Influence of ambient temperature on the CO2 emitted of light-duty vehicle

Because of global warming, people have paid more attention to greenhouse gas emitted by vehicles. To quantify the impact of temperature on vehicle CO2 emissions, this study was conducted using the world light vehicle test cycle on two light-duty E10 gasoline vehicles at ambient temperatures of -10, 0, 23, and 40℃, and found that CO2 emission factors of Vehicle 1 in the low-speed phase were 22.07% and 20.22% higher than those of Vehicle 2 at cold start and hot start under -10℃. The reason was vehicle 1 had a larger displacement and more friction pairs than vehicle 2. There was the highest CO2 emission at the low-speed phase due to low average speed, frequent acceleration, and deceleration. The CO2 temperature factor and the ambient temperature had a strong linear correlation (R2 = 0.99). According to CO2 temperature factors and their relationships, CO2 emission factors of other ambient temperatures could be calculated when the CO2 emission factor of 23℃ was obtained, and the method also could be used to obtain the CO2 temperature factors of different vehicles. To separate the effect of load setting and temperature variation on CO2 emission quantitatively, a method was proposed. And results showed that the load setting was dominant for the CO2 emission variation. Compared with 23℃, the CO2 emission for vehicle 1 caused by load setting variation were 62.83 and 47.42 g/km, respectively at -10 and 0℃, while those for vehicle 2 were 45.01 and 35.63 g/km, respectively.

Visual hazardous models: A hybrid approach to investigate road hazardous events

Road users (drivers, passengers, pedestrians, and Animals) are exposed to hazardous events during their commute. With 23 % of global fatalities among pedestrians, their safety continues to be a principal interest for policymakers worldwide. Owing to limited budgets available, there is a growing emphasis on data-driven stochastic models to decide on policies. However, statistical models have limitations due to crash data having redundant features, inherent heterogeneity, and unobserved characteristics. The random parameter model framework addresses the unobserved heterogeneity, but redundant features and inherent heterogeneity among the data's characteristics still compute the biased estimates. This is further complicated if the data has spatiotemporal attributes. To address this, we developed two visual hazardous (VH) models: (i) addresses the unobserved heterogeneity in the data, and (ii) addresses the dimensionality, inherent heterogeneity among the characteristics and unobserved heterogeneity in the collected data after spatiotemporal pattern identification. The feature selection model reduces the dimensionality, whereas latent class clustering classifies the data into maximum heterogeneity between classes. This integration reduces bias in the estimates. As a use-case, pedestrian crosswalk crashes for a decade (2009-2018) in the Indian state of Tamil Nadu extracted from the Road Accident Database Management System (RADMS) was used to understand model performance. This data comprises the crash location, road, vehicle, driver, pedestrian, and environment details. Results show that visual hazardous model 2 allows for generating crash scenarios with five homogeneous sub-classes and the magnitude with marginal effects of contributing factors impacting it. For example, pedestrians during their crosswalks are likely to sustain 82% more chance of fatal/grievous injuries on expressways (posted speed limit: 100 km per hour) in annual hazardous zone locations. Working pedestrian age group (25-64 years), an older pedestrian (>64 years), the pedestrian position on a pedestrian crossing and not in the centre of the road, pedestrian action: walking along the edge of the road, multiple lanes, two lanes, paved shoulder, straight and flat road, motorcycle, bus, truck, medium-duty vehicle, illegal driver (<=17 years), going ahead/ overtaking, high speed, expressways, and rural region were statistically significant (positively) contributing to the fatal/grievous injury pedestrian crashes during their crosswalk. This technique serves as a structure for engineers, researchers, and policymakers to formulate effective countermeasures that enhance road safety.

Lateral safety evaluation of autonomous trucks in overtaking maneuvers and adverse weather: Exploratory comparison of different lateral control modes

As the detrimental impact of the commonly recommended centered driving mode for autonomous trucks on road longevity is gaining attention, more lateral control modes are being proposed to enhance road sustainability. However, there is currently a lack of research on the lateral safety analysis of autonomous trucks with different lateral control modes, especially in complex driving scenarios (such as overtaking) and adverse weather conditions. Therefore, this study developed a safety assessment framework to comparatively analyze the risk probability differences in lateral accidents during overtaking maneuvers by autonomous trucks with different lateral control modes under adverse weather conditions. Based on aerodynamics and vehicle dynamics simulations to capture the multifactorial influences on truck lateral deviation, the results are used for model validation and training. In the reliability approach, Support Vector Machine Regression (SVR) is introduced to establish the SVR response surface model with optimal predictive performance, and combined with Monte Carlo simulations for safety assessment, quantifying safety indices. The results indicate that trucks being overtaken during overtaking maneuvers are more prone to lateral accidents under crosswind influences. The overall impact of lateral control modes on the lateral safety trends is minor. Compared to other lateral control modes, following the centered zero-drift mode is generally safer. However, in conditions of low wind speeds (below 20 km/h) or on highly slippery road surfaces (road friction coefficient below 0.1), autonomous trucks following a uniform distribution mode can better maintain a low-risk level. This study provides crucial insights for future considerations integrating road longevity and truck safety in a collaborative manner, and the proposed methodology has broad applications.

From sky to road: Incorporating the satellite imagery into analysis of freight truck-related crash factors

Freight truck-related crashes in urban contexts have caused significant economic losses and casualties, making it increasingly essential to understand the spatial patterns of such crashes. Limitations regarding data availability have greatly undermined the generalizability and applicability of certain prior research findings. This study explores the potential of emerging geospatial data to delve deeply into the determinants of these incidents with a more generalizable research design. By synergizing high-resolution satellite imagery with refined GIS map data and geospatial tabular data, a rich tapestry of the road environment and freight truck operations emerges. To navigate the challenges of zero-inflated issues of the crash datasets, the Tweedie Gradient Boosting model is adopted. Results reveal a pronounced spatial heterogeneity between highway and urban non-highway road networks in crash determinants. Factors such as freight truck activity, intricate road network patterns, and vehicular densities rise to prominence, albeit with varying degrees of influence across highways and urban non-highway terrains. Results emphasize the need for context-specific interventions for policymakers, encompassing optimized urban planning, infrastructural overhauls, and refined traffic management protocols. This endeavor may not only elevate the academic discourse around freight truck-related crashes but also champion a data-driven approach towards safer road ecosystems for all.

A method for generating case-specific vehicle models from a single-view vehicle image for accurate pedestrian injury reconstructions

Developing vehicle finite element (FE) models that match real accident-involved vehicles is challenging. This is related to the intricate variety of geometric features and components. The current study proposes a novel method to efficiently and accurately generate case-specific buck models for car-to-pedestrian simulations. To achieve this, we implemented the vehicle side-view images to detect the horizontal position and roundness of two wheels to rectify distortions and deviations and then extracted the mid-section profiles for comparative calculations against baseline vehicle models to obtain the transformation matrices. Based on the generic buck model which consists of six key components and corresponding matrices, the case-specific buck model was generated semi-automatically based on the transformation metrics. Utilizing this image-based method, a total of 12 vehicle models representing four vehicle categories including family car (FCR), Roadster (RDS), small Sport Utility Vehicle (SUV), and large SUV were generated for car-to-pedestrian collision FE simulations in this study. The pedestrian head trajectories, total contact forces, head injury criterion (HIC), and brain injury criterion (BrIC) were analyzed comparatively. We found that, even within the same vehicle category and initial conditions, the variation in wrap around distance (WAD) spans 84-165 mm, in HIC ranges from 98 to 336, and in BrIC fluctuates between 1.25 and 1.46. These findings highlight the significant influence of vehicle frontal shape and underscore the necessity of using case-specific vehicle models in crash simulations. The proposed method provides a new approach for further vehicle structure optimization aiming at reducing pedestrian head injury and increasing traffic safety.

Wildlife-vehicle collisions: The disproportionate risk of injury faced by motorcyclists

We applied a generalized linear mixed-effects model to determine the factors leading to injuries from wildlife-vehicle collisions (WVCs). We used the Police database representing WVCs which took place on the Czech road network between 2009 and 2022. The majority of WVCs in Czechia are with roe deer, followed by wild boar, i.e., both relatively small ungulates. Less than 2 % of these encounters ends with an injury to the motor vehicle occupants. We found that the probability of sustaining injury was systematically higher for motorcyclists than for car occupants. The odds of sustaining an injury during WVC were roughly 1600 times higher for motorcyclists than for car occupants. When applying an evading manoeuvre, the odds of sustaining an injury were approximately 68 times higher for car occupants while only 2.3 times higher for motorcyclists compared to a direct hit to an animal. The lack of helmets (for motorcyclists) and missing seat belts (for car occupants) were additional factors which made the outcomes worse for WVCs. While the acceptance of a direct hit (preceded by braking) seems to be a reasonable strategy for car drivers, WVC awareness (including maintaining a lower speed during critical times and places) should be raised among motorcyclists as both manoeuvres are almost comparably dangerous for them.

Modeling occupant injury severities for electric-vehicle-involved crashes using a vehicle-accident bi-layered correlative framework with matched-pair sampling

This study seeks to investigate occupant injury severities for electric-vehicle-involved crashes and inspect if electric vehicles lead to more serious injuries than fuel-powered vehicles, which have commonly been neglected in past studies. A Bayesian random slope model is proposed aiming to capture interactions between occupant injury severity levels and electric vehicle variable. The random slope model is developed under a vehicle-accident bi-layered correlative framework, which can account for the interactive effects of vehicles in the same accident. Based on the crash report sampling system (CRSS) 2020 and 2021 database, the extracted observations are formed into inherently matched pairs under certain matching variables including restraint system use, air bag deployed, ejection and rollover. The introduced data structure is able to ensure the standard error of the modeling parameters are not affected by these matching variables. Meanwhile, a comprehensive modeling performance comparison is conducted between the Bayesian random slope model and the Bayesian random intercept model, the Bayesian basic model. According to the empirical results, the bi-layered Bayesian random slope model presents a strong ability in model fitting and analysis, even when the sample size is small and the error structure is complex. Most importantly, occupants in electric vehicles are more likely to suffer serious injuries, especially incapacitating and fatal injuries, in the event of an accident compared to fuel-powered vehicles, which disproving the long-held misconception that green and safety are related.

Fatigue at the wheel: A non-visual approach to truck driver fatigue detection by multi-feature fusion

BACKGROUND

Monitoring of long-haul truck driver fatigue state has attracted considerable interest. Conventional fatigue driving detection methods based on the physiological and visual features are scarcely applicable, due to the intrusiveness, reliability, and cost-effectiveness concerns.

METHODS

We elaborately developed a fatigue driving detection method by fusion of non-visual features derived from the customized wristbands, vehicle-mounted equipment, and trip logs. To capture the spatiotemporal information within the sequential data, the bidirectional long short-term memory network with attention mechanism was proposed to determine whether the truck driver was fatigued within a fine-grained episode of one minute. The model was validated using a natural driving dataset with nine truck drivers on real-world roads in Guiyang, China during June and July 2021.

RESULTS

Our approach yielded 99.21 %, 84.44 %, 82.01 %, 99.63 %, and 83.21 % in accuracy, precision, recall, specificity, and F1-score, respectively. Compared with the mainstream visual-based methods, our approach outperformed particularly in terms of precision and recall. Photoplethysmogram stood out as the most important feature for truck driver fatigue state detection. Vehicle load, driving forward angle, cumulative driving time, midnight, and recent working hours were found to be positively associated with the probability of fatigue driving, while the galvanic skin response, vehicle acceleration, current time, and recent rest hours had a negative relationship. Specifically, truck drivers were more likely to fatigue when driving at 20-40 km/h, braking abruptly at 5-10 m/s2, with vehicle loads over 70 tons, and driving more than 100 min consecutively.

CONCLUSIONS

Our study is among the first to harness the natural driving dataset to delve into the real-life fatigue pattern of long-haul truck drivers without disruptions on routine driving tasks. The proposed method holds pragmatic prospects by providing a privacy-preserving, robust, real-time, and non-intrusive technical pathway for truck driver fatigue monitoring.

Improper Restraint Use in Fatal Pediatric Motor Vehicle Collisions

PURPOSE

Motor vehicle collisions (MVC) are the second leading cause of death in children and adolescents, but appropriate restraint use remains inadequate. Our previous work shows that about half of pediatric MVC victims presenting to our trauma center were unrestrained. This study evaluates restraint use among children and adolescents who did not survive after MVC. We hypothesize that restraint use is even lower in this population than in pediatric MVC patients who reached our trauma center.

METHODS

We reviewed the local Medical Examiner's public records for fatal MVCs involving decedents <19 years old from 2010 to 2021. When restraint use was not documented, local Fire Rescue public records were cross-referenced. Patients were excluded if restraint use was still unknown. Age, demographics, and restraint use were compared using standard statistical methods.

RESULTS

Of 199 reviewed cases, 92 met selection criteria. Improper restraint use was documented in 72 patients (78%). Most decedents were White (72% versus 28% Black) and male (74%), with a median age of 17 years [15-18]. Improper restraint use was more common among Black (92% vs 73% White, p = 0.040) and male occupants (85% vs 58% female, p = 0.006). Improper restraint use was lower in the Hispanic population (73%) compared to non-Hispanic individuals (89%), but this difference was not statistically significant (p = 0.090).

CONCLUSION

Most pediatric patients who die from MVCs in our county are improperly restrained. While male and Black patients are especially high-risk, the overall dismal rates of restraint use in our pediatric population present an opportunity to improve injury prevention measures.

TYPE OF STUDY

Retrospective Comparative Study.

LEVEL OF EVIDENCE

Level III.

Evaluating infection risks in buses based on passengers' dynamic temporal and typical spatial scenarios: A case study of COVID-19

Conventional buses, as an indispensable part of the urban public transport system, impose cross-infection risks on passengers. To assess differential risks due to dynamic staying durations and locations, this study considered four spatial distributions (i = 1-4) and six temporal scenarios (j = 1-6) of buses. Based on field measurements and a risk assessment approach combining both short-range and room-scale effects, risks are evaluated properly. The results showed that temporal asynchrony between infected and susceptible individuals significantly affects disease transmission rates. The Control Case assumes that infected and susceptible individuals enter and leave synchronously. However, ignoring temporal asynchrony scenarios, i.e., the Control Case, resulted in overestimation (+30.7 % to +99.6 %) or underestimation (-15.2 % to -69.9 %) of the actual risk. Moreover, the relative difference ratios of room-scale risks between the Control Case and five temporal scenarios are impacted by ventilation. Short-range risk exists only if infected and susceptible individuals have temporal overlap on the bus. Considering temporal and spatial asynchrony, a more realistic total reproduction number (R) can be obtained. Subsequently, the total R was assessed under five temporal scenarios. On average, for the Control Case, the total R was estimated to be +27.3 % higher than j = 1, -9.3 % lower than j = 2, +12.8 % higher than j = 3, +33.0 % lower than j = 4, and + 77.6 % higher than j = 5. This implies the need for a combination of active prevention and real-time risk monitoring to enable rigid travel demand and control the spread of the epidemic.

Long-term trends of black carbon and particle number concentrations and their vehicle emission factors in Stockholm

Black carbon (BC) and particle number (PN) concentrations are usually high in cities due to traffic emissions. European mitigation policies, including Euro emission standards, have been implemented to curb these emissions. We analyzed BC and PN (particle diameter Dp > 4 nm) concentrations in Stockholm spanning the years 2013-2019 (BC) and 2009-2019 (PN) measured at street canyon and rooftop sites to assess the effectiveness of the implemented policies. Combining these data with inverse dispersion modeling, we estimated BC and PN emission factors (EFBC and EFPN) for the mixed fleet, reflecting real-world driving conditions. The pollutants showed decreasing trends at both sites, but PN concentrations remained high at the canyon site considering the World Health Organization (WHO) recommendations. BC concentrations declined more rapidly than PN concentrations, showing a -9.4% and -4.9% annual decrease at the canyon and -7.2% and -0.5% at the rooftop site in the years 2013-2019. The EFBC and EFPN trends showed that the mitigation strategies for reducing particulate emissions for on-road vehicles were successful over the study period. However, the introduction of biofuels in the vehicle fleet -ethanol and later rapeseed methyl ester (RME)- increased the concentrations of particles with Dp < 10 nm before the adoption of particulate filters in the exhausts. Stricter Euro emission regulations, especially with diesel particulate filters (DPF) in Euro 5, 6, and VI vehicles, led to 66% decrease in EFBC and 55% in EFPN. Real-world EFBC surpassed HBEFA (Handbook Emission Factors for Road Transport) database values by 2.4-4.8 times; however, direct comparisons between real-world and HBEFA EFPN are difficult due to differences in lower cut-off sizes and measurement techniques. Our results underscore the necessity for revising the HBEFA database, updating laboratory testing methods and portable emission measuring systems (PEMS) measurements to account for liquid condensate contributions to PN measurements.

Enhancing vehicular emissions monitoring: A GA-GRU-based soft sensors approach for HDDVs

Vehicle emissions have a serious impact on urban air quality and public health, so environmental authorities around the world have introduced increasingly stringent emission regulations to reduce vehicle exhaust emissions. Nowadays, PEMS (Portable Emission Measurement System) is the most widely used method to measure on-road NOx (Nitrogen Oxides) and PN (Particle Number) emissions from HDDVs (Heavy-Duty Diesel Vehicles). However, the use of PEMS requires a lot of workforce and resources, making it both costly and time-consuming. This study proposes a neural network based on a combination of GA (Genetic Algorithm) and GRU (Gated Recurrent Unit), which uses CC (Pearson Correlation Coefficient) to determine and simplify OBD (On-board Diagnosis) data. The GA-GRU model is trained under three real driving conditions of HDDVs, divided by vehicle driving parameters, and then embedded as a soft sensor in the OBD system to monitor real-time emissions of NOx and PN within the OBD system. This research addresses the existing research gap in the development of soft sensors specifically designed for NOx and PN emission monitoring. In this study, it is demonstrated that the described soft sensor has excellent R2 values and outperforms other conventional models. This research highlights the ability of the proposed soft sensor to eliminate outliers accurately and promptly while consistently tracking predictions throughout the vehicle's lifetime. This method is a groundbreaking update to the vehicle's OBD system, permanently adding monitoring data to the vehicle's OBD, thus fundamentally improving the vehicle's self-monitoring capabilities.

Towards zero pollution vehicles by advanced fuels and exhaust aftertreatment technologies

Vehicular emissions deteriorate air quality in urban areas notably. The aim of this study was to conduct an in-depth characterization of gaseous and particle emissions, and their potential to form secondary aerosol emissions, of the cars meeting the most recent emission Euro 6d standards, and to investigate the impact of fuel as well as engine and aftertreatment technologies on pollutants at warm and cold ambient temperatures. Studied vehicles were a diesel car with a diesel particulate filter (DPF), two gasoline cars (with and without a gasoline particulate filter (GPF)), and a car using compressed natural gas (CNG). The impact of fuel aromatic content was examined for the diesel car and the gasoline car without the GPF. The results showed that the utilization of exhaust particulate filter was important both in diesel and gasoline cars. The gasoline car without the GPF emitted relatively high concentrations of particles compared to the other technologies but the implementation of the GPF decreased particle emissions, and the potential to form secondary aerosols in atmospheric processes. The diesel car equipped with the DPF emitted low particle number concentrations except during the DPF regeneration events. Aromatic-free gasoline and diesel fuel efficiently reduced exhaust particles. Since the renewal of vehicle fleet is a relatively slow process, changing the fuel composition can be seen as a faster way to affect traffic emissions.

Fatal hyperthermia among children in school buses: Analysis of 47 cases in China

The school bus is an important mode of transportation for school-age children, and safety-related issues are always the focus of public concern. Fatal hyperthermia occurring in school buses is an uncommon type of school bus-related injury. An internet search using Chinese internet search engines based on various combinations of keywords including 'vehicles', 'school bus', 'children or babies', 'hyperthermia or heat stroke' and 'death' was performed. Forty-seven cases of fatal hyperthermia in children which occurred in school buses were retrieved in the study. High ambient temperature, younger age and poor management were identified as risk factors. There is a lack of consensus regarding the legal nature and liability for fatal hyperthermia occurring in school buses. Pre-employment education should be focused on awareness of the dangers of leaving children alone in a school bus. Most importantly, the relevant legislation and regulations on school buses should be implemented. An internal alarm-raising system is recommended to avoid this kind of tragedy.

Understanding the relationship between road users and the roadway infrastructure in Ghana

Ghana exemplifies the contribution of road crashes to mortality and morbidity in Africa, partly due to a growing population and increasing car ownership, where fatalities have increased by 12 to 15 % annually since 2008 (National Road Safety Authority (NRSA), 2017). The study described in this paper focused on understanding driver behavior at unsignalized junctions in the Ashanti Region of Ghana. Understanding driver behavior at unsignalized junctions is particularly important since failure to stop or yield can seriously affect vulnerable road users. The study's objectives were to develop relationships between driver behavior and junction characteristics. Understanding the characteristics that lead to determining what factors influence a driver's behavioral response at rural junctions provides information for policy makers to determine the best strategies to address these behaviors. The study evaluated stopping behavior at rural junctions. Driver behavior was extracted from video views of ten junctions in the Ashanti Region of Ghana. A total of 3,420 vehicles were observed across all ten junctions during data collection before any analysis was conducted. The type of stop was selected as a surrogate measure of safety. Logistic regression was used to model stopping behavior at the selected junctions. The analysis showed drivers were more likely to stop when going straight (versus a left turn) and left turning vehicles were more likely to stop than right turning vehicles. Additionally, single unit trucks and tro-tros were more likely to stop than other vehicle types. Drivers were also much more likely to stop when channelization, intersection lighting, or speed humps were present. Drivers at junctions with 4-approaches were also more likely to stop than those with 3 approaches. The results from this research contribute valuable information about what factors contribute to positive safety behaviors at rural junctions. This provides guidance for safety professionals to select solutions and can be a valuable tool to predict the economical effectiveness of solutions to addressing junction safety in low- and middle-income countries (LMIC) such as Ghana. The results can also provide insight and recommendations to Ghanaian road safety agencies and launch sustainable efforts to raise community awareness toward decreasing road crash fatalities in Ghana.

Risk of motor vehicle collision associated with cannabis and alcohol use among patients presenting for emergency care

BACKGROUND

The objective of this study was to examine the relationship between cannabis and alcohol use and occurrence of motor vehicle collision (MVC) among patients in the emergency department (ED).

METHODS

This was a cross-sectional study of visits to EDs in Denver, CO, Portland, OR, and Sacramento, CA by drivers who were involved in MVCs and presented with injuries (cases) and non-injured drivers (controls) who presented for medical care. We obtained blood samples and measured delta-9-THC and its metabolites. Alcohol levels were determined by breathalyzer or samples taken in the course of clinical care. Participants completed a research-assistant-administered interview consisting of questions about drug and alcohol use prior to their visit, context of use, and past-year drug and alcohol use. Multiple logistic regression was used to estimate the association between MVC and cannabis/alcohol use, adjusted for demographic characteristics. We then stratified participants based on levels of cannabis use and calculated the odds of MVC across these levels, first using self-report and then using blood levels for delta-9-THC in separate models. We conducted a case-crossover analysis, using 7-day look-back data to allow each participant to serve as their own control. Sensitivity analyses examined the influence of usual use patterns and driving in a closed (car, truck, van) versus open (motorcycle, motorbike, all-terrain vehicle) vehicle.

RESULTS

Cannabis alone was not associated with higher odds of MVC, while acute alcohol use alone, and combined use of alcohol and cannabis were both independently associated with higher odds of MVC. Stratifying by level of self-reported or measured cannabis use, higher levels were not associated with higher odds for MVC, with or without co-use of alcohol; in fact, high self-reported acute cannabis use was associated with lower odds of MVC (odds ratio [OR] 0.18, 95% confidence interval [CI] 0.05-0.65). In the case-crossover analysis, alcohol use alone or in combination with cannabis was associated with higher odds of MVC, while cannabis use alone was again associated with decreased odds of MVC.

CONCLUSIONS

Alcohol use alone or in conjunction with cannabis was consistently associated with higer odds for MVC. However, the relationship between measured levels of cannabis and MVC was not as clear. Emphasis on actual driving behaviors and clinical signs of intoxication to determine driving under the influence has the strongest rationale.

Exploratory Analysis of Farm Vehicle and Farm Labor Transportation-Related Crashes

OBJECTIVE

The purpose of this study was to investigate characteristics associated with farm vehicle and farm labor transportation-related roadway crashes in the state of Florida. Agricultural roadway crashes related to these vehicles have not been explored in Florida.

METHODS

The data for this study comes from the Florida Department of Highway Safety and Motor Vehicles (FLHSMV) for the years 2013-2021. The data is recorded by the Florida Highway Patrol when a vehicular crash has occurred. The data is then coded to allow the selection of farm and agricultural-related vehicles. Specifically, this study focuses on farm labor transport vehicles and farm vehicles (e.g. farm tractors).

RESULTS

There were 744 farm vehicle- and 209 farm labor transportation-related crashes during the 9-year study period. The farm labor transport vehicle crashes involved a total of 420 vehicles and 1,329 individuals, and the farm vehicle crashes involved 1,458 vehicles and 1,652 individuals. Injury risk for non-farm labor transportation vehicle occupants was significantly higher than injury risk for farm labor transportation vehicle occupants.

CONCLUSIONS

The average fatality rate for farm vehicle-involved (2.1 fatalities per 100 crashes) and farm labor transportation vehicle-involved crashes (2.9 fatalities per 100 crashes) were much higher than the overall FL roadway crash fatality rate (0.45 fatalities per 100 crashes).

Are electric vehicles really the optimal option for the transportation sector in China to approach pollution reduction and carbon neutrality goals?

Profound worldwide fleet electrification is thought to be the primary route for achieving the target of carbon neutrality. However, when and how electrification can help mitigate environmental impacts and carbon emissions in the transport sector remains unclear. Herein, the overall life-cycle environmental impacts and carbon saving range of two typical A-class vehicles in China, including electric vehicle (EV) and internal combustion engine vehicle (ICEV), were quantified by the life cycle assessment model for endpoint damage with localization parameters. The results showed that the EV outperformed the ICEV for the total environment impact after a travel distance of 39,153 km and for carbon emissions after 32,292 km. The ICEV was more carbon-friendly only when the driving distance was less than 3229 km/a. Considering a full lifespan travel distance of 150,000 km, the whole life-cycle average environmental impacts of EV and ICEV were calculated as 8.6 and 17.5 mPt/km, respectively, but the EV had 2.3 times higher impacts than the ICEV in the production phase. In addition, the EV unit carbon emission was 140 g/km, 46.8% lower than that of the ICEV. Finally, three potential reduction scenarios were considered: cleaner power mix, energy efficiency improvement and composite scenario. These scenarios contributed 19.1%, 13.0% and 32.1% reductions, respectively. However, achieving carbon peak and neutrality goals in China remains a great challenge unless fossil fuels are replaced by renewable energy. The research can provide scientific reference for the method and practice of emission reduction link identification, eco-driving choice and emission reduction path formulation.

Assessing the collective safety of automated vehicle groups: A duration modeling approach of accumulated distances between crashes

Ideally, the evaluation of automated vehicles would involve the careful tracking of individual vehicles and recording of observed crash events. Unfortunately, due to the low frequency of crash events, such data would require many years to acquire, and potentially place the motorized public at risk if defective automated technologies were present. To acquire information on the safety effectiveness of automated vehicles more quickly, this paper uses the collective crash histories of a group of automated vehicles, and applies a duration modeling approach to the accumulated distances between crashes. To demonstrate the applicability of this approach as a method compare automated and conventional vehicles (human drivers), an empirical assessment was undertaken using two comparable sources of data. For conventional vehicles, police and non-police-reportable crashes were collected from the Second Strategic Highway Research Program's naturalistic driving study, and for automated vehicles, data from the California Department of Motor Vehicles Autonomous Vehicle Tester program were used (105 crashes from 59 permit holders driving ∼2.8 million miles were used for the analysis). The results of the empirical study showed that automated driving was safer at the 95% confidence level, with a higher number of miles between crashes, relative to their conventional vehicle counterparts. The findings indicate that the number of miles between crashes would be increased by roughly 27% when switching from conventional vehicles to automated vehicles. Despite limited data which mandated a group-vehicle approach, this study can be considered a reasonable initial approximation of automated vehicle safety.

Optimization of Forward Collision Warning Algorithm Considering Truck Driver Response Behavior Characteristics

Forward collision warning (FCW) systems have been widely used in trucks to alert drivers of potential road situations so they can reduce the risk of crashes. Research on FCW use shows, however, that there are differences in drivers' responses to FCW alerts under different scenarios. Existing FCW algorithms do not take differences in driver response behavior into account, with the consequence that the algorithms' minimum safe distance assessments that trigger the warnings are not always appropriate for every driver or situation. To reduce false alarms, this study analyzed truck driver behavior in response to FCW warnings, and k-means clustering was adopted to classify driver response behavior into three categories: Response Before Warning (RBW), Response After Warning (RAW), and No Response (NR). Results showed that RBW clusters tend to occur at long following distances (>19 m), and drivers applied braking before the warning. In RAW clusters, deceleration after warning is significantly more forceful than before warning. NR clusters occur at short distances, and deceleration fluctuates only slightly. To optimize the FCW algorithm, the warning distance was divided into reaction distance and braking distance. The linear support vector machine was used to fit the driver reaction distance. The long short-term memory method was used to predict braking distance based on each of the three response scenarios: R2 was 0.896 for RAW scenarios, 0.927 for RBW scenarios, and 0.980 for NR scenarios. Verification results show that the optimized truck FCW algorithm improved safety by 1 % to 5.1 %; accuracy reached 97.92 %, and the false alarm rate was 1.73 %.

"Braking bad": The influence of haptic feedback and tram driver experience on emergency braking performance

Trams are experiencing a resurgence with worldwide network expansion driven by the need for sustainable and efficient cities. Trams often operate in shared or mixed-traffic environments, which raise safety concerns, particularly in hazardous situations. This paper adopts an international, mixed-methods approach, conducted through two interconnected studies in Melbourne (Australia) and Birmingham (UK). The first study involved qualitative interviews, while the second was an experimental study involving a virtual reality (VR) simulator and haptic master controller (i.e., speed lever). In tram operations, master controllers play a critical role in ensuring a smooth ride, which directly influences passenger safety and comfort. The objective was to understand how a master control system, enhanced with additional haptic feedback, could improve tram driver braking performance and perceptions in safety-critical scenarios. Interview results indicate that the use of the emergency brake is considered the final or ultimate choice by drivers, and their driving experience is a moderating factor in limiting its application. Combined with the experimental results, this paper highlights how implementing haptic feedback within a master controller can reduce the performance disparity between novice and experienced tram drivers.

Upgrading Passenger Vehicle Emission Standard Helps to Reduce China's Air Pollution Risk from Uncertainty in Electrification

Upgrading to the CHINA 7 standard is crucial for managing air pollution from passenger vehicles in China. Meanwhile, China aims to achieve carbon neutrality by 2060, which necessitates large-scale replacement of gasoline vehicles with electric vehicles in the future. Consequently, the public might view upgrading gasoline vehicles to the CHINA 7 standard as redundant. However, the emission reduction benefits of upgrading standards in the context of uncertain electrification ambitions have not received adequate attention. Here, we show that upgrading standards will compensate for the absence of emissions reductions due to hindered electrification efforts. In the best scenario, China's CO2 emissions can be reduced to 0.047 Gt and NOx to 8.2 × 103 t in 2050. In nonextreme electrification scenarios with CHINA 7 standard, the emission intensity reduction will remain the main driver for emission reductions, outweighing the electrification contribution. In extreme electrification scenarios, upgrading standards will tackle the increased emissions from plug-in hybrid electric vehicles. Our fleet-level results advocate for early standards upgrades to enhance resilience against air pollution risks arising from uncertainties in electrification. Our evidence from China, with one of the most stringent emission standards, can provide a reference point for the world on the upgrading passenger vehicle emission standard issue.

Surveillance methods and interventions implemented in American Indian and Alaska Native communities to increase child restraint device and seat belt use in motor vehicles: a systematic review

BACKGROUND

American Indian/Alaska Native (AI/AN) children are disproportionately affected by injuries and deaths related to motor vehicle crashes. We aimed to synthesise published evidence on surveillance methods and interventions implemented in AI/AN communities and analyse characteristics that make them successful in increasing child restraint devices and seat belt use.

METHODS

Studies were collected from the PubMed, Scopus, and TRID databases and the CDC Tribal Road Safety website, Community Guide, and Indian Health Service registers. Included studies collected primary data on AI/AN children (0-17) and reported morbidity/mortality outcomes related to child restraint devices or seat belt use. Studies with poor methodological quality, published before 2002, whose data were collected outside of the USA, or were non-English, were excluded. Checklists from the Joanna Briggs Institute were used to assess the risk of bias. In the synthesis of results, studies were grouped by whether a surveillance method or intervention was employed.

RESULTS

The final review included 9 studies covering 72 381 participants. Studies conducted surveillance methods, interventions involving law enforcement only and multipronged interventions. Multipronged approaches were most effective by using the distribution of child restraint devices combined with at least some of the following components: educational programmes, media campaigns, enactment/enforcement of child passenger restraint laws, incentive programmes and surveillance.

DISCUSSION

Although this review was limited by the number and quality of included studies, available resources suggest that we need multipronged, culturally tailored and sustainable interventions fostered by mutually beneficial and trusting partnerships. Continued investment in AI/AN road safety initiatives is necessary.

Risk factors for collisions attributed to microsleep-related behaviors while driving in professional truck drivers

Sleep-disordered breathing (SDB) is prevalent among professional drivers. Although SDB is a known risk factor for truck collisions attributed to microsleep-related behaviors at the wheel (TC-MRBs), the usefulness of overnight pulse oximetry for predicting TC-MRBs is debatable. This retrospective study assessed the association between overnight pulse oximetry parameters, the Epworth Sleepiness Scale (ESS), and TC-MRBs, confirmed by dashcam footage. This study included 108 matched professional truck drivers (TC-MRBs: N = 54; non-TC-MRBs: N = 54), with a mean age and body mass index of 41.9 ± 11.3 years and 23.0 ± 3.7 kg/m2, respectively. Night-time drivers, 4% oxygen desaturation index (ODI), and nadir oxygen saturation (SpO2) were associated with TC-MRBs (odds ratio [95% confidence interval]: 25.63 [5.88-111.77], p < 0.0001; 2.74 [1.02-7.33], p = 0.045; and 3.87 [1.04-14.39], p = 0.04, respectively). The area under the curve of 4% ODI and nadir SpO2 for TC-MRBs were 0.50 and 0.57, respectively. In conclusion, night-time driving, 4% ODI, and nadir SpO2 were significantly associated with TC-MRBs in professional truck drivers. However, the sensitivity of overnight pulse oximetry parameters to predict TC-MRBs in a real-world application was poor. Therefore, combining subjective and objective assessments such as dashcam video footage may be needed to achieve high accuracy for predicting TC-MRBs among professional truck drivers.

Ocular injuries associated with motor vehicle accidents: long term effects on quality of life

PURPOSE

To describe the prevalence and outcome of motor vehicle accidents-associated ocular injuries.

METHODS

A survey of patients who presented to the emergency room at a level 1 trauma center with motor vehicle accidents-associated ocular injuries. A patient questionnaire and review of clinical notes were conducted for all patients.

RESULTS

Of 274 motor vehicle accident victims with ocular injuries who presented to the emergency room, 40 (15%) responded to the survey. Over half of them were driving a vehicle, and most reported wearing a seat belt or a helmet. Most ocular injuries were mild. The most common injuries were bone fractures, subconjunctival hemorrhage, eyelid involvement and corneal injury. Most respondents had no change in vision and perceived their ocular involvement as a minor part of their injury. Most respondents returned to work and to driving within a year.

CONCLUSION

Our study sheds light on the details and extent of ocular involvement and the visual ability to perform daily activities following motor vehicle accidents.

Strategies to enhance the level of service and safety of rural roads: A case study

Faced with the contradiction between the increasing traffic volume and the aging road infrastructures in the rural area, this paper aims to propose feasible strategies to enhance the level of service and safety, by a case study of the rural area in the north Jintan district. In order to figure out current issues related to rural roads, a carefully designed investigation has been conducted, and the results of the two-week investigation include roads' basic information, traffic signs and protective facilities, surrounding landscape, and etc. Based on the field driving tests, specific problems including signs category, signs installation and facility maintenance have been fully analyzed. Meanwhile, the problem of roadnet connectivity has also been pointed out through the theory of complex network, and results show that the average node clustering coefficient and shortest path length perform worse than the demonstration plot of other rural districts. For the sake of rural traffic safety and management efficiency, both quantified and qualified strategies have been put forward. The quantified strategies include the regular inspection indicators, the safety sight distance at T-type crossings, as well as the risk severity of sections and the crossings. The qualified strategies involve the management of trucks and roadworks, the setting of signalized intersections, and the timely updates of traffic signs and facilities. Finally, an intelligent management system framework has been established for rural road traffic, with highly interconnected modules of data acquisition, risk identification and information publishing.

Summary of influences that led to introduction of frontal airbags on US passenger vehicles

OBJECTIVE

The history of airbags for occupant protection in frontal crashes is reviewed from the perspective of a former Senior Executive at NHTSA from the early 1970's to the late 1980's. This paper summarizes the factors that led to regulatory delays as well as those that led to voluntary adoption of airbags by several manufacturers.

METHODS

The regulatory history and interactions with airbag suppliers and vehicle manufactures is recounted citing key steps in the evolution of frontal airbags.

RESULTS

When the Advanced Notice of Proposed Rulemaking for Standard 208, "Occupant Protection" was issued on July 2, 1969 the Safety Agency anticipated that the industry response would provide automatic frontal crash protection from airbags that deployed to protect all front seat occupants from injury in severe frontal crashes. It was not until, September 1, 1998 that airbags were required in all cars and light trucks. The interim 29 years involved a series of stops and starts during which most of the original airbag suppliers lost interest and abandoned the airbag market. The issues associated with airbags and their place in Standard 208 were directly influenced by interventions from the President, the Congress, the Supreme Court, Secretaries of Transportation, NHTSA Administrators, the Presidents of US Auto Companies and Senior Executives of Insurance Companies.

CONCLUSION

In 1966, there was support from the US auto industry for a single source of safety regulations that apply to new vehicles sold in the US. This is evidenced by the unanimous passage by the House of Representatives and Senate of the Law that created the Federal auto safety regulatory framework. The Law also required seatbelts in new cars and prohibited States from making separate safety rules. However, the large safety benefits offered by seatbelts were negated because they were rarely used. Consequently, finding ways of providing high levels of protection without requiring action by occupants became a goal of the new Safety Agency. The airbag offered the possibility of achieving that goal.From the initial airbag notice of proposed rulemaking on July 2, 1969, Safety Agency required 2 years to resolve objections before a final Standard 208 could be issued (on July 8,1971). The subsequent industry opposition to the Standard 208 employed Presidential influence and Court suits to cause a 5½ year delay until the Coleman Decision on December 6, 1976. Changes in regulatory approach of the Ford, Carter, and Reagan Administrations and associated Court suits caused another 7½ year delay until the Dole Decision on July 17, 1984. It required another 7 ½, until December 18, 1991, for market forces to reduce industry opposition to airbags and permit Congress to pass a law that mandated them. Another 6½ years of lead time was required before all cars and light trucks were required to meet the airbag standard. During the mid and late 1980's vehicle safety ratings, seatbelt use laws, and Vince and Larry PSA's had all acted to increase safety awareness and safety belt use. Consequently, added public demand for vehicle safety features slowly developed. Changes in economic incentives encouraged a number of vehicle lines to install driver airbags as standard equipment and the feature was being widely advertised by Chrysler. This combination of events made it possible for Congress to pass and President Bush 41 to sign legislation requiring airbags in cars and light trucks by September 1, 1998 - more than 29 years after the initial rulemaking notice in 1969.

Are front crash prevention systems less effective at preventing rear-end crashes where trucks and motorcycles are struck?

OBJECTIVE

Automatic emergency braking (AEB) and forward collision warning (FCW) are effective at preventing rear-end crashes, but they may perform better in some rear-end crash scenarios than others. The goal of this study was to estimate the effects of front crash prevention systems equipped to passenger vehicles in crashes where another passenger vehicle, a medium/heavy truck, or a motorcycle is struck and compare effectiveness by struck vehicle type.

METHODS

More than 160,000 two-vehicle rear-end crashes were identified where a passenger vehicle with or without FCW and AEB was the striking vehicle and another passenger vehicle, medium/heavy truck, or motorcycle was the struck vehicle. Poisson regression was used to estimate the effect of front crash prevention by struck vehicle type on rear-end crash rates per registered vehicle year, accounting for the state and year of the crash and the make, model year, class, and engine type of the striking vehicle.

RESULTS

Front crash prevention was associated with a 53% reduction in rear-end crash rates when striking another passenger vehicle, which was significantly larger than the reductions of 38% when striking a medium/heavy truck and 41% when striking a motorcycle. Reductions in rear-end injury crash rates when striking a passenger vehicle also were larger than when striking a medium/heavy truck and when striking a motorcycle.

DISCUSSION

If all passenger vehicles were equipped with FCW and AEB that were as effective in crashes striking a truck or motorcycle as they are in crashes with another passenger vehicle, over 5,500 additional crashes with medium/heavy trucks and 500 with motorcycles could potentially be prevented annually in the United States above what would be expected from current front crash prevention systems. Extending front crash prevention testing in consumer information programs to include motorcycle and truck targets could encourage auto manufacturers to improve performance in these crash scenarios.

Electrification of Transit Buses in the United States Reduces Greenhouse Gas Emissions

The transportation sector is the largest emitter of greenhouse gas emissions (GHGs) in the United States. Increased use of public transit and electrification of public transit could help reduce these emissions. The electrification of public transit systems could also reduce air pollutant emissions in densely populated areas, where air pollution disproportionally burdens vulnerable communities with high health impacts and associated social costs. We analyze the life cycle emissions of transit buses powered by electricity, diesel, gasoline, and compressed natural gas and model GHGs and air pollutants mitigated for a transition to a fully electric U.S. public transit bus fleet using transit agency-level data. The electrification of the U.S. bus fleet would reduce several conventional air pollutants and has the potential to reduce transit bus GHGs by 33-65% within the next 14 years depending on how quickly the transition is made and how quickly the electricity grid decarbonizes. A levelized cost of driving analysis shows that with falling capital costs and an increase in annual passenger-kilometers of battery electric buses, the technology could reach levelized cost parity with diesel buses when electric bus capital costs fall below about $670 000 per bus.

Investigating safety and cost-effectiveness of cable median barriers in Louisiana

OBJECTIVES

Cable median barriers (CMBs) are installed on freeway medians to prevent cross-median crashes and reduce the severity of median-related crashes. Though CMBs are effective in preventing cross-median crashes, they are also known to increase the number of property damage-only (PDO) crashes. The higher frequency of PDO crashes could result in increased CMB maintenance and repair expenses. The aim of this study is to evaluate the safety impact and economic justification of CMBs in Louisiana.

METHODS

Initially, a flowchart was developed using Louisiana crash data to identify targeted crashes, such as median-related and cross-median crashes. This was followed by a 3-year observational before-and-after crash analysis with an emphasis on head-on collisions and crashes involving large trucks. Using a 4-step improved prediction method, crash modification factors were then developed to quantitatively assess the impact of CMBs on crash outcomes, accounting for and adjusting to changes in the annual average daily traffic (AADT) and relevant crash frequencies before and after CMB implementation. Finally, an exhaustive benefit-cost analysis was conducted to determine the cost-effectiveness of CMBs.

RESULTS

The results revealed that CMBs significantly reduced cross-median crashes of all severities. However, an increase in PDO crashes was observed in both total and median-related crashes. Large truck cross-median crashes and head-on collisions also decreased significantly after CMB implementation. Testing Level 4 (TL-4) CMBs were found to be more effective in preventing vehicles from crossing the median and in reducing crashes of higher severity levels. The benefit-cost ratios, calculated using economic crash unit costs for both total and targeted crashes, were greater than 1. Notably, the estimated benefit-cost ratios were considerably higher, demonstrating that CMBs are cost-effective countermeasures for enhancing traffic safety.

CONCLUSION

This study contributes to the understanding of CMB performance from both traffic safety and economic perspectives. The findings may assist transportation agencies in making decisions regarding the management of CMB systems. Based on the comprehensive analysis of CMBs on Louisiana freeways, this project has revealed that CMBs are an effective and economically justified crash countermeasure. Thus, further implementation of CMBs is recommended until better alternatives are available.

School Bus Rebate Program and Student Educational Performance Test Scores

Importance

Students who ride older school buses are often exposed to high levels of exhaust during their commutes, which may adversely affect health and school attendance. As a result, the US Environmental Protection Agency (EPA) has awarded millions of dollars to school districts to replace older, highly polluting school buses with newer, cleaner buses.

Objective

To leverage the EPA's randomized allocation of funding under the 2012-2016 School Bus Rebate Programs to estimate the association between replacing old, highly polluting buses and changes in district-average standardized test scores.

Design, Setting, and Participants

This study examined changes in reading and language arts (RLA) and math test scores among US school district applicants to the EPA's 2012-2016 national School Bus Rebate Programs 1 year before and after each lottery by selection status. Data analysis was conducted from January 15 to July 30, 2023.

Exposure

Selection to receive EPA funding to replace older school buses with newer, cleaner alternatives.

Main Outcomes and Measures

School district changes in RLA and math test scores among students in grades 3 through 8 before and after the EPA funding lotteries by selection status were measured using an intention-to-treat approach.

Results

This study included 1941 school district applicants to the 2012-2106 EPA School Bus Rebate Programs. These districts had a mean (SD) of 14.6 (33.7) schools per district, 8755 (23 776) students per district, and 41.3% (20.2%) of students with free lunch eligibility. Among the applicants, 209 districts (11%) were selected for the clean bus funding. District-average student test scores did not improve among selected districts overall. In secondary analyses, however, districts replacing the oldest, highest polluting buses (ie, pre-1990) experienced significantly greater improvements in district-average test scores in the year after the lottery for RLA and math (SD improvement in test scores, 0.062 [95% CI, 0.050-0.074] and 0.025 [95% CI, 0.011-0.039], respectively) compared with districts without replacements.

Conclusions and Relevance

In this study, the EPA funding was not associated with student test scores overall, but in secondary analyses, the replacement of the oldest school buses was associated with improved educational performance. These findings support prioritizing clean bus replacement of the oldest buses as an actionable way for improving students' educational performance.

A systematic review and meta-analysis of data linkage between motor vehicle crash and hospital-based datasets

Motor vehicle crash data linkage has emerged as a vital tool to better understand the injury outcomes and the factors contributing to crashes. This systematic review and meta-analysis aims to explore the existing knowledge on data linkage between motor vehicle crashes and hospital-based datasets, summarize and highlight the findings of previous studies, and identify gaps in research. A comprehensive and systematic search of the literature yielded 54 studies for a qualitative analysis, and 35 of which were also considered for a quantitative meta-analysis. Findings highlight a range of viable methodologies for linking datasets, including manual, deterministic, probabilistic, and integrative methods. Designing a linkage method that integrates different algorithms and techniques is more likely to result in higher match rate and fewer errors. Examining the results of the meta-analysis reveals that a wide range of linkage rates were reported. There are several factors beyond the approach that affect the linkage rate including the size and coverage of both datasets and the linkage variables. Gender, age, crash type, and roadway geometry at the crash site were likely to be associated with a record's presence in a linked dataset. Linkage rate alone is not the only important metric and when linkage rate is used as a metric in research, both police and hospital rates should be reported. This study also highlights the importance of examining and accounting for population and bias introduced by linking two datasets.

Health benefits of vehicle electrification through air pollution in Shanghai, China

Vehicle electrification has been recognized for its potential to reduce emissions of air pollutants and greenhouse gases in China. Several studies have estimated how national-level policies of electric vehicle (EV) adoption might bring very large environmental and public health benefits from improved air quality to China. However, large-scale adoption is very costly, some regions derive more benefits from large-scale EV adoption than others, and the benefits of replacing internal combustion engines in specific cities are less known. Therefore, it is important for policymakers to design incentives based on regional characteristics - especially for megacities like Shanghai - which typically suffer from worse air quality and where a larger population is exposed to emissions from vehicles. Over the past five years, Shanghai has offered substantial personal subsidies for passenger EVs to accelerate its electrification efforts. Still, it remains uncertain whether EV benefits justify the strength of incentives. The purpose of our study is to evaluate the health and climate benefits of replacing light-duty gasoline vehicles (ICEVs) with battery EVs in the city of Shanghai. We assess health impacts due to ICEV emissions of primary fine particulate matter, NOx, and volatile organic compounds, and to powerplant emissions of NOx and SO2 due to EV charging. We incorporate climate benefits from reduced greenhouse gas emissions based on existing research. We find that the benefit of replacing the average ICEV with an EV in Shanghai is US$6400 (2400-14,700), with health impacts of EVs about 20 times lower than the average ICEV. Larger benefits ensue if older ICEVs are replaced, but replacing newer China ICEVs also achieves positive health benefits. As Shanghai plans to stop providing personal subsidies for EV purchases in 2024, our results show that EVs achieve public health and climate benefits and can help inform policymaking strategies in Shanghai and other megacities.

Assessment of waste workers occupational risk to microbial agents and cytotoxic effects of mixed contaminants present in the air of waste truck cabin and ventilation filters

Workers in the waste-processing industry are potentially exposed to high concentrations of biological contaminants, leading to respiratory and digestive problems and skin irritations. However, few data on the exposure of waste collection truck (WCT) drivers are available. The goal was to document the microbial risk of the waste collection truck (WCT) workers while in the vehicle cab. Long-period sampling using the truck air filters (CAF) and short time ambient air sampling in the cab were used. The potential release of microbial particles from CAFs was also investigated since it could contribute to the microbial load of the cabin air. A combination of analytical methods also helped assess the complex mixture of the biological agents. Aspergillus sections Fumigati and Flavi, E. coli, Enterobacter spp. and Legionella spp. were detected in the CAF of trucks collecting three types of waste. The highest levels of bacteria and fungi were found in the CAF from organic WCT. The highest endotoxin concentrations in CAF were 300 EU/cm2. Most of the CAF showed cytotoxic effects on both lung cells and hepatocytes. Only one mycotoxin was detected in a CAF. The maximal concentrations in the ambient WCT air varied according to the type of waste collected. The highest proportion (84%) of the air samples without cytotoxic effects on the lungs cells was for the recyclable material WCTs. The results revealed the potential microbial risk to workers from a complex mixture of bio-contaminants in the cabs of vehicles collecting all types of waste. The sustained cytotoxic effect indicates the potential adverse health-related impact of mixed contaminants (biological and non-biological) for the workers. Overall, this study highlights the benefits of using complementary sampling strategy and combined analytical methods for a the assessment of the microbial risk in work environments and the need to implement protective measures for the workers.Implications: Exposure to microbial agents is a well-known occupational hazard in the waste management sector. No previous study had evaluated the cytotoxicity of ambient air and ventilation filters to document worker exposure to a combination of contaminants during waste collection. This research confirms the usefulness of ventilation filters for long-term characterization of exposure to infectious agents, azole-resistant fungi, coliform bacteria and mycotoxin. Overall, this study highlights the importance of using several sampling and analysis methods for a comprehensive assessment of microbial risk in work environments, as well as the need to implement appropriate protective measures for collection workers.

Impact of alcohol driving-while-impaired license suspension duration on future alcohol-related license events and motor vehicle crash involvement in North Carolina, 2007 to 2016

BACKGROUND/PURPOSE

License suspensions are a strategy to address alcohol-impaired driving behavior and recidivism following an alcohol driving while impaired (alcohol-DWI) conviction. Little is known about the specific impacts of conviction-related suspensions on safety outcomes and given recent fluctuations in alcohol-impaired driving behavior, crashes, and suspension trends, updated and focused assessments of this intervention are necessary. This study aimed to 1) examine the association between type of recent alcohol-DWI suspension and having a secondary alcohol-related license outcome and/or future crash event in North Carolina (NC) between 2007 and 2016; and 2) assess potential modification of these associations by race/ethnicity.

METHODS

We used linked NC licensing data, NC crash data, and county-level contextual data from a variety of data sources. We compared individuals ages 21 to 64 who sustained initial (1-year) versus repeat (4-year) suspensions for alcohol-related license and crash involvement outcomes. We estimated unadjusted and adjusted hazard ratios (aHRs) using Cox proportional hazards models and produced Kaplan-Meier (KM) survival curves using a three-year follow-up period. After observing statistically significant modification by race/ethnicity, we calculated stratified aHRs for each outcome (Black and White subgroups only, as other subgroups had low numbers of outcomes).

RESULTS

122,002 individuals sustained at least one alcohol-DWI conviction suspension (117,244 initial, 4,758 repeat). Adjusted KM survival curves indicated that within three years of the index suspension, the predicted risks of having a license outcome and crash outcome were about 8 % and 15 %, respectively, among individuals with an initial suspension and 5 % and 10 %, respectively, among individuals with a repeat suspension. After adjusting for potential confounding, we found that compared to those with an initial suspension, those with repeat suspensions had a lower incidence of future license (aHR: 0.49; 95 % CI: 0.42, 0.57) and crash outcomes (aHR: 0.67; 95 % CI: 0.60, 0.75). Among Black individuals, license outcome incidence was 162 % lower among repeat versus initial index suspension groups (aHR: 0.38; 95 % CI: 0.26, 0.55), while for White individuals, the incidence was 87 % lower (aHR: 0.54; 95 % CI: 0.45, 0.64). Similarly, crash incidence for repeat versus initial suspensions among Black individuals was 56 % lower (aHR: 0.64; 95 % CI: 0.50, 0.83), while only 39 % lower among White individuals (aHR: 0.72; 95 % CI: 0.63, 0.81).

CONCLUSIONS

Decreased incidence of both license and crash outcomes were observed among repeat versus initial index suspensions. The magnitude of these differences varied by race/ethnicity, with larger decreases in incidence among Black compared to White individuals. Future research should examine the underlying mechanisms leading to alcohol-impaired driving behavior, convictions, recidivism, and crashes from a holistic social-ecological perspective so that interventions are designed to both improve road safety and maximize other critical public health outcomes, such as access to essential needs and services (e.g., healthcare and employment).

Migraine headaches are associated with motor vehicle crashes and driving habits among older drivers: Prospective cohort study

BACKGROUND

Migraine headache is common in older adults, often causing symptoms that may affect driving safety. This study examined associations of migraine with motor vehicle crashes (MVCs) and driving habits in older drivers and assessed modification of associations by medication use.

METHODS

In a multi-site, prospective cohort study of active drivers aged 65-79 (53% female), we assessed prevalent migraine (i.e., ever had migraine, reported at enrollment), incident migraine (diagnosis first reported at a follow-up visit), and medications typically used for migraine prophylaxis and treatment. During 2-year follow-up, we recorded self-reported MVCs and measured driving habits using in-vehicle GPS devices. Associations of prevalent migraine with driving outcomes were estimated in multivariable mixed models. Using a matched design, associations of incident migraine with MVCs in the subsequent year were estimated with conditional logistic regression. Interactions between migraine and medications were tested in all models.

RESULTS

Of 2589 drivers, 324 (12.5%) reported prevalent migraine and 34 (1.3%) incident migraine. Interactions between migraine and medications were not statistically significant in any models. Prevalent migraine was not associated with MVCs in the subsequent 2 years (adjusted OR [aOR] = 0.98; 95% CI: 0.72, 1.35), whereas incident migraine significantly increased the odds of having an MVC within 1 year (aOR = 3.27; 1.21, 8.82). Prevalent migraine was associated with small reductions in driving days and trips per month and increases in hard braking events in adjusted models.

CONCLUSION

Our results suggest substantially increased likelihood of MVCs in the year after newly diagnosed migraine, indicating a potential need for driving safety interventions in these patients. We found little evidence for MVC risk or substantial changes in driving habits associated with prevalent migraine. Future research should examine timing, frequency, and severity of migraine diagnosis and symptoms, and use of medications specifically prescribed for migraine, in relation to driving outcomes.

Evaluating real-world emissions from in-use buses and taxis using on-road remote sensing

Assessing real-world emissions from buses and taxis is vital to comprehend their impact on urban air quality. Such vehicles differ significantly from the majority of the fleet owing to their higher mileage rates. However, few studies have focused on specifically assessing the emissions from this segment of the vehicle fleet. In this context, this study evaluated the real-world emissions of nitrogen oxides (NOx) from in-use buses and taxis in Dublin, Ireland, using crossroad remote sensing technology. The remote sensing system was deployed at strategic locations throughout the city to capture on-road emissions from passing vehicles. The collected data included vehicle related information such as emission standard, make, and mileage, and pollutants including NOx. Based on this data, analysis was aimed to understand the impact of Euro emission standard, ambient temperature, mileage, and make of the vehicle on NOx emissions. The results reveal that the average emissions from taxis reduce by 37% from Euro 5 to Euro 6b, and average emissions from Euro 6 buses are 87% lower compared to Euro 5. The trends in emission factors (EFs) of buses and taxis were similar during summer and winter sampling. Moreover, on comparing the emissions from the top five taxi manufacturers, different trends in the emission factors were observed. Finally, the study found that the effect of vehicle mileage on emissions was unclear for both buses and taxis. In any case, these findings provide valuable insights into the real-world emission performance of the existing fleet of buses and taxis in Dublin and highlight the need for targeted measures to reduce emissions from these vehicles. The results can assist policymakers and urban planners in formulating evidence-based strategies to improve air quality in Dublin and other cities facing similar challenges.

Barriers to adoption of electric vehicles in Texas

Sustainable mobility options such as electric vehicles (EVs) have the potential to improve the quality of life for Americans as well as those in other countries, as they can enhance the quality of the air we breathe, while reducing greenhouse gas emissions, fossil fuel consumption, and the adverse impacts of global warming. Despite their many benefits, however, the demand for EVs remains low. Therefore, this study aims to identify the barriers that affect the widespread EV adoption in the United States. Seventeen barriers were identified from the literature, and a questionnaire survey was designed and distributed to potential consumers of EVs. The survey yielded 733 responses, and various statistical tests like cluster analysis and chi-squared tests were performed. The results revealed that the high purchase price of the vehicle, high battery replacement cost, and the lack of public infrastructures for charging them were the primary concerns. The results also revealed that middle-aged men with high education and income are more enthusiastic about adopting EVs. The results presented in this study indicate a range of developments that different stakeholders could implement. To surmount the economic barriers to EV adoption, policymakers should strengthen incentives countrywide, and automakers should introduce more affordable EVs to the market. To overcome the challenges associated with charging, it is necessary to make investments in rapid charging infrastructure along the primary toll routes.

An interpretable clustering approach to safety climate analysis: Examining driver group distinctions

The transportation industry, particularly the trucking sector, is prone to workplace accidents and fatalities. Accidents involving large trucks accounted for a considerable percentage of overall traffic fatalities. Recognizing the crucial role of safety climate in accident prevention, researchers have sought to understand its factors and measure its impact within organizations. While existing data-driven safety climate studies have made remarkable progress, clustering employees based on their safety climate perception is innovative and has not been extensively utilized in research. Identifying clusters of drivers based on their safety climate perception allows the organization to profile its workforce and devise more impactful interventions. The lack of utilizing the clustering approach could be due to difficulties interpreting or explaining the factors influencing employees' cluster membership. Moreover, existing safety-related studies did not compare multiple clustering algorithms, resulting in potential bias. To address these problems, this study introduces an interpretable clustering approach for safety climate analysis. This study compares five algorithms for clustering truck drivers based on their safety climate perceptions. It also proposes a novel method for quantitatively evaluating partial dependence plots (QPDP). Then, to better interpret the clustering results, this study introduces different interpretable machine learning measures (Shapley additive explanations, permutation feature importance, and QPDP). The Python code used in this study is available at https://github.com/NUS-DBE/truck-driver-safety-climate. This study explains the clusters based on the importance of different safety climate factors. Drawing on data collected from more than 7,000 American truck drivers, this study significantly contributes to the scientific literature. It highlights the critical role of supervisory care promotion in distinguishing various driver groups. Moreover, it showcases the advantages of employing machine learning techniques, such as cluster analysis, to enrich the scientific knowledge in this field. Future studies could involve experimental methods to assess strategies for enhancing supervisory care promotion, as well as integrating deep learning clustering techniques with safety climate evaluation.

Examining factors influencing the severity of motorized two-wheeler crashes in Delhi

Failure to meet road safety targets has necessitated urgent actions from stakeholders worldwide, especially in developing countries like India. Road safety of motorized two-wheelers (MTWs), one of India's most preferred travel modes for urban commutes, is in danger and witnessing threatening figures of fatalities and injuries. Most of the studies in the domain of MTW safety were conducted in developed countries, with very limited research in countries having a significant proportion of MTWs. The present work investigates police-reported crash data to identify the contributory factors of motorized two-wheeler crash severity. Data from MTW crash-prone areas were selected from Delhi, which is leading in road traffic fatalities among the million-plus urban cities in India. A binary logistic regression model was developed using the data for 2016-2018 period. The model results show that the odds of fatal motorized two-wheeler crashes increase when the following circumstances apply: crash occurs on underpasses; involves bus, truck, heavy motor vehicle (lorry, crane) as the striking vehicle; when hit-and-run type of crash occurs and when older age-group (> = 55) riders are involved. Finally, based on the findings, countermeasures were suggested to facilitate policymakers and traffic enforcement agencies, in improving the road safety situation of MTW users.

Modeling of single-vehicle and multi-vehicle truck-involved crashes injury severities: A comparative and temporal analysis in a developing country

Truck-involved crashes persist as a significant concern, yielding noteworthy human casualties and causing economic ramifications, particularly in developing countries. This paper aims to undertake a comprehensive analysis of the associated factors influencing injury severity in truck-involved crashes, with a particular emphasis on discerning variations between single-vehicle and multi-vehicle incidents, as well as accounting for heterogeneity and temporal stability. The data analysis involves a meticulous examination of crash data spanning the entirety of Thailand from 2017 to 2020. Employing three distinct levels of injury severities, namely PDO injury, moderate injury, and severe injury, the study employs a series of mixed logit models that account for unobserved heterogeneity in both means and variances. Results revealed significant instability in injury risk determinants over time among both single and multi-vehicle events. Aligning predictive assessments further spotlighted fluctuations in projected burdens across models and years - collectively underscoring the imperative to integrate temporal considerations into modeling and prevention. Several crash-type distinctions and priorities emerged. For single-truck events, key risks included roadway alignments and geometry, speeding, fatigue, and lighting conditions. However multi-truck collisions concentrated around exposure factors like highway traits, sightline limitations, and vulnerable road users. Ultimately, the technique permitted responsive countermeasure targeting and recalibration opportunities keyed to each crash form's evolving landscapes. While it is indeed noteworthy that several variables have exhibited instability in their effects, it is equally important to acknowledge the existence of certain variables that maintain a relative degree of temporal stability. This underscores their pivotal role in shaping the foundation of enduring strategies aimed at enhancing traffic safety in the long run. The multifaceted investigation constitutes an invaluable reference for diverse transportation stakeholders seeking to curb rising truck fatalities through evidence-based improvements in policy, engineering, usage protocols, and technologies. It provides a blueprint for nimble safety planning within complex modernizing road systems.

Development of an intervention program to reduce whole-body vibration exposure based on occupational and individual determinants among dumper operators

Objectives. Studies related to a systematic approach for intervention design to reduce whole-body vibration (WBV) exposure are scarce. This study presents a systematic approach to identifying, selecting and prioritizing safety interventions to fulfill that research gap. Methods. A total of 130 vibration readings for dumper operators were taken from two surface iron ore mines to identify significant determinants of WBV exposure. Initially, age, weight, seat design, awkward posture, machine's age, load tonnage, dumper speed and haul road condition were hypothesized as determinants. Data were collected through standardized questionnaires and field-based observation. A multivariate statistical approach was applied for the practical use of the intervention program. Results. As some of the hypothesized factors were correlated, exploratory factor analysis (EFA) followed by multiple linear regression (MLR) was used to investigate their association with WBV exposure. As per EFA results, hypothesized factors were clubbed under individual, ergonomics and occupational factors. Occupational and ergonomics factors were found to be significantly associated with WBV exposure through MLR and used to form safety interventions to reduce WBV exposure. Conclusions. Our methodological approach is original in the occupational health research area and can be helpful to tailor the safety interventions for the unit level with minimum effort.

Fuel consumption rate and emissions variability in waste collection services routes: case study of Cascais Ambiente

In the design waste collection systems, it is common practice to use a constant specific fuel consumption (e.g. litres per amount of waste collected or distance travelled). This is also the approach used in many cases for fleet management, namely, for decision-making on more fuel-efficient equipment acquisition. However, the specific fuel consumption is not constant and there are spatial and temporal variations. Accounting for this variability becomes relevant if a more refined cost or environmental optimization is intended. The present research effort evaluates the energy intensity of the waste collection service in the Cascais municipality, reporting the differences and the magnitude of the variability for the mixed waste collection service. Statistically significant differences are found between the circuits, the trucks, months of the year and days of the week. It is discussed that extrapolating average fuel consumption rates for decision-making in new waste collection systems may be prone to substantial error.

Unraveling the differences in distracted driving injury severities in passenger car, sport utility vehicle, pickup truck, and minivan crashes

Distracted driving poses a significant risk on the roadway users, with the level of distraction and crash outcomes varying depending on the type of vehicle. Drivers of passenger cars, sport utility vehicles (SUVs), pickup trucks, minivans experience distinct levels of distraction, leading to potential crashes. This study investigates into the severity of driver injuries resulting from distracted driving in these vehicle categories, shedding light on the variations in single-vehicle crashes. Focusing on single-vehicle crashes in Florida during 2019, involving passenger cars, SUVs, pickup trucks, and minivans caused by distracted driving, the study examines various distractions such as, electronic communication devices (cell phones), electronic devices (navigation systems, music players), internal and external disturbances, texting, and inattentive driving. To analyze the severity of injuries resulting from distracted driving in passenger cars, SUVs, pickup trucks, and minivans, the study employs random parameter multinomial logit models with heterogeneity in means and variances. The model estimates highlight thirty-five significant factors influencing the severity of driver injuries resulting from distracted driving. Notably, the impact of these factors varies significantly depending on the vehicle type (i.e., passenger cars, SUVs, pickup trucks, and minivans). While many explanatory variables are specific to each vehicle type, only one factor (restraint belt usage) is common across all vehicle types, with varying magnitudes in injury outcomes. The likelihood ratio tests indicate that injury severity must be analyzed and modeled separately for passenger cars, SUVs, pickup trucks, and minivans. Vehicle characteristics play a crucial role in driver distraction and crash outcomes. Analyzing a year of crash data, categorized by four vehicle types, has provided valuable insights into distracted driving patterns in passenger cars, SUVs, pickup trucks, and minivans, influencing potential prevention strategies. To combat against distracted driving effectively, priority should be given to driver education and training, roadway design, vehicle technology, enforcement, and automobile insurance. The automobile industry, especially for passenger cars, SUVs, pickup trucks, and minivans, should consider implementing advanced in-vehicle technologies tailored to the specific characteristics of each vehicle type (e.g., advanced driver assistance systems (ADAS)) to proactively prevent driver distraction. These proactive measures will contribute significantly to enhancing road safety and reducing the risks associated with distracted driving.

Is Adolescent Obesity Associated With a Higher Risk for Pelvic Fractures in Motor Vehicle Collisions?

INTRODUCTION

The impact of obesity on the incidence of blunt pelvic fractures in adults is unclear, and adolescents may have an increased risk of fracture due to variable bone mineral density and leptin levels. Increased subcutaneous adipose tissue may provide protection, though the association between obesity and pelvic fractures in adolescents has not been studied. This study hypothesized that obese adolescents (OAs) presenting after motor vehicle collision (MVC) have a higher rate of pelvic fractures, and OAs with such fractures have a higher associated risk of complications and mortality compared to non-OAs.

METHODS

The 2017-2019 Trauma Quality Improvement Program database was queried for adolescents (12-16 y old) presenting after MVC. The primary outcome was a pelvic fracture. Adolescents with a body mass index ≥30 (OA) were compared to adolescents with a body mass index <30 (non-OA). Subgroup analyses for high-risk and low-risk MVCs were performed. Multivariable logistic regression analyses were also performed adjusting for age and sex.

RESULTS

From 22,610 MVCs, 3325 (14.7%) included OAs. The observed rate of pelvic fracture was similar between all OA and non-OA MVCs (10.2% versus 9.4%, P = 0.16), as well as subanalyses of minor or high-risk MVC (both P > 0.05). OAs presenting with a pelvic fracture after high-risk MVC had a similar risk of complications, pelvic surgery, and mortality compared to non-OAs (all P > 0.05). However, OAs with a pelvic fracture after minor MVC had a higher associated risk of complications (OR 2.27, CI 1.10-4.69, P = 0.03), but a similar risk of requiring pelvic surgery, and mortality (all P > 0.05).

CONCLUSIONS

This national analysis found a similar observed incidence of pelvic fractures for OAs versus non-OAs involved in an MVC, including subanalyses of minor and high-risk MVC. Furthermore, there was no difference in the associated risk of morbidity and mortality except for OAs involved in a minor MVC had a higher risk of complication.

A case of a motor vehicle collision suspected as associated with development of angiosarcoma

Trauma has been suspected as a factor leading to development of angiosarcoma, a malignant soft-tissue tumor. We conducted a forensic autopsy to investigate a putative relation between a motor vehicle collision and the driver's later death from angiosarcoma. A vehicle operated by a man in his 60 s collided with an oncoming vehicle at a curve. The victim noticed no injury at the scene. However, 45 days later, he was transferred to an emergency room with dyspnea and bloody sputum. After diagnosis of angiosarcoma, he died of respiratory failure 132 days later. The bereaved family speculated about a relation between the collision and angiosarcoma onset. At autopsy, tumor cells of the scalp had metastasized to the lung, pleura, liver, and spleen. Histopathological examinations revealed characteristic features of angiosarcoma with positive immune-staining for CD31, CD34, and factor VIII. When a person dies some time after a collision, it is designated as a delayed death. In such cases, the relevance of trauma to the person's death is often an issue of concern. Because the interval between trauma and angiosarcoma development was short, only 45 days, the angiosarcoma might be coincidental. Therefore, we rejected the relation. Forensic experts sometimes need to investigate such inquiries.

A correlated random parameters ordered probit approach to analyze the injury severity of bicycle-motor vehicle collisions at intersections

Bicycle-motor vehicle (BMV) accidents hold paramount importance due to their substantial impact on public safety. Specifically, road intersections, being critical conflict points, demand focused attention to reduce BMV crashes effectively and mitigate their severity. The existing research on the severity analysis of these crashes appears to have certain gaps that warrant further contribution. To address the mentioned limitations, this study first integrates multiple pre-collision features of the bicycles and vehicles to classify crash types based on the mechanism of the crashes. Then, the correlated random parameters ordered probit (CRPOP) model is employed to examine the factors influencing injury severity among bicyclists involved in intersection BMV crashes in Pennsylvania from 2013 to 2018. To gain deeper insights, this study conducts a separate analysis of crash data from 3-leg intersections, 4-leg intersections, and their combined scenarios, followed by a comparative examination of the results. The findings revealed that the presented crash typing approach yields new insights regarding injury severity outcomes. Moreover, in addition to exhibiting a comparable statistical performance contrasting to the more restricted models, the CRPOP model identified hidden correlations between three random parameters. Furthermore, the study demonstrated that analyzing combined crash data from the two intersection types obscured certain factors that were found significantly influential in the injury outcomes through analyzing sub-grouped data. Consequently, it is recommended to implement tailored countermeasures for each type of intersection.

An unusual case of high-voltage electrocution of a truck driver due to inadvertent contact of the truck with an overhead wire

Electrocution deaths are mostly accidental. However, reconstruction of events in unusual electrocution death is challenging. This article reports an accidental death due to electrocution in a highly unusual circumstance, in which a truck driver reversing his vehicle was electrocuted when his truck inadvertently touched an overhead high-voltage wire. The electric injury marks were present over the sole of the right foot. The scene investigation revealed that the high-voltage wire was loose and was below the level of the prescribed height. The truck was passing over an elevated area made up of dirt and stone. The interior of the cabin of the truck revealed a few non-insulated metallic areas over the floor of the truck, between the accelerator and the brake, which were attributed as the sources of entry of electricity into the body. The electric injury marks were different than those usually seen in high-voltage electrocution as there was an intermediate object (truck) involved, and the contact period between the truck and the electric wire was minimal. This fatality was attributed to the non-proper insulation of the interior of the truck, the negligent driving of the truck driver over the elevated surface, and the loose high-voltage wire without proper maintenance.

Newspaper framing of motor vehicle crashes and road safety in Argentina

The media plays a key role in shaping the public's perception of road safety. This study analyzes the newspaper coverage and framing of motor vehicle crashes (MVCs) and road safety in Argentina, South America. The content of 304 articles published by 15 newspapers in November 2020 was reviewed. The results show that episodically framed news stories (focused on a single event or incident) prevail over thematically framed articles. MVCs are presented primarily as 'police' events and tend to receive more coverage when fatalities are involved. There is limited information provided on contextual and risk factors, and road safety advice is rarely included. Speeding, infrastructure, alcohol and other human-related variables are the most cited risk factors. Very few articles mention the use of protective devices (seat-belt, helmet and child restraint system). Although motorcyclists represent 40% of RTC deaths in Argentina, only 20% of the news coverage was about them. News coverage was quite similar in national and regional newspapers. There is an opportunity for the media to help build a better road safety culture, but significant changes in news framing are required. Practical recommendations for editors, journalists and road safety practitioners are provided.