number of entrances at an intersection increased the perceived crash risk while higher visibility at the intersection reduced the perception of risk. Associations with traffic-calming measures were weak,suggesting that the measures used in the study areas were not effective in reducing the perceived crash risk. The results of a police-reported crash model showed that school-aged children have a relatively accurate perception of crash risk and that the perceived crash risk of school-aged children may provide valuable information on the intersection characteristics in need of attention near school sites.

Out and about: association of the built environment with physical activity behaviors of adolescent females.

Locational data, logged on portable GPS units and matched with accelerometer data, was used to examine associations of the built environment with physical activity and sedentary behaviors of adolescent females. In a sample of 293 adolescent females aged 15 to 18 years old in Minneapolis and San Diego, the built environment around each GPS point and its corresponding sedentary, light, and moderate-to-vigorous intensity physical activity was examined using random intercept multinomial logistic regression models. The odds of higher physical activity intensity (3-level outcome: sedentary, light, MVPA) were higher in places with parks, schools, and high population density, during weekdays, and lower in places with more roads and food outlets. Understanding the places where physical activity and sedentary behaviors occur appears to be a promising strategy to clarify relationships and inform policy aimed at increasing physical activity and reducing sedentary behaviors.

Identifying walking trips from GPS and accelerometer data in adolescent females.

BACKGROUND: Studies that have combined accelerometers and global positioning systems (GPS) to identify walking have done so in carefully controlled conditions. This study tested algorithms for identifying walking trips from accelerometer and GPS data in free-living conditions. The study also assessed the accuracy of the locations where walking occurred compared with what participants reported in a diary. METHODS: A convenience sample of high school females was recruited (N = 42) in 2007. Participants wore a GPS unit and an accelerometer, and recorded their out-of-school travel for 6 days. Split-sample validation was used to examine agreement in the daily and total number of walking trips with Kappa statistics and count regression models, while agreement in locations visited by walking was examined with geographic information systems. RESULTS: Agreement varied based on the parameters of the algorithm, with algorithms exhibiting moderate to substantial agreement with self-reported daily (Kappa = 0.33-0.48) and weekly (Kappa = 0.41-0.64) walking trips. Comparison of reported locations reached by walking and GPS data suggest that reported locations are accurate. CONCLUSIONS: The use of GPS and accelerometers is promising for assessing the number of walking trips and the walking locations of adolescent females.

Identifying Walking Trips Using GPS Data.

PURPOSE: this study developed and tested algorithms to identify outdoor walking trips from portable global positioning system (GPS) units in free-living conditions. METHODS: the study included a calibration and a validation phase. For the calibration phase, we determined the best algorithm from 35 person-days of data. Measures of agreement regarding the daily number and duration of diary-reported and GPS-identified trips were used. In the validation phase, the best algorithm was applied to an additional and separate 136 person-days of diary and GPS data. RESULTS: the preferred algorithm in the calibration phase resulted in 90% of trips identified from the GPS data being found in the diary, whereas 81% of trips reported in the diary being found in the GPS data. The preferred algorithm used 1) a maximum 3-min gap between points to define a trip, 2) at least 5 min or more of continuous GPS points, 3) a speed range between 2 and 8.0 km·h, 4) at least 30 m of displacement between the start and end points of a trip, and 5) merged walking trips when the time gap between trips was less than 3 min. With the validation data, substantial agreement between the GPS and the diary was achieved, with 86% of trips identified from the GPS data found in the diary and 77% of trips reported in the diary found in the GPS data. CONCLUSIONS: the algorithm identified free-living walking trips of more than 5 min in duration. The ability to identify outdoor walking trips from GPS data can be improved by reducing recording intervals used in the GPS units and monitoring participant compliance. Further research is desirable to determine whether concurrent wearing of an accelerometer may improve the ability to detect walking more accurately.