Pre-crash scenarios for safety testing of autonomous vehicles: A clustering method for in-depth crash data.

Autonomous vehicles (AVs) provide an opportunity to enhance traffic safety. However, AVs market penetration is still restricted due to their safety concerns and dependability. For widespread adoption, it is crucial to thoroughly assess the safety response of AVs in various high-risk scenarios. To achieve this objective, a clustering method was used to construct typical testing scenarios based on the China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database. Initially, 222 car-to-powered two-wheelers (PTWs) crashes and 180 car-to-car crashes were reconstructed from CIMSS-TA database. Second, six variables were extracted and analyzed, including the motion of the two vehicles involved, relative movement, lighting condition, road condition, and visual obstruction. Third, these variables were clustered using the k-medoids algorithm, identifying five typical pre-crash scenarios for car-to-PTWs and seven for car-to-car. Additionally, we extracted the velocities and surrounding environmental information of the crash-involved parties to enrich the scenario description. The approach used in this study used in-depth case review and thus provided more insightful information for identifying and quantifying representative high-risk scenarios than prior studies that analyzed overall descriptive variables from Chinese crash databases. Furthermore, it is crucial to separately test car-to-car scenarios and car-to-PTWs scenarios due to their distinct motion characteristics, which significantly affect the resulting typical scenarios.

Impacts of information quantity and display formats on driving behaviors in a connected vehicle environment.

The emerging connected vehicle (CV) technologies facilitate the development of integrated advanced driver assistance systems (ADASs), with which various functions are coordinated in a comprehensive framework. However, challenges arise in enabling drivers to perceive important information with minimal distractions when multiple messages are simultaneously provided by integrated ADASs. To this end, this study introduces three types of human-machine interfaces (HMIs) for an integrated ADAS: 1) three messages using a visual display only, 2) four messages using a visual display only, and 3) three messages using visual plus auditory displays. Meanwhile, the differences in driving performance across three HMI types are examined to investigate the impacts of information quantity and display formats on driving behaviors. Additionally, variations in drivers' responses to the three HMI types are examined. Driving behaviors of 51 drivers with respect to three HMI types are investigated in eight field testing scenarios. These scenarios include warnings for rear-end collision, lateral collision, forward collision, lane-change, and curve speed, as well as notifications for emergency events downstream, the specified speed limit, and car-following behaviors. Results indicate that, compared to a visual display only, presenting three messages through visual and auditory displays enhances driving performance in four typical scenarios. Compared to the presentation of three messages, a visual display offering four messages improves driving performance in rear-end collision warning scenarios but diminishes the performance in lane-change scenarios. Additionally, the relationship between information quantity and display formats shown on HMIs and driving performance can be moderated by drivers' gender, occupation, driving experience, annual driving distance, and safety attitudes. Findings are indicative to designers in automotive industries in developing HMIs for future CVs.

How would autonomous vehicles behave in real-world crash scenarios?

Autonomous Vehicles (AVs) have the potential to revolutionize transportation systems by enhancing traffic safety. Safety testing is undoubtedly a critical step for enabling large-scale deployment of AVs. High-risk scenarios are particularly important as they pose significant challenges and provide valuable insights into the driving capabilities of AVs. This study presents a novel approach to assess the safety of AVs using in-depth crash data, with a particular focus on real-world crash scenarios. First, based on the high-definition video recording of the whole process prior to the crash occurrences, 453 real-world crashes involving 596 passenger cars from China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database were reconstructed. Pertinent static and dynamic elements needed for the construction of the testing scenarios were extracted. Subsequently, 596 testing scenarios were created via each passenger car's perspective within the simulation platform. Following this, each of the crash-involved passenger cars was replaced with Baidu Apollo, a famous automated driving system (ADS), for counterfactual simulation. Lastly, the safety performance of the AV was assessed using the simulation results. A logit model was utilized to identify the fifteen crucial scenario elements that have significant impacts on the test results. The findings demonstrated that the AV could avoid 363 real-world crashes, accounting for approximately 60.91% of the total, and effectively mitigated injuries in the remaining 233 unavoidable scenarios compared to a human driver. Moreover, the AV maintain a smoother speed in most of the scenarios. The common feature of these unavoidable scenarios is that the AV is in a passive state, and the crashes are not caused by the AV violating traffic rules, but rather caused by abnormal behavior exhibited by the human drivers. Additionally, seven specific scenarios have been identified wherein AVs are unable to avoid a crash. These findings demonstrate that, compared to human drivers, AVs can avoid crashes that are difficult for humans to avoid, thereby enhancing traffic safety.

A variable speed limit control approach for freeway tunnels based on the model-based reinforcement learning framework with safety perception.

To improve the traffic safety and efficiency of freeway tunnels, this study proposes a novel variable speed limit (VSL) control strategy based on the model-based reinforcement learning framework (MBRL) with safety perception. The MBRL framework is designed by developing a multi-lane cell transmission model for freeway tunnels as an environment model, which is built so that agents can interact with the environment model while interacting with the real environment to improve the sampling efficiency of reinforcement learning. Based on a real-time crash risk prediction model for freeway tunnels that uses random deep and cross networks, the safety perception function inside the MBRL framework is developed. The reinforcement learning components fully account for most current tunnels' application conditions, and the VSL control agent is trained using a deep dyna-Q method. The control process uses a safety trigger mechanism to reduce the likelihood of crashes caused by frequent changes in speed. The efficacy of the proposed VSL strategies is validated through simulation experiments. The results show that the proposed VSL strategies significantly increase traffic safety performance by between 16.00% and 20.00% and traffic efficiency by between 3.00% and 6.50% compared to a fixed speed limit approach. Notably, the proposed strategies outperform traditional VSL strategy based on the traffic flow prediction model in terms of traffic safety and efficiency improvement, and they also outperform the VSL strategy based on model-free reinforcement learning framework when sampling efficiency is considered together. In addition, the proposed strategies with safety triggers are safer than those without safety triggers. These findings demonstrate the potential for MBRL-based VSL strategies to improve traffic safety and efficiency within freeway tunnels.

Enhancing H2O2 Electrosynthesis at Industrial-Relevant Current in Acidic Media on Diatomic Cobalt Sites.

Electrocatalytic synthesis of hydrogen peroxide (H2O2) in acidic media is an efficient and eco-friendly approach to produce inherently stable H2O2, but limited by the lack of selective and stable catalysts under industrial-relevant current densities. Herein, we report a diatomic cobalt catalyst for two-electron oxygen reduction to efficiently produce H2O2 at 50-400 mA cm-2 in acid. Electrode kinetics study shows a >95% selectivity for two-electron oxygen reduction on the diatomic cobalt sites. In a flow cell device, a record-high production rate of 11.72 mol gcat-1 h-1 and exceptional long-term stability (100 h) are realized under high current densities. In situ spectroscopic studies and theoretical calculations reveal that introducing a second metal into the coordination sphere of the cobalt site can optimize the binding strength of key H2O2 intermediates due to the downshifted d-band center of cobalt. We also demonstrate the feasibility of processing municipal plastic wastes through decentralized H2O2 production.

Analysis of dynamic determinants of vehicles involved in crash affecting severity based on in-depth crash data.

OBJECTIVE: The dynamic characteristics of vehicles involved in crashes may be an important factor affecting the crash severity. This study investigates the relationship between the dynamic characteristics of vehicles involved in crashes in the five seconds before the occurrence and the crash severity. The findings aim to offer insights for preventing severe crashes and advancing autonomous vehicle technology. METHODS: This study aims to investigate the impact of dynamic features, such as speed, acceleration, and relative distance of vehicles involved in the crash in the five seconds before the crash, on the crash severity. Five hundred ninety-six crash samples from the China In-depth Mobility Safety Study-Traffic Accident database were selected for crash reconstruction. A random parameters logit model was used to extract and analyze the effect of dynamic features of the vehicles involved in the crash on the crash severity. RESULTS: The random parameters logit model demonstrated a satisfactory fit. Analysis of the parameter estimation results of the model showed that the variables of speed, acceleration, and relative distance between vehicles involved in the crash at some time points during the five seconds before the crash significantly affected the crash severity. Notably, the coefficient of variation of relative distance over 5 s emerged as the most influential positive determinant of the crash severity. CONCLUSIONS: Certain dynamic characteristics of vehicles involved in a crash in the five seconds before a crash significantly impact the crash severity. The study's findings can serve as a reference for preventing severe crashes and advancing the development of autonomous vehicles.

Multi-objective deep reinforcement learning approach for adaptive traffic signal control system with concurrent optimization of safety, efficiency, and decarbonization at intersections.

This study introduces a novel approach to adaptive traffic signal control (ATSC) by leveraging multi-objective deep reinforcement learning (DRL) techniques. The proposed scheme aims to optimize control strategies at intersections while concurrently addressing the objectives of safety, efficiency, and decarbonization. Traditional ATSC schemes primarily emphasize traffic efficiency and often lack the ability to adapt to real-time dynamic traffic conditions. To overcome these limitations, the study proposes a DRL-based ATSC algorithm that integrates the Dueling Double Deep Q Network (D3QN) framework. The performance of the proposed algorithm is evaluated through a simulated intersection in Changsha, China. Specifically, the proposed ATSC algorithm outperforms both traditional ATSC and ATSC with efficiency optimization only algorithms by achieving more than a 16% reduction in traffic conflicts and a 4% reduction in carbon emissions. In terms of traffic efficiency, waiting time reduces by 18% compared to traditional ATSC, but slightly increases (0.64%) compared to DRL-based ATSC algorithm that integrates D3QN framework. This small increase indicates a trade-off between efficiency and other objectives such as safety and decarbonization. Moreover, the proposed scheme demonstrates superior performance specifically in highly traffic-demand scenarios in terms of all three objectives. The findings of this study contribute to the advancement of traffic control systems by providing a practical and effective solution for optimizing signal control strategies in real-world traffic scenarios.

Diatomic iron nanozyme with lipoxidase-like activity for efficient inactivation of enveloped virus.

Enveloped viruses encased within a lipid bilayer membrane are highly contagious and can cause many infectious diseases like influenza and COVID-19, thus calling for effective prevention and inactivation strategies. Here, we develop a diatomic iron nanozyme with lipoxidase-like (LOX-like) activity for the inactivation of enveloped virus. The diatomic iron sites can destruct the viral envelope via lipid peroxidation, thus displaying non-specific virucidal property. In contrast, natural LOX exhibits low antiviral performance, manifesting the advantage of nanozyme over the natural enzyme. Theoretical studies suggest that the Fe-O-Fe motif can match well the energy levels of Fe2 minority ß-spin d orbitals and pentadiene moiety π* orbitals, and thus significantly lower the activation barrier of cis,cis-1,4-pentadiene moiety in the vesicle membrane. We showcase that the diatomic iron nanozyme can be incorporated into air purifier to disinfect airborne flu virus. The present strategy promises a future application in comprehensive biosecurity control.

Terrace-Rich Ultrathin PtCu Surface on Earth-Abundant Metal for Oxygen Reduction Reaction.

The activity and stability of the platinum electrode toward the oxygen reduction reaction are size-dependent. Although small nanoparticles have high Pt utilization, the undercoordinated Pt sites on their surface are assumed to have too strong oxygen binding strength, thus often leading to compromised activity and surface instability. Herein, we report an extended nanostructured PtCu ultrathin surface to reduce the number of low-coordination sites without sacrificing the electrochemical active surface area (ECSA). The surface shows (111)-oriented characteristics, as proven by electrochemical probe reactions and spectroscopies. The PtCu surface brings over an order of magnitude increase in specific activity relative to commercial Pt/C and nearly 4-fold enhancement in ECSA compared to traditional thin films. Moreover, due to the weak absorption of air impurities (e.g., SO2, NO, CO) on highly coordinated sites, the catalyst displays enhanced contaminant tolerance compared with nanoparticulate Pt/C. This work promises a broad screening of extended nanostructured surface catalysts for electrochemical conversions.

Spatio-temporal dynamic change mechanism analysis of traffic conflict risk based on trajectory data.

Analyzing risk dynamic change mechanism under spatio-temporal effects can provide a better understanding of traffic risk, which helps reinforce the safety improvement. Traditionally, spatio-temporal studies based on crash data were mostly conducted to explore crash risk evolution mechanism from a macroscopic perspective. Dynamic change mechanism of short-term risk within a small-scale area deserves exploration, which cannot be captured in macroscopic crash-based studies. It is practical to analyze traffic conflict risk as a surrogate safety measure, which can preferably overcome the limitations of crash-based studies. This study aims to explore the spatio-temporal dynamic change mechanism of conflict risk based on trajectory data. Both conflict frequency and severity are integrated and assessed by applying fuzzy logic theory to develop the whole risk indicator. Trajectories on U.S. Highway101 from NGSIM dataset are utilized and aggregated. A two-step framework is proposed to analyze the risk dynamic change mechanism. The spatial Markov model is firstly applied to explore the transition probability of risk level, and then the panel regression approach is employed to quantify the relationship between spatio-temporal risk and traffic characteristics. Modeling results show that (1) the dynamic change trend of safety states differs under different spatial lag conditions, and it can be well depicted by the spatial Markov model; (2) dynamic spatial panel data modeling method performs better than the model that only considers temporal or spatial dependency. The novel proposed framework promotes a systematic exploration of conflict risk from a mesoscopic perspective, which contributes to assess the real-time road safety more comprehensively.

Identifying typical pre-crash scenarios based on in-depth crash data with deep embedded clustering for autonomous vehicle safety testing.

Choosing appropriate scenarios is critical for autonomous vehicles (AVs) safety testing. Real-world crash scenarios can be used as critical scenarios to test the safety performance of AVs. As one of the dominant types of traffic crashes, the car to powered-two-wheelers (PTWs) crash results in a higher possibility of fatality than ordinary car-to-car crashes. Generally, typical testing scenarios are chosen according to the subjective understanding of the safety experts with limited static features of crashes (e.g., geometric features, weather). This study introduced a novel method to identify typical car-to-PTWs crash scenarios based on real-world crashes with dynamic pre-crash features investigated from the China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database. First, we present crash data collection and construction methods of the CIMSS-TA database to construct testing scenarios. Second, the stacked autoencoder methods are used to learn and obtain embedded features from the high-dimensional data. Third, the extracted features are clustered using k-means clustering algorithm, and then the clustering results are interpreted. Six typical car-to-PTWs scenarios are obtained with the proposed processes. This study introduces a typical high-risk scenario construction method based on deep embedded clustering. Unlike existing researches, the proposed method eliminates the negative impacts of manually selecting clustering variables and provides a more detailed scenario description. As a result, the typical scenarios obtained from AV testing are more robust.

Conditioned nausea induced by cisplatin and emetine identified by a taste reactivity test in rats.

No prior studies have shown that gaping reactions are produced with the avoidance of conditioned taste caused by cisplatin and emetine. Therefore, we tried to demonstrate it using a taste reactivity test in rats and found the gaping reactions induced when saccharin is readministered after gustatory conditioning that paired saccharin with cisplatin or emetine. Since conditioned gaping reactions indicate the aversion to saccharin taste and conditioned nausea, the present study suggest that the taste aversion is induced by cisplatin and emetine. It was also found that with intraperitoneal injections of emetine alone, gaping almost never occurs.

Effects of collision warning characteristics on driving behaviors and safety in connected vehicle environments.

With the emerging connected vehicle (CV) technologies, a novel in-vehicle omni-direction collision warning system (OCWS) is developed. For example, vehicles approaching from different directions can be detected, and advanced collision warnings caused by vehicles approaching from different directions can be provided. Effectiveness of OCWS in reducing crash and injury related to forward, rear-end and lateral collision is recognized. However, it is rare that the effects of collision warning characteristics including collision types and warning types on micro-level driver behaviors and safety performance is assessed. In this study, variations in drivers' responses among different collision types and between visual only and visual plus auditory warnings are examined. In addition, moderating effects by driver characteristics including drivers' demographics, years of driving experience, and annual driving distance are also considered. An in-vehicle human-machine interface (HMI) that can provide both visual and auditory warnings for forward, rear-end, and lateral collisions is installed on an instrumented vehicle. 51 drivers participate in the field tests. Performance indicators including relative speed change, time taken to accelerate/decelerate, and maximum lateral displacement are adopted to reflect drivers' responses to collision warnings. Then, generalized estimation equation (GEE) approach is applied to examine the effects of drivers' characteristics, collision type, warning type and their interaction on the driving performance. Results indicate that age, year of driving experience, collision type, and warning type can affect the driving performance. Findings should be indicative to the optimal design of in-vehicle HMI and thresholds for the activation of collision warnings that can increase the drivers' awareness to collision warnings from different directions. Also, implementation of HMI can be customized with respect to individual driver characteristics.

General Synthesis of a Diatomic Catalyst Library via a Macrocyclic Precursor-Mediated Approach.

Heterogeneous catalysts containing diatomic sites are often hypothesized to have distinctive reactivity due to synergistic effects, but there are limited approaches that enable the convenient production of diatomic catalysts (DACs) with diverse metal combinations. Here, we present a general synthetic strategy for constructing a DAC library across a wide spectrum of homonuclear (Fe2, Co2, Ni2, Cu2, Mn2, and Pd2) and heteronuclear (Fe-Cu, Fe-Ni, Cu-Mn, and Cu-Co) bimetal centers. This strategy is based on an encapsulation-pyrolysis approach, wherein a porous material-encapsulated macrocyclic complex mediates the structure of DACs by preserving the main body of the molecular framework during pyrolysis. We take the oxygen reduction reaction (ORR) as an example to show that this DAC library can provide great opportunities for electrocatalyst development by unlocking an unconventional reaction pathway. Among all investigated sites, Fe-Cu diatomic sites possess exceptional high durability for ORR because the Fe-Cu pairs can steer elementary steps in the catalytic cycle and suppress the troublesome Fenton-like reactions.

Jointly analyzing freeway primary and secondary crash severity using a copula-based approach.

A copula-based model is developed in this study to jointly model the severity of freeway primary crashes and secondary crashes. The copula-based model can concurrently account for the severity levels in the crash and the correlation among primary-secondary crash pairs' severity. The model comprehensively considers a series of explanation variables, including temporal characteristics, crash characteristics, roadway characteristics and real-traffic conditions, and is estimated using traffic crash data from 2016 through 2019 for Los Angeles County, California. The proposed copula model is then contrasted with the traditional binary probit model and the results show a remarkable advantage of the copula model, which is evidenced by better fitting performance. It is found that weather, whether towed away, unsafe speed, collision type, road condition, terrain, road weaving and truck involvement have significant impact on primary crash severity propensity and collision type, road width, road condition, traffic volume and vehicle speed have significant impact on secondary crash severity propensity. In light of the findings, a number of countermeasures are proposed to mitigate freeway crashes, including emergency services, vehicle and roadway engineering, traffic law enforcement and driver education.

Exploring factors affecting the injury severity of freeway tunnel crashes: A random parameters approach with heterogeneity in means and variances.

Generally, freeway tunnels are built to overcome the complex driving environments in mountainous terrains. However, crashes in these tunnels can be more severe than those on the open road sections due to their closed driving environment. Despite the higher crash severity, very few studies have attempted to investigate the severity of injuries in freeway tunnel crashes. Also, the existing studies on the injury severity analysis of tunnels did not fully consider the unobserved heterogeneity and its interactive effects. To address these issues, the present study first collected a comprehensive dataset containing five-year of police-reported tunnel crashes from Hunan province, China. A random parameters model with heterogeneity in means and variances was then developed to explore the influence of different variables related to the environment, drivers, crashes, vehicles, and tunnels. The study observed that the presence of curves and speeding indicators produce random parameters with heterogeneity in means and variances for freeway tunnels, which is influenced by the young drivers and outside exit zone variables. Also, the results reveal that factors, including weekdays, daytime, speeding, fatigue driving, rear-end collisions, collisions with fixtures, large passenger vehicles, and downgrades increase, while rain reduces the probability of severe injury outcomes in freeway tunnel crashes. More importantly, considering the unique tunnel driving environment, the summer, young drivers, novice drivers, presence of curves, and different tunnel sections (access, entrance, and outside exit zones) also significantly affect the risk of severe injury outcomes. Finally, the study's findings could be used as a basis for developing plans and technologies to minimize the severity of crash injuries in freeway tunnels.

An assessment of the effect of green signal countdown timers on drivers' behavior and on road safety at intersections, based on driving simulator experiments and naturalistic observation studies.

INTRODUCTION: Motor-vehicle crashes at signalized intersections are a significant traffic safety problem. To address this problem, many Asian cities have installed signal countdown displays at signalized intersections, aiming to assist drivers to make correct decisions in response to traffic signals. METHOD: In this study, we assessed the short-term and long-term effects of green signal countdown timers (GSCTs) on road safety, using a combination of driving simulator experiments and naturalistic observations. RESULTS: In our driving simulator experiments, 80 participants drove at 50 km/h in scenarios in which a car either approached a signalized intersection alone or following another car. In naturalistic observations, short-term (1-week) and long-term (1-year) intersection safety in the presence and absence of GSCTs were compared. These observations revealed that GSCTs reduced the number of red-light-running violations over the short term, but not over the long term. In fact, GSCTs appeared to lead to an overall increase in rear-end crash risk at intersections, as their presence resulted in drivers exhibiting more sudden acceleration and braking, and altered intersection-crossing speeds and patterns. CONCLUSIONS: The results suggest that GSCTs worsen safety at signalized intersections, and thus their removal should be considered.

Velocity control in car-following behavior with autonomous vehicles using reinforcement learning.

Car-following behavior is a common driving behavior. It is necessary to consider the following vehicle in the car-following model of autonomous vehicle (AV) under the background of the vehicle-to-vehicle transportation system. In this study, a safe velocity control method for AV based on reinforcement learning with considering the following vehicle is proposed. First, the mixed driving environment of AVs and human-driven vehicles is constructed, and the trajectories of the leading and following vehicles are extracted from the naturalistic High D driving dataset. Next, the soft actor-critic (SAC) algorithm is used as the velocity control algorithm, in which the agent is AV, the action is acceleration, and the state is the relative distance and relative speed between the AV and the leading and following vehicles. Then, a reward function based on state and corresponding action is designed to guide AV to choose acceleration without collision between the leading and following vehicles. Furthermore, AVs are gradually able to learn to avoid collisions between the leading and following vehicles after training the model. The test result of the trained model shows that the SAC agent can achieve complete collision avoidance, resulting in zero collision. Finally, the driving performance of the SAC agent and that of human driving are compared and analyzed for safety and efficiency. The results of this study are expected to improve the safety of the car-following process..

Understanding drivers' awareness, habits and intentions inside road tunnels for effective safety policies.

Tunnels have a unique driving environment; thus, a small incident in a tunnel may result in severe consequences and a high probability of secondary crashes. Fortunately, studies have found that adopting safe driving behavior in a tunnel minimizes the severe outcomes of an incident. Therefore, implementing driver-oriented safety policies and conducting public awareness campaigns that emphasize safe behavior when driving through tunnels are essential. However, before devising policies and campaigns on the right issues, it is necessary to understand drivers' current level of knowledge regarding tunnel safety, their habits, behavioral intentions, and psychological condition while driving through tunnels. To achieve this objective, a sample of 841 responses was collected from China using a questionnaire survey consisting of fifty-two items. The results showed that several gaps exist in drivers' knowledge regarding tunnel safety and equipment. Drivers often adopt inappropriate habits and behaviors while driving through tunnels. Also, the tunnel environment has a significant influence on the psychological condition of the drivers. Moreover, drivers' demographic characteristics significantly affect their knowledge, reported habits and behavioral intentions, and psychological condition. The authorities and safety analysts could employ the suggestions highlighted in the present study for improving tunnel safety.

What Factors Would Make Single-Vehicle Motorcycle Crashes Fatal? Empirical Evidence from Pakistan.

The existing research on motorcycle safety has shown that single-vehicle motorcycle crashes (SVMC) account for a higher fatality rate than other types of crashes. Also, motorcycle safety has become one of the critical traffic safety issues in many developing countries, such as Pakistan, due to the growing number of motorcycles and lack of sufficient relevant infrastructure. However, the available literature on SVMC and motorcycle safety in developing countries is limited. Therefore, the present study attempted to investigate the factors that contribute to the injury severity of SVMC in a developing country, Pakistan. For this purpose, a random parameter logit model with heterogeneity in means and variances is developed using two years of data extracted from the road traffic injury research project in Karachi city, Pakistan. The study's findings show that the presence of pillion passengers and young motorcyclists indicators result in random parameters with heterogeneity in their means and variances. The study's results also reveal that the summer, morning time, weekends, older motorcyclists, collisions with fixed objects, speeding, and overtaking are positively, while younger motorcyclists and the presence of pillion passengers are negatively associated with fatal crashes. More importantly, in the particular Pakistan's context, female pillion passenger clothes trapped in the wheel, riding under the influence, intersections, U-turns, and collisions due to loss of control are also found to significantly influence the injury severity of SVMC. Based on these research findings, multiple appropriate countermeasures are recommended to enhance motorcycle safety in Pakistan and other developing countries with similar problems.