The impact of COVID-19 restrictions on motorcycle crashes in Taiwan.

Taiwan is one of the countries with the highest motorcycle per capita globally, and motorcycle crashes are predominant among traffic crashes. This study examines the impact of coronavirus disease 2019 restrictions on motorcycle crashes. We analyzed the trend of motorcycle crashes in Taipei City from 2019 to 2020 using the dataset provided by the Department of Transportation, Taipei City Government, Taiwan. We found 47,108 and 51,441 motorcycle crashes in 2019 and 2020, involving 61,141 and 67,093 motorcycles, respectively. Mopeds had the highest risk in 2020, followed by heavy motorcycles [≥550 cubic capacity (cc)] and scooters compared to 2019. Food delivery motorcycle crashes increased for scooters (0.93% in 2019 to 3.45% in 2020, P < .0001) and heavy motorcycles (250 < cc < 550) (0.90% in 2019 to 3.38% in 2020, P < .0001). While fatalities remained under 1%, 30% to 51% of motorcyclists sustained injuries. Food delivery with scooters or heavy motorcycles (250 < cc < 550) was significantly associated with motorcyclist injuries and deaths. Compared with 2019, the adjusted odds ratios of motorcyclist injuries and deaths in 2020 were 1.43 (95% confidence interval = 1.05-1.94) for heavy motorcycles (≥550 cc) and 1.07 (95% confidence interval = 1.04-1.09) for scooters. This study shows that coronavirus disease 2019 restrictions was associated with elevated risks of crashes, injuries, and deaths among motorcyclists, reflecting the general preference for private transport over public transport. The popularity of food delivery services also contributed to increased motorcycle crashes.

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

OBJECTIVE: Automatic emergency braking (AEB) and forward collision warning (FCW) are effective at preventing rear-end crashes, but they may perform better in some rear-end crash scenarios than others. The goal of this study was to estimate the effects of front crash prevention systems equipped to passenger vehicles in crashes where another passenger vehicle, a medium/heavy truck, or a motorcycle is struck and compare effectiveness by struck vehicle type. METHODS: More than 160,000 two-vehicle rear-end crashes were identified where a passenger vehicle with or without FCW and AEB was the striking vehicle and another passenger vehicle, medium/heavy truck, or motorcycle was the struck vehicle. Poisson regression was used to estimate the effect of front crash prevention by struck vehicle type on rear-end crash rates per registered vehicle year, accounting for the state and year of the crash and the make, model year, class, and engine type of the striking vehicle. RESULTS: Front crash prevention was associated with a 53% reduction in rear-end crash rates when striking another passenger vehicle, which was significantly larger than the reductions of 38% when striking a medium/heavy truck and 41% when striking a motorcycle. Reductions in rear-end injury crash rates when striking a passenger vehicle also were larger than when striking a medium/heavy truck and when striking a motorcycle. DISCUSSION: If all passenger vehicles were equipped with FCW and AEB that were as effective in crashes striking a truck or motorcycle as they are in crashes with another passenger vehicle, over 5,500 additional crashes with medium/heavy trucks and 500 with motorcycles could potentially be prevented annually in the United States above what would be expected from current front crash prevention systems. Extending front crash prevention testing in consumer information programs to include motorcycle and truck targets could encourage auto manufacturers to improve performance in these crash scenarios.

Temporal trends of transport-related injuries on New Zealand roads.

AIM: This observational study aimed to investigate temporal trends in transport-related injuries in New Zealand by mode of transport and explore whether specific population groups and localities have a relatively higher incidence of injury. These trends provide insight into changes in injury patterns from road trauma. METHODS: A retrospective study of hospitalised road trauma in New Zealand was conducted between 1 July 2017 to 30 June 2021. Data were obtained from the National Minimum Dataset of hospital admissions, and the New Zealand Trauma Registry (NZTR). Road trauma was identified using ICD-10 coding, and major trauma using Abbreviated Injury Scale (AIS) coding. Analysis included road trauma by mode, ethnicity, rurality and population rates. Statistical analysis included Interrupted Time Series (ITS) analysis to account for the impact of COVID-19 on road trauma. RESULTS: Over the 4-year period there were 20,607 incidents of transport-related injury that resulted in admission to a New Zealand hospital. Of these, 14.5% (2,992) involved injuries that were classified as major trauma. Car occupants accounted for 62% of hospitalisations, followed by motorcyclists (23%), pedestrians (9%) and pedal cyclists (4%). Temporal trends showed no reduction in injuries from cars, pedal cyclists and pedestrian injuries, but an increase in motorcycling injuries. Maori had an age-standardised incidence rate almost 3.5 times higher than the rate for Asian peoples. CONCLUSION: The increases in motorcycling injuries and no changes in pedestrian and cycling injuries, as well as demographic variation, highlight the need to focus on vulnerable road users. Effective and targeted initiatives on vulnerable road users will support objectives to reduce deaths and serious injury on New Zealand roads. Enhanced exposure data is needed for vulnerable road users to account for mobility changes over time. Linked data across population-based datasets is an important asset that enhances our understanding of road traffic injuries and allows evidence-based countermeasures to be developed.

Traffic flow detection method based on improved SSD algorithm for intelligent transportation system.

With the development of the new generation communication system in China, the application of intelligent transportation system is more extensive, which brings higher demands for vehicle flow detection and monitoring. Traditional traffic flow detection modes often cannot meet the high statistical accuracy requirement and high-speed detection simultaneously. Therefore, an improved Inception module is integrated into the single shot multi box detector algorithm. An intelligent vehicle flow detection model is constructed based on the improved single shot multi box detector algorithm. According to the findings, the convergence speed of the improved algorithm was the fastest. When the test sample was the entire test set, the accuracy and precision values of the improved method were 93.6% and 96.0%, respectively, which were higher than all comparison target detection algorithms. The experimental results of traffic flow statistics showed that the model had the highest statistical accuracy, which converged during the training phase. During the testing phase, except for manual statistics, all methods had the lowest statistical accuracy on motorcycles. The average accuracy and precision of the designed model for various types of images were 96.9% and 96.8%, respectively. The calculation speed of this intelligent model was not significantly improved compared to the other two intelligent models, but it was significantly higher than manual monitoring methods. Two experimental data demonstrate that the intelligent vehicle flow detection model designed in this study has higher detection accuracy. The calculation speed has no significant difference compared with the traditional method, which is helpful to the traffic flow management in intelligent transportation system.

Exploring the spatial relationship of e-bike and motorcycle crashes: Implications for risk reduction.

INTRODUCTION: Electric bicycles, or e-bikes, have become very popular over the past decade. In order to reduce the risk of crashes, it is necessary to understand the contributing factors. While several researchers have examined these elements, few have considered the spatial heterogeneity between crashes and environmental variables, such as Points of Interest (POI). In addition, there is a scarcity of studies comparing the crash-related factors of e-bikes and motorcycles. Despite their differing speed and range capabilities, different POIs also tend to impact area/bandwidths differently because e-bikes cannot cover the same range that motorcycles can. METHOD: In this study, we compared e-bike and motorcycle crashes at 11 different types of POIs in Taipei from 2016 to 2020. Since crashes are sparse events and easily affected by the Modifiable Areal Unit Problem (MAUP), Kernel Density Estimation (KDE) was employed to transform crash points (count data) to crash risk surfaces (continuous data). Additionally, an advanced variant of Geographical Weighted Regression (GWR), Multiscale Geographically Weighted Regression (MGWR) utilized to predict crash risk because each predictor is allowed to have a different bandwidth. RESULTS: The results showed: (a) For e-bike crashes, the MGWR model outperformed the GWR and OLS models in terms of AIC values, while the MGWR and GWR performed similarly with regard to motorcycle crashes; (b) The analysis revealed e-bike and motorcycle crash risk to be associated with various types of POIs. E-bike crashes tended to occur more frequently in areas with more schools, supermarkets, intersections, and elderly people. Meanwhile, motorcycle crashes were more likely to occur in areas with a high number of restaurants and intersections. The search bandwidths of e-bikes are inconsistent and narrower than those of motorcycles.

Evaluating helmet-wearing of single-vehicle overspeeding motorcycle crashes: Insights from temporal instability in parsimonious pooled framework.

OBJECTIVE: A lower helmet-wearing rate and overspeeding in Pakistan are critical risk behaviors of motorcyclists, causing severe injuries. To explore the differences in the determinants affecting the injury severities among helmeted and non-helmeted motorcyclists in motorcycle crashes caused by overspeeding behavior, single-vehicle motorcycle crash data in Rawalpindi city for 2017-2019 is collected. Considering three possible crash injury severity outcomes of motorcyclists: fatal injury, severe injury and minor injury, the rider, roadway, environmental, and temporal characteristics are estimated. METHODS: To provide a mathematically simpler framework, the current study introduces parsimonious pooled random parameters logit models. Then, the standard pooled random parameters logit models without considering temporal effects are also simulated for comparison. By comparing the goodness of fit measure and estimation results, the parsimonious pooled random parameters logit model is suitable for capturing the temporal instability. Then, the non-transferability among helmeted and non-helmeted overspeeding motorcycle crashes is illustrated by likelihood ratio tests and out-of-sample prediction, and two types of models provide robust results. The marginal effects are also calculated. RESULTS: Several variables, such as age, cloudy and weekday indicators illustrate temporal instability. Moreover, several variables are observed to only show significance in non-helmeted models, showing non-transferability across helmeted and non-helmeted models. CONCLUSIONS: More educational campaigns, regulation and enforcement, and management countermeasures should be organized for non-helmeted motorcyclists and overspeeding behavior. Such findings also provide research reference for the risk-compensating behavior and self-selected group issues under overspeeding riding considering the usage of helmets.

Analysis of increased motorcycle accidents during the COVID-19 pandemic: a single-center study from Türkiye.

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic has led to a unique set of circumstances, straining health-care systems and affecting the way of life in societies around the world. Measures such as social isolation, travel restrictions, and workplace closures have led to an increase in motorcycle use. Consequently, motorcycle accidents have become a significant problem during this period. This study presents detailed research conducted to examine motorcycle accidents during the COVID-19 pandemic and to understand the causes and consequences of the increase in these accidents. METHODS: This research evaluated records from a single health examination and used various models to analyze motorcycle acci-dents within a specified time period. Additionally, retrospective analyses were conducted to examine associations between motorcycle use and crashes in our country before and after the pandemic. The records of 386 patients who were injured in motorcycle accidents and followed up, received treatment, and were recorded at Biruni University Hospital between November 2015 and April 2023 were retrospectively examined. Noted details included the victims' age, gender, injury mechanism, injury site, injury severity, helmet use, presence and location of fractures, time distribution of the accident, and the severity of other important tissue injuries. The relationship between the injury site, fractures, and accident details, and the "Injury Severity Score" (ISS) was also investigated. RESULTS: Among the 386 injured victims in motorcycle accidents, 333 were male and 53 were female. Of these, 168 (43.5%) were motorcycle drivers, 137 (35.5%) were motorcycle couriers, and 81 (21%) were pedestrians. A total of 186 (48%) injuries occurred before the pandemic (November 2015-March 2020), while 200 (52%) were sustained during the pandemic. The study indicates a noticeable increase in motorcycle injuries, particularly among motor couriers, especially during the pandemic quarantine periods. Post hoc analysis revealed that motor couriers had significantly lower ISS compared to other professions (p=0.009 and p=0.045, respectively). Motorcyclists who wore helmets were found to have significantly lower ISS than those who did not wear helmets (p<0.05). Furthermore, it was found that the ISS was positively correlated with the number of bone fractures, total soft tissue injury, and significant clinical characteristics (r=0.758, r=0.756, and p<0.001, respectively). CONCLUSION: This clinical study's findings demonstrate that the measures implemented during the pandemic to limit society's mobility have led to an increase in motorcycle accidents. Notably, there has been a significant rise in the number of accidents, particularly involving individual motorcycle use and motorcycle courier services.

Near-crash risk identification and evaluation for takeout delivery motorcycles using roadside LiDAR.

The proliferation of motorcycles in urban areas has raised concerns regarding traffic safety. However, traditional sensors struggle to obtain precise high-resolution trajectory data, which hinder the accurate identification and quantification of near-crash risks for takeout delivery motorcycles. To fill this gap, this study presents a novel approach utilizing roadside light detection and ranging (LiDAR) to identify and evaluate the risk of near crashes of takeout delivery motorcycles. First, a trajectory amendment method incorporating speed and steering angle was introduced to enhance the accuracy and continuity of the trajectory prediction. Second, a trajectory prediction method combining the steering intention and a repulsive force model was proposed for near-crash risk prediction. Subsequently, a near-crash identification method was developed that relied on the closest distance and risk radius. Finally, near-crash risk fields were constructed to quantify risk levels by leveraging velocity, position, and weight. The experimental results demonstrated 92.10 % accuracy in intention prediction, with mean absolute error (MAE) and root mean square error (RMSE) values of 0.53 m and 0.45 m, respectively. In addition to its higher accuracy, the proposed method makes it easier to quantify near-crash risk and supports a proactive approach for visualizing and analyzing traffic safety.

The role of public policy in reducing moped and scooter injuries in South Carolina.

INTRODUCTION: Mopeds and electric scooters have grown in popularity in recent years. A South Carolina (SC) law was passed on November 19, 2018, aimed to regulate mopeds and scooters. This study aims to evaluate whether this SC law was associated with a decrease in the moped injury rate in a Level 1 Trauma Center. METHODS: A retrospective review of trauma registry data was used to identify a cohort of patients 14 years and older who came to a Level 1 trauma center for a moped/scooter accident between January 2014 - December 2022. The proportion of moped injuries before and after the passing of the law was calculated. The chi-square test and Wilcoxon Rank Sum test were used to compare differences in proportions for categorical factors and continuous factors, respectively. RESULTS: A total of 350 moped injury cases were identified. There was a significant decrease in the moped injury rate after the passing of the 2018 SC law (0.9 % vs 1.8 %, p<0.001). Additionally, those treated post-law implementation were significantly older (47.4 vs 43.2 years, p = 0.013) and more likely to be male (95.5 % vs 87.9 %, p = 0.025) than those treated pre-law. Patients treated post-law were significantly more likely to be uninsured (45.1 % vs 42.7 %, p = 0.009) and less likely to have commercial (16.2 % vs 20.1 %, p = 0.009), or government (29.7 % vs 35.6 %, p = 0.009) health insurance compared to those treated pre-law. There was no significant difference between the two groups in Glascow Coma Scale, Injury severity score, Trauma Injury Severity Score, or rate of fatalities. CONCLUSION: After the implementation of a SC law, we found that the local proportion of injuries due to moped and scooter accidents was significantly lower than pre-law proportions. These findings suggest that public policies aimed at increasing regulations for mopeds may decrease the rate of injury, but not severity, from moped use.

Motorcycle-related crashes before and during the COVID-19 pandemic: A comparative retrospective observational study from the Middle East.

BACKGROUND: During the COVID-19 pandemic, there was a boom in the delivery sector, with a significant increase in the demand and number of motorcycle delivery drivers in Qatar, which made them vulnerable to injury. We aimed to evaluate the incidence, pattern, and outcome of patients injured by motorcycle-related crashes (MCC) before and during the pandemic. METHODS: A retrospective observational study included all adult patients admitted with motorcycle-related injuries before the pandemic (March 2018 to February 2020) and during the pandemic (March 2020 to March 2022). Comparative analyses were performed based on (work versus non-work related MCCs) and (pre- versus during the pandemic injuries). RESULTS: 510 patients with MCC were identified, of which 172 (33.7 %) were admitted in the pre-pandemic and 338 (66.3 %) during the pandemic. The mean age of patients was 29.2±7.8 years; 56 % were aged 20-29 years, and 99.4 % were males. Work-related MCCs were more frequent among the younger age group (60.9 % vs. 52.1 %; p=0.001) during the early evening, i.e., 6:00 to 9:00 p.m. (21.9 % vs. 13.9 %; p=0.004). However, non-work related MCC occurred more frequently between midnight and 3:00 am (20.2 % vs. 10.9 %; p=0.004), and such patients were more likely non-compliant for protective devices use (19.3 % vs. 6.1 %; p=0.001) and ride under the influence of alcohol (13.2 % vs. 7.4 %; p=0.03). During the pandemic, the proportion of alcohol consumers (13 % vs. 5.8 %; p=0.01) and work-related MCC (50.9 % vs. 22.7 %; p=0.001) increased significantly compared to the pre-pandemic period. CONCLUSION: The overall burden of MCC increased during the pandemic, and the frequency of MCC involving commercial drivers surged significantly during the pandemic period as opposed to the non-work MCC, which predominated in the pre-pandemic period. Work-related MCCs were more frequent among younger age groups, mainly involving South Asians with frequent accidents in the evening time. However, recreation-related MCCs occurred more frequently at midnight, and victims were non-compliant with the protective gear. Furthermore, there is a need for prospective studies to examine the broader scope of risk factors that are associated with the work-related MCC, especially involving food deliveries, and for focused safety programs for motorcycle delivery drivers and recreational motorcyclists.

Classifying Motorcyclist Behaviour with XGBoost Based on IMU Data.

Human behaviour detection is relevant in many fields. During navigational tasks it is an indicator for environmental conditions. Therefore, monitoring people while they move along the street network provides insights on the environment. This is especially true for motorcyclists, who have to observe aspects such as road surface conditions or traffic very careful. We thus performed an experiment to check whether IMU data is sufficient to classify motorcyclist behaviour as a data source for later spatial and temporal analysis. The classification was done using XGBoost and proved successful for four out of originally five different types of behaviour. A classification accuracy of approximately 80% was achieved. Only overtake manoeuvrers were not identified reliably.

Substance use and type of Road Traffic Injury in Mexico City.

Limited data are available in Mexico on the prevalence of alcohol and drug use and the possible differences in their effects on types of road traffic injury (RTI), such as those involving pedestrians, drivers or passengers of motorcycles or other motor vehicles, and the association between substance use and driving behaviors, for preventive purposes. The sample comprised 433 adult RTI patients, admitted to the emergency department (ED) of a public hospital in Mexico City (January to April 2022). Breath Alcohol Concentration (BAC) was measured using a breath tester, and six types of drugs (amphetamines, barbiturates, benzodiazepines, cannabis, cocaine, and methamphetamine) were assessed using a saliva screen test. RTI patients also self-reported their alcohol and drug use in the six hours prior to the accident. Approximately 62% of respondents had been involved in a motorcycle crash. One in three patients self-reported or had traces of a substance in their saliva or breath. The most common substance was alcohol (23.6%), followed by cannabis and stimulants (10.9%). One in five patients reported having used a cell phone ten minutes before the injury. One in three had not been using any safety device, the only behavior exacerbated by substance use. We found a high prevalence of substance use in the sample of RTI patients admitted to the ED, regardless of the type of the RTI, together with high cell phone rates. Motorcycle passengers under the influence were particularly likely not to have been wearing a helmet.

Getting patients to the right level of trauma center after motorcycle crashes in a rural trauma system.

PURPOSE: After a motorcycle crash (MCC), emergency medical services (EMS) responders must balance trauma center proximity with clinical needs of patients, which is especially challenging in rural states. The study purpose was to determine if MCC patients treated at lower-level trauma centers (LLTC) experienced higher mortality when compared to patients transported directly to the highest level of trauma care available in the state at Level II trauma centers. PROCEDURES: A retrospective study was conducted on MCC patients transported by EMS to Montana hospitals and met registry inclusion criteria in 2020-2021. The first study group included patients initially transported to state-designated trauma centers (equivalent to Level III-V) or non-designated hospitals (LLTC), and the second group included patients transported directly to American College of Surgeon verified Level II trauma centers (L2TC). Secondary transfer was defined as initial transport to a LLTC and subsequent transfer to a L2TC. Primary study outcome was mortality at the L2TC. Chi-square tests and Wilcoxon rank sum tests were used for analysis. FINDINGS: In the study period, 337 MCC patients were transported by EMS; 186 (55%) patients were transported to a LLTC while 151 patients (45%) were transported to a L2TC. There were no statistically significant differences in mortality (12% vs 8%, p = 0.30) when comparing secondary transfer patients to patients transported directly to a L2TC. CONCLUSIONS: Nearly half of patients initially evaluated at a LLTC required transfer to a higher-level of care. Secondary transfer was not associated with increased mortality.

Body Mass Index Does Not Predict Injury Pattern or Outcome After Motorcycle Crash.

INTRODUCTION: The obesity epidemic plagues the United States, affecting approximately 42% of the population. The relationship of obesity with injury severity and outcomes has been poorly studied among motorcycle collisions (MCC). This study aimed to compare injury severity, mortality, injury regions, and hospital and intensive care unit length of stay (LOS) between obese and normal-weight MCC patients. METHODS: Trauma registries from three Pennsylvania Level 1 trauma centers were queried for adult MCC patients (January 1, 2016, and December 31, 2020). Obesity was defined as adult patients with body mass index ≥ 30 kg/m2 and normal weight was defined as body mass index < 30 kg/m2 but > 18.5 kg/m2. Demographics and injury characteristics including injury severity score (ISS), abbreviated injury score, mortality, transfusions and LOS were compared. P ≤ 0.05 was considered significant. RESULTS: One thousand one hundred sixty-four patients met the inclusion criteria: 40% obese (n = 463) and 60% nonobese (n = 701). Comparison of ISS demonstrated no statistically significant difference between obese and normal-weight patients with median ISS (interquartile range) 9 (5-14) versus 9 (5-14), respectively (P = 0.29). Obese patients were older with median age 45 (32-55) y versus 38 (26-54) y, respectively (P < 0.01). Comorbidities were equally distributed among both groups except for the incidence of hypertension (30 versus 13.8%, P < 0.01) and diabetes (11 versus 4.4%, P < 0.01). There was no statistically significant difference in Trauma Injury Severity Score or abbreviated injury score. Hospital LOS, intensive care unit LOS, and 30-day mortality among both groups were similar. CONCLUSIONS: Obese patients experiencing MCC had no differences in distribution of injury, mortality, or injury severity, mortality, injury regions, and hospital compared to normal-weight adults. Our study differs from current data that obese motorcycle drivers may have different injury characteristics and increased LOS.

Geriatric Motorcycle-Related Outcomes: A Pennsylvania Multicenter Study.

INTRODUCTION: Geriatric patients (GeP) often experience increased morbidity and mortality following traumatic insult and as a result, require more specialized care due to lower physiologic reserve and underlying medical comorbidities. Motorcycle injuries (MCCI) occur across all age groups; however, no large-scale studies evaluating outcomes of GeP exist for this particular subset of patients. Data thus far are limited to elderly participation in recreational activities such as water and alpine skiing, snowboarding, equestrian, snowmobiles, bicycles, and all-terrain vehicles. We hypothesized that GeP with MCCI will have a higher rate of mortality when compared with their younger counterparts despite increased helmet usage. METHODS: We performed a multicenter retrospective review of MCCI patients at three Pennsylvania level I trauma centers from January 2016 to December 2020. Data were extracted from each institution's electronic medical records and trauma registry. GeP were defined as patients aged more than or equal to 65 y. The primary outcome was mortality. Secondary outcomes included ventilator days; hospital, intensive care unit, and intermediate unit length of stays; complications; and helmet use. 3:1 nongeriatric patients (NGeP) to GeP propensity score matching (PSM) was based on sex, abbreviated injury scale (AIS), and injury severity score (ISS). P ≤ 0.05 was considered significant. RESULTS: One thousand five hundred thirty eight patients were included (GeP: 7% [n = 113]; NGP: 93% [n = 1425]). Prior to PSM, GeP had higher median Charlson Comorbidity Index (GeP: 3.0 versus NGeP: 0.0; P ≤ 0.001) and greater helmet usage (GeP: 73.5% versus NGeP: 54.6%; P = 0.001). There was a statistically significant difference between age cohorts in terms of ISS (GeP: 10.0 versus NGeP: 6.0, P = 0.43). There was no significant difference for any AIS body region. Mortality rates were similar between groups (GeP: 1.7% versus NGeP: 2.6%; P = 0.99). After PSM matching for sex, AIS, and ISS, GeP had significantly more comorbidities than NGeP (P ≤ 0.05). There was no difference in trauma bay interventions or complications between cohorts. Mortality rates were similar (GeP: 1.8% versus NGeP: 3.2%; P = 0.417). Differences in ventilator days as well as intensive care unit length of stay, intermediate unit length of stay, and hospital length of stay were negligible. Helmet usage between groups were similar (GeP: 64.5% versus NGeP: 66.8%; P = 0.649). CONCLUSIONS: After matching for sex, ISS, and AIS, age more than 65 y was not associated with increased mortality following MCCI. There was also no significant difference in helmet use between groups. Further studies are needed to investigate the effects of other potential risk factors in the aging patient, such as frailty and anticoagulation use, before any recommendations regarding management of motorcycle-related injuries in GeP can be made.

Factors affecting injury severity in motorcycle crashes: Different age groups analysis using Catboost and SHAP techniques.

OBJECTIVE: Motorcycle crashes often result in severe injuries on roads that affect people's lives physically, financially, and psychologically. These injuries could be notably harmful to drivers of all age groups. The main objective of this study is to investigate the risk factors contributing to the severity of crash injuries in different age groups. METHODS: This Objective is achieved by developing accurate machine learning (ML) based prediction models. This research examines the relationship between potential risk factors of motorcycle-associated crashes using (ML) and Shapley Additive explanations (SHAP) technique. The SHAP technique further helped interpreting ML methods for traffic injury severity prediction. It indicates the significant non-linear interactions between dependent and independent variables. The data for this study was collected from the Provincial Emergency Response Service RESCUE 1122 for the Rawalpindi region (Pakistan) over three years (from 2017 to 2020). The Synthetic Minority Oversampling Technique (SMOTE) is employed to balance injury severity classes in the pre-processing phase. RESULTS: The results demonstrate that age, gender, posted speed limit, the number of lanes, and month of the year are positively associated with severe and fatal injuries. This research also assesses how the modeling framework varies between the ML and classical statistical methods. The predictive performance of proposed ML models was assessed using several evaluation metrics, and it is found that Catboost outperformed the XGBoost, Random Forest (RF) and Multinomial Logit (MNL) model. CONCLUSION: The findings of this study will assist road users, road safety authorities, stakeholders, policymakers, and decision-makers in obtaining substantial and essential guidance for reducing the severity of crash injuries in Pakistan and other countries with prevailing conditions.

Lateral Evasive Maneuver with Shared Control Algorithm: A Simulator Study.

Shared control algorithms have emerged as a promising approach for enabling real-time driver automated system cooperation in automated vehicles. These algorithms allow human drivers to actively participate in the driving process while receiving continuous assistance from the automated system in specific scenarios. However, despite the theoretical benefits being analyzed in various works, further demonstrations of the effectiveness and user acceptance of these approaches in real-world scenarios are required due to the involvement of the human driver in the control loop. Given this perspective, this paper presents and analyzes the results of a simulator-based study conducted to evaluate a shared control algorithm for a critical lateral maneuver. The maneuver involves the automated system helping to avoid an oncoming motorcycle that enters the vehicle's lane. The study's goal is to assess the algorithm's performance, safety, and user acceptance within this specific scenario. For this purpose, objective measures, such as collision avoidance and lane departure prevention, as well as subjective measures related to the driver's sense of safety and comfort are studied. In addition, three levels of assistance (gentle, intermediate, and aggressive) are tested in two driver state conditions (focused and distracted). The findings have important implications for the development and execution of shared control algorithms, paving the way for their incorporation into actual vehicles.

The catastrophic cost of motorcycle road traffic injuries: Experience from a major reference centre in a lower-middle income country.

BACKGROUND: Motorcycle crashes are an increasing public health problem in low- and middle-income countries (LMICs). An accurate estimation of the economic burden of these crashes could be complex owing to a prevalent system of out-of-pocket (OOP) payment for health care services in these countries. Our study aims to objectively evaluate the cost implication of motorcycle Road Traffic Injuries (RTIs) among road crash victims managed at a major trauma reference hospital in Nigeria. Two economic evaluation methods were used to accurately reflect the cost-of-care (C-o-C) of each victim as well as for cross-validation. METHOD: This is a prospective cohort study conducted between August 2020 and May 2021. All patients involved in motorcycle road traffic crashes presenting to the Emergency Department of the University College Hospital, Ibadan, Nigeria, were included in the study. For each patient, all medical expenses from the time of injury (T0) to 30 days after injury (T30) or Time to death (TD) - whichever occurred first, were valued in costs, and added (Activity-based costing or ABC), while also estimating overall cost-of-care (C-o-C) at T30 or TD, using the willingness-to-pay (WTP) method. Following the WHO definition, catastrophic expenditure was defined as expenditure > 25% of the patient's estimated annual household income. RESULTS: Of the 150 consecutively managed motorcycle crashes victims during the study period, 112 had complete data. The median monthly household income for the cohort was $121 with 75% of them earning less than $180. The median cost-of-care (C-o-C), by ABC, was $242 ($143 - 828). For individual care items, expenditure on surgical intervention(s) was the highest followed by prosthesis and implant procurement, and radiological investigations. On the other hand, the estimated medical cost was $2356 (IQR $938 - 6475) by WTP. Only 14% had health insurance coverage. The overall expenditure was catastrophic for 46% of the patients. Monthly household income of < $180 (AOR=9.2; 95% CI=2.6-32.8; p < 0.001), absence of health insurance coverage (AOR=10.7; 95% CI=1.1-101.6; p = 0.040), and prolonged hospital stay above 14 days (AOR=25.1; 95% CI=5.5 -115.1; p = 0.001) were predictors of catastrophic expenditure. There was a weak positive correlation between actual cost-of-care using the ABC method and WTP (r = 0.247; p = 0.102). CONCLUSION: The aggregate cost of motorcycle RTIs is catastrophic for nearly half of the victims attending the University College Hospital, Ibadan. The willingness-to-pay method, though less tedious is often less reliable in these settings owing to a prevalent OOP payment system. This study identified the need to implement effective financial protection mechanisms against the high OOP expenditure faced by motorcycle crash victims in LMICs.

Life satisfaction and occupational safety relationship: Research on motorcycle couriers in Turkey.

OBJECTIVES: Rising e-commerce trend with Covid-19 pandemic has significantly increased the need for motorcycle-couriers. The aim of this research was to investigate the relationship between job satisfaction and occupational safety and health (OSH) of motorcycle-couriers and to evaluate the opinions of OSH specialists about the importance of motorcycle-couriers' life satisfaction and OSH perception levels. METHODS: The relationship between life satisfaction and perception of OSH of 750 motorcycle-couriers was addressed using quantitative research methods through two different scales (Life Satisfaction Scale and Occupational Safety Perception Scale). The views of 20 OSH specialists about the importance of motorcycle-couriers' perception of life satisfaction and OSH levels were addressed using qualitative research methods. RESULTS: There was a negative linear relationship between perception of OSH and life satisfaction levels of motorcycle-couriers. All OSH specialists evaluated the level of life satisfaction as an important factor in OSH practice of motorcycle-couriers and all were of the opinion that the job was quite risky and adequate OSH measures were not sufficiently guaranteed. CONCLUSIONS: The findings suggest that there is a need for some legal regulation of this occupation and ensuring necessary occupational standards are applied. This could include increased responsibility of companies/employers and could provide training programs to improve OSH.

Alternative approaches to modeling heterogeneity to analyze injury severity sustained by motorcyclists in two-vehicle crashes.

The presence of unobserved factors in the motorcycle involved two-vehicle crashes (MV) data could lead to heterogenous associations between observed factors and injury severity sustained by motorcyclists. Capturing such heterogeneities necessitates distinct methodological approaches, of which random and scale heterogeneity models are paramount. Herein, we undertake an empirical evaluation of random and scale heterogeneity models, exploring their efficacy in delineating the influence of external determinants on the degree of injury severity in crashes. Within the effects of scale heterogeneity, this study delves into two dominant models: the scaled multinomial logit model (S-MNL) and its generalized counterpart, the G-MNL, which encompasses both the S-MNL and the random parameters multinomial logit model (RPL). While the random heterogeneity domain is represented by the random parameters multinomial logit and an upgraded variant - the random parameters multinomial logit model with heterogeneity in means and variances (RPLHMV). Motorcycle involved two-vehicle crashes data were extracted from the UK STATS19 dataset from 2016 to 2020. Likelihood ratio tests are computed to assess the temporal variability of the significant factors. The test result demonstrates the temporal variations over a five-year study period. Some very important differences started to show up across the years based on the model estimation results: that the RPLHMV model statistically outperforms the G-MNL model in the 2016, 2018, and 2019 models, while the S-MNL model is statistically superior in the 2017 and 2020 years. These important findings suggest that the origin of heterogeneity in explaining factor weights can be captured by scale effects, not just random heterogeneity. In addition, the model results further show that motorcyclists' injury severities are significantly affected by motorcycle-related characteristics; there is the added factor of external influences, such as non-motorcycle drivers (males, young drivers, and elderly drivers) and vehicles (the moving status, age, and types of vehicles) that collide with motorcycles. The results of this paper are anticipated to help policymakers develop effective strategies to mitigate motorcycle involved two-vehicle crashes by implementing appropriate measures.