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The advancement of intelligent road systems in developing countries poses unique challenges in identifying risk factors and implementing safety strategies. The variability of factors affecting crash injury severity leads to different risks across levels of roadway smartness, especially in hazardous terrains, complicating the adaptation of smart technologies. Therefore, this study investigates the temporal instability of factors affecting injury severities in crashes across various terrains, with a focus on the evolution of road smartness. Crash data from selected complex terrain regions in Shaanxi Province during smart road adaptation were used, and categorized into periods before, during, and after smart road implementations. A series of mixed logit models were employed to account for unobserved heterogeneity in mean and variance, and likelihood ratio tests were conducted to assess the spatio-temporal instability of model parameters across different topographic settings and smart processes. Moreover, a comparison between partially constrained and unconstrained temporal modeling approaches was made. The findings reveal significant differences in injury severity determinants across terrain conditions as roadway intelligence progressed. On the other hand, certain factors like pavement damage, truck and pedestrian involvement were identified that had relatively stable effects on crash injury severities. Out-of-sample predictions further emphasize the need for modeling across terrain and roadway development stages. These insights are crucial for developing tailored safety measures for smart road retrofitting in different terrain conditions, thereby supporting the transition towards smarter road systems in developing regions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Occupational motor vehicle (OMV) crashes are a leading cause of occupation-related injury and fatality in the United States. Statewide crash databases provide a good source for identifying crashes involving large commercial vehicles but are less optimal for identifying OMV crashes involving light or medium vehicles. This has led to an underestimation of OMV crash counts across states and an incomplete picture of the magnitude of the problem. The goal of this study was to develop and pilot a systematic process for identifying OMV crashes in light and medium vehicles using both state crash and health-related surveillance databases. A two-fold process was developed that included: 1) a machine learning approach for mining crash narratives and 2) a deterministic data linkage effort with crash state data and workers compensation (WC) claims records and emergency medical service (EMS) data, independently. Overall, the combined process identified 5,302 OMV crashes in light and medium vehicles within one year's worth of crash data. Findings suggest the inclusion of multi-method approaches and multiple data sources can be implemented and used to improve OMV crash surveillance in the United States., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Pedestrian fatalities comprise a quarter of all traffic deaths in Low-and-Middle-Income Countries (LMICs). The use of safer modes of transport such as buses can reduce road trauma as well as air pollution and traffic congestion. Although travelling by bus is safer than most other modes, accessing bus stops can be risky for pedestrians. This paper systematically reviews factors contributing to the safety of pedestrians near bus stops in countries of differing income levels. The review included forty-one studies from high (20), upper-middle (13) and lower-middle income countries (8) during the last two decades. The earliest research was conducted in high-income countries (HICs), but research has spread in the last decade. The factors influencing pedestrian safety fell into three groups: (a) characteristics of road users, (b) characteristics of bus stops and (c) characteristics of the road traffic environment. Pedestrians near bus stops are frequently exposed to a high risk of collisions and fatalities due to factors such as unsafe pedestrian behaviours (e.g., hurrying to cross the road), lack of bus stop amenities such as safe footpaths, high traffic speeds and traffic volumes, multiple lanes, and roadside hazards (e.g., parked cars obscuring pedestrians). Road crash statistics are commonly used to identify unsafe bus stops in HICs but the unavailability and unreliability of data have prevented more widespread use in LMICs. Future research is recommended to focus on surrogate safety measures to identify hazardous bus stops for pedestrians., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The rapid economic development and accelerating urbanization have led to a significant mismatch between the urban bus network allocation and the population flow. Therefore, this paper investigates this challenge by exploring the intricate relationship between the population flow dynamics, traffic congestion conditions, and the efficient allocation of bus resources. In response, two key indexes were introduced based on spatial matching patterns to assess the urban bus network: the Population-Bus Match Index evaluates the matching degree between supply and demand, and the Population-Congestion Match Index evaluates the matching degree between utilization and saturation. Additionally, two distinct optimization strategies have been proposed to enhance the urban bus network. The first optimization strategy considers the bus network's current status, while the second aspires to an idealized scenario. Subsequently, the potential contributions of each bus station in reducing CO2 emission reduction after implementing the two optimization strategies are quantified. Utilizing a case study focused on Harbin, the proposed methods are validated. The findings unveil a substantial misalignment between supply and demand within the bus network during peak periods, with nearly half of the bus stations experiencing a disparity between utilization and saturation. Comparative experiments across different optimization strategies reveal that the second optimization strategy significantly outperforms the first, but the first optimization strategy has a higher degree of CO2 emission reduction contribution. The results of this study provide decision-makers with an environmentally oriented vantage point for the discerning selection of optimization strategies and leave valuable insights for urban areas confronting transportation challenges., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Gao, Liu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objectives: Road traffic injuries (RTIs) pose a significant public health burden, and more than half of these fatalities are attributed to vulnerable road users (VRUs). This study aimed to evaluate the epidemiology and outcomes of severe RTIs in Korea by focusing on different types of road users., Methods: This is nationwide retrospective observational study. Using data from the Korean Nationwide Severe Trauma Registry, this study analyzed severe RTI cases from 2016 to 2020. The study included EMS-treated severe trauma patients, defining severe RTI as cases with an injury severity score (ISS) ≥16 or out-of-hospital cardiac arrest (OHCA). The main variable of interest was the road user type, classified as motor vehicle occupants (MVOs), pedestrians, motorcyclists, and bicyclists. Trends and injury characteristics by road user type were analyzed, and multivariate logistic regression was conducted to calculate the adjusted odds ratios (AORs) and 95 % confidence intervals (CIs) of road user type for in-hospital mortality., Results: Of the 143,021 EMS-treated severe trauma cases, 24,464 were included in this study. Pedestrians represented the largest group (n = 8,782; 35.9 %). More than half of the patients died (n = 12,620, 51.6 %), and a high proportion of patients had OHCA (n = 10,048, 41.1 %). There was no significant change in the overall severe RTI numbers from 2016 to 2020, but a decrease in pedestrian cases and an increase in motorcyclist cases were noted (both p for trend<0.05). Low usage of safety devices was observed (28.2 % of motor vehicle occupants used seat belts, 35.9 % of motorcyclists used helmets, and 9.6 % of bicyclists used helmets). Head injuries were most common, particularly among bicyclists (77.0 %) and motorcyclists (69.8 %). Compared to motor vehicle occupants, pedestrians (AOR [95 % CI] 1.12 [1.04-1.20]) and others (AOR [95 % CI] 1.30 [1.02-1.65]) had higher odds of mortality, while motorcyclists (AOR [95 % CI] 0.64 [0.59-0.69]) and bicyclists (AOR [95 % CI] 0.68 [0.60-0.76]) had lower odds of mortality., Conclusion: We found varying trends and injury characteristics in severe RTIs according to road user type. Adapting prevention strategies for evolving road user patterns, with particular attention to increasing safety device usage and addressing the high mortality associated with severe RTIs are warranted., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

This exploratory study is a follow-up to a 2014 study that investigated factors associated with large truck at-fault crash outcomes in Alabama. To assess unobserved temporal changes in the effects of the crash factors, this study re-creates the original crash models developed in the 2014 study using crash data from 2017 to 2019. Four mixed logit models were re-created using the same variables used in the previous study to analyze contributing crash factors to injury severity of single-vehicle (SV) and multi-vehicle-involved (MV) large truck at-fault crashes in urban and rural settings. It was found that there have been temporal changes in how many of the factors influenced crash severity with some of them no longer showing any significant association with crash outcomes, while others remained significant. Further, it was observed that some of the variables that remained significant had different relationships with crash injury severity in the newer severity models. For instance, while factors such as fatigued driver (in rural crashes), clear weather (in urban crashes), single-unit truck (in rural SV crashes), truck rollover (in urban SV crashes) maintained consistent significance over time, the effects of variables such as at-fault male drivers (in urban MV crashes), at-fault female drivers (in urban MV crashes), and hitting fixed object (in rural MV crashes) have changed. One such notable difference is the variable for absence of traffic control which increased the probability of major injury in rural SV crashes by 49.50% in the 2014 model but decreased the probability of recording major injuries by 108.90% using the 2017-2019 data. Considering the temporal changes that were observed in the recreated models, newer models were developed, revealing the emergence of new variables such as truck age that are significantly associated with truck crash severity. The findings of this study provide evidence to suggest that some crash severity factors for at-fault large truck collisions vary over time, with newer ones also emerging over time. These findings can also help trucking companies, transportation engineers, and other industry experts in developing measures to reduce large truck crashes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective: Exposure to flood, one of the most widespread disasters caused by natural hazards, increases the risk of drowning. Driving through flooded waterways is a cause of death due to flood-related drowning, especially in flood-prone areas. This study aimed at identifying the risk factors for motor vehicle-related drowning in floods and its prevention strategies., Methods: International and national databases (WOS, PubMed, Scopus, Google Scholar, Magiran, and SID) were searched in the time span from 2000 to 2022. The studies investigating the risk factors relevant to land motor vehicle-related drowning in floods and its prevention strategies were included and analyzed using thematic content analysis., Results: In 14 eligible studies, risk factors for land motor vehicle-related drowning in floods were identified and categorized in 3 subthemes: driver (3 categories: socio-demographic characteristics, knowledge and attitude, and beliefs); technology (1 category: land motor vehicles); and environment (2 categories: physical and socio-economic environment). Physical and structural measures (1 category: road safety improvement) and nonstructural measures (4 categories: research and education and raising awareness, risk management, promoting social-cognitive beliefs, and reconstruction and improvement of legal infrastructure) were proposed as drowning prevention strategies., Conclusions: The knowledge, attitude, and belief of the driver; the vehicle; and the environment were the most important risk factors of driving through flooded waterways. These factors should be considered when designing programs and physical and structural strategies for future interventions to curb this dangerous and potentially fatal driving behavior.

Introduction: Vehicles play an important role in pedestrian injury risk in crashes. This study examined the association between vehicle front-end geometry and the risk of fatal pedestrian injuries in motor vehicle crashes., Method: A total of 17,897 police-reported crashes involving a single passenger vehicle and a single pedestrian in seven states were used in the analysis. Front-end profile parameters of vehicles (2,958 vehicle makes, series, and model years) involved in these crashes were measured from vehicle profile photos, including hood leading edge height, bumper lead angle, hood length, hood angle, and windshield angle. We defined a front-end-shape indicator based on the hood leading edge height and bumper lead angle. Logistic regression analysis evaluated the effects of these parameters on the risk that a pedestrian was fatally injured in a single-vehicle crash., Results: Vehicles with tall and blunt, tall and sloped, and medium-height and blunt front ends were associated with significant increases of 43.6%, 45.4%, and 25.6% in pedestrian fatality risk, respectively, when compared with low and sloped front ends. There was a significant 25.1% increase in the risk if a hood was relatively flat as defined in this study. A relatively long hood and a relatively large windshield angle were associated with 5.9% and 10.7% increases in the risk, respectively, but the increases were not significant., Conclusions: Vehicle front-end profiles that were significantly associated with increased pedestrian fatal injury risk were identified., Practical Applications: Automakers can make vehicles more pedestrian friendly by designing vehicle front ends that are lower and more sloped. The National Highway Traffic Safety Administration (NHTSA) can consider evaluations that account for the growing hood heights and blunt front ends of the vehicle fleet in the New Car Assessment Program or regulation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 National Safety Council and Elsevier Ltd. All rights reserved.)

Introduction: Road death risk is often characterized as deaths per volume of traffic in geographic regions, the denominator in miles or kilometers supposedly indicative of the magnitude of risk exposure. This paper reports an examination of the differences in the predictive value of factors hypothesized to influence traffic volume and road death risk., Method: The association of 11 risk factors in U.S. counties during the first 7 months of 2020 was examined for consistency of predictions of road death and traffic volume measured by cell phone and vehicle location data. The study employed least squares regression for traffic volume and Poisson regression for deaths with the population as the offset variable., Results: The directions of the regression coefficients for traffic volume and odds of road deaths per population were opposite from one another for 9 of the 11 variables in the analysis of vehicle occupant deaths. Only the coefficients for maximum daily temperature and Saturday travel were in the same direction. The confidence intervals of three risk ratios for pedestrian deaths indicated low reliability but most of the predictor variables were opposite in association with traffic volume and odds of death. Although traffic volume plunged in the first weeks of the pandemic, the results for the months before and during the COVID-19 pandemic were similar., Practical Applications: Traffic volume is an inverse risk factor for road deaths at the local level, likely the result of lower speeds on congested roads. Without the application of countermeasures aimed at reducing speed and other risk factors, the reduction of road congestion is likely to increase deaths., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

An analysis of crash data spanning four years (January 1, 2015, to December 31, 2018) from the State of Washington is conducted to investigate factors influencing injury severity outcomes in large truck-involved crashes. The study utilizes a mixed logit model that accounts for unobserved heterogeneity to capture the variation influenced by other variables. Transferability and temporal stability across the years are assessed using the likelihood ratio test. A wide range of attributes, including driver characteristics, vehicle features, crash-related attributes, roadway conditions, environmental factors, and temporal elements, are considered. Despite a significant temporal instability warranted by the likelihood ratio test across the years, twenty-one parameters consistently exhibit stable effects on injury severity over the years of which thirteen are new. The identified stable parameters included over speeding, following too closely, falling asleep, missing/ faulty airbags, head-on collisions, crashes involving two or more than three vehicles, rear-end collisions, lane width, low-light conditions, sag curves, New Jersey barriers, snowy weather, and morning hours. The temporally stable factors affecting injury severities in large truck crashes are crucial in developing the needed to address these crashes. The findings of this study offer valuable insights for researchers, stakeholders in the trucking industry, and policymakers, empowering them to develop targeted policies that not only improve traffic safety but also alleviate associated economic losses., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Public transport priority systems such as Bus Rapid Transit (BRT) and Buses with High Level of Service (BHLS) are top-rated solutions to mobility in low-income and middle-income cities. There is scientific agreement that the safety performance level of these systems depends on their functional, operational, and infrastructure characteristics. However, there needs to be more evidence on how the different characteristics of bus corridors might influence safety. This paper aims to shed some light on this area by structuring a multivariate negative binomial model comparing crash risk on arterial roads, BRT, and BHLS corridors in Bogotá, Colombia. The analyzed infrastructure includes 712.1 km of arterial roads with standard bus service, 194.1 km of BRT network, and 135.6 km of BHLS network. The study considered crashes from 2015 to 2018 -fatalities, injuries, and property damage only- and 30 operational and infrastructure variables grouped into six classes -exposure, road design, infrastructure, public means of transport, and land use. A multicriteria process was applied for model selection, including the structure and predictive power based on [i] Akaike information criteria, [ii] K-fold cross-validation, and [iii] model parsimony. Relevant findings suggest that in terms of observed and expected accident rates and their relationship with the magnitude of exposure -logarithm of average annual traffic volumes at the peak hour (LOG&#95;AAPHT) and the percentage of motorcycles, cars, buses, and trucks- the greatest risk of fatalities, injuries, and property damage occurs in the BHLS network. BRT network provides lower crash rates in less severe collisions while increasing injuries and fatalities. When comparing the BHLS network and the standard design of arterial roads, BHLS infrastructure, despite increasing mobility benefits, provides the lowest safety performance among the three analyzed networks. Individual factors of the study could also contribute to designing safer roads related to signalized intersection density and curvature. These findings support the unique characteristics and traffic dynamics present in the context of Bogotá that could inform and guide decisions of corresponding authorities in other highly dense urban areas from developing countries., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Introduction: Motor vehicle collisions are a leading source of mortality and injury on urban highways. From a temporal perspective, the determination of a road segment as being collision-prone over time can fluctuate dramatically, making it difficult for transportation agencies to propose traffic interventions. However, there has been limited research to identify and characterize collision-prone road segments with varying collision density patterns over time., Method: This study proposes an identification and characterization framework that profiles collision-prone roads with various collision density variations. We first employ the spatio-temporal network kernel density estimation (STNKDE) method and time-series clustering to identify road segments with different collision density patterns. Next, we characterize collision-prone road segments based on spatio-temporal information, consequences, vehicle types, and contributing factors to collisions. The proposed method is applied to two-year motor vehicle collision records for New York City., Results: Seven clusters of road segments with different collision density patterns were identified. Road segments frequently determined as collision-prone were primarily found in Lower Manhattan and the center of the Bronx borough. Furthermore, collisions near road segments that exhibit greater collision densities over time result in more fatalities and injuries, many of which are caused by both human and vehicle factors., Conclusions: Collision-prone road segments with various collision density patterns over time have distinct differences in the spatio-temporal domain and the collisions that occur on them., Practical Applications: The proposed method can help policymakers understand how collision-prone road segments change over time, and can serve as a reference for more targeted traffic treatment., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Fentanyl, a type of opioid, in impaired driving cases increased across cities in the United States., Objectives: No empirical studies have examined motor vehicle overdoses with fentanyl use. We investigated the magnitude of the motor vehicle overdose problem in Providence, RI, and the environmental, socioeconomic, and geographic conditions associated with motor vehicle overdose occurrence., Methods: This was a retrospective observational study of emergency medical services data on all suspected opioid overdoses between January 1, 2017, and October 31, 2020. The data contain forced-choice fields, such as age and biological sex, and an open-ended narrative in which the paramedic documented clinical and situational information. The overdoses were geocoded, allowing for the extraction of sociodemographic data from the U.S. Census Bureau's American Community Survey. Seven other data sources were included in a logistic regression to understand key risk factors and spatial patterns of motor vehicle overdoses., Results: Of the 1,357 opioid overdose cases in this analysis, 15.2% were defined as motor vehicle overdoses. In adjusted models, we found a 61% increase in the odds of a motor vehicle overdose involvement for men versus women, a 16.8% decrease in the odds of a motor vehicle overdose for a one-unit increase in distance to the nearest gas station, and a 10.7% decrease in the odds of a motor vehicle overdose for a one-unit increase in distance to a buprenorphine clinic., Conclusion: There is a need to understand the interaction between drug use in vehicles to design interventions for decreasing driving after illicit drug use., Competing Interests: The authors have no conflicts of interest to report., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Introduction: Despite their important role in the economy, truck drivers face several challenges, including adapting to advancing technology. The current study investigated the occupational experiences of Dutch truck drivers to detect common patterns., Methods: A questionnaire was distributed to professional drivers in order to collect data on public image, traffic safety, work pressure, transport crime, driver shortage, and sector improvements., Results: The findings based on 3,708 respondents revealed a general dissatisfaction with the image of the industry and reluctance to recommend the profession. A factor analysis of the questionnaire items identified two primary factors: 'Work Pressure', more common among national drivers, and 'Safety & Security Concerns', more common among international drivers. A ChatGPT-assisted analysis of textbox comments indicated that vehicle technology received mixed feedback, with praise for safety and fuel-efficiency improvements, but concerns about reliability and intrusiveness., Discussion: In conclusion, Dutch professional truck drivers indicate a need for industry improvements. While the work pressure for truck drivers in general may not be high relative to certain other occupational groups, truck drivers appear to face a deficit of support and respect., Competing Interests: AC is CEO of Transporteffect BV and Chauffeurnieuws. He was not involved in the current analysis and interpretation of the data. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 de Winter, Driessen, Dodou and Cannoo.)

One consequence of population aging is an increase in the number of older wheelchair users. They often board the motor vehicle from the rear for moving. Recently, wheelchair user vehicle passengers have involved in motor vehicle collisions and died. A three-point seatbelt does not adequately fit most wheelchair user passengers because of the way that the wheelchair is constructed. Therefore, owing to the movement of the body immediately after the collision, the wheelchair user passengers attacked their body to the interior of the vehicle or suffered from the intrusion of the lap belt into the abdomen, subsequently suffered from severe head, chest or abdominal injuries. According to the review of all fatal motor vehicle collisions in Shiga Prefecture, Japan, which has a population of approximately 1.4 million, from 2017 to 2022, the rate of wheelchair users in fatal motor vehicle passenger increased from 3.6% in 2017 to 2019 to 7.8% in 2020 to 2022. Therefore, there is a risk that substantial numbers of wheelchair user passengers involved in motor vehicle collisions will die. However, in Japan, there are no official statistics on the involvement of wheelchair user passengers in motor vehicle collisions. Therefore, we propose a nationwide registration of injuries and fatalities in wheelchair user passengers. Investigating the mechanisms of injury in wheelchair user passengers would contribute to the development of safety measures, especially for restraint systems. Established preventive measure would contribute to the decrease of fatally or severely injured motor vehicle collision passengers.

Monitoring and understanding the trends in motor vehicle traffic (MVT) mortality is crucial for developing effective interventions and tracking progress in reducing deaths related to MVT. This study aimed to assess the trends in MVT mortality in New York City from 1999 through 2020. Publicly available de-identifiable mortality data were abstracted from the Centers for Disease Control and Prevention's Wide-ranging Online Data for Epidemiologic Research. MVT deaths were identified using the International Classification of Diseases Codes, 10th Revision: V02-V04 (.1, .9), V09.2, V12-V14 (.3-.9), V19 (.4-.6), V20-V28 (.3-.9), V29-V79 (.4-.9), V80 (.3-.5), V81.1, V82.1, V83-V86 (.0-.3), V87 (.0-.8), and V89.2. Age adjusted mortality rates (AAMR) were abstracted by county (Bronx; Kings; Queens; New York), age (in years) (< 25; 25-44; 45-64; ≥ 65), sex (male; female), race/ethnicity (Non-Hispanic Black; Non-Hispanic White; Asian/Pacific Islander; Hispanic), and road user type (motor vehicle occupant; motorcyclist; pedal cyclist; pedestrian). Joinpoint regression models were fitted to estimate the annual percentage change (APC) and average annual percentage change (AAPC) in AAMR during the study period. The Parametric Method was used to compute 95% confidence intervals (CI). Between 1999 and 2020, a total of 8,011 MVT deaths were recorded in New York City. Mortality rates were highest among males (age adjusted mortality rate (AAMR) = 6.4 per 100,000; 95% CI: 6.2, 6.5), Non-Hispanic Blacks (AAMR = 4.8; 95% CI: 4.6, 5.0), older adults (AAMR = 8.9; 95% CI: 8.6, 9.3), and persons from Richmond County (AAMR = 5.2; 95% CI: 4.8, 5.7). MVT death rates, overall, have declined by 3% per year (95% CI: -3.6, -2.3) from 1999 to 2020. The rates have fallen or stabilized by race/ethnicity, county of residence, road user type, and age group. In contrast, rates have increased by 18.1% per year among females and by 17.4% per year in Kings County from 2017 to 2020.The results of this study draw attention to the worsening trends in MVT mortality among females and in Kings County, New York City. Further investigation is needed to determine the underlying behavioral, social, and environmental factors contributing to this increase, such as polysubstance or alcohol abuse, psychosocial stressors, access to medical and emergency care, and compliance with traffic laws. These findings emphasize the importance of developing targeted interventions to prevent MVT deaths and ensure the health and safety of the community., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

We investigate the connection between the choice of transportation mode used by commuters and the probability of COVID-19 transmission. This interplay might influence the choice of transportation means for years to come. We present data on commuting, socioeconomic factors, and COVID-19 disease incidence for several US metropolitan areas. The data highlights important connections between population density and mobility, public transportation use, race, and increased likelihood of transmission. We use a transportation model to highlight the effect of uncertainty about transmission on the commuters' choice of transportation means. Using multiple estimation techniques, we found strong evidence that public transit ridership in several US metro areas has been considerably impacted by COVID-19 and by the policy responses to the pandemic. Concerns about disease transmission had a negative effect on ridership, which is over and above the adverse effect from the observed reduction in employment. The COVID-19 effect is likely to reduce the demand for public transport in favor of lower density alternatives. This change relative to the status quo will have implications for fuel use, congestion, accident frequency, and air quality. More vulnerable communities might be disproportionally affected as a result. We point to the need for additional studies to further quantify these effects and to assist policy in planning for the post-COVID-19 transportation future., (© 2021. The Author(s).)

Traffic congestion has become common in urban areas worldwide. To solve this problem, the method of searching a solution using artificial intelligence has recently attracted widespread attention because it can solve complex problems such as traffic signal control. This study developed two traffic signal control models using reinforcement learning and a microscopic simulation-based evaluation for an isolated intersection and two coordinated intersections. To develop these models, a deep Q-network (DQN) was used, which is a promising reinforcement learning algorithm. The performance was evaluated by comparing the developed traffic signal control models in this research with the fixed-time signal optimized by Synchro model, which is a traffic signal optimization model. The evaluation showed that the developed traffic signal control model of the isolated intersection was validated, and the coordination of intersections was superior to that of the fixed-time signal control method., Competing Interests: The authors have declared that no competing interests exist.

Vehicles' trajectory prediction is a topic with growing interest in recent years, as there are applications in several domains ranging from autonomous driving to traffic congestion prediction and urban planning. Predicting trajectories starting from Floating Car Data (FCD) is a complex task that comes with different challenges, namely Vehicle to Infrastructure (V2I) interaction, Vehicle to Vehicle (V2V) interaction, multimodality, and generalizability. These challenges, especially, have not been completely explored by state-of-the-art works. In particular, multimodality and generalizability have been neglected the most, and this work attempts to fill this gap by proposing and defining new datasets, metrics, and methods to help understand and predict vehicle trajectories. We propose and compare Deep Learning models based on Long Short-Term Memory and Generative Adversarial Network architectures; in particular, our GAN-3 model can be used to generate multiple predictions in multimodal scenarios. These approaches are evaluated with our newly proposed error metrics N-ADE and N-FDE, which normalize some biases in the standard Average Displacement Error (ADE) and Final Displacement Error (FDE) metrics. Experiments have been conducted using newly collected datasets in four large Italian cities (Rome, Milan, Naples, and Turin), considering different trajectory lengths to analyze error growth over a larger number of time-steps. The results prove that, although LSTM-based models are superior in unimodal scenarios, generative models perform best in those where the effects of multimodality are higher. Space-time and geographical analysis are performed, to prove the suitability of the proposed methodology for real cases and management services., Competing Interests: The authors have declared that no competing interests exist.

Advanced Driver Assistance Systems (ADAS) are being developed and installed in increasing numbers. Some of the most popular ADAS include blind spot monitoring and cruise control which are fitted in the majority of new vehicles sold in high-income countries. With more drivers having access to these technologies, it is imperative to develop policy and strategies to guarantee the safe uptake of ADAS. One key issue is that ADAS education has been primarily centred on the user manual which are not widely utilised. Moreover, it is unclear if user manuals are an adequate source of education in terms of content and readability. To address this research gap, a content analysis was used to assess the differences in ADAS-related content and readability among the manuals of the highest selling vehicles in Australia. The qualitative findings showed that there are seven themes in the user manuals: differences between driving with and without ADAS, familiarisation requirements, operational limits of the ADAS, potential ADAS errors, behaviour adaptation warnings, confusion warnings, and malfunction warnings. The quantitative analysis found that some of the manuals require several years of education above the recommended for a universal audience (>8 years) to be understood. Additionally, there is a notable number of text diversions and infographics which could make comprehension of the user manual difficult. This investigation shows that there is a lack of standardisation of ADAS user manuals (in both content and delivery of information) which requires regulatory oversight. Driver ADAS education needs to be prioritised by policymakers and practitioners as smart technology continues to increase across the transport system. It seems that current strategies based on user manuals are insufficient to achieve successful adoption and safe use of these technologies., Competing Interests: The authors have declared that no competing interests exist.

We collected COVID-19 epidemiological and epidemic control measures-related data in mainland China during the period January 1 to February 19, 2020, and empirically tested the practical effects of the epidemic control measures implemented in China by applying the econometrics approach. The results show that nationally, both traffic control and social distancing have played an important role in controlling the outbreak of the epidemic, however, neither of the two measures have had a significant effect in low-risk areas. Moreover, the effect of traffic control is more successful than that of social distancing. Both measures complement each other, and their combined effect achieves even better results. These findings confirm the effectiveness of the measures currently in place in China, however, we would like to emphasize that control measures should be more tailored, which implemented according to each specific city's situation, in order to achieve a better epidemic prevention and control., Competing Interests: The authors have declared that no competing interests exist.

Introduction: This study performed a path analysis to uncover the behavioral pathways (from contributing factors, pre-crash actions to injury severities) in bicycle-motor vehicle crashes., Method: The analysis investigated more than 7,000 bicycle-motor vehicle crashes in North Carolina between 2007 and 2014. Pre-crash actions discussed in this study are actions of cyclists and motorists prior to the event of a crash, including "bicyclist failed to yield," "motorist failed to yield," "bicyclist overtaking motorist," and "motorist overtaking bicyclist.", Results: Model results show significant correlates of pre-crash actions and bicyclist injury severity. For example, young bicyclists (18 years old or younger) are 23.5% more likely to fail to yield to motor traffic prior to the event of a crash than elder bicyclists. The "bicyclist failed to yield" action is associated with increased bicyclist injury severity than other actions, as this behavior is associated with an increase of 5.88 percentage points in probability of a bicyclist being at least evidently injured. The path analysis can highlight contributing factors related to risky pre-crash actions that lead to severe injuries. For example, bicyclists traveling on regular vehicle travel lanes are found to be more likely to involve the "bicyclist failed to yield" action, which resulted in a total 44.38% (7.04% direct effect + 37.34% indirect effect) higher likelihood of evident or severe injuries. The path analysis can also identify factors (e.g., intersection) that are not directly but indirectly correlated with injury severity through pre-crash actions. Practical Applications: This study offers a methodological framework to quantify the behavioral pathways in bicycle-motor vehicle crashes. The findings are useful for cycling safety improvements from the perspective of bicyclist behavior, such as the educational program for cyclists., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021.)

Introduction: Teen drivers experience higher crash risk than their experienced adult counterparts. Legislative and community outreach methods have attempted to reduce this risk; results have been mixed. The increasing presence of vehicle safety features across the fleet has driven fatality numbers down in the past decades, but the disparity between young drivers and others remains., Method: We merged Fatality Analysis Reporting System (FARS) data on fatal crashes with vehicle characteristic data from the Highway Loss Data Institute (HLDI). The analysis compared the vehicle type, size, age, and the presence of select safety features in vehicles driven by teens (ages 15-17 years) and adult drivers (ages 35-50 years) who were killed in crashes from 2013 to 2017. Results were compared with a similar analysis conducted on data from 2007 to 2012., Results: Teen drivers were more likely than their adult counterparts to be killed while driving older, smaller vehicles that were less likely to have the option to be equipped with side airbags., Discussion: Teenage drivers remain more likely to be killed while driving older, smaller vehicles than adult drivers. Parents and guardians are mainly responsible for teen vehicle choice, and should keep vehicle size, weight, and safety features in mind when placing their teen in a vehicle. Practical Application: These findings can help guide safer vehicle choice for new teen drivers., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021. Published by Elsevier Ltd.)

Introduction: The European Union (EU) has developed different strategies to internalize the costs of excessive motor traffic in the road freight transport sector. One of these is a relaxation of restrictions on the size and load capacity of trucks that circulate between member States and a proposal has been made for Longer and Heavier Vehicles (LHVs) to be allowed to circulate across borders. LHVs are the so-called "megatrucks" (i.e., trucks with a length of 25 meters and a weight of 60 tonnes). Megatrucks have allowed to circulate for decades in some European countries such as Norway, Finland, and Sweden, world leaders in traffic accident prevention, although the impact that cross-border traffic would have on road safety is still unknown., Methods: This article provides an econometric analysis of the potential impact on road safety of allowing the circulation of "megatrucks" throughout the EU., Results: The findings show that countries that currently allow megatrucks to circulate present lower traffic accident and fatality levels, on average., Conclusions: The circulation of this type of vehicle is only advisable in countries where there is a certain degree of maturity and demonstrated achievements in the field of road safety. Practical applications: European countries that have allowed megatruck circulation obtaining better road safety outcomes in terms of accidents, although the accident lethality rate seems to be higher. Consequently, introducing megatruck circulation requires a prior proper preparation and examination., (Copyright © 2021. Published by Elsevier Ltd.)

Introduction: Although stop signs are popular in North America, they have become controversial in cities like Montreal, Canada where they are often installed to reduce vehicular speeds and improve pedestrian safety despite limited evidence demonstrating their effectiveness. The purpose of this study is to evaluate the impact of stop-control configuration (and other features) on safety using statistical models and surrogate measures of safety (SMoS), namely vehicle speed, time-to-collision (TTC), and post-encroachment time (PET), while controlling for features of traffic, geometry, and built environment., Methods: This project leverages high-resolution user trajectories extracted from video data collected for 100 intersections, 336 approaches, and 130,000 road users in Montreal to develop linear mixed-effects regression models to account for within-site and within-approach correlations. This research proposes the Intersection Exposure Group (IEG) indicator, an original method for classifying microscopic exposure of pedestrians and vehicles., Results: Stop signs were associated with an average decrease in approach speed of 17.2 km/h and 20.1 km/h, at partially and fully stop-controlled respectively. Cyclist or pedestrian presence also significantly lower vehicle speeds. The proposed IEG measure was shown to successfully distinguish various types of pedestrian-vehicle interactions, allowing for the effect of each interaction type to vary in the model., Conclusions: The presence of stop signs significantly reduced approach speeds compared to uncontrolled approaches. Though several covariates were significantly related to TTC and PET for vehicle pairs, the models were unable to demonstrate a significant relationship between stop signs and vehicle-pedestrian interactions. Therefore, drawing conclusions regarding pedestrian safety is difficult. Practical Applications: As pedestrian safety is frequently used to justify new stop sign installations, this result has important policy implications. Policies implementing stop signs to reduce pedestrian crashes may be less effective than other interventions. Enforcement and education efforts, along with geometric design considerations, should accompany any changes in traffic control., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021.)

Vehicular Ad hoc Networks (VANETs) an important category in networking focuses on many applications, such as safety and intelligent traffic management systems. The high node mobility and sparse vehicle distribution (on the road) compromise VANETs network scalability and rapid topology, hence creating major challenges, such as network physical layout formation, unstable links to enable robust, reliable, and scalable vehicle communication, especially in a dense traffic network. This study discusses a novel optimization approach considering transmission range, node density, speed, direction, and grid size during clustering. Whale Optimization Algorithm for Clustering in Vehicular Ad hoc Networks (WOACNET) was introduced to select an optimum cluster head (CH) and was calculated and evaluated based on intelligence and capability. Initially, simulations were performed, Subsequently, rigorous experimentations were conducted on WOACNET. The model was compared and evaluated with state-of-the-art well-established other methods, such as Gray Wolf Optimization (GWO) and Ant Lion Optimization (ALO) employing various performance metrics. The results demonstrate that the developed method performance is well ahead compared to other methods in VANET in terms of cluster head, varying transmission ranges, grid size, and nodes. The developed method results in achieving an overall 46% enhancement in cluster optimization and an F-value of 31.64 compared to other established methods (11.95 and 22.50) consequently, increase in cluster lifetime., Competing Interests: The authors have declared that no competing interests exist

Introduction: Given the specificity of the occupation of professional drivers, fatigue is a very serious problem. Tired drivers can pose a huge threat to themselves and to other users of public roads. The effects of fatigue can lead to loss of drivers' and other users' health or life., Objective: The aim of the study was to analyze some aspects of fatigue in Polish professional drivers (e.g. symptoms of fatigue reported by drivers, their frequency and number, critical hours, and fatigue management) in relation to such variables as the job seniority, the mode of transport, and the covered distance., Material and Methods: The study involved 398 professional drivers. The method of research based on an anonymous electronic survey., Results: The survey showed that 89% of the respondents declared that they suffered from fatigue at work. The most common symptoms of fatigue reported by the drivers included drowsiness (76% of the respondents), eye strain (67%), lack of concentration (45%), and lack of energy (44%). The mean number of fatigue symptoms indicated by drivers involved in different modes of transport and covering different distances did not differ significantly., Conclusions: The analysis revealed that the everyday experience of fatigue reported by the surveyed drivers depends on job seniority. There is a significant difference in the occurrence of critical hours experienced by drivers covering different distances.

Introduction: Understanding how the COVID-19 pandemic has impacted our health and safety is imperative. This study sought to examine the impact of COVID-19's stay-at-home order on daily vehicle miles travelled (VMT) and MVCs in Connecticut., Methods: Using an interrupted time series design, we analysed daily VMT and MVCs stratified by crash severity and number of vehicles involved from 1 January to 30 April 2017, 2018, 2019 and 2020. MVC data were collected from the Connecticut Crash Data Repository; daily VMT estimates were obtained from StreetLight Insight's database. We used segmented Poisson regression models, controlling for daily temperature and daily precipitation., Results: The mean daily VMT significantly decreased 43% in the post stay-at-home period in 2020. While the mean daily counts of crashes decreased in 2020 after the stay-at-home order was enacted, several types of crash rates increased after accounting for the VMT reductions. Single vehicle crash rates significantly increased 2.29 times, and specifically single vehicle fatal crash rates significantly increased 4.10 times when comparing the pre-stay-at-home and post-stay-at-home periods., Discussion: Despite a decrease in the number of MVCs and VMT, the crash rate of single vehicles increased post stay-at-home order enactment in Connecticut after accounting for reductions in VMT., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Between March and May 2020, Japan experienced a lockdown due to the COVID-19 crisis. Empty roads possibly triggered speed-related traffic violations that caused fatal motor vehicle collisions (MVCs). Using police data on the monthly number of fatal MVCs between January 2010 and February 2020 in which motor vehicle drivers were at fault, we forecasted the numbers of fatal MVCs due to the speed-related violations during the lockdown and compared these with those observed. We also compared the observed to forecasted using the ratio of the number of speed-related fatal MVCs to that of non-speed related fatal MVCs. The observed numbers of speed-related fatal MVCs were within the 95% CIs of the forecasted numbers. The observed ratio was higher than the forecasted ratio in April (p=0.016). In the second month of the lockdown, drivers were more likely to commit speed-related violations that caused fatal MVCs than before the lockdown., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: There is a paucity of data on the incidence and severity of tram tracks related cycling injuries. The aim of this study is to get insight into the incidence, severity and characteristics of tram tracks related cycling injuries, potentially defining significant 'hotspots' in the Ghent city area., Methods: A one-year, multicenter, prospective, observational study was conducted. Patients presenting to the emergency departments of all 4 Ghent hospitals with tram tracks related cycling injury, were included. Data on patient demographics, circumstances of the accident and type of injury were collected., Results: 149 patients were included, with a median age of 31 years. 42 patients had fractures, 39 patients required wound sutures, 79 and 49 patients suffered from bruising and abrasions respectively. Only 5 patients required admission. No patients died or suffered life-threatening injuries. Women (65.1%) presented more frequently than men (34.9%). Forty-tree percent of all accidents happened in autumn ( p  < 0.001). Accidents happened more frequently in rainy conditions ( p  < 0.001). Mean number of days off work was 2.7 days, significantly increasing to 6.56 days when sustaining a fracture or dislocation ( p  = 0.02)., Conclusion: Tram tracks are potentially dangerous and may lead to clinically important injuries and significant number of days off work.

Injuries caused by mobile machinery or special purpose vehicles (SPVs) can lead to high socio-medical cost and fatality. In this descriptive study, we compared the epidemiology and injury profile of injured patients involved in SPVs-related incidents. We analyzed a nationwide database of SPV-related injured patients between January 2011 and December 2016. Injured patients were classified into three groups: pedestrian, motor vehicle occupant (MVO), and SPV operator groups. Of 1,419 cases, the highest number of SPV-related injured patients were found in the age group 40-59 years (671 cases, 47.3%) and at transport area (771 cases, 54.3%). The injury was most severe in the SPV operator group. The lower extremities were the most common fracture site, and intrathoracic injury was the most common visceral regions for SPV-related injured patients. SPV operator could lead to fatal intrathoracic injuries.

Parked vehicles can create deadly environments for young children who are left unattended. This study was a descriptive analysis to describe circumstances leading to death in children left unattended in parked motor vehicles in India. Between 2011 and 2018, there were 16 incidents that resulted in 28 fatalities across India, mostly in the summer months. The majority of children (19/28) were aged 4-6 years, of whom 25 (89%) gained access to unattended vehicles and became accidentally locked in. Hyperthermia-related deaths are a poorly recognised type of vehicular injury in India; there has been no analysis to describe circumstances leading to such fatalities.

Objective: Forward collision warning (FCW) and automatic emergency braking (AEB) have the potential to prevent or mitigate many large truck crashes. While these systems are known to be effective in passenger vehicles, less is known about their effectiveness in large trucks. The objective of this study was to estimate the effectiveness of these systems in reducing real-world crash rates of large trucks. Methods: Data on Class 8 trucks operating on limited-access highways during 2017-2019 were obtained from SmartDrive Systems. Detailed data on exposure measures and crash circumstances were extracted from video footage by both automated means and manual coding. Crash rates were compared by front crash prevention technology (FCW, AEB, neither), both for all police-reportable crashes overall and for relevant crash types. Results: FCW was associated with a statistically significant 22% reduction in the rate of police-reportable crashes per vehicle miles traveled, and a significant 44% reduction in the rear-end crash rate of large trucks. AEB also was associated with significant reductions-12% overall and 41% for rear-end crashes. Warnings were issued in 31% of rear-end crashes for FCW-equipped trucks. AEB intervened in 43% of rear-end crashes; about two thirds of these interventions involved autobrake activations. On average, speed was reduced by over half between the time of the intervention and impact for both systems. Observed reductions in same-direction sideswipe and roadway departure crashes per mile traveled were smaller in magnitude than those of rear-end crashes; these were consistent with other crash avoidance technologies suspected to be bundled with FCW/AEB in some cases, and very few front crash prevention interventions occurred in these types of crashes. Conclusions: FCW and AEB are effective countermeasures for crashes in which large trucks rear-end other vehicles. Large truck safety is expected to improve as new trucks are increasingly equipped with these systems. FCW has the advantage that some of these systems can be retrofitted to existing trucks, so benefits can be realized sooner and with less investment.

Objective: The objective of this study is to estimate the potential safety benefits of Emergency Lane Keeping (ELK) and Autonomous Emergency Steering (AES) systems with precise and reliable lateral positioning., Methods: The material comprised in-depth studies of fatal road crashes in Sweden in 2017. Crashes resulting in passenger car occupant fatality ( n  = 114) were included; single vehicle ( n  = 60) and head-on ( n  = 31), intersection ( n  = 9), rear-end ( n  = 5), overtaking ( n  = 5), animal ( n  = 1), other ( n  = 1, U-turn), and train ( n  = 2). Additionally, fatal collisions between motor vehicles and vulnerable road users (VRUs) were included; pedestrians ( n  = 36), cyclists/moped users ( n  = 15), and motorcyclists ( n  = 17). A case-by-case analysis was conducted to identify crashes potentially prevented by ELK and AES with precise (within 0.1 m accuracy) and reliable lateral positioning. The identified crashes potentially avoided by ELK systems involved unintentional drift-out-of-lane on roads with posted speed limits of ≥70 km/h (43 mph). For comparison, the same material was used to identify the potential safety benefits of the more traditional lane departure warning (LDW) and Lane Keeping Assist (LKA) systems without enhanced lateral positioning. Crashes potentially avoided by LDW/LKA occurred on roads with visible lane markings and without rumble strips on the departure side while ELK with enhanced lateral positioning is not reliant on lane markings. Collisions potentially avoided by AES occurred on roads with sufficient escape zones and road friction., Results: The study shows that ELK systems with enhanced lateral positioning could potentially avoid 33 - 45 (36 - 49%) out of 91 head-on and single car crashes resulting in passenger car occupant fatality, which corresponds to a further 18% (5/28) compared to traditional lane support (LDW/LKA) without enhanced lateral positioning. The improved lane keeping addresses crashes involving absent or nonvisible lane markings that are covered by snow. Furthermore, the study shows that the evasive steering features of AES systems with enhanced lateral positioning could potentially prevent 28 (54%) out of 52 collisions resulting in passenger car occupant fatalities (i.e., head-on, intersection, rear-end, overtaking, and animal collision). Additionally, AES with enhanced lateral positioning could potentially prevent 16 (31%) out of 51 collisions between motor vehicles and vulnerable road users resulting in fatally injured pedestrians, cyclists or moped users. The total potential safety benefits of AES include both lane keeping and evasive steering features including lane escaping. The study identified that the total number of crashes potentially avoided by AES corresponds to 42% out of all crashes resulting in passenger car occupant fatalities ( n  = 48/114)., Conclusions: This is a first attempt to identify and quantify the increase in potential safety benefits, based on the assumption that robust lateral vehicle positioning has been applied to lane keeping as well as lane escaping. In addition to the potentially increased number of prevented crashes compared to traditional lane support, a robust lateral positioning system may have the potential to improve the redundancy in positioning systems. Previous research suggests several techniques to improve lateral vehicle positioning. However, further research is required, including testing for implementation of adequately improved lateral vehicle awareness to establish which techniques are appropriate.

Objective: Speeding in work zones is common and poses significant safety hazards to motorists and workers. Previous studies have demonstrated that speeding is reduced when workers are visible to the drivers, suggesting that concern for the safety of workers influences drivers' speed choice. Conversely, the extent of speeding when workers are not visible suggests that drivers underestimate the increased risk of crashes or other damage to their vehicles associated with the poorer road conditions common at roadworks (loose surfaces and debris, narrower lane width and drop-offs etc.). To better understand the factors influencing drivers' speeds in work zones, this paper examined the extent to which drivers' speed choices are influenced by their perceived likelihoods of injuring workers and damaging their own vehicles., Methods: Driver-nominated speeds and perceived likelihoods of worker injury and vehicle damage were collected in an online survey of 405 drivers from Queensland, Australia, by showing photographs of 12 real-world work zones. The data were analyzed using regression techniques to examine the factors influencing driver-nominated speeds and likelihood of speeding., Results: The results supported the hypothesis that both drivers' perceived likelihood of injuring workers and damaging vehicles strongly influence their nominated speeds (p < 0.001). Young and middle-aged drivers nominated 2.5-6.3 km/h higher speeds than older drivers (p < 0.001). Car drivers who also held truck licenses nominated 4.5 km/h higher speeds (p < 0.001) and 81% higher odds of nominating speeds higher than posted limits (p < 0.001) than car-only licensed drivers. Drivers nominated lower speeds at road curves (12.1 km/h lower speed and 53% lower odds of speeding, p < 0.001), if adjacent areas of travel lanes were unpaved or had loose materials (11.5 km/h lower speed and 66% lower odds, p < 0.001), and when workers were visible in work zones (1.4 km/h lower speed, p = 0.004, and 27% lower odds, p = 0.041)., Conclusions: In addition to driver demographic and work zone characteristics, drivers' perceptions of safe speeds depend on their perceptions of the likelihood of worker injury and vehicle damage at work zones. These findings suggest that interventions to heighten drivers' perceptions of the likelihood of damage to their vehicles may be useful in moderating speeds at roadworks.

Objective: Automated Truck-Mounted Attenuators (ATMAs) have the potential to improve work zone safety by removing the human driver out of a vehicle that is positioned in work zones to absorb impact from errant vehicles. However, this automated technology is expensive and can be detrimental to safety and project success if operated incorrectly (e.g., operating limitations and procedures not followed). Therefore, it is important to understand users' perceptions of ATMAs and how training can improve appropriate adoption of this technology. The objective of this study was to evaluate how work zone workers perceive the usefulness of and the capabilities of automation in Truck-Mounted Attenuators., Methods: A survey study was conducted with 13 Department of Transportation (DOT) workers in Colorado and California. Each of the DOT workers in this study had some previous experience with the ATMA, either in real-world applications and/or formal training. The survey collected information on participant job specifications, experience with the ATMA, training received, trust in the ATMA, usability of the HMIs, and operating capabilities of the automation., Results: Workers reported an overall positive acceptance of this technology. This was supported by their expectation that it would reduce crash severity; that there was a reasonable workload associated with operating procedures for the automation; and by their overall trust in the automation's reliability. However, workers noted concerns regarding their trust in the automation under various contexts, such as poor visibility and denser traffic volumes. Further, trust in the technology was greatest among workers with higher levels of ATMA training and longer experience working with the ATMA., Conclusions: This research presents a novel perspective on user acceptance of ATMA technology. These findings can help jurisdictions achieve the safety improvements that investment and deployment of automation in work zones offers, by identifying the disconnect between operators and technology.

Importance: Evidence of whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19), can be transmitted as an aerosol (ie, airborne) has substantial public health implications., Objective: To investigate potential transmission routes of SARS-CoV-2 infection with epidemiologic evidence from a COVID-19 outbreak., Design, Setting, and Participants: This cohort study examined a community COVID-19 outbreak in Zhejiang province. On January 19, 2020, 128 individuals took 2 buses (60 [46.9%] from bus 1 and 68 [53.1%] from bus 2) on a 100-minute round trip to attend a 150-minute worship event. The source patient was a passenger on bus 2. We compared risks of SARS-CoV-2 infection among at-risk individuals taking bus 1 (n = 60) and bus 2 (n = 67 [source patient excluded]) and among all other individuals (n = 172) attending the worship event. We also divided seats on the exposed bus into high-risk and low-risk zones according to the distance from the source patient and compared COVID-19 risks in each zone. In both buses, central air conditioners were in indoor recirculation mode., Main Outcomes and Measures: SARS-CoV-2 infection was confirmed by reverse transcription polymerase chain reaction or by viral genome sequencing results. Attack rates for SARS-CoV-2 infection were calculated for different groups, and the spatial distribution of individuals who developed infection on bus 2 was obtained., Results: Of the 128 participants, 15 (11.7%) were men, 113 (88.3%) were women, and the mean age was 58.6 years. On bus 2, 24 of the 68 individuals (35.3% [including the index patient]) received a diagnosis of COVID-19 after the event. Meanwhile, none of the 60 individuals in bus 1 were infected. Among the other 172 individuals at the worship event, 7 (4.1%) subsequently received a COVID-19 diagnosis. Individuals in bus 2 had a 34.3% (95% CI, 24.1%-46.3%) higher risk of getting COVID-19 compared with those in bus 1 and were 11.4 (95% CI, 5.1-25.4) times more likely to have COVID-19 compared with all other individuals attending the worship event. Within bus 2, individuals in high-risk zones had moderately, but nonsignificantly, higher risk for COVID-19 compared with those in the low-risk zones. The absence of a significantly increased risk in the part of the bus closer to the index case suggested that airborne spread of the virus may at least partially explain the markedly high attack rate observed., Conclusions and Relevance: In this cohort study and case investigation of a community outbreak of COVID-19 in Zhejiang province, individuals who rode a bus to a worship event with a patient with COVID-19 had a higher risk of SARS-CoV-2 infection than individuals who rode another bus to the same event. Airborne spread of SARS-CoV-2 seems likely to have contributed to the high attack rate in the exposed bus. Future efforts at prevention and control must consider the potential for airborne spread of the virus.

Background: Pedestrian and cyclist injuries are a major concern globally, but especially in low-income countries. Locally conducted research is needed to measure the size of the problem and advise policy on road safety interventions. We wanted to investigate the precise circumstances of these injuries in Lilongwe, Malawi and to identify risk factors for severe injuries., Methods: Cross-sectional study of all adult pedestrian and cyclist injuries presenting to a large central hospital. This was a sub-study of a larger study with all types of road users included. All patients provided detailed information about the incidents leading to injury and were tested for alcohol., Results: There were 222 pedestrians, 183 bicycle riders and 42 bicycle passengers among the 1259 adult road traffic injury victims that were treated at Kamuzu Central Hospital during a 90-day period in 2019. Of these injuries, 60.2% occurred while the victim was walking/cycling along the road and 22.3% when the victim was trying to cross the road. The majority of the victims were men (89.1%). Helmet use for bicyclists was almost non-existent. Only 1 patient had used reflective devices when injured in the dark, despite 44.7% of these injuries occurring in reduced light conditions. There was an increased risk for serious and fatal injuries for pedestrians compared with bicyclists, and also compared with all types of road users. Patients injured in rural areas and those hit by lorries were more severely injured. Consuming alcohol before being injured was associated with more severe injuries in bicyclists. Being injured while crossing the road at painted zebra crossings was associated with an increased risk of serious and potentially fatal injuries., Conclusion: This study identified important risk factors for severe injuries in pedestrians and cyclists. Implications for preventive measures are presented in a Haddon Matrix., (© 2020 The College of Medicine and the Medical Association of Malawi.)

Due to its direct relevance to post-disaster operations, meter reading and civil refuse collection, the Uncertain Capacitated Arc Routing Problem (UCARP) is an important optimisation problem. Stochastic models are critical to study as they more accurately represent the real world than their deterministic counterparts. Although there have been extensive studies in solving routing problems under uncertainty, very few have considered UCARP, and none consider collaboration between vehicles to handle the negative effects of uncertainty. This article proposes a novel Solution Construction Procedure (SCP) that generates solutions to UCARP within a collaborative, multi-vehicle framework. It consists of two types of collaborative activities: one when a vehicle unexpectedly expends capacity ( route failure ), and the other during the refill process. Then, we propose a Genetic Programming Hyper-Heuristic (GPHH) algorithm to evolve the routing policy used within the collaborative framework. The experimental studies show that the new heuristic with vehicle collaboration and GP-evolved routing policy significantly outperforms the compared state-of-the-art algorithms on commonly studied test problems. This is shown to be especially true on instances with larger numbers of tasks and vehicles. This clearly shows the advantage of vehicle collaboration in handling the uncertain environment, and the effectiveness of the newly proposed algorithm.

Many researchers use life cycle assessment methodology to investigate the energy and environmental impacts of energy-saving and new energy vehicles. However, in the context of China, the life cycle energy-saving and emission-reduction effects of extended-range electric vehicles (EREVs), and the optimal applicable vehicle size and driving conditions for EREVs have been rarely studied. In this study, based on the life cycle assessment theory, the resource consumption, energy exhaustion, and environmental impact of EREVs were comprehensively analyzed. In addition, a differential evaluation model of ecological benefits was established for comparing EREVs with other vehicles with different power sources. Finally, scenario analysis was performed in terms of different vehicle sizes and driving conditions. The results have shown that EREV has great advantages in reducing mineral resource consumption and fossil energy consumption. The consumption of mineral resources of EREV is 14.68% lower than that of HEV, and the consumption of fossil energy is 34.72% lower than that of ICEV. In terms of environmental impact, EREV lies in the middle position. The scenario analysis has revealed that, for EREV in China, the optimal vehicle size is the passenger car and the optimal driving condition is the suburban condition. This work helps to understand the environmental performance of EREVs in China and may provide a decision-making reference for the government., Competing Interests: The authors have declared that no competing interests exist.

Objective: On March 23, 2018 a Tesla Model X driver was killed in Mountain View, CA after colliding with a previously collapsed crash attenuator at a speed of 31.7 m/s (70.8 mph). The attenuator, which must be repaired following a collision, had been struck 11 days prior by a 2010 Toyota Prius at a minimum speed of 33.9 m/s (75.8 mph). The Toyota driver survived. The maintenance of traffic safety hardware and benefit of the crash attenuator are evaluated., Methods: Public information from an NTSB investigation is used to evaluate crash impact severity. Vehicle crash data from Event Data Recorders (EDRs) are compared with test data for the SCI Smart Cushion 100GM crash attenuator. An idealized triangular crash pulse is used to estimate longitudinal peak impact acceleration, and longitudinal impact energy is calculated. The medical outcomes for drivers are considered., Results: The California Department of Transportation (Caltrans) maintenance and repair program for safety critical traffic safety hardware was found to be ineffective. The Tesla collision into the nonfunctional attenuator was estimated have roughly three times the impact force to the passenger compartment as it would have been if striking a functional attenuator., Conclusions: The Tesla driver could have survived the collision had the crash attenuator been functional. Roadway management and timely maintenance of safety critical traffic hardware are necessary to ensure safety.

Objective: To determine whether occupants of collisions involving at least one vehicle with an available Automatic Collision Notification (ACN) system have quicker times from collision to 1) Emergency Medical Services (EMS) notification and 2) arrival to a medical center., Methods: Using data from the 2017 Crash Investigation Sampling System, vehicles were categorized as whether ACN was available using data from the CISS's dataset of crash avoidance system availability (in which ACN is included though notably not a crash avoidance system). A Cox proportional hazards model-overall and stratified by urbanization-was used to compare the time from collision to both EMS notification and EMS arrival to a medical center., Results: A total of 2,034 collisions (weight n: 2,775,512) involving 4235 occupants (weighted n: 4,987,669) were included. An estimated 259,021 (9.3%) and 546,223 occupants (11.0%) were in a collision in which one vehicle had ACN equipped. The median time to EMS notification was longer for collisions in which no involved vehicles had ACN available (median 4, IQR 2-9 minutes) than ACN-exposed collisions (median 2, IQR 1-5 minutes). There was a marginally significant higher hazard (i.e., instantaneous risk) of EMS notification for collisions with at least one vehicle having ACN available (HR 1.77, 95% CI 0.99-3.15). Likewise, there was a higher instantaneous risk of medical center arrival for occupants (HR 1.80, 95% CI 1.41-2.30) involved in collisions in which at least one vehicle had ACN available. ACN was associated with quicker EMS notification only in urban areas (HR 3.06, 95% CI 1.57-5.97) and associated with a greater reduction in time to medical facility in less urban areas (median 36 vs 45 minutes, HR 2.12, 95% CI 1.22-3.63)., Conclusions: This is the first study to directly compare EMS response-related times between collisions involving vehicles with and without ACN available. The current data corroborate prior literature reporting quicker EMS notification times among collisions involving ACN-equipped vehicles. This is the first study to find that ACN is also associated to quicker times to medical center arrival, particularly for collisions occurring in less urban areas. Future research examining whether these decreased times are associated with better clinical outcomes are needed in order to fully assess ACN's ability to prevent trauma-related mortality and morbidity.

Objective: To understand which users' characteristics influence their preferences in the selection of vehicle seating configurations and positions across different traveling scenarios involving a fully automated vehicle (FAV)., Methods: Participants ( n  = 730) completed an online survey in which they were asked to imagine traveling in a FAV across three hypothetical scenarios. Participants were asked to select between five different seating configurations and four positions for each scenario and about their anthropometry and their driving/riding experience. Multinomial regression analyses were conducted to identify the factors that influenced users' preferences., Results: FAV Configuration #3 (traditional light vehicle seating configuration) was the preferred seating configuration for 74% of the participants, followed by FAV #2 (in which the two seating rows face each other, 13%) in Scenario 1 (riding by oneself). Similar numbers were observed in Scenario 3 (riding with an unknown person). In Scenario 2 (riding with their partner), participants preferred FAV #2 (12.5%) and FAV #5 (in which the front seat passengers point toward a common point in the front, conventional rear seat configuration, 17.5%). Having close family was significant to choose the traditional vehicle configuration over other vehicle configurations, but only when the participants were driving by themselves. Having previously experienced motion sickness was significant to prefer a forward seating configuration when the trip was shared with partners. Belonging to a particular height or weight group was significantly associated with the preferred seat within the preferred FAV configuration (the driver's position in FAV #3; a rear-facing seat in FAV #2), although there was not a clear trend between increasing weight and/or height and preferring one seating position over the others., Conclusions: Previous work had shown differences in participants' preferences for seating configurations and positions depending on age, sex and country. While increasing the sample size, the current study analyses other factors that were associated with choosing one vehicle configuration and seating position over others. As these factors are directly related to the likelihood of sustaining injuries in the event of a crash, the current study provides important insights regarding the potential risk factors for FAV occupants.

Objectives: Driving simulation is an important platform for studying vehicle automation. There are different approaches to using this platform - with most using scripting or programmatic tools to simulate vehicle automation. A less frequently used approach, the Wizard-of-Oz method, has potential for increased flexibility and efficiency in designing and conducting experiments. This study designed and evaluated an experimental setup to examine the feasibility of this approach as an alternative for conducting automation studies., Methods: Twenty-four participants experienced simulated vehicle automation in two platforms, one where the automation was controlled by algorithms, and the other where the automation was simulated by an external operator. Surveys were administered after each drive and the drivers' takeover performance after the automation disengaged was measured., Results: Results indicate that while the kinematic parameters of the driving differed significantly for the two platforms, there were no significant differences in the perceptions of participants and in their takeover performance between the two platforms., Conclusion: These results provide evidence for the use of alternative approaches for the conduct of human factors studies on vehicle automation, potentially lowering barriers to undertaking such experiments while increasing flexibility in designing more complex studies.

Technical vehicle inspection centers are widespread in Kuwait. All vehicles should pass the test every 2 years if not older than 6 years or every 1 year for other vehicles. This study provided an insight into the data collected from test centers and the emission test utilized in Kuwait. Data were collected from test centers in the six Kuwaiti governorates. European standards were selected as limits for emission violations. Independent variables included the place of vehicle manufacture, vehicle's age, and odometer reading. A multinomial logit model was used to identify the significant predictors and determine the correlation between dependent and independent variables. Artificial neural network was employed to compare prediction estimates of neural network and multinomial logit. The findings showed that the place of vehicle's manufacture, vehicle's age, and odometer reading were significant regarding violating emission standards of carbon monoxide (CO). Asian vehicles, vehicles with more than 150,000 km mileage, and vehicles older than 15 years had a higher probability of failing the CO test compared to the place of manufacture. In contrast, the odometer reading was the only significant indicator for vehicles that have failed the hydrocarbons test, especially for vehicles with 150,000 km odometer reading. The findings of this study can reduce air-pollution, time, and money by targeting the most polluting vehicles; thus, more efficient test can be performed.

Motor vehicle crashes remain the leading cause of adolescent mortality and injury in the United States. For young drivers, crash risk peaks immediately after licensure and declines during the next two years, making the point of licensure an important safety intervention opportunity. Legislation in Ohio established a unique health-transportation partnership among the State of Ohio, Children's Hospital of Philadelphia, and Diagnostic Driving, Inc., to identify underprepared driver license applicants through a virtual driving assessment system. The system, a computer-based virtual driving test, exposes drivers to common serious crash scenarios to identify critical skill deficits and is delivered in testing centers immediately before the on-road examination. A pilot study of license applicants who completed it showed that the virtual driving assessment system accurately predicted which drivers would fail the on-road examination and provided automated feedback that informed drivers on their skill deficits. At this time, the partnership's work is informing policy changes around integrating the virtual driving assessment system into licensing and driver training with the aim of reducing crashes in the first months of independent driving. The system can be developed to identify deficits in safety-critical skills that lead to crashes in new drivers and to address challenges that the coronavirus disease 2019 pandemic has introduced to driver testing and training.

The consequences of crashes, including injury, loss of lives, and damage to properties, are further worsened when buses plying expressways are involved in the crash. Previous studies have separately analyzed crash severity in terms of monetary cost, injuries and loss of lives, and the size of crashes in terms of the number of vehicles involved. However, as both outcome variables are correlated, it is imperative to perform a combined analysis using an appropriate econometric model to achieve a better model fit. This study contributes to the literature by jointly exploring the factors influencing the severity and size of express bus-involved crashes that occur on expressways and characterizes the dependence between both outcome variables by employing a more plausible copula regression framework. Likelihood ratio tests were also conducted to investigate the temporal stability of the factors that affect both crash severity and size. Based on the goodness-of-fit statistics, the Frank copula model proved superior to the independent ordered probit model. The estimate of the underlying dependence between the outcome variables provided a better comprehension of the correlation between them. Temporal instability was detected for the individual parameters in the models and is attributed to the changing driving behavior due to the heightened road safety campaigns. The results suggest that traffic exposure measures are significantly associated with a higher propensity of observing increased bus crash severity and size. Insights into the factors influencing the size and severity of express bus crashes are discussed, and appropriate engineering, enforcement, and education-related countermeasures are proposed., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The increase in data amount makes the traditional Internet of Vehicles (IoV) fail to meet users' needs. Hence, the IoV is explored in series. To study the construction of freight integer linear programming (ILP) model based on fog computing (FG), and to analyze the application of the model in the optimization of the networking deployment (ND) of the IoV. FG and ILP are combined to build a freight computing ILP model. The model is used to analyze the application of ND optimization in the IoV system through simulations. The results show that while analyzing the ND results in different scenarios, the model is more suitable for small-scale scenarios and can optimize the objective function; however, its utilization rate is low in large-scale scenarios. While comparing and analyzing the network cost and running time, compared with traditional cloud computing solutions, the ND solution based on FG requires less cost, shorter running time, and has apparent effectiveness and efficiency. Therefore, it is found that the FG-based model has low cost, short running time, and apparent efficiency, which provides an experimental basis for the application of the later deployment of freight vehicles (FVs) in the Internet of Things (IoT) system for ND optimization. The results will provide important theoretical support for the overall deployment of IoV., Competing Interests: The authors have declared that no competing interests exist.

Although tires maintain the only contact between the vehicle and the ground, tire failures are still underrepresented in traffic safety assessments. Vehicle stability and safety can deteriorate significantly by a sudden tire failure. The current body of literature on tire failure-related crashes is limited, and no previous study was found to extensively investigate the factors associated with tire failures and the corresponding injury severity. The contributions of this study include (i) investigating the factors affecting tire failures, (ii) assessing the impacts of tire failures on occupant injury severity, and (iii) demonstrating the necessity of statewide tire inspection regulations. An extensive exploratory analysis was performed using ten years (2007-2016) of historical crash data along I-80 in Wyoming. Binary logistic regression with the Bayesian inference approach was applied to develop two separate models: tire failure and injury severity model. The results from the tire failure model showed that vehicle speeds greater than 75 mph, commercial motor vehicles, summer season, daytime, the presence of rough surface, downgrades, and concrete pavement are all related to higher tire failure occurrences. On the other hand, the incidence of a tire failure in a crash significantly contributed to more severe injuries when combined with any of the following instances: fire or explosion, rollover, guardrail hits, runoff road, angle, rear-end, clear weather, speeding, downgrades, and curved segments. With the incorporation of the random intercept in the modeling procedure, the injury severity analysis found a strong presence (42 %) of intra-crash correlation (effects of the common crash-specific unobserved factors) in occupant injury severity within the same crash. Finally, based on the findings of the study, recommendations are provided to alleviate tire-related problems., (Copyright © 2020 Elsevier Ltd. All rights reserved.)