Car Drivers Research Articles

Assessment of Driver's Stress using Multimodal Biosignals and Regularized Deep Kernel Learning.

In this work, we classify the stress state of car drivers using multimodal physiological signals and regularized deep kernel learning. Using a driving simulator in a controlled environment, we acquire electrocardiography (ECG), electrodermal activity (EDA), photoplethysmography (PPG), and respiration rate (RESP) from N = 10 healthy drivers in experiments of 25min duration with different stress states (5min resting, 10min driving, 10min driving + answering cognitive questions). We manually remove unusable segments and approximately 4h of data remain. Multimodal time and frequency features are extracted and employed to regularized deep kernel machine learning based on a fusion framework. Task-specific representations of different physiological signals are combined using intermediate fusion. Subsequently, the fused multimodal features are fed a support vector machine (SVM) and a random forest (RF) for stress classification. The experimental results show that the proposed approach can discriminate between stress states. The combination of PPG and ECG using RF as classifier yields the highest F1-score of 0.97 in the test set. PPG only and RF yield a maximum F1-score of 0.90. Furthermore, subject-specific cross-validation improves performance. ECG and PPG signals are reliable in classifying the stress state of a car driver. In summary, the proposed framework could be extended to real-time stress state assessment in driving conditions.

Drowsy Eyes and Face Mask Detection for Car Drivers using the Embedded System

Efforts to prevent the spread of the COVID-19 virus have underscored the critical importance of mask-wearing as a preventive measure. Concurrently, road traffic accidents, often resulting from human error, have emerged as a significant contributor to global mortality rates. This study endeavors to address these pressing issues by employing advanced Deep Learning techniques to detect mask usage and identify drowsy eyes, thus contributing to the prevention of COVID-19 and accidents due to driver fatigue. To achieve this objective, an embedded system was developed, utilizing the integration of hardware and software components. The system effectively utilizes MobileNetV2 for face mask detection and employs HOG and SVM algorithms for drowsy eye detection. By seamlessly integrating these detection systems into a single embedded device, the simultaneous detection of both mask usage and drowsy eyes is made possible. The results demonstrates a commendable accuracy rate of 80% for face mask detection and 75% for drowsy eye detection. Furthermore, the mask detection component exhibits a remarkable training accuracy of 99%, while the drowsy eye detection component demonstrates an 80% training accuracy, affirming the system's efficacy in precisely identifying masks and drowsy eyes. The proposed embedded system offers potential applications in enhancing road safety. Its capability to effectively detect drowsy eyes and mask usage in car drivers contributes significantly to preventing accidents due to driver fatigue. Additionally, it plays a vital role in mitigating COVID-19 transmission by promoting widespread mask-wearing among individuals. This study exemplifies the potential of integrating Deep Learning methodologies with embedded systems, thus paving the way for future research and development in the realm of driver safety and virus prevention.

Thermal Comfort of Automobile Driver Based on Local Air Conditioning Vent Arrangement

Thermal Comfort of Automobile Driver Based on Local Air Conditioning Vent Arrangement

Back to the Present of Automated Mobility

This article focuses on how car drivers domesticate technologies of automation and the way this might inform our understanding of potential shifts to a more automated mobility system. The current literature on automated mobility has mainly addressed drivers’ roles in terms of their attitudes towards—and acceptance of—an anticipated shift to high-level driving automation. In this article, however, we take a step back from expectations around automated mobility to explore the domestication of driving assistance technologies and systems already in use. The analysis is built on qualitative interviews with drivers of private cars in Norway. Based on our findings, we develop a typology of user-technology characterizations highlighting three themes of the drivers’ use (comfort, safety, and novelty) as well as two modes of engagements (modulation and non-use). Our analysis suggests that automation is likely to be an incremental and gradual process and that its eventual application depends on the specificities of the practices that it seeks to disrupt. Moreover, we argue that the governance of automated mobility needs to be attentive to the dynamic and unpredictable roles technology will have in processes of socio-technical change. In this context, we highlight the key roles of users in shaping processes of appropriation of both new technologies and broader innovations and argue that knowledge about technology domestication provides important insights to changes towards automation in our current mobility systems.

Understanding and modeling willingness-to-pay for public policies to enhance road safety: A perspective from Pakistan

Evaluating road safety improvements becomes important because it can assist policymakers in allocating economic resources to improve safety and implementing effective policy interventions. As such, this study aims to estimate the value of road safety risk measures using a new modeling approach for willingness-to-pay (WTP). Specifically, this study integrates a machine learning technique (decision tree) with a correlated random parameters Tobit with heterogeneity-in-means model. The decision tree identifies a priori relationships for higher-order interactions, while the model captures unobserved heterogeneity and the correlation between random parameters. The proposed modeling framework examines the determinants of public WTP for improving road safety using a sample of car drivers from Peshawar, Pakistan. WTP for fatal and severe injury risk reductions is estimated and used to calculate the values of corresponding risk reductions, which can be used for monetizing the cost of road traffic crashes in the country. Modeling results reveal that most respondents are willing to contribute to road safety improvement policies. Further, the model also uncovers significant heterogeneity in WTP corresponding to the safer perception of the overall road infrastructure and perceived risk of accident involvement. Systematic preference heterogeneity is also found in the model by including higher-order interactions, providing additional insights into the complex relationship of WTP with its determinants. Further, the marginal effects of explanatory variables indicate different sensitivities toward WTP, which can help to quantify the impacts of these variables on both the probability and magnitude of WTP. Overall, the proposed modeling framework has a twofold contribution. First, the modeling framework provides valuable insights into the determinants of public WTP, mainly when the heterogeneous effects of variables are interactive. Second, its implementation and consequent findings shall help prioritize different road safety policies/projects by better understanding public sensitivity to WTP.

Metacognition for Every Clinician

A call to action, Surgical Metacognition: Smarter Decision-making for Surgeons challenges surgeons to understand the processes that underpin decision-making and performance, and how we can use this new knowledge to improve a surgeon's performance both in and outside the operating theatre.
 Decision-making is a core professional activity for surgeons of all levels. It is a distinct high-level skill that is closely associated with the quality of surgical outcomes, patient satisfaction, teamwork, and a surgeon's well-being. Yet it has so far been woefully neglected in surgical training and professional development, with an assumption that these skills develop on their own and cannot be taught or improved. This is an unfortunate and costly oversight that it is time we redress.
 But how are good decisions and expertise made? And how can we as a profession improve our thinking to optimize our decision-making and train expertise?
 The answer lies not only in gaining more knowledge and experience, which although essential for good decision-making, do not address the fact that so few of us understand the process itself, let alone how to optimize and teach it. The first step, and focus of this book, is understating 'metacognition'; 'thinking about thinking, ' to improve self-awareness, critical thinking and how we make decisions.
 The profound developments of psychology and decision-science are now well established and have been successfully incorporated into other high-stakes professions such as senior business management, the military and aviation, to significantly improve both professional and personal outcomes. That surgery has yet to implement this knowledge is like a race car driver using a car with technology that is decades old, ignoring developments that would significantly increase performance. It is time for surgery to embrace this knowledge; to advance and modernize the profession with sharper critical thinking and teaching.
 With many real-world surgical examples, Surgical Metacognition: Smarter Decision-making for Surgeons reviews the psychological understanding of decisions and decision-making from recent decades of research, giving us the tools to improve how we think and teach this fundamental skill of expertise.

Atypical intersection of a bicycle path and a carriageway with marked car priority: Which car drivers give way anyway?

The issue of the safety of vulnerable road users is particularly important at points of collision with other roads and road users. The most common solution at the intersection of a carriageway and a cycle path is a cycle crossing, with specific rules of priority for cyclists and drivers. However, the constantly increasing number of accidents involving cyclists prompts the creation of new and innovative solutions in infrastructure and traffic organisation. One novel solution in this respect takes the form of intersections of cycle paths with carriageways with traffic organisation that always gives priority to drivers, introduced in the city of Rybnik in Poland. The aim of the study is to analyse the behaviour of drivers at the intersection of the cycle path and the carriageway with signage indicating car priority in a situation when simultaneously a driver was coming along the carriageway and a cyclist was preparing to cross and crossed the intersection. The study took the form of a trial in which two cyclists—a man and a woman—negotiated the designated intersection in two directions. Drivers’ decisions to give or not to give priority were analysed in the context of the cyclist’s gender and direction of travel, as well as observable driver characteristics such as gender and residence based on the vehicle registration number. The study confirms that a significant number of drivers give priority to cyclists despite the rules at the intersection under study. However, no significant differences were found between the behaviour of female and male drivers. The study found that only one variable—place of vehicle registration—had a statistically significant relationship with giving priority to cyclists, contrary to the variables defining the gender of drivers and cyclists.

Drivers’ willingness to shift towards electronic toll collection system in india

Electronic toll collection (ETC), locally known as the FASTag system in India, has been made compulsory for payment of tolls on all National Highways (NHs) from January 2020. The users are thus adopting the newly developed FASTag system for toll payment, but the penetration rates are very low. The present research aims at studying the factors affecting road users’ willingness or unwillingness to use the FASTag. The nationwide survey data are collected from the different vehicle users using an online survey and personal interview methods. Various trip-related parameters like vehicle type, trip purpose, etc.; attitudinal parameters like awareness of toll roads, and current toll rates; FASTag-related parameters like awareness of FASTag, and having or not having the FASTag, and socioeconomic and demographic parameters like age, gender, monthly income, etc., are considered in developing the questionnaire. Analysis of these survey data showed that drivers of big cars, Bus, multi axel vehicle (MAV) and Truck Trailers are more willing to use the FASTag facility to pay toll. A willingness to adopt the FASTag model is developed using the binary choice model. The results showed that trip-related attributes such as vehicle class, trip purpose and trip frequency significantly impact willingness to own FASTag. Further, awareness about FASTag is one of the important factors for shifting towards the FASTag system. Moreover, frequent users passing during peak hours are more likely to use FASTag. Also, the users who are in service, either Government or Private, have shown more willingness to adopt FASTag than those who are unemployed. Further, the stated preference analysis is also carried out for framing possible policy measures to increase the FASTag share. The outcome of the present study is expected to provide essential inputs to the policymakers for enhancing the adoption of FASTag among toll road users.

How do the affective and symbolic factors of private car driving influence car users’ travel behavior in a car restriction policy scenario?

The number of private car trips a person makes is usually linked to external factors such as economic incentives or disincentives, legislation, infrastructure, and the quality of transport systems, among others. The impact of these options are typically analyzed to evaluate urban transport policies and projects. However, internal factors related to an individuals' attitudes and perceptions also play an essential role in the amount of private car driving they do and on urban mobility as well. Therefore, insight into private car driving's affective and symbolic aspects is important in order to formulate appropriate transport policies and strategies to reduce car-use dependency and encourage private car drivers to use public transport. Congestion charging schemes make drivers pay the full social cost of driving, they achieve congestion reduction, pollution reduction, they make cities more attractive for pedestrians and cyclists, and help improve quality of life. Congestion charging schemes are often difficult to implement and expensive to administer. As a result of these reasons, several cities worldwide have adopted a variety of car restriction schemes that try to reduce air pollution and congestion as well, instead of establishing congestion charges. The effects of driving restrictions in the short term are positive, but in the medium and long term, there are unwanted consequences. However, by giving private car drivers the option to pay a toll so that their cars are exempted from the restriction, these perverse incentives could be eliminated. This paper analyzes the impacts of internal factors related to individuals' attitudes and perceptions about the travel behavior of car users affected by car restriction policies in urban areas. We designed a stated preference survey conducted among car owners in Cali, Colombia, where a License Plate Restriction Charging (LPRC) policy has been in place since January 2017. Through hybrid discrete choice modeling, we demonstrated that latent variables, such as the feelings of being in control, independence, and higher social status, positively influence the decision to use cars for daily trips, thus impacting the urban modal split. The heterogeneity captured through these latent variables allowed us to understand more deeply how individuals deal with the LPRC policy in order to travel to their destination.

INVESTIGATION OF THE USE OF STUDDED WINTER AND UNIVERSAL TIRES IN VEHICLES OF M1 AND N1 CATEGORIES IN ŠIAULIAI

Tires are one of the most important elements of vehicles in terms of traffic safety. This is even more evident in unfavourable weather conditions on winter roads. The wrong choice of tires quite often becomes the cause of a traffic accident. It is very important to pay attention to the correct use of tires for drivers of trucks and cars. The vehicle is in contact with the road surface only on the four surfaces of the human palm. Road traffic safety depends on various traffic elements: driver education, road quality and technical condition of cars. Tires are one of the components of a car. This paper examines the types of tires suitable for use in winter, the tire tread patterns, in order to determine the types of winter tires used, the prevalence of studded tires is studied. The aim is to determine the reasons why some European countries prohibit the use of studded winter tires. Keywords: winter tires, studded tires, traffic safety, road surface

Noise levels at traffic intersections and awareness of noise pollution among traffic policemen and automobile drivers

Introduction: Noise pollution is an important health hazard in modern times and traffic policemen and automobile drivers are exposed to unregulated vehicular noise. This study aims to estimate the exposure levels from traffic noise for traffic policemen and automobile drivers and assess their awareness regarding noise pollution and the use of noise prevention measures. Methods: An analytical cross-sectional study was carried out comprising the exposed and the control group with 121 people in each group. Sound level estimation was carried out across 20 traffic intersections in the city at various times to assess the noise levels. All the participants were administered a pretested questionnaire to assess their awareness regarding noise pollution and as well as the use of noise prevention strategies. The chi-square test was used to estimate the difference between the two groups. P <0.05 was considered statistically significant. Results: The noise levels at traffic intersections are above the permitted and acceptable limits. The mean ±SD of sound levels at traffic points range from 76.7±5.8 dB to 83.4±6.3 dB across various traffic intersections. The mean noise level at the traffic intersection was in the high-risk category (81-86dB) in 25% of traffic points while 75% of traffic points had noise levels in the moderate risk (76 - 81dB) category. 88% of the exposed group and 95% of the control group considered noise to be a form of pollution even though they were not aware of all the harmful effects. In practice, only 34% of study participants used some form of hearing protection measures, even though the majority were aware of the need to do so. Conclusion: The noise levels at traffic intersections are high. This is coupled with a lack of adequate use of hearing protective devices which can be detrimental to the inner ear. Frequent awareness programs are necessary to educate the occupationally exposed personnel on proper noise prevention strategies. At the same, time it is necessary to take decisive measures to curb the ever-increasing menace of occupational noise exposure.

FeduLPM: Federated Unsupervised Learning-Based Predictive Model for Speed Control in Customizable Automotive Variants

Over speeding of vehicles is considered as one of the most important factors for occurring road accidents. An advanced sensor technology motivates the researchers to move towards automatic vehicles, whereas the speed decisions were taken based on the information collected from the sensor. Beyond its success, the expensiveness, and implementation failure in bad road conditions make it less practicable. In this work, a novel FeduLPM technique has been proposed for controlling the speed in customizable automotive variants using data analysis from the location of the accident. Instead of sharing the whole accident data on a Heroku server to train the model, a pre-trained Random Forest algorithm is designed on each nearby location and the accident data is updated on the local model. The attributes such as weather, automotive variants, time, location, and zone geometry are taken for providing an exact speed limit. Finally, trained parameters of the local model are aggregated; then a global model is generated and distributed to local clients without sharing local data. The experimental setup is implemented using a Python-based ML algorithm that draws information from the vehicle based on the gathered attributes. The proposed FeduLPM achieved 96.7% of accuracy while processing with the different locations of Bangalore city. From the experimental results, the proposed FeduLPM is a better assistant to bike and car drivers by providing an optimum solution to prevent accidents.

THE EFFECT OF TRAM MANAGEMENT ON ROAD TRAFFIC FLUIDITY AND ITS INFLUENCE ON DRIVERS’ BEHAVIORS AT A CRITICAL JUNCTION IN ALGERIA

This research paper aims to study the effect of tram management on traffic fluidity and its impact on car drivers’ behaviors at junctions crossed by trams. The methodology used in this research is based on a mathematical model and an investigation of car drivers. The first step is to analyze the data of annual travelers’ attendance and assess the number of trams offered and needed in operation to respond adequately to the factual demand. The second step proceeds to show how the previous results of the trams’ fleet influence traffic jams. That is, this step identifies how the number of trams used in operation blocks other motorists and reduces traffic flow capacity at junctions. Finally, the purpose of the questionnaire is to determine car drivers’ opinions of the causes of traffic congestion at junctions and understand how this phenomenon affects their behaviors. The outcomes demonstrate that tram management is ineffective because there is a considerable gap between the annual offered tram fleet and the actual one needed according to the real statistical data. The high number of trams utilized is the leading cause of traffic congestion. Furthermore, this situation disturbs the control of traffic lights at common intersections. Unfortunately, this outcome is the main reason for drivers’ poor behavior, as 75.20% of car drivers are always stressed. These issues have intensified traffic jams in several junctions along the tram line. The article recommends some solutions to improve tram management and traffic fluidity to avoid the substandard behavior of car drivers at junctions.

ACCIDENT DETECTION & ALERT MESSAGING –ANDROID APP

Accidents are the main cause of fatalities. These can occasionally lead to patients staying in the hospital for an extended period of time. Because of the witnesses` late arrival at the scene of the accident, the victim frequently dies. In this project, a real-time Police and ambulance drivers will be informed and given an alert system as a remedy to this issue. In order to detect falls, mobile phones are employed as readily available technical instruments. For fall detection, an Android smart phone with an embedded accelerometer is employed. The phone`s vibration frequency will be evaluated by the accelerometer in order to determine the frequency of the fall. Various factors, including vibration frequency and height, are taken into account while evaluating the threshold. A pop-up notice is displayed for the user`s input if it exceeds the predetermined threshold. The next step is decided upon by the user`s response. If users don`t answer within a given time frame, an alarm and notification will be promptly sent to the necessary pre-specified people, whose contacts the user gave while enrolling for the programme. Drivers of the police car and am balance are also provided an alert and notice, or SMS, with the location of the victim`s accident. The technology described in this research uses an intuitive, straightforward Android app to detect fall at a low cost.

State policy in the field of ensuring road safety in Ukraine through the prism of regulatory initiatives: analysis and prospects for implementation

A comparative legal analysis of draft legal acts aimed at improving road safety (draft laws No. 8369 of 19.01.2023, No. 8353 of 12.01.2023, No. 8356 of 12.01.2023) has been carried out. It has been noted that today the legislator has carried out meaningful work aimed at improving the current legislation in terms of road safety.
 It has been proved that the adoption of a certain act should not have adverse consequences in the lawmaking process and create a situation where there is competition of legal norms, as well as contradictions between other legal acts in force in the area under study. In addition, it has been stated that the draft laws under consideration are to some extent controversial and debatable, they contain terminological uncertainty, and may cause the unplanned (additional) burden on the budget of Ukraine and local budgets. It has been offered proposals and comments on how to solve these problems and improve the law-making process.
 It has been emphasised that there is a need to implement the strategic directions of the State to ensure the education of conscious road users, strengthening road safety, infrastructure, constructive vehicle safety, as well as planning of public authorities to ensure road safety at various levels of government, which in turn should contribute to the implementation of the national policy in the field under study. The position that road users, including children, should be trained in three stages has been supported: 1) elementary level is for preschool children; 2) safety basics is for primary school (teaching safe driving for themselves and others on electric scooters, monowheels, segways and similar vehicles); 3) full-fledged study, in particular, according to the programme for training drivers of scooters, motorcycles and cars, with mandatory testing of knowledge by means of an exam, is for secondary school.

The Correlation of Work-Related Stress with Work Fatigue on Surabaya Integrated Terminal Tank Car Driver

Background: Many industrial locations have been observed to have a high prevalence of work fatigue, which poses health and safety issues. Driving can be tiring, especially if the body is not rested so it can increase the risk of accidents. Accidents to tank car drivers can be fatal because they can cause massive losses to the company. Subjective work fatigue can be induced by various factors, such as work-related stress. Objectives: The purpose of this research was to analyze the relationship between work-related stress with work fatigue on the tank car driver of the PT Pertamina Patra Niaga and the fatigue and stress management that should be carried out. Methods: This research was an observational analytic conducted on a sample of 205 tank car drivers. The dependent variable in this research was subjective work fatigue. Data analysis using Spearman Correlation test. Results: The Spearman correlation coefficient was 0.071, which means that the variables of work-related stress and subjective work fatigue did not have a significant relationship. Conclusion: Variable work-related stress was not related to work fatigue may occur because other variables were not analyzed so it did not show a description of the causes of fatigue experienced. Companies must perform driver intervention programs to prevent stress and fatigue from worsening.

What triggers road traffic fatalities among older adult drivers? An investigation based on the Swedish register for in-depth studies of fatal crashes

IntroductionRoad traffic fatalities among drivers result not only from erroneous driving manoeuvres. They can also be triggered by poor mental or physical health like suicidal acts and acute disease attacks, the latter being more frequent with age. There are few studies comprising all the different triggers of fatal crashes among older drivers and on how these triggers evolve over time. This Swedish national study aims to investigate this, considering drivers 50+ years. MethodFor the period 2010–2019, data on non-commercial car drivers aged 50 and older were extracted from the Swedish Transport Administration’s in-depth studies of fatal crashes, compiled in a register encompassing all road traffic fatalities. The crash triggers reported were classified as suicide, acute disease, own manoeuvre (single or other type of crash), external, and undetermined. Total and annual frequencies were presented by crash trigger, overall and stratified by sex and age groups. ResultsIn total, 762 fatalities were identified and with 709 of them, it was possible to classify the crash trigger: 12% resulting from suicide; 29% from acute disease; 49% from the driver’s own manoeuvre; and 10% from an external crash trigger. Suicides increased from 9% in average 2010–2014 to 15% in 2015–2019 while acute disease decreased (from 32% to 27%). The overall male-to-female ratio was 4.5, and as high as 8.7 for acute disease. The relative importance of each trigger varied with age with, for instance, acute disease being more frequent in the two younger age groups; 31% for 50–64 and 52% for 65–79 years old drivers, while own manoeuvre was more prominent for drivers aged 80 and older (23%). ConclusionThe distribution of the four different triggers of road traffic fatalities varies only slightly over time and, a majority are triggered by the driver’s own manoeuvre. However, a substantial proportion, about four out of ten,are triggered by a health condition, of which some are suicide but the major part an acute disease, mainly cardiovascular. In some instances, such crashes can be harmful to other road users. Having in mind the need for safety and mobility of older drivers and the protection of all road users, there is a need for the promotion of road safety policies that encompass a wider range of measures to help reducing the likelihood of fatal and severe crashes to happen e.g., targeting incapacitated drivers.

Unraveling preference heterogeneity in willingness-to-pay for enhanced road safety: A hybrid approach of machine learning and quantile regression

Investing in road safety enhancement programs highly depends on the economic valuation of road traffic accidents and their outcomes. Such evaluation underpins road safety interventions in cost-benefit analysis. To this end, understanding and modeling public willingness-to-pay for enhanced road safety have received significant attention in the past few decades. However, despite considerable modeling efforts, some issues still persist in earlier studies, namely, (i) using standard regression approaches that assume a homogeneous impact of explanatory variables on willingness-to-pay, not accounting for heterogeneity, and depends on a priori distribution of the dependent variable, and (ii) the absence of higher-order interactions from models, leading to omitted variable bias and erroneous model inferences. To overcome this critical research gap, our study proposes a new modeling framework, integrating a machine learning technique (decision tree) to identify a priori relationships for higher-order interactions and a quantile regression model to account for heterogeneity along the entire range of willingness-to-pay. The proposed framework examines the determinants of willingness-to-pay for enhanced road safety using a sample of car drivers from Peshawar, Pakistan. Modeling results indicate that variables not significant in a linear model become significant at specific quantiles of the willingness-to-pay distribution. Further, including higher-order interactions among the explanatory variables provides additional insights into the complex relationship between willingness-to-pay and its determinants. In addition, willingness-to-pay for fatal and severe injury risk reductions is estimated at different quartiles and used to calculate the values of corresponding risk reductions. Overall, the proposed framework provides a better understanding of public sensitivities to willingness-to-pay for enhanced road safety.

Bivariate joint analysis of injury severity of drivers in truck-car crashes accommodating multilayer unobserved heterogeneity

Truck-involved crashes, especially truck-car crashes, are associated with serious and even fatal injuries, thus necessitating an in-depth analysis. Prior research focused solely on examining the injury severity of truck drivers or developed separate performance models for truck and car drivers. However, the severity of injuries to both drivers in the same truck-car crash may be interrelated, and influencing factors of injury severities sustained by the two parties may differ. To address these concerns, a random parameter bivariate probit model with heterogeneity in means (RPBPHM) is applied to examine factors affecting the injury severity of both drivers in the same truck-car crash and how these factors change over the years. Using truck-car crash data from 2017 to 2019 in the UK, the dependent variable is defined as slight injury and serious injury or fatality. Factors such as driver, vehicle, road, and environmental characteristics are statistically analyzed in this study. According to the findings, the RPBPHM model demonstrated a remarkable statistical fit, and a positive correlation was observed between the two drivers' injury severity in truck-car crashes. More importantly, the effects of the explanatory factors showing relatively temporal stability vary across different types of vehicle crashes. For example, car driver improper actions and lane changing by trucks, have a significant interactive effect on the severity of injuries sustained by drivers involved collisions between trucks and cars. Male truck drivers, young truck drivers, older truck drivers, and truck drivers' improper actions, elevate the estimated odds of only truck drivers; while older car and unsignalized crossing increase the possibility of injury severity of only car drivers. Finally, due to shared unobserved crash-specific factors, the 30-mph speed limit, dark no lights, and head-on collision, significantly affect the severity of injuries sustained by drivers involved in collisions between trucks and cars. The modeling approach provides a novel framework for jointly analyzing truck-involved crash injury severities. The findings will help policymakers take the necessary actions to reduce truck-car crashes by implementing appropriate and accurate safety countermeasures.

Evaluating overtaking and filtering maneuver of motorcyclists and car drivers using advanced trajectory data analysis

The present paper compares motorized two-wheeler (MTW) and passenger car’s interactions with the rest of the traffic in urban roads while performing overtaking and filtering maneuvers. To better understand filtering maneuvers of motorcyclists and car drivers, an attempt was made to propose a new measure, i.e. pore size ratio. Additionally, the factors affecting lateral width acceptance for motorcyclists and car drivers while overtaking and filtering were studied using advanced trajectory data. A regression model was developed to predict the significant factors affecting motorcyclist’s and car driver’s decisions to accept lateral width with the adjacent vehicle while performing overtaking and filtering maneuvers. Finally, a comparative analysis between machine learning and the probit model revealed that, in the present case, machine learning models perform better than the probit model in terms of the model’s discernment power. The findings of this study will help ameliorate the power of existing microsimulation tools.