Comparative analysis of real-world vehicular emissions from BS-IV and BS-VI cars in India.

This study investigates real-world carbon dioxides (CO2) and nitrogen oxides (NOx) emissions from diesel (Bharat Stage-IV (BS-IV)) and petrol/gasoline (BS-IV and BS-VI) cars in Indian driving conditions using a portable emission measurement system (PEMS). The paired sample t-test revealed a significant difference ( p < 0.05) in NOx and CO2 emissions among the three types of cars, except for CO2 emissions ( p > 0.05) between BS-IV petrol and BS-VI petrol cars. The highest NOx emission rates were observed in all car types during acceleration (> 1 m/s2) and deceleration (- 2 m/s2). CO2 emission rates were also high during acceleration (> 1 m/s2) for all car types. At low speeds (around 20 kmph), all car types had low emissions of CO2 and NOx, with acceleration and deceleration rates ranging from - 0.5 to 0.5 m/s2. BS-IV diesel cars emit significantly higher NOx emissions compared to petrol cars, especially at vehicle-specific power (VSP) bin 0 (deceleration to idling mode) and during VSP bin 7 (acceleration mode). BS-IV diesel cars emit 228% and 530% higher NOx emissions than BS-IV and BS-VI petrol cars at VSP bins 0 and 7, respectively. CO2 emissions from BS-VI petrol cars were 10% lower than those from BS-IV petrol cars across all VSP bins, indicating moderate reductions. Furthermore, diesel cars emit 140% less CO2 emissions than petrol cars across various VSP bins. The findings highlight the need for cleaner technologies and responsible driving practices to address vehicular emission concerns.

A matched case-control analysis of autonomous vs human-driven vehicle accidents.

Despite the recent advancements that Autonomous Vehicles have shown in their potential to improve safety and operation, considering differences between Autonomous Vehicles and Human-Driven Vehicles in accidents remain unidentified due to the scarcity of real-world Autonomous Vehicles accident data. We investigated the difference in accident occurrence between Autonomous Vehicles' levels and Human-Driven Vehicles by utilizing 2100 Advanced Driving Systems and Advanced Driver Assistance Systems and 35,113 Human-Driven Vehicles accident data. A matched case-control design was conducted to investigate the differential characteristics involving Autonomous' versus Human-Driven Vehicles' accidents. The analysis suggests that accidents of vehicles equipped with Advanced Driving Systems generally have a lower chance of occurring than Human-Driven Vehicles in most of the similar accident scenarios. However, accidents involving Advanced Driving Systems occur more frequently than Human-Driven Vehicle accidents under dawn/dusk or turning conditions, which is 5.25 and 1.98 times higher, respectively. Our research reveals the accident risk disparities between Autonomous Vehicles and Human-Driven Vehicles, informing future development in Autonomous technology and safety enhancements.

Classification of autonomous vehicle crash severity: Solving the problems of imbalanced datasets and small sample size.

Only a few researchers have shown how environmental factors and road features relate to Autonomous Vehicle (AV) crash severity levels, and none have focused on the data limitation problems, such as small sample sizes, imbalanced datasets, and high dimensional features. To address these problems, we analyzed an AV crash dataset (2019 to 2021) from the California Department of Motor Vehicles (CA DMV), which included 266 collision reports (51 of those causing injuries). We included external environmental variables by collecting various points of interest (POIs) and roadway features from Open Street Map (OSM) and Data San Francisco (SF). Random Over-Sampling Examples (ROSE) and the Synthetic Minority Over-Sampling Technique (SMOTE) methods were used to balance the dataset and increase the sample size. These two balancing methods were used to expand the dataset and solve the small sample size problem simultaneously. Mutual information, random forest, and XGboost were utilized to address the high dimensional feature and the selection problem caused by including a variety of types of POIs as predictive variables. Because existing studies do not use consistent procedures, we compared the effectiveness of using the feature-selection preprocessing method as the first process to employing the data-balance technique as the first process. Our results showed that AV crash severity levels are related to vehicle manufacturers, vehicle damage level, collision type, vehicle movement, the parties involved in the crash, speed limit, and some types of POIs (areas near transportation, entertainment venues, public places, schools, and medical facilities). Both resampling methods and three data preprocessing methods improved model performance, and the model that used SMOTE and data-balancing first was the best. The results suggest that over-sampling and the feature selection method can improve model prediction performance and define new factors related to AV crash severity levels.

Safety in higher level automated vehicles: Investigating edge cases in crashes of vehicles equipped with automated driving systems.

With emerging Automated Driving Systems (ADS) representing Automated Vehicles (AVs) of Level 3 or higher as classified by the Society of Automotive Engineers, several AV manufacturers are testing their vehicles on public roadways in the U.S. The safety performance of AVs has become a major concern for the transportation industry. Several ADS-equipped vehicle crashes have been reported to the National Highway Traffic Safety Administration (NHTSA) in recent years. Scrutinizing these crashes can reveal rare or complex scenarios beyond the normal capabilities of AV technologies called "edge cases." Investigating edge-case crashes helps AV companies prepare vehicles to handle these unusual scenarios and, as such, improves traffic safety. Through analyzing the NHTSA data from July 2021 to February 2023, this study utilizes an unsupervised machine learning technique, hierarchical clustering, to identify edge cases in ADS-equipped vehicle crashes. Fifteen out of 189 observations are identified as edge cases, representing 8 % of the population. Injuries occurred in 10 % of all crashes (19 out of 189), but the proportion rose to 27 % for edge cases (4 out of 15 edge cases). Based on the results, edge cases could be initiated by AVs, humans, infrastructure/environment, or their combination. Humans can be identified as one of the contributors to the onset of edge-case crashes in 60 % of the edge cases (9 out of 15 edge cases). The main scenarios for edge cases include unlawful behaviors of crash partners, absence of a safety driver within the AV, precrash disengagement, and complex events challenging for ADS, e.g., unexpected obstacles, unclear road markings, and sudden and unexpected changes in traffic flow, such as abrupt road congestion or sudden stopped traffic from a crash. Identifying and investigating edge cases is crucial for improving transportation safety and building public trust in AVs.

Sources of Tobacco Smoke Exposure and Their Associations With Serum Cotinine Levels Among US Children and Adolescents.

INTRODUCTION: We assessed tobacco smoke exposure (TSE) levels based on private and public locations of TSE according to race and ethnicity among US school-aged children ages 6-11 years and adolescents ages 12-17 years. AIMS AND METHODS: Data were from 5296 children and adolescents who participated in the National Health and Nutrition Examination Survey (NHANES) 2013-2018. Racial and ethnic groups were non-Hispanic white, black, other or multiracial, and Hispanic. NHANES assessed serum cotinine and the following TSE locations: homes and whether smokers did not smoke indoors (home thirdhand smoke [THS] exposure proxy) or smoked indoors (secondhand [SHS] and THS exposure proxy), cars, in other homes, restaurants, or any other indoor area. We used stratified weighted linear regression models by racial and ethnic groups and assessed the variance in cotinine levels explained by each location within each age group. RESULTS: Among 6-11-year-olds, exposure to home THS only and home SHS + THS predicted higher log-cotinine among all racial and ethnic groups. Non-Hispanic white children exposed to car TSE had higher log-cotinine (ß = 1.64, 95% confidence interval [CI] = 0.91% to 2.37%) compared to those unexposed. Non-Hispanic other/multiracial children exposed to restaurant TSE had higher log-cotinine (ß = 1.13, 95% CI = 0.23% to 2.03%) compared to those unexposed. Among 12-17-year-olds, home SHS + THS exposure predicted higher log-cotinine among all racial and ethnic groups, except for non-Hispanic black adolescents. Car TSE predicted higher log-cotinine among all racial and ethnic groups. Non-Hispanic black adolescents with TSE in another indoor area had higher log-cotinine (ß = 2.84, 95% CI = 0.85% to 4.83%) compared to those unexposed. CONCLUSIONS: TSE location was uniquely associated with cotinine levels by race and ethnicity. Smoke-free home and car legislation are needed to reduce TSE among children and adolescents of all racial and ethnic backgrounds. IMPLICATIONS: Racial and ethnic disparities in TSE trends have remained stable among US children and adolescents over time. This study's results indicate that TSE locations differentially contribute to biochemically measured TSE within racial and ethnic groups. Home TSE significantly contributed to cotinine levels among school-aged children 6-11 years old, and car TSE significantly contributed to cotinine levels among adolescents 12-17 years old. Racial and ethnic differences in locations of TSE were observed among each age group. Study findings provide unique insight into TSE sources, and indicate that home and car smoke-free legislation have great potential to reduce TSE among youth of all racial and ethnic backgrounds.

User characteristics and service satisfaction of car sharing systems: Evidence from Hangzhou, China.

Car sharing has become a new mode of transport during the past two decades in the world. Its rapid growth in China has attracted a wide range of users and posed some problems. The main focus is on service efficiency and user satisfaction. To explore possible service enhancement and management intervention, this study aims at capturing the user characteristics according to different user types and scrutinizing their satisfaction with station-based one-way car sharing service. The study firstly illustrates descriptive statistics of user profile. This is followed by a study of user satisfaction influenced by user rates on staffs, the efficiency of rental process, vehicle situation, the use of credit card and their familiarity towards rental station. Furthermore, by clustering users according to the total travel time and distance during one rent, two different types of users are identified and defined as User Group A (UGA) and User Group B (UGB). To examine how fully do users utilize the shared cars, ANOVA was conducted implying family car ownership, total travel distance and main travel purpose have strong impact on total rental time for UGB, while for UGA, travel purpose and age have strong impact. Finally, ordinal logistic regression was introduced to find that for UGB, "shopping" is the main travel purpose with longer rental time, whereas for UGA, "out for business", "shopping", "visit friends" or "pick up others" are the main travel purposes with longer total travel time. Based on the findings, advices for operators on how to improve service quality and suggestions for government management strategy are discussed, respectively.

Beyond the dichotomy: How ride-hailing competes with and complements public transport.

Since ride-hailing has become an important travel alternative in many cities worldwide, a fervent debate is underway on whether it competes with or complements public transport services. We use Uber trip data in six cities in the United States and Europe to identify the most attractive public transport alternative for each ride. We then address the following questions: (i) How does ride-hailing travel time and cost compare to the fastest public transport alternative? (ii) What proportion of ride-hailing trips do not have a viable public transport alternative? (iii) How does ride-hailing change overall service accessibility? (iv) What is the relation between demand share and relative competition between the two alternatives? Our findings suggest that the dichotomy-competing with or complementing-is false. Though the vast majority of ride-hailing trips have a viable public transport alternative, between 20% and 40% of them have no viable public transport alternative. The increased service accessibility attributed to the inclusion of ride-hailing is greater in our US cities than in their European counterparts. Demand split is directly related to the relative competitiveness of travel times i.e. when public transport travel times are competitive ride-hailing demand share is low and vice-versa.

Space-time clustering-based method to optimize shareability in real-time ride-sharing.

Real-time ride-sharing has become popular in recent years. However, the underlying optimization problem for this service is highly complex. One of the most critical challenges when solving the problem is solution quality and computation time, especially in large-scale problems where the number of received requests is huge. In this paper, we rely on an exact solving method to ensure the quality of the solution, while using AI-based techniques to limit the number of requests that we feed to the solver. More precisely, we propose a clustering method based on a new shareability function to put the most shareable trips inside separate clusters. Previous studies only consider Spatio-temporal dependencies to do clustering on the mobility service requests, which is not efficient in finding the shareable trips. Here, we define the shareability function to consider all the different sharing states for each pair of trips. Each cluster is then managed with a proposed heuristic framework in order to solve the matching problem inside each cluster. As the method favors sharing, we present the number of sharing constraints to allow the service to choose the number of shared trips. To validate our proposal, we employ the proposed method on the network of Lyon city in France, with half-million requests in the morning peak from 6 to 10 AM. The results demonstrate that the algorithm can provide high-quality solutions in a short time for large-scale problems. The proposed clustering method can also be used for different mobility service problems such as car-sharing, bike-sharing, etc.

Markovian city-scale modelling and mitigation of micro-particles from tires.

The recent uptake in popularity in vehicles with zero tailpipe emissions is a welcome development in the fight against traffic induced airborne pollutants. As vehicle fleets become electrified, and tailpipe emissions become less prevalent, non-tailpipe emissions (from tires and brake disks) will become the dominant source of traffic related emissions, and will in all likelihood become a major concern for human health. This trend is likely to be exacerbated by the heavier weight of electric vehicles, their increased power, and their increased torque capabilities, when compared with traditional vehicles. While the problem of emissions from tire wear is well-known, issues around the process of tire abrasion, its impact on the environment, and modelling and mitigation measures, remain relatively unexplored. Work on this topic has proceeded in several discrete directions including: on-vehicle collection methods; vehicle tire-wear abatement algorithms and controlling the ride characteristics of a vehicle, all with a view to abating tire emissions. Additional approaches include access control mechanisms to manage aggregate tire emissions in a geofenced area with other notable work focussing on understanding the particle size distribution of tire generated PM, the degree to which particles become airborne, and the health impacts of tire emissions. While such efforts are already underway, the problem of developing models to predict the aggregate picture of a network of vehicles at the scale of a city, has yet to be considered. Our objective in this paper is to present one such model, built using ideas from Markov chains. Applications of our modelling approach are given toward the end of this note, both to illustrate the utility of the proposed method, and to illustrate its application as part of a method to collect tire dust particles.

Assessment of end-of-life vehicle recycling: Remanufacturing waste sheet steel into mesh sheet.

The automobile industry contributes significantly to global energy use and carbon emissions. Hence, there are significant economic and environmental benefits in recovering materials from end-of-life vehicles (ELVs). Here, the remanufacturing of waste steel sheet (WSS) from ELVs into useful mesh steel sheet (MSS) for metal forming applications was evaluated based on its technological, economic, and environmental feasibility. A remanufacturing plant with a dismantling capacity of over 30,171 ELV/year and a recovery capacity of 1000 m2/d of WSS was used as a case study. Remanufacturing can achieve a total reduction of ~3800 kg CO2/ELV and an economic benefit of ~775 USD/ELV compared with conventional recycling. The calculated feasibility indexes were similar to or exceeded standard feasibility thresholds, indicating that WSS remanufacturing is a viable sustainable development route and has synergistic benefits when combined with existing recycling plants, especially in developing countries as small-to-medium enterprises.

Research on the internal flow and macroscopic characteristics of a diesel fuel injection process.

The internal flow and macroscopic spray behaviors of a fuel injection process were studied with schlieren spray techniques and simulations. The injection pressures(Pin)and ambient pressures(Pout)were applied in a wide range. The results showed that increasing the Pin is likely to decrease the flow performance of the nozzle. Furthermore, increasing the Pin can increase the spray tip penetration. However, the effect of Pin on the spray cone angle was not evident. The spray cone angle at an injection pressure of 160MPa was 21.7% greater than at a pressure of 100MPa during the initial spraying stage. Additionally, the discharge coefficient increased under high Pout, and the decrease in Pout can promote the formation of cavitation. Finally, increasing the Pout can decrease the penetration, while the spray angle becomes wider, especially at the initial spray stage, and high Pout will enhance the interaction of the spray and the air, which can enhance the spray quality.

Determining passenger car equivalent (PCEs) for pretimed signalized intersections with severe motorcycle composition using Bayesian linear regression.

Pretimed signalized intersection is known as a common source of congestion, especially in urban heterogeneous traffic. Furthermore, the accuracy of saturation flow rate is found to cause efficient and vital capacity estimation, in order to ensure optimal design and operation of the signal timings. Presently, the traffic also consists of diverse vehicle presence, each with its own static and dynamic characteristics. The passenger car equivalent (PCE) in an essential unit is also used to measure heterogenous traffic into the PCU (Passenger Car Unit). Based on the collection of observed data at three targets in Banda Aceh City, this study aims to redetermine the PCEs by using Bayesian linear regression, through the Random-walk Metropolis-Hastings and Gibbs sampling. The result showed that the obtained PCE values were 0.24, 1.0, and 0.80 for motorcycle (MC), passenger car (PC), and motorized rickshaw (MR), respectively. It also showed that a significant deviation was found between new and IHCM PCEs, as the source of error was partially due to the vehicle compositions. The present traffic characteristics were also substantially different from the prevailing conditions of IHCM 1997. Therefore, the proposed PCEs enhanced the accuracy of base saturation flow prediction, provided support for traffic operation design, alleviated congestion, and reduced delay within the city, which in turn improved the estimation of signalized intersection capacity.

Evaluation of the effectiveness of vehicle roll stability control (RSC) for high center of gravity light passenger vehicles in Australasia.

OBJECTIVE: Rollover crashes, which occur when the vehicle's side or roof makes impact with the ground, present particularly serious injury risk. Higher rollover risk has been found for high riding vehicles - those with a relatively high center of gravity compared to the width of the wheel track. Electronic Stability Control (ESC), which automatically applies brakes to individual wheels and reduces engine power to help drivers regain control when traction is lost, has been shown to be effective in preventing a proportion of rollovers. A newer safety technology, Roll Stability Control (RSC), uses similar technology aimed specifically to reduce rollover risk. This study sought to estimate rollover crash rates associated with the fitment of RSC compared to non-fitment for high center of gravity (CG) light passenger vehicles using an induced exposure analysis. METHODS: Police-recorded Australasian crash data were studied for the years 2008-2017. A quasi-induced exposure analysis was restricted to vehicles already equipped with ESC as vehicles fitted with RSC always have ESC fitted. Rollover risk associated with RSC fitment was assessed, controlling for year of crash, speed limit at crash location, year of vehicle manufacture, vehicle market group, driver age, driver gender and jurisdiction identifier. RESULTS: The analysis found a statistically significant rollover risk ratio of 0.76 (95% CI 0.62-0.93), representing a 24% reduction in rollover risk, associated with RSC fitment for vehicles manufactured between 2008 and 2017. Analysis by particular market groups found significant risk ratio reductions for commercial utilities and large SUVs, but not for the other high CG market groups individually. CONCLUSIONS: These results suggest that RSC is a highly effective safety feature for high CG vehicles. Fleet data from Australia and New Zealand showed declining rates of RSC fitment over recent years for SUVs, meaning the potential road safety benefits of the technology are not being fully realized.

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

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

Statistical modeling of numbers of human deaths per road traffic accident in the Oromia region, Ethiopia.

BACKGROUND: Globally, road traffic accidents are the leading causes of death among young people in general, and the main cause of death among young people aged 15-29 years. Recently, in Ethiopia, the number of road traffic accidents has been increasing. The study aimed to identify the potential factors associated with the number of human deaths by road traffic accidents in the Oromia Regional State, Ethiopia. METHODS: We used data obtained from the Oromia region traffic police office recorded on daily basis road traffic accidents from July 2016 up to July 2017. Count regression models were was used to analyses the factors associated with the number of human deaths from road traffic accidents. RESULTS: Age of the driver's 31-50 years (AOR = 0.289, 95%CI: 0.175, 0.479) and higher than 50 years old (AOR = 0.311, 95%CI: 0.129, 0.751), driver's years of experience 5-10 years (AOR = 0.014, 95%CI: 0.007, 0.027), and more than 10 years (AOR = 0.101, 95%CI: 0.057, 0.176), automobile vehicle type (AOR = 8.642, 95%CI: 2.7644, 27.023), vehicle years of service 5-10 years (AOR = 2.484, 95%CI: 1.194, 5.169), and more than 10 years (AOR = 2.639, 95%CI: 1.268, 5.497), vehicle upside down accidents (AOR = 5.560, 95%CI: 2.506, 12.336), turning illegal position (AOR = 0.454, 95%CI: 0.226, 0.913), residential areas (AOR = 108.506, 95%CI: 13.725, 857.798), and working areas (AOR = 129.606, 95%CI: 16.448, 1021.263) were significant associated number of human deaths per road traffic accident factors in the study area. CONCLUSION: Human deaths per road traffic accidents occurred due to the younger age of the driver, driver's lack of sufficient experience, vehicle serviced for long years, driving on a wet road, driving in the afternoon, driving near/around residential places and vehicle to driver's relation. Thus, the regional traffic police should give special attention to younger drivers, less experienced drivers, old vehicles, driving near residential areas, driving automobiles, and driving in the afternoon to control traffic system to reduce the number of human deaths pear road traffic accident.

Description of the Nature of Musculoskeletal Shoulder Injuries in a Cohort of Commercial Truck Drivers: A Retrospective Cross-Sectional Study.

BACKGROUND: Commercial truck drivers (CTDs) are significantly affected by shoulder injuries; however, little is known about the unique mechanisms of injury (MOIs), specific injuries, or possible preventive measures among this group of workers. This study characterized the MOIs, musculoskeletal disorders (MSDs), and factors associated with MSDs of the shoulder among a group of CTDs. METHODS: A retrospective medical record review was conducted of CTDs between 21 and 65 years of age who were seen for MSDs of the shoulder between 2007 and 2015. RESULTS: A total of 130 CTDs were included, who were aged 21 to 65 years. Commercial truck drivers were most often injured during a fall (35%) or while using chains, tarps, or straps (31%). The two most common MSDs were unspecified sprains/strains (58%) and rotator cuff tears (24%). Age was found to be associated with all MSDs (p = .001) and an increased risk of developing rotator cuff tears (p =.005). Seventy-four percent of CTDs who experienced a rotator cuff tear were 46 years of age or older. CONCLUSION/APPLICATION TO PRACTICE: This study highlights the course of the injury in terms of diagnostics such as magnetic resonance imaging (MRI) and referral for surgery and describes the occupational activities associated with CTDs. These findings can inform employer injury prevention programs, patient and health care provider education, and future interventional research.

Associating ridesourcing with road safety outcomes: Insights from Austin, Texas.

Improving road safety and setting targets for reducing traffic-related crashes and deaths are highlighted as part of the United Nations sustainable development goals and worldwide vision zero efforts. The advent of transportation network companies and ridesourcing expands mobility options in cities and may impact road safety outcomes. We analyze the effects of ridesourcing use on road crashes, injuries, fatalities, and driving while intoxicated (DWI) offenses in Travis County, Texas. Our approach leverages real-time ridesourcing volume to explain variation in road safety outcomes. Spatial panel data models with fixed-effects are deployed to examine whether the use of ridesourcing is significantly associated with road crashes and other safety metrics. Our results suggest that for a 10% increase in ridesourcing trips, we expect a 0.12% decrease in road crashes, a 0.25% decrease in road injuries, and a 0.36% decrease in DWI offenses in Travis County. On the other hand, ridesourcing use is not significantly associated with road fatalities. This study augments existing work because it moves beyond binary indicators of ridesourcing availability and analyzes crash and ridesourcing trips patterns within an urbanized area rather than their metropolitan-level variation. Contributions include developing a data-rich approach for assessing the impacts of ridesourcing use on the transportation system's safety, which may serve as a template for future analyses for other cities. Our findings provide feedback to policymakers by clarifying associations between ridesourcing use and traffic safety and uncover the potential to achieve safer mobility systems with transportation network companies.

Crash rates of convertible cars.

OBJECTIVE: Convertible cars have existed since among the first automobiles, and the lack of substantial roof structure creates some safety concerns. Though crash tests have demonstrated that convertibles can resist excessive intrusion in front and side crashes and that strong A-pillars and roll bars can help maintain survival space in rollovers, little work has been done examining the real-world crash experience of these vehicles. The objective of this study was to compare the crash experience of recent convertibles with nonconvertible versions of the same cars using the most recent crash data. METHODS: Crash and exposure data were obtained from the U.S. Department of Transportation and IHS Markit, respectively. Rates of driver deaths and police-reported crash involvements were compared for 1- to 5-year-old convertible cars and their nonconvertible versions during 2014-2018. Exposure measures included registered vehicle years (RVY) and vehicle miles traveled (VMT). These rates were compared using the standardized mortality ratio to account for possible differences in exposure distribution. Crash circumstances (e.g., point of impact, rollover, ejection) and behavioral outcomes (e.g., speeding, alcohol impairment, seat belt use) were compared for drivers killed in crashes. RESULTS: Convertibles had lower driver death rates and police-reported crash involvement rates on the basis of both RVY and VMT. However, the differences in driver death rates were not statistically significant. Driver deaths per 10 billion VMT were 11% lower for convertibles, and driver involvement in police-reported crashes per 10 million VMT was 6% lower. On average, convertibles were driven 1,595 fewer miles per year than the nonconvertible versions of these cars. Among fatally injured drivers, convertibles had slightly higher rates of ejection, and behavioral differences were minimal. The number of rollovers was small and their rate did not substantially differ between convertibles and their nonconvertible versions. CONCLUSIONS: Safety concerns associated with convertibles' retractable roof structures were not supported by the results of this study.

Large-scale simulation of traffic flow using Markov model.

Modeling and simulating movement of vehicles in established transportation infrastructures, especially in large urban road networks is an important task. It helps in understanding and handling traffic problems, optimizing traffic regulations and adapting the traffic management in real time for unexpected disaster events. A mathematically rigorous stochastic model that can be used for traffic analysis was proposed earlier by other researchers which is based on an interplay between graph and Markov chain theories. This model provides a transition probability matrix which describes the traffic's dynamic with its unique stationary distribution of the vehicles on the road network. In this paper, a new parametrization is presented for this model by introducing the concept of two-dimensional stationary distribution which can handle the traffic's dynamic together with the vehicles' distribution. In addition, the weighted least squares estimation method is applied for estimating this new parameter matrix using trajectory data. In a case study, we apply our method on the Taxi Trajectory Prediction dataset and road network data from the OpenStreetMap project, both available publicly. To test our approach, we have implemented the proposed model in software. We have run simulations in medium and large scales and both the model and estimation procedure, based on artificial and real datasets, have been proved satisfactory and superior to the frequency based maximum likelihood method. In a real application, we have unfolded a stationary distribution on the map graph of Porto, based on the dataset. The approach described here combines techniques which, when used together to analyze traffic on large road networks, has not previously been reported.