Pedestrians and vehicles frequently interact at signalized intersections, where many pedestrian crashes involve right-turning vehicles. Toward improving safety, it is essential to understand the factors influencing pedestrian–vehicle interactions and conflicts. Using post-encroachment time (PET) as a surrogate safety measure, this study investigated associations of various conflict- and location-specific factors with pedestrian conflict severity, and ascertained variations across locations. Data were collected for 639 pedestrian–right-turn vehicle conflicts observed from over 1,000 hours of video at 33 intersections in Utah. Next, correlations were calculated and multilevel models (conflicts nested within intersections) were estimated. Conflicts tended to be more severe (shorter PET) when pedestrians were younger, men, leaving the curb, and using the second crosswalk that drivers encounter when turning right. Conversely, conflicts were less severe (longer PET) for women, those using strollers or wheelchairs, and pedestrians approaching the curb or using the first crosswalk. Concerning traffic signal operations, more severe conflicts occurred when vehicles were turning right on green and when pedestrians were crossing against a “Steady Don’t Walk” indication. Conflicts tended to be slightly less severe at locations with larger corner radii, high-visibility crosswalk markings, shared thru/right-turn lanes, channelized and unsignalized right-turn lanes, nonskewed intersections, and more pedestrian volumes. These results offer practical implications, such as using corner geometry and continental crosswalk markings to increase visibility between pedestrians and drivers, or considering warning signs, leading pedestrian intervals, and prohibiting right-turns-on-red in some locations. Agencies can leverage these insights to reduce conflict severity and advance Vision Zero safety goals for pedestrians.

In this study, we explored whether and how area-wide air pollution affected individuals' activity participation and travel behaviors, and how these effects differed by neighborhood context. Using multi-day travel survey data provided by 390 adults from 223 households in a small urban area in northern Utah, United States, we analyzed a series of 20 activity and travel outcomes. We investigated the associations of three different metrics of (measured and perceived) air quality with these outcomes, separately for residents of urban and suburban/rural neighborhoods, while weighting and controlling for personal/household characteristics and weather. Our regression models detected measurable changes in activity and travel patterns on days with poor air quality. People engaged in more mandatory (work/school) and fewer discretionary activities. The total travel time for urban residents increased, driven by increases in trip-making and travel time by public modes (bus) and increases in travel time by private modes (car). On the other hand, suburban/rural residents exhibited behavior consistent with mode shifts from driving to active transportation, such as: less car travel (distance and time), longer transit distances, more walking/bicycling (trips, distances, and time), and greater odds of being an active mode user. Air quality perceptions also seemed to play a role, with some evidence for increased active transportation and public transit usage on days with worse perceived air pollution. Overall, the results offer more evidence of altruistic than risk-averse travel behavioral responses to episodes of area-wide air pollution, although more research is needed.

This study addressed key knowledge gaps by forecasting electric air taxi (EAT) demand for airport access-one of the earliest and most promising use cases for EAT deployment. Unlike prior EAT studies focused on short urban trips, this research analyzed a long-distance corridor in northern Utah, United States, where the substantial travel time savings make it a highly feasible setting for early adoption. Methods combined a revealed preference-stated preference (RP-SP) mixed logit model with observed origin-destination travel data. The first objective assessed mode shifts following the introduction of EAT service for airport trips. Results show EAT could divert approximately one-third of trips from private vehicles and ride-hailing, reducing corridor traffic by over 160 vehicles daily. This shift could reduce airport parking demand and tailpipe emissions, improving air quality. Forecasts also predict a modest decline in public transportation ridership from approximately 6% to 3%, potentially undermining bus service revenue and disproportionately impacting lower-income travelers. The second objective conducted sensitivity analyses to evaluate the robustness of EAT demand to various factors. Findings revealed that in-vehicle travel time, travel cost, service frequency, autonomy, and the number of parking days significantly influenced mode shifts. EAT market share was projected to increase from 17% at a higher EAT travel cost of $4.50/mile (near-term) to 63% at a lower EAT travel cost of $0.50/mile (long-term). Vertiport expansion in the study area showed diminishing returns beyond one site, suggesting locations for priority deployment. These findings support evidence-based planning and equitable implementation of EAT services.

To examine how regional air pollution affects multimodal traffic volumes, we analyzed two years (2018–2019) of daily automobile traffic counts, estimated pedestrian volumes, and transit ridership in Cache County, Utah, United States. Multilevel models accounted for locational differences while controlling for weather and other temporal variables. When the air quality was poor, pedestrian volumes decreased, automobile traffic increased, and system-wide bus ridership showed no significant change. Walking declines were larger in neighborhoods with higher car ownership and smaller in areas with greater street connectivity and more commercial land use.

Microscopic evacuation models often represent heterogeneous crowds as one average pedestrian, limiting prediction of mobility-class differences. We develop and evaluate a disability-aware social force model (DiSFM) using radio-frequency-identification trajectories from a controlled evacuation drill with 47 participants, including 13 individuals with disabilities (IWD). A strict 13-scenario calibration and three-scenario hold-out evaluation separates model fitting from physical-model assessment. Under equal calibration budgets, the two-class DiSFM reduces held-out class-speed loss by 85.8% and IWD speed error by 88.0% relative to a fully homogeneous social force model; a matched ablation attributes this gain to class-specific kinematics, whereas directional attraction provides no consistent additional gain. We then apply the calibrated physical model to a map-derived, full-building scenario ensemble with an accessibility-aware capacity-aware route planner and imposed route-guidance uptake. Across 100 matched scenarios, mean total completion within 360 s rises from 83.3% without guidance to 99.9% at uptake C=0.7; mean IWD completion rises from 84.7% to 99.7%. The full-building analysis evaluates route-guidance outcomes under the specified model, building, and scenario conditions, providing a direct simulation framework for disability-aware evacuation-policy screening.

Effective evacuation simulations can be useful for assessing building safety design and optimizing emergency responses. However, configuring these simulations is often manual, time-consuming, and error-prone, especially with complex building geometries and diverse occupant characteristics. This paper introduces an automated workflow that integrates openBIM-based Occupant Movement Analysis data with fire safety regulations from the US and UK using Retrieval-Augmented Generation (RAG) methods and Large Language Models (LLMs). We benchmarked multiple parsing tools, with LlamaParse emerging as the most accurate for extracting text and tables from regulatory documents. We then tested eight RAG approaches with various LLMs across multiple question types and identified Knowledge Graph Enhanced RAG and Neo4j GraphRAG with GPT-4o as top performers in accuracy and consistency. These methods enabled on-demand interpretation of the US and UK regulatory documents for calculating occupant load from IFC-derived geometry data, generating a population input file for an Evacuation simulation as a selected evacuation software in this study. Our evaluation confirms that Knowledge Graph Enhanced RAG excelled in complex reasoning, while Neo4j GraphRAG offered higher stability. This automation enables efficient and reliable safety assessments, contributing to safer building design and emergency response planning.

The global rise in urbanization and population density has intensified crowding challenges at large events, increasing the risk of disasters during pedestrian evacuations. Understanding pedestrian behavior and decision-making during evacuations is critical for enhancing safety. The presence of a leader during evacuations can facilitate quicker and safer navigation, enhancing crowd management and mitigating potential risks. This study utilized the Virtual Immersive Reality Experiment (VIRE) to investigate conditions influencing individuals' leader-following behavior in simulated outdoor mass gatherings, involving 27 participants (74% male, 26% female) aged 18-39. Seven independent variables-stress level, leadership type, time of day, leader credibility, leader gender, route familiarity, and crowd-following behavior-were analyzed. Participants' heart rates were monitored to assess physiological responses to emergency conditions. Results revealed significant effects of route familiarity, stress level, leader credibility, and time of day on leader-following behavior. Increased heart rates in emergencies highlighted heightened anxiety, reducing compliance with leaders. Findings emphasize the importance of credible leaders providing accurate guidance and improving evacuation safety in unfamiliar, low-light environments during mass gatherings.

Transportation is at a historic turning point. In the United States alone, vehicles move over 20 billion tons of freight annually and account for more than 3 trillion vehicle miles traveled. This places the annual cost of diesel and gasoline fuel for transportation in the United States at over US$500 billion. Additionally, the sector produces more air pollution and greenhouse gas emissions than any other and contributes to economic volatility and national security concerns through global oil markets. Electrified transportation presents a compelling solution: substantially higher energy efficiency, the potential for zero tailpipe emissions, stable energy prices using local energy resources, and reduced total cost of ownership. Since electric drivetrains use four times less energy to do the same work as an internal combustion engine, they have the potential to save hundreds of billions of dollars annually in the United States through reduced fuel and maintenance costs. The challenge is capturing this fundamental advantage at scale without losing it to today’s underutilized charging infrastructure, expensive and heavy batteries, slow adoption rates, and significant grid upgrades. Each of these areas presents both obstacles and opportunities for systemic innovation.

As congestion and environmental concerns grow, innovative solutions like electric air taxis (EATs) are gaining attention as efficient and sustainable options for improving airport trips. To understand preferences for EATs for long-distance airport access/egress trips, we conducted a stated choice experiment and estimated integrated choice and latent variable (ICLV) models. The study analyzed choices among EAT, private vehicle (PV), public transport (PT), and transportation network companies (TNC), collecting data in 2024 from 1028 US adults traveling 75–200 miles one-way to/from airports. In-vehicle travel time, travel cost, and access time for all modes showed significant negative associations with mode choice, with access time being particularly critical for EAT. A decrease in service frequency of EAT significantly reduced its selection odds, and autonomy in travel modes was found to present a disutility, with the most substantial negative impact on EAT. Perceived ease of use was highest for EAT, while perceived trust was highest for PT. Travelers were more inclined to choose EAT when it is perceived as dependable and reliable. Including luggage costs in the total travel cost for EAT significantly increased preferences compared to scenarios with extra fees. Based on our findings, the highest value of travel time savings (VTTS) was for EAT at $46.09/hr, followed by PV at $38.63/hr, PT at $37.81/hr, and TNCs at $30.40/hr. EAT’s access time VTTS was highest, at $53.46/hr. These findings provide valuable insights for airlines, airport operators, EAT companies, and policymakers regarding the market potential of EATs for long-distance airport trips.

This study investigates pedestrian crossing behaviors at signalized intersections, focusing on spatial and temporal violations. Using a comprehensive dataset comprising 5,589 pedestrian crossing events at 47 crosswalks across 39 intersections in Utah, the research employs multilevel regression models to identify the factors influencing these violation behaviors. Key findings indicate that individual characteristics such as gender and mobility device use, environmental conditions including temperature and time of day, and social dynamics such as the presence of other pedestrians significantly impact violation behaviors. The study reveals that female-presenting pedestrians and those using mobility devices are generally more compliant, while higher temperatures and specific times-of-day are associated with increased violation rates. The analysis also highlights that longer waiting time is linked to a reduced likelihood of violations, and that social dynamics play a crucial role, with the presence of other pedestrians discouraging violations. Additionally, neighborhoods with larger shares of Hispanic or non-white populations have higher violation rates, and mid-block crossings see more temporal violations. These findings aim to enhance pedestrian safety and compliance at signalized intersections, ultimately reducing pedestrian fatalities and injuries.

This study’s objectives were to (1) understand (geometric, traffic, operational, and other) factors associated with bicycle safety (crash frequency and severity) at signalized intersections; and (2) investigate whether the “safety in numbers” phenomenon applies to bicycling in the US. To accomplish these objectives, data for 2312 bicycle crashes over a ten-year (2010–2019) period were linked to crowdsourced Strava ridership data (as a measure of bicycle exposure) and other information at 2232 signalized intersections in Utah. Zero-inflated negative binomial models of bicycle crash frequencies and ordered logit models of bicycle crash severities were estimated, accounting for different levels of data availability. Also, an aggregate time period analysis compared bicycle crash rates and severity levels for different weekdays and months. Bicycle crashes were more frequent at signals with four legs, longer crossing distances, no channelized right turn lanes, more far-side bus stops, higher population densities, no places of worship, and in neighborhoods with lower incomes and greater shares of people of Hispanic or non-White race/ethnicity. Bicycle crashes were more severe when involving larger or left-turning vehicles, road users who disregarded the traffic control device, on arterial roadways, and at locations with vertical grades and without street lighting. Bicycle crash rates and the share of fatal/serious injury crashes were lower during the highest-ridership months of the year (May–August). Overall, the study found strong support for the “safety in numbers” effect, in which bicycle crash rates decrease with increasing bicycle volumes, when looking both across locations and over time.

This study explores the factors affecting travel mode choices and the relative importance of those factors. Based on two nationwide consumer surveys in 2019 in the US, the objective of this study is to measure and explain heterogeneity in the self-reported importance ratings of eight specific types of factors-time, cost, convenience, safety, health, emotions, environment, social-in travel mode choice decisions. To help investigate the relative importance people attributed to the factors, respondents were clustered into groups using a hierarchical clustering algorithm based on their modality styles (mode use frequencies). Next, we fit two seemingly unrelated regression (SUR) models: one for each survey dataset, with the eight importance factors as the dependent variables. The independent variables consisted of respondents' demographic characteristics carefully selected based on previous research. The results reveal interesting similarities between the two datasets. For instance, non-white respondents assigned greater importance to safety considerations in their mode choice than did white respondents. Also, people from higher-income households cared more about the convenience of their trip when selecting a mode. More educated people tended to care more about the environmental effects of their travel mode. Individuals who drive almost every day but rarely use public transit placed lower importance on factors related to health, emotions, the environment, and social relationships; other factors like convenience, travel time, and cost were more important to them. These findings extend prior literature by suggesting that greater attention ought to be paid to measuring and including subjective factors (in addition to instrumental considerations) in mode choice analyses.

The high number of pedestrian crashes and fatalities in the United States warrants increased attention, including at intersections where road users cross paths and interact. The primary objective of this study is to explore driver and pedestrian behaviors and their interactions during right-turn conflicts at signalized intersections, under both pedestrian post-encroachment (vehicle passes before pedestrian) and vehicle post-encroachment (vehicle passes after pedestrian) scenarios. Using data from 846 pedestrian–right-turning-vehicle conflicts observed over 1,000 h of video collected from 34 intersections in Utah, we analyzed how these behaviors influence conflict severity, measured using a time-based surrogate safety metric. Based on path analysis, the influence of drivers’ reactions on the severity of pedestrian post-encroachment conflicts and pedestrians’ reactions on the severity of post-encroachment conflicts are mediated by the reactions of the other road users (pedestrians and drivers, respectively). Specifically, the reactions of pedestrians have a more significant impact on the severity of the conflict than the reactions of drivers. Based on mediation analysis, larger total effect sizes revealed that pedestrians’ reactions—stopping and slowing down (0.506) or taking evasive actions (1.121)—during pedestrian post-encroachment conflicts do more to affect collision severity than driver’s reactions. Similarly, the effect of pedestrians taking evasive actions to avoid collisions (0.303) was slightly higher than right-turning drivers’ actions of stopping or slowing (0.200) during vehicle post-encroachment conflicts. However, because of the higher severity of post-encroachment conflicts, drivers have a greater responsibility to prevent collisions in these situations. Policy measures should consider design and operational strategies—including signs, pavement markings, and signal timing—to improve driver yielding and pedestrian protection. Focusing on the interactions between drivers and pedestrians in right-turn conflict scenarios could significantly enhance road safety.

Effective crowd management during evacuations depends on timely information and leadership that guides evacuees to safety. This study investigates factors influencing pedestrian behavior in outdoor evacuations using immersive virtual reality (VR). Data from 27 participants, collected through eye-tracking and physiological sensors, examined leader credibility, leadership style (visual versus visual-verbal), stress levels, leader gender, familiarity, and crowd behavior. Mann-Whitney U tests analyzed attention and physiological responses, while a mixed binary logistic regression model identified predictors of leader-following. Results indicated that leader credibility and environmental familiarity were key predictors of leader-following. Participants were more likely to follow leaders in scenarios that included both visual and verbal leadership, male leaders, and normal conditions. Female participants showed a greater likelihood to follow leaders. Analysis of eye-tracking measures revealed increased attention and scanning spans among followers, particularly at night and under normal conditions. These findings highlight the importance of leadership dynamics and inform targeted strategies for effective evacuation management.

People with disabilities make up nearly 13% of the US population, yet there remains a lack of attention to their mobility needs, which affects their social integration and quality of life. Utilizing data from the 2022 US National Household Travel Survey, this study investigates the mobility patterns of people with disabilities. We applied a Negative Binomial model and Gradient Boosting Regression machine learning method to analyze the factors influencing daily trip production among this population group. Our findings reveal that mobility for people with disabilities is significantly lower compared to their counterparts. Being a non-driver significantly reduces trip frequency, with a higher effect size on people with disabilities. Employment significantly increases mobility, with a higher effect size for people with disabilities. Additionally, being in a lower income group, rather than a high-income group, significantly decreases mobility, particularly for people with disabilities. These insights provide a foundation for policymakers to enhance mobility for people with disabilities, ensuring more inclusive and accessible transportation solutions.

Based on surveys of 910 long-distance airport access travelers in the United States, we observed early adopters with higher intentions to adopt Electric Air Taxis (EAT) and laggards with lower intentions and notable resistance toward acceptance, compared to majority, thus validating Rogers’ innovativeness-based categorization. The intention to adopt EAT was motivated by perceived ease of use, social validation, and the utility offered by EAT during airport access trips. Performance expectancy emerged as a key predictor of intention to use EAT among the majority and laggards, while social influence had a stronger impact on intention to use EAT among early adopters.

Pavement friction is crucial for road safety, especially in adverse weather. This study investigates the relationship between pavement friction—measured as skid number (SN)—and crash frequency on Utah highways. Using data from 2016 to 2019 for I-15 (interstate) and US-89 (non-interstate), negative binomial models were estimated to establish safety performance functions (SPFs) and crash modification factors (CMFs). The models accounted for traffic volume, segment length, and roadway geometric characteristics, examining various crash types, including dry- and wet-weather, property damage only, and injury-related crashes. Results show a significant negative association between SN and crash frequency for all crash types on both highway types. Higher SN values (more friction) were linked to fewer crashes. A 10-unit increase in SN was associated with a 7% to 8% reduction in dry-weather crashes on both highway segments. For wet-weather crashes, the same increase in SN resulted in a 14% decrease on non-interstate and a 21% decrease on interstate highways. The impact of SN on reducing crashes was particularly strong on interstate highways during wet conditions, indicating that pavement friction is vital for safety in these scenarios. Additionally, the safety impact of skid resistance was greater on segments with more curved portions. These findings suggest that enhancing pavement friction through measures such as high friction surface treatments could significantly improve traffic safety. The results support the continued collection of skid data by transportation agencies to identify high-risk locations and prioritize friction improvement efforts to enhance roadway safety.

Familiar and unfamiliar drivers may exhibit different behaviours in response to the road environment. Overall familiarity with the road environment is a human factor believed to play a role in road crash injury severity due to its effect on a driver's decision-making process, reaction time, etc. Hence, there is a need to separately analyse familiar and unfamiliar drivers regarding the injury severity of crashes. Using a six-year database of 30,481 rural two-vehicle crashes in Guilan province, Iran, this research first defined four categories of crashes, reflecting various levels of the involved drivers’ familiarity with the environment (72% of drivers were from the same vs. 28% from a different province). Next, the injury severity of crashes in each familiarity crash category was analysed using both non-parametric (classification and regression trees) and parametric (logistic regression) methods. When both crash parties were unfamiliar, several results are different compared to when both parties were familiar or when ignoring driver familiarity. For instance, young at-fault drivers increased the injury severity of crashes if they were unfamiliar, while they decreased the crash severity if they were familiar. Also, crashes in winter tended to be more severe when one or especially both crash parties were unfamiliar, but winter crashes were less severe when both drivers were familiar or when driver familiarity was ignored. Overall, when both drivers were familiar, 63% of crashes were injury/fatal; however, when both drivers were unfamiliar, only 31% of crashes involved an injury or fatality.

Studying the tradeoffs between commuting, working, and money—including the willingness to pay (WTP) for changes to travel time and work time—is of interest in both travel behavior research and transportation practice. Our study's objectives were to: (1) quantify WTP for travel time and work time changes; (2) measure preference heterogeneity in these time values; and (3) examine sensitivities to changes in income and travel cost. First, we conducted a stated choice experiment that added work time and income to traditional travel time and cost attributes, collecting data from 675 US adult commuters in late 2020. Second, we estimated pseudo panel mixed multinomial logit models with random and systematic preference heterogeneity. Average WTP was larger for travel time changes than for work time changes, although values varied across personal, household, and commute/work characteristics. Overall and on the margin, US commuters appear to dislike commuting more than they dislike working, a finding which differs from previous studies in Chile and Austria. Although future work could improve the realism of the choice experiment, our research offers a simple stated preference method to help work towards advancing an understanding of the values and tradeoffs involving both working and commuting.

This research project's objective was to investigate the impacts of transit stop location (near-side versus far-side) on pedestrian safety and traffic operations. Three different video-based behavioral observation data collections at signalized intersections in Utah were utilized, studying: (1) transit vehicle stop events and transit rider crossing behaviors and vehicle conflicts; (2) pedestrian conflicts with right-turning vehicles (driver/pedestrian reactions, conflict severity); and (3) pedestrian crossing behaviors (crossing location, crossing behaviors). These outcomes were statistically compared for near-side versus far-side transit stop locations. Far-side transit stops appear better for general traffic operations. Although transit departure delays are more likely and impactful at far-side stops, actions can be taken to improve transit operations there. On the other hand, far-side transit stops appear to be worse for pedestrian safety, corroborating prior crash-based research findings. Specifically, conflicts at far-side stops were more severe, and drivers were less likely to slow or stop for pedestrians. Reconciling these differing findings likely requires improving pedestrian safety at some far-side transit stops, and prioritizing safety over operational efficiency at other near-side transit stops.