Forward dynamics computational modelling of a cyclist fall with the inclusion of protective response using deep learning-based human pose estimation.

Single bicycle crashes, i.e., falls and impacts not involving a collision with another road user, are a significantly underestimated road safety problem. The motions and behaviours of falling people, or fall kinematics, are often investigated in the injury biomechanics research field. Understanding the mechanics of a fall can help researchers develop better protective gear and safety measures to reduce the risk of injury. However, little is known about cyclist fall kinematics or dynamics. Therefore, in this study, a video analysis of cyclist falls is performed to investigate common kinematic forms and impact patterns. Furthermore, a pipeline involving deep learning-based human pose estimation and inverse kinematics optimisation is created for extracting human motion from real-world footage of falls to initialise forward dynamics computational human body models. A bracing active response is then optimised for using a genetic algorithm. This is then applied to a case study of a cyclist fall. The kinematic forms characterised in this study can be used to inform initial conditions for computational modelling and injury estimation in cyclist falls. Findings indicate that protective response is an important consideration in fall kinematics and dynamics, and should be included in computational modelling. Furthermore, the novel reconstruction pipeline proposed here can be applied more broadly for traumatic injury biomechanics tasks. The tool developed in this study is available at https://kevgildea.github.io/KinePose/.

Comparison of motor vehicle-involved e-scooter and bicycle crashes using standardized crash typology.

INTRODUCTION: The market share of e-scooters in the United States has proliferated in cities: 86 million trips were made on shared e-scooters in 2019, a more than 100% increase compared to 2018. However, the interaction of e-scooters with other road users and infrastructure remains uncertain. METHOD: This study scrutinized 52 e-scooter and 79 bicycle police-reported crashes in Nashville, Tennessee, from April 2018 to April 2020 from the Tennessee Integrated Traffic Analysis Network (TITAN) database. We used descriptive analysis and a recent prototype version of the Pedestrian and Bicycle Crash Analysis Tool (PBCAT) to classify crashes based on the locations of the crashes relative to roadway segments or intersections, as well as the maneuver of the motor vehicle and e-scooter/bicycle relative to the motor vehicle. RESULTS: Two crash typologies can explain the majority of e-scooter crashes, while bicycle crashes are distributed over several crash typologies. Additionally, 1 in 10 e-scooter- and bicycle-motor vehicle crashes leads to the injury or fatality of the e-scooter rider or bicyclist. Furthermore, we noted statistically significant differences in spatial and temporal distribution, demographics, lighting conditions, and crash distance from home for e-scooter and bicycle crashes. CONCLUSIONS: The police crash report provides a comprehensive picture of e-scooter safety complementing existing literature. We found that e-scooter crash characteristics do not fully overlap with features of bicycle crashes. PRACTICAL IMPLICATIONS: A generalized engineering, education, and enforcement treatment to reduce and prevent e-scooter and bicycle crashes, injuries, and fatalities might not result in equal outcomes for each mode. More rigorous enforcement could be implemented to deter e-scooters riders under the age of 18 years and e-scooter safety campaigns could target female riders.

A geographically weighted regression to estimate the comprehensive cost of traffic crashes at a zonal level.

Global road safety records demonstrate spatial variation of comprehensive cost of traffic crashes across countries. To the best of our knowledge, no study has explored the variation of this matter at a local geographical level. This study proposes a method to estimate the comprehensive crash cost at the zonal level by using person-injury cost. The current metric of road safety attributes safety to the location of the crash, which makes it challenging to assign the crash cost to home-location of the individuals who were involved in traffic crashes. To overcome this limitation, we defined Home-Based Approach crash frequency as the expected number of crashes by severity that road users who live in a certain geographic area have during a specified period. Using crash data from Tennessee, we assign those involved in traffic crashes to the census tract corresponding to their home address. The average Comprehensive Crash Cost at the Zonal Level (CCCAZ) for the period of the study was $18.2 million (2018 dollars). Poisson and Geographically Weighted Poisson Regression (GWPR) models were used to analyzing the data. The GWPR model was more suitable compared to the global model to address spatial heterogeneity. Findings indicate population of people over 60-years-old, the proportion of residents that use non-motorized transportation, household income, population density, household size, and metropolitan indicator have a negative association with CCCAZ. Alternatively, VMT, vehicle per capita, percent educated over 25-year-old, population under 16-year-old, and proportion of non-white races and individuals who use a motorcycle as their commute mode have a positive association with CCCAZ. Findings are discussed in line with road safety literature.

Underreporting, crash severity and fault assignment of minor crashes in China - a study based on self-reported surveys.

Unreported minor crashes have importance as a surrogate for more serious crashes that require infrastructure, education, and enforcement strategies; and they still inflict damages. To study factors that influence underreporting, cause, and severity of minor crashes; a survey was performed in Kunming and Beijing to collect self-reported personal characteristics and crash history data of the three major urban road users in China: automobile drivers, bicycle riders and electric bike (e-bike) riders. Underreporting rates of automobile to automobile, automobile to non-motorized vehicle, and non-motorized vehicle to non-motorized vehicle crashes are 56%, 77% and 94%, respectively. Minor crashes with higher reported injury severity levels are more likely to be reported. E-bike riders without a driver's license are more likely to cause crashes. Licensing and education could be an effective way to reduce their crashes. The party that is not at fault in a crash is more likely to sustain high level of injury.

Neighborhood-level factors affecting seat belt use.

Despite the well-known safety benefits of seat belt use, some vehicle occupants still do not use them. This is a challenge in Tennessee, which has a lower seat belt use rate compared to the United States national average. Roadside observations and interviews are the two main sources for estimating seat belt use rate and have several limitations (e.g., small sample size, social desirability bias). To address these limitations, we attributed seat belt use of individuals who were involved in traffic crashes (N = 542,776) to their corresponding home-addresses. Home-addresses were retrieved from police crash reports and were geocoded, and assigned to their corresponding census tract revealing added information about the spatial distribution of seat belt use and socioeconomics of the areas surrounding the crash victim's home. The average seat belt use rate in the metropolitan area was 88% and for the non-metropolitan area was 87%. A Tobit model was used to evaluate the relationship between the seat belt use rate for both drivers and passengers over 16 years old, with neighborhood sociodemographic variables. Population, age cohorts, race, household vehicles' ownership, household size, and education were among the predictors of the seat belt use rate. Results of this analysis could be used in safety campaign design to reach specific geographic areas and groups with a lower seat belt use rate.

Using CyclePhilly data to assess wrong-way riding of cyclists in Philadelphia.

PROBLEM: The increasing use of smartphones and low cost GPS have provided new sources for collecting data and using them to explain travel behavior. This study aims to use data collected from a smartphone application (CyclePhilly) to explain wrong-way riding behavior of cyclists on one-way segments to help better identify the demographic and network factors influencing the wrong-way riding decision making. METHODS: The data used in this study consist of two different sources: (a) Route trips data downloaded from the CyclePhilly Website contained trips detailed up to segment level, collected from May 2014 to April 2016 (12,202 trips by 300 unique users); and (b) Open Street Maps (OSM). Using ArcGIS, we calculate detour routes for each wrong way segment. We then built a mixed logistic regression model to identify the trip and riders' characteristics affecting wrong-way riding behavior. Next, we explore the characteristics of road facilities associated with wrong-way riding behavior. RESULTS AND DISCUSSION: Only 2.7% of travel distance is wrong-way, yet 42% of trips include a wrong-way segment. Commute trips have a higher chance of wrong-way riding. The longer the trips also include more wrong-way riding. Segments with higher detour ratios (ratio of distance with a detour to the wrong-way distance) are found to be associated with more wrong-way behavior. Compared to roads with no bike lane, roads with sharrow markings and buffered bike lane discourage wrong way riding. PRACTICAL APPLICATIONS: This study proposes new methods that can be adapted to use naturalistic and probe data and analyze city-wide aberrant riders' behavior. These help planners and engineers choose between various types of bike infrastructure. Wrong-way riding is one application that can be investigated, but probe bicycle datasets provide unprecedented resolution and volume of data that will allow for more sophisticated safety and planning analyses.

Walking under the influence of the alcohol: A case study of pedestrian crashes in Tennessee.

Alcohol adversely affects human behavior and judgment, and it is one of the contributing factors in traffic crashes. Although a large body of research has investigated driving behavior under the influence of the alcohol, to the best of our knowledge, no study has investigated the crash characteristics of the pedestrians under the influences of the alcohol. Tennessee Police Crash Data from 2011 to 2016 was used in this study to identify crashes between motor vehicles and pedestrians who were walking under the influence of alcohol (WUI). Results indicate that the number of fatally injured pedestrians for WUI cases has increased since 2011. Alcohol was present in 7% of the pedestrian crashes. Tested pedestrians averaged BAC levels of 0.17 g/dL. As pedestrian injury severity increased, the share of the WUI crashes increased. WUI contributed in 22% of the fatally injured pedestrian and only in 2% of the pedestrian crashes with no-injury. Comparisons indicate that the WUI crashes had their characteristics, which distinguished them from non-WUI crashes. Analysis indicates that 83% of the WUI crashes occurred in the nights; moreover, 54%, 69%, and 85% of WUI crashes respectively occurred on weekends, mid-block section of the road, and areas with no traffic control device. Results of a binary logit regression indicate that pedestrian's age, males, posted speed limit, and nighttime crashes had a positive association with the WUI crashes. On the other hand, urban context, intersection crashes, driver maneuvers (i.e., parking-related, turning, and straight), and daylight had a negative association with the WUI crashes. Findings are discussed in line with road safety countermeasures.

Environmental Justice Aspects of Exposure to PM2.5 Emissions from Electric Vehicle Use in China.

Plug-in electric vehicles (EVs) in China aim to improve sustainability and reduce environmental health impacts of transport emissions. Urban use of EVs rather than conventional vehicles shifts transportation's air pollutant emissions from urban areas (tailpipes) to predominantly rural areas (power plants), changing the geographic distribution of health impacts. We model PM2.5-related health impacts attributable to urban EV use for 34 major cities. Our investigation focuses on environmental justice (EJ) by comparing pollutant inhalation versus income among impacted counties. We find that EVs could increase EJ challenge in China: most (~77%, range: 41-96%) emission inhalation attributable to urban EVs use is distributed to predominately rural communities whose incomes are on average lower than the cities where EVs are used. Results vary dramatically across cities depending on urban income and geography. Discriminant analysis reveals that counties with low income and high inhalation of urban EV emissions have comparatively higher agricultural employment rates, higher mortality rates, more children in the population, and lower education levels. We find that low-emission electricity sources such as renewable energy can help mitigate EJ issues raised here. Findings here are not unique to EVs, but instead are relevant for nearly all electricity-consuming technologies in urban areas.

Risky riding: Naturalistic methods comparing safety behavior from conventional bicycle riders and electric bike riders.

As electric bicycles (e-bikes) have emerged as a new transportation mode, their role in transportation systems and their impact on users have become important issues for policy makers and engineers. Little safety-related research has been conducted in North America or Europe because of their relatively small numbers. This work describes the results of a naturalistic GPS-based safety study between regular bicycle (i.e., standard bicycle) and e-bike riders in the context of a unique bikesharing system that allows comparisons between instrumented bike technologies. We focus on rider safety behavior under four situations: (1) riding in the correct direction on directional roadway segments, (2) speed on on-road and shared use paths, (3) stopping behavior at stop-controlled intersections, and (4) stopping behavior at signalized intersections. We find that, with few exceptions, riders of e-bike behave very similarly to riders of bicycles. Violation rates were very high for both vehicles. Riders of regular bicycles and e-bikes both ride wrong-way on 45% and 44% of segments, respectively. We find that average on-road speeds of e-bike riders (13.3kph) were higher than regular bicyclists (10.4kph) but shared use path (greenway) speeds of e-bike riders (11.0kph) were lower than regular bicyclists (12.6kph); both significantly different at >95% confidence. At stop control intersections, both bicycle and e-bike riders violate the stop signs at the similar rate with bicycles violating stop signs at a slightly higher rate at low speed thresholds (∼80% violations at 6kph, 40% violations at 11kph). Bicycles and e-bikes violate traffic signals at similar rates (70% violation rate). These findings suggest that, among the same population of users, e-bike riders exhibit nearly identical safety behavior as regular bike riders and should be regulated in similar ways. Users of both technologies have very high violation rates of traffic control devices and interventions should occur to improve compliance.

Greenway siting and design: relationships with physical activity behaviors and user characteristics.

BACKGROUND: Greenways (GW) can be sited and designed in a variety of ways. However, the extent to which siting and design relate to GW user's demographic characteristics and physical activity (PA) is unknown. The purpose of this study was to compare 2 GWs that differed in terms of their siting and design, with respect to the aforementioned variables. METHODS: A trail intercept survey measuring PA, modes of GW access, and demographics was administered on 2 GWs (GWlinear vs. GWloop), which varied in siting and design characteristics. RESULTS: GWlinear (n = 216), compared with GWloop users (n = 400), accumulated significantly greater volumes of PA from both accessing and using the GW (P = .012). GW linear users were more likely to be younger, male, and never married; they were also more likely to engage in transportational PA (10.6 vs. 0.3%, P ≤ .001) and access the GW via active transit modes (37.0% vs. 4.2%, P ≤ .001). CONCLUSIONS: GW siting and design appears to be related to user characteristics, and the types and volumes of PA performed. These results should be considered by GW planners and designers to best serve the PA needs of the community.

Health hazards of China's lead-acid battery industry: a review of its market drivers, production processes, and health impacts.

Despite China's leaded gasoline phase out in 2000, the continued high rates of lead poisoning found in children's blood lead levels reflect the need for identifying and controlling other sources of lead pollution. From 2001 to 2007, 24% of children in China studied (N = 94,778) were lead poisoned with levels exceeding 100 µg/L. These levels stand well above the global average of 16%. These trends reveal that China still faces significant public health challenges, with millions of children currently at risk of lead poisoning. The unprecedented growth of China's lead-acid battery industry from the electric bike, automotive, and photovoltaic industries may explain these persistently high levels, as China remains the world's leading producer, refiner, and consumer of both lead and lead-acid batteries.This review assesses the role of China's rising lead-acid battery industry on lead pollution and exposure. It starts with a synthesis of biological mechanisms of lead exposure followed by an analysis of the key technologies driving the rapid growth of this industry. It then details the four main stages of lead battery production, explaining how each stage results in significant lead loss and pollution. A province-level accounting of each of these industrial operations is also included. Next, reviews of the literature describe how this industry may have contributed to mass lead poisonings throughout China. Finally, the paper closes with a discussion of new policies that address the lead-acid battery industry and identifies policy frameworks to mitigate exposure.This paper is the first to integrate the market factors, production processes, and health impacts of China's growing lead-acid battery industry to illustrate its vast public health consequences. The implications of this review are two-fold: it validates calls for a nationwide assessment of lead exposure pathways and levels in China as well as for a more comprehensive investigation into the health impacts of the lead-acid battery industry. The continuous growth of this industry signals the urgent need for effective regulatory action to protect the health and lives of China's future generations.

Electric vehicles in China: emissions and health impacts.

E-bikes in China are the single largest adoption of alternative fuel vehicles in history, with more than 100 million e-bikes purchased in the past decade and vehicle ownership about 2× larger for e-bikes as for conventional cars; e-car sales, too, are rapidly growing. We compare emissions (CO(2), PM(2.5), NO(X), HC) and environmental health impacts (primary PM(2.5)) from the use of conventional vehicles (CVs) and electric vehicles (EVs) in 34 major cities in China. CO(2) emissions (g km(-1)) vary and are an order of magnitude greater for e-cars (135-274) and CVs (150-180) than for e-bikes (14-27). PM(2.5) emission factors generally are lower for CVs (gasoline or diesel) than comparable EVs. However, intake fraction is often greater for CVs than for EVs because combustion emissions are generally closer to population centers for CVs (tailpipe emissions) than for EVs (power plant emissions). For most cities, the net result is that primary PM(2.5) environmental health impacts per passenger-km are greater for e-cars than for gasoline cars (3.6× on average), lower than for diesel cars (2.5× on average), and equal to diesel buses. In contrast, e-bikes yield lower environmental health impacts per passenger-km than the three CVs investigated: gasoline cars (2×), diesel cars (10×), and diesel buses (5×). Our findings highlight the importance of considering exposures, and especially the proximity of emissions to people, when evaluating environmental health impacts for EVs.