Driver Profile Research Articles

The influence of middle-aged male driver profile on driving performance and the effects of three perceptual countermeasures: A simulator study

The human-environment-vehicle triad and how it relates to crashes has long been a topic of discussion, in which the human factor is consistently seen as the leading cause. Recently, more sophisticated approaches to Road Safety have advocated for a road-driver interaction view, in which human characteristics influence road perception and road environment affects driver behavior. This study focuses on road-driver interaction by using a driving simulator. The objective is to investigate how the driver profile influences driving performance and the effects of three countermeasures (peripheral transverse lines before and after the beginning of the curves and roadside poles in the curves). Fifty-six middle-aged male participants drove a non-challenging rural highway simulated scenario based on a real road where many single-vehicle crashes occurred. The drivers' profiles were assessed through their behavioral history measured by a validated version of the Driver Behavior Questionnaire (DBQ) comprising three dimensions: Errors (E), Ordinary Violations (OV), and Aggressive Violations (AV). The relationship between speed and trajectory measures and drivers' profiles was investigated using random-parameter models with heterogeneity in the means. The models' results showed that the DBQ subscale scores in OV explained a considerable part of the heterogeneity found in drivers' performance. Furthermore, the heterogeneity in the means caused by the DBQ subscale scores in OV and E in the presence of peripheral transverse lines indicates a difference in how drivers react to the countermeasures. The peripheral lines were more efficient than roadside poles to moderate speed but did not positively influence all drivers' trajectories. Although the peripheral lines could be seen as an alternative to change driver behavior in a non-challenging or monotonous road environment, the design used in this study should be reviewed.

A psychological profile of drivers convicted of driving under the influence of alcohol

Driving under the influence of alcohol (driving while intoxicated [DWI]) is a rather large social problem. It can be influenced by the driver’s personality, risk perception, difficulties related to hyperactivity, or attention deficit and alcohol dependencies; all of which share complex relations with each other, increasing the probability of criminal behaviour while driving and of road accidents. The purpose of this study is to explore the way in which personality, personality disorders, risk perception and behaviour while driving, hyperactivity or attention deficit, and the problematic consumption of alcohol are expressed in a sample of Spanish drivers who had been found guilty of committing DWI offences. These results are compared with those recorded for a sample of drivers with no history of such violations. For this study, a total of 115 subjects sentenced to perform road safety-related community service work (CSW) as a result of DWI convictions, as well as 97 drivers with no violations, were evaluated using Driver Behaviour Questionnaire (DBQ) tests, the NEO Five-Factor Inventory (NEO-FFI), the International Personality Disorder Examination (IPDE), the Adult ADHD Self-Report Scale Version 1.1, and the Alcohol Use Disorders Identification Test (AUDIT). Our results reveal that DWI traffic offenders exhibit risky consumption and signs of dependence (on alcohol and/or other drugs), and low levels of risk perception that are significantly lower than those observed for drivers without infractions. The logistic regression model provides data that supports the implication of a low perception of risk while driving under the influence of alcohol or other drugs, the violation of rules, extroverted personality traits, openness and cordiality, the indications of possible paranoid and antisocial PDs, and a below-university level of education as predictors of road crime due to DWI. This statistical solution offers a classificatory model with adequate internal and external validity. This scientific evidence could be useful for the design and improvement of prevention and reinsertion plans.

Consistency Analysis of Drivers’ Car-Following Behaviors

Car-following models are calibrated to account for various driver behaviors such as speed and space headway. Because drivers do not all drive the same way, they are typically classified based on their level, or profile, of aggressiveness. This approach to model calibration assumes that a single set of car-following parameters applies to an individual driver consistently, that is, during the entire driving period a study considers. The purpose of this research is to challenge that assumption by analyzing the heterogeneity and inconsistency of driver behaviors in different traffic conditions. A total of 3,262 urban expressway car-following periods from 51 drivers were extracted from the Shanghai Naturalistic Driving Study database. The intelligent driver model (IDM) was selected as this study’s car-following model for its favorable performance in highly dynamic situations; its parameters, that is, desired speed, desired time headway, maximum acceleration, comfortable deceleration, acceleration exponent, and minimum spacing, were calibrated. To reflect the effect of traffic conditions, the car-following periods were categorized into three regimes from low-speed to high-speed. Two-way analysis of variance was used to evaluate heterogeneity and inconsistency in the car-following parameters. The findings of this study show that (1) the main significant variable that distinguished driver profile was desired time headway, while the comfortable acceleration was commonly affected by both driver profile and speed regime; (2) variability of individual driver parameter estimates greatly depended on the traffic regime (low-, medium-, and high-speed), showing that driver behavior was highly influenced by traffic conditions; and (3) speed information was demonstrated to be a useful tool by which to divide car-following periods for modeling, as the car-following parameters exhibited distinct differences among the regimes in the varied traffic environment.

Understanding the Operating Speed Profile Patterns Using Unsupervised Machine Learning Approach: Short-Term Naturalistic Driving Study

Several studies have measured the minimum operating speed on horizontal curves to model the operating speed to assess the geometric design consistency. Most of these studies approximated equal lengths of deceleration and acceleration in the operating speed profiles for the curves and assumed the minimum operating speed position at the midpoint of the curve. In contrast, a few recent studies showed different percentages of deceleration lengths on the curve and measured the minimum operating speed at the deceleration end on the curve to model the operating speed. A defined pattern of the operating speed profile on the horizontal curve was not reported in the previous studies and therefore presents opportunities to determine the patterns of the operating speed profiles on curves. In this study, the operating speed profiles of different drivers for the given features of the horizontal curve were studied, and the clustering technique was used to categorize the different patterns in the operating speed profiles on horizontal curves. The optimal number of clusters was determined using four methods: silhouette, elbow, gap statistic, and NbClust function. The different patterns observed from the clustering results are as follows: (1) complete deceleration on the curve, (2) complete acceleration on the curve, (3) deceleration length slightly greater or lower than acceleration length, and (4) longer deceleration/acceleration lengths followed by shorter acceleration/deceleration lengths, respectively. The study results imply that all operating speed profiles are not symmetric around the midpoint of the curve (MC), and the group of drivers exhibited defined patterns of the operating speed profiles on the curves. This study helps in understanding the different patterns of operating speed profiles exhibited by the drivers and the measurement of the minimum operating speed at the deceleration end to model the operating speed to assess the geometric design consistency.

Decline in Other Instrumental Activities of Daily Living as Indicators of Driving Risk in Older Adults at an Academic Memory Clinic.

Decisions around driving retirement are difficult for older persons living with cognitive decline and their caregivers. In many jurisdictions, physicians are responsible for notifying authorities of driving risks. However, there are no standardized guidelines for this assessment. Having access to a driving risk assessment tool could help older adults and their caregivers prepare for discussions around driving retirement. This study compares the clinical profiles of older adult drivers assessed in an academic memory clinic who were referred to the driving authority to older drivers who were not with a focus on instrumental activities of daily living (iADLs). Data on referred (R) and not-referred (NR) drivers were extracted from medical records. Elements from the medical history, cognitive history, functional abilities, Modified Mini-Mental State (3MS) examination, Trails A/B, and clock drawing were included in the analysis. Four risk factors of interest were examined in separate logistic regression analyses, adjusted for demographic variables. 50 participants were identified in each group. The R group was older on average than the NR. As expected, R were more likely to have Trails B scores over 3 min and have significantly abnormal clock drawing tests. R also showed lower 3MS scores and a higher average number of functional impairments (including managing appointments, medications, bills, or the television). Beyond standard cognitive tests, impairment in iADLs may help general practitioners identify at-risk drivers in the absence of standardized guidelines and tools. This finding can also inform the design of a risk assessment tool for driving and could help with approaches for drivers with otherwise borderline test results.

Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions

This study reviews the Artificial Intelligence and Machine Learning approaches developed thus far for driver profile and driving pattern recognition, representing a set of macroscopic and microscopic behaviors respectively, to enhance the understanding of human factors in road safety, and therefore reduce the number of crashes. It provides a definition of the two scientific fields in terms of safety, and identifies the most efficient approaches used regarding methodology, data collection and driving metrics. Results show that K-means and Neural Networks are the most commonly used methodologies for driver profile identification, and Dynamic Time Warping for driving pattern detection. Most studies discovered driver profiles related to aggressiveness, considering mainly speed and acceleration as driving metrics. Based on the gaps and challenges identified, this paper provides a new framework for combining microscopic and macroscopic driving behavior analysis, instead of examining them separately as is the state-of-theart. Such combined results can potentially improve the development of traffic risk models, which could be exploited in applications that monitor drivers in real-time and provide feedback. These models will represent human behavior more accurately, which can eventually lead to the recognition of “optimal” human driving patterns that Automated Vehicles (AV) could ‘mimic’ to become safer.

Vehicles lateral acceleration and speed profiles investigation at the entry area of interchange ramps as a criterion of geometric road design

The subject of road safety is the main subject of many research programs regarding the road network infrastructure. In the present study, lateral acceleration is recorded through modern instruments, depending on passing speed for each participant driver, for four (4) interchanges and six (6) horizontal curves in total. Essentially, this study emphasizes on how the geometric roads design affects driving behavior. The main purpose is to determine the effect of on-ramp applied speed and curve geometry on lateral acceleration and to create a clear speed profile of drivers during their passage from an interchange ramp. The data collection took place in morning weekend hours, in order to avoid high traffic flows and record the vehicles motions in free flow conditions. As a result, the analysis can examine the main human factors, which affect the driving behavior, too. The selected interchange ramps are mainly trumpets, but also clover and diamond sections and the under-investigation curve radii varies between 28 and 175 meters.For each curve, the presented figures correlate the speed within the curve and the lateral acceleration, while comparisons are made between the lateral acceleration recorded in this research and the literature and guidelines limits. Additionally, V85, V50 and V15 are calculated from the total measurements, taking also into account the data from three additional horizontal curves of Schistos-Skaramagas interchange investigated one year earlier. Therefore, aggregate speed profiles are created and presented.In conclusion, the results show that an increase in the speed leads to an increase in the lateral acceleration as well. Also the limit values of lateral acceleration received by drivers into smaller-radii curves, significantly exceed the maximum driver comfort limit and the maximum driver safety limit, as well as the provided -by design guidelines- values, in contrast to larger-radii curves. Regarding the speed within the curve in respect to the horizontal radius, it was found that as the radius of the curve increases the applied speed also increases. However, this increase is more abrupt for the smaller radius curves compared to the larger ones. Moreover, de Jong's models include higher values than the measurements made in the present study and much more than those presented by Jafarov's and Zaluga's model (2020), especially in cases of large horizontal radii.

Strong predictors of offender drivers: Drug and alcohol addiction and the inability to dissociate binge alcohol or drug consumption from driving. Revoking their driver’s licence may not be enough

The causes underlying traffic offender behaviour might be the tip of the iceberg that represents a persistent burden on global health and serves to detect other serious social, family, work problems. Better understanding and prevention of recidivism following a First-time driving under the influence conviction and analysis of High Risk offender driver behaviors are needed. This study seeks to identify the factors that predict an offender driver profile, analysing not only drivers’ general use of alcohol and/or drugs, but also their ability to dissociate the use of alcohol and other substances from driving. A total of 315 drivers — 97 offenders (95.9 % men) and 218 non-offenders (47.2 % men) responded to a battery of self-report evaluation questionnaires on: alcohol use habits (measured with the AUDIT); drug use (measured with the DAST-20); the ability of dissociating alcohol and substance use from driving; educational level; self-reported traffic violations, errors and lapses (measured with the DBQ, Driver Behaviour Questionnaire); and sociodemographic questions. The model showed good fit indicators (R2 = 0.74). It also displayed good predictive power: (1). It correctly classified 91 % of participants as offender or non-offender drivers. (2). More specifically, its sensitivity was 88 %, having correctly classified offenders as offenders. And its specificity was 92 %, having correctly classified non-offenders as non-offenders. The findings underscores that not only is alcohol use a key predictor of offending behaviour, but so is drug use. These addictions are a health problem, and their consequences are more serious when the afflicted individual drives a vehicle. This article makes evident that repeat offenders have a lower ability of dissociating alcohol and substance use from driving than do non-offender drivers. Non-offenders are shown to be more capable than offenders of dissociating consumption from driving: offender drivers are unable to dissociate the consumption of more than 5 spirit drinks, or the consumption of cannabis, from driving. More than half of non-offender drivers admitted to driving after having had 1 or 2 beers. Along these same lines, we found that offenders believe they have fewer lapses than non-offenders. This could be due to the optimism bias of bold offender drivers, who overestimate their abilities and underestimate their lapses. We also found that offender drivers have a lower educational level. Revoking the High Risk offender’s driver’s licence may not be enough. Instead, High Risk Offenders should be given the support they need to give up the consumption of alcohol and drugs, at least while driving. This could be achieved if they receive motivational interventions and are referred for detoxification treatment.

Effects of microforms on the evaporation of peat‐bryophyte‐litter column in a montane peatland in Canadian Rocky Mountain

AbstractPeatland microtopography contains hummocks (local high points) and hollows (local low points). Little is known about how the evaporation of peat (P), peat‐bryophyte (BP), peat‐litter (LP) and peat‐bryophyte‐litter (LBP) columns varies with peatland microforms. That is, whether there are fine‐scale variations in peatland evaporation, and if they are critical when being upscaled to the entire peatland ecosystem is yet to be answered. This study found that evaporation was significantly affected by cover type (P, BP, LP or LBP) and the interaction effect of the cover type and microform, based on the field evaporation experiments in a montane peatland in the Canadian Rocky Mountains, during the growing season of 2021. Mean daily evaporation of P‐Hummock and P‐Hollow is 14.16 and 11.76 g day−1, respectively; BP‐Hummock and BP‐Hollow is 9.57 and 14.38 g day−1, respectively; LBP‐Hummock and LBP‐Hollow is 9.44 and 9.91 g day−1, respectively; and evaporation of LP‐Hummock and LP‐Hollow is 5.68 and 7.64 g day−1, respectively. Peatland microform indirectly affected evaporation through interactions with cover type, modifying the vertical profile of soil temperature and changing key environmental drivers of evaporation. Moreover, the ability of two widely used models in modelling the spatial variation of peatland evaporation was also tested. It was found that Penman–Monteith (P–M) model and the bryophyte layer model in the Atmosphere‐Plant Exchange Simulator (APES) were able to yield satisfactory results based on field measurements of soil temperature and soil moisture. This study supports developing more practical evaluation tools on the hydrological state of peatland ecosystems.

IvCDS: An End-to-End Driver Simulator for Personal In-Vehicle Conversational Assistant.

An advanced driver simulator methodology facilitates a well-connected interaction between the environment and drivers. Multiple traffic information environment language processing aims to help drivers accommodate travel demand: safety prewarning, destination navigation, hotel/restaurant reservation, and so on. Task-oriented dialogue systems generally aim to assist human users in achieving these specific goals by a conversation in the form of natural language. The development of current neural network based dialogue systems relies on relevant datasets, such as KVRET. These datasets are generally used for training and evaluating a dialogue agent (e.g., an in-vehicle assistant). Therefore, a simulator for the human user side is necessarily required for assessing an agent system if no real person is involved. We propose a new end-to-end simulator to operate as a human driver that is capable of understanding and responding to assistant utterances. This proposed driver simulator enables one to interact with an in-vehicle assistant like a real person, and the diversity of conversations can be simply controlled by changing the assigned driver profile. Results of our experiment demonstrate that this proposed simulator achieves the best performance on all tasks compared with other models.

Driver Identification Through Heterogeneity Modeling in Car-Following Sequences

Intra-driver and inter-driver heterogeneity has been confirmed to exist in human driving behaviors by many studies. This research proposes a driver identification method by modeling such heterogeneities in car following sequences. It is assumed that all drivers share a pool of driver states; under each state, a car-following data sequence obeys a specific probability distribution in feature space; each driver has his/her own probability distribution over the states, called driver profile, which characterize the intra-driver heterogeneity, while the difference between the driver profile of different drivers depicts the inter-driver heterogeneity. Thus, the driver profile can be used to distinguish a driver from others. Based on the assumption, a method of driver identification is proposed to take both intra- and inter-driver heterogeneity into consideration, and a method is developed to jointly learn parameters in behavioral feature extractor, driver states, and driver profiles. Experiments demonstrate the performance of the proposed method in driver identification on naturalistic car-following data: accuracy of 82.3% is achieved in an 8-driver experiment using 10 car-following sequences of duration 15 seconds for online inference. The potential of fast registration of new drivers is demonstrated and discussed.

Drivers of African Filovirus (Ebola and Marburg) Outbreaks.

Outbreaks of African filoviruses often have high mortality, including more than 11,000 deaths among 28,562 cases during the West Africa Ebola outbreak of 2014-2016. Numerous studies have investigated the factors that contributed to individual filovirus outbreaks, but there has been little quantitative synthesis of this work. In addition, the ways in which the typical causes of filovirus outbreaks differ from other zoonoses remain poorly described. In this study, we quantify factors associated with 45 outbreaks of African filoviruses (ebolaviruses and Marburg virus) using a rubric of 48 candidate causal drivers. For filovirus outbreaks, we reviewed >700 peer-reviewed and gray literature sources and developed a list of the factors reported to contribute to each outbreak (i.e., a "driver profile" for each outbreak). We compare and contrast the profiles of filovirus outbreaks to 200 background outbreaks, randomly selected from a global database of 4463 outbreaks of bacterial and viral zoonotic diseases. We also test whether the quantitative patterns that we observed were robust to the influences of six covariates, country-level factors such as gross domestic product, population density, and latitude that have been shown to bias global outbreak data. We find that, regardless of whether covariates are included or excluded from models, the driver profile of filovirus outbreaks differs from that of background outbreaks. Socioeconomic factors such as trade and travel, wild game consumption, failures of medical procedures, and deficiencies in human health infrastructure were more frequently reported in filovirus outbreaks than in the comparison group. Based on our results, we also present a review of drivers reported in at least 10% of filovirus outbreaks, with examples of each provided.

Driving under the influence of drugs (DUID) profiling in Spain: Data from oral fluid results between 2016 and 2022, including the effect of COVID-19 lockdown

To provide information about driving under the influence of drugs (DUID) cases in Spain during the last 6 years and to evaluate the impact of the Spanish lockdown (March 14th to May 6th 2020) on drivers’ consumption profile. In total, 16,461 oral fluid samples were collected from drivers in Spain that tested positive on the roadside between January 1st 2016 and April 15th 2022. Samples were collected at different locations throughout the country and analyzed by LC-MS/MS at the Toxicology Service of the Institute of Forensic Sciences of the University of Santiago de Compostela. For data analysis, drugs and their metabolites were classified in 8 different groups (cannabis, cocaine, amphetamines, opioids, benzodiazepines, ketamine, methadone and zolpidem). Demographic characteristics and drug profile were evaluated during time. The impact of COVID-19 pandemic lockdown was also considered, applying Chi 2 test. Drivers were mainly young adults (33.3 ± 9.9 years old) and men (94.2% male). In 97.6% of cases, LC-MS/MS results confirmed at least one of the positive results detected on the roadside. Cannabis (72.6%) and cocaine (64.4%) were the most detected drugs, followed by amphetamines (13.8%), opioids (12.9%), benzodiazepines (7.7%), and methadone (7.4%). Polydrug use was observed in 55.1% of drivers, being the combination of substances from two different groups the most prevalent. Cannabis and cocaine were the most common combination (either only the two of them or mixed with more substances). Changes in drivers’ consumption profile was noticed when comparing data from the specimens collected during the Spanish lockdown period ( n = 151) vs. specimens obtained during 2020 before (“pre-lockdown”, n = 557) and after (“post-lockdown”, n = 1974) that period. During the lockdown period drivers were 3 years older in average, and polyconsumers of 4 and 5 different groups of substances doubled (in average, 4.5% vs. 9.9% for 4 groups; 0.7% vs. 1.3% for 5 groups). Nevertheless, results were only statistically different for consumers of 4 groups of substances during lockdown, either compared with pre- and post-lockdown results. Also, for polyconsumers, the combination of cannabis and cocaine with amphetamines, with high prevalence before lockdown, was substituted by the combination of cannabis and cocaine with methadone. Furthermore, opiates use was two times higher (in average, 14.0% vs. 24.5%), being this difference statistically significant when comparing the 3 periods. On the other hand, amphetamines and ketamine were less consumed by drivers during lockdown (in average, 11.2% vs. 9.9% for amphetamines; 3.2% vs. 0.7% for ketamine). Nevertheless, these differences were only statistically significant when considering ketamine consumption post-lockdown (either compared with pre-lockdown or lockdown results). The use of oral fluid samples was able to confirm the vast majority of the screening results found in roadside screening tests. In Spain, cannabis and cocaine remain the most detected drugs on the roadside, as previously reported from our group in the period 2013–2015 (Lema-Atán JA. Drug Alcohol Depend 2019;195:106–113). During the lockdown period in Spain, due to stay-at-home orders, mobility was only allowed for “essential workers”. Samples collected in those months showed an increase in polydrug use, especially for opiates and methadone, as well as a decrease in the use of “party drugs” (amphetamines and ketamine). LC-MS/MS confirmation result was negative in only 2.4% of the cases. The DUID driver profile was a young man, consuming either cannabis or a combination of cannabis and cocaine, alone or combined with more substances. During lockdown, drivers consumed less “party drugs” and more opiates and methadone. Moreover, there was a higher rate of polyconsumers (4 or 5 groups of substances).

Portraits of wagon drivers’ communities in southern Brazil

Technological developments have made motor vehicles for cargo transportation available to human beings, but animal-powered vehicles are still found circulating in urban areas. The use of horse-drawn wagons for cargo transport guarantees the survival of populations in urban areas and little is known about the reality of this population. In this sense, the aim of this study was to portray the profile of the wagon drivers in a municipality from metropolitan region of Rio Grande do Sul, Brazil. Informations were obtained by interviews conduct during home visits. Thirty-eight drivers were interviewed, of which 84.86% were male with an average age of 38 years and about 60% did not complete elementary school. Cargo transportation was the main source of income for 57.8% of respondents, in 63.15% the income was below a minimum monthly wage and 28.4% was related to working from 5 to 7 days per week. It has been found that the reality of car drivers is verified in other communities of Brazilian urban centers and is marked by low incomes, excessive working hours and little information on the health of horses.

Public Acceptability of Environmentally Linked Congestion and Parking Charging Policies in Greek Urban Centers

Nowadays, urban centers face the challenge to upgrade life quality by reducing traffic congestion, air pollution emissions and road casualties. Transport charging policies applied in cities at a quick pace are a key tool for sustainable mobility. However, public acceptability is an important precondition to be adopted for such policies. In this context, the scope of this paper is the investigation of the public acceptability of environmentally linked urban charging policies in Greek urban centers. Specifically, the paper’s objective is the investigation of Greek drivers’ acceptability of the implementation of a congestion charging policy and a parking charging policy with the charging being adjusted according to the Euro class and technology of the vehicle in favor of less polluting cars. A structural equation model (SEM) was developed using data from a questionnaire survey which provided a sample encompassing 733 respondent drivers from three main urban centers of Greece: Athens, Thessaloniki and Volos. Several statistical relationships were detected and quantified correlating the two examined urban environmental charging policies with five latent unobserved variables. Based on the results, public acceptability of environmental congestion charging policies and the public acceptability of environmental parking charging policies were found to be positively correlated with each other, meaning that a driver who supports one environmentally linked transport charging policy is more likely to support the other one as well. The environmental sensitivity and high commuting profiles of drivers are influential factors that positively affect the acceptability of the two examined transport charging policies’ implementation in Greek urban centers. Analysis has also shown that younger, higher-educated respondents and females are more likely to accept the environmental charging policies under consideration.

Modeling Motorcyclists' Aggressive Driving Behavior Using Computational and Statistical Analysis of Real-Time Driving Data to Improve Road Safety and Reduce Accidents.

Driving behavior is considered one of the most important factors in all road crashes, accounting for 40% of all fatal and serious accidents. Moreover, aggressive driving is the leading cause of traffic accidents that jeopardize human life and property. By evaluating data collected by various collection devices, it is possible to detect dangerous and aggressive driving, which is a huge step toward altering the situation. The utilization of driving data, which has arisen as a new tool for assessing the style of driving, has lately moved the concentration of aggressive recognition research. The goal of this study is to detect dangerous and aggressive driving profiles utilizing data gathered from motorcyclists and smartphone APPs that run on the Android operating system. A two-stage method is used: first, determine driver profile thresholds (rules), then differentiate between non-aggressive and aggressive driving and show the harmful conduct for producing the needed outcome. The data were collected from motorcycles using -Speedometer GPS-, an application based on the Android system, supplemented with spatiotemporal information. After the completion of data collection, preprocessing of the raw data was conducted to make them ready for use. The next steps were extracting the relevant features and developing the classification model, which consists of the transformation of patterns into features that are considered a compressed representation. Lastly, this study discovered a collection of key characteristics which might be used to categorize driving behavior as aggressive, normal, or dangerous. The results also revealed major safety issues related to driving behavior while riding a motorcycle, providing valuable insight into improving road safety and reducing accidents.

Influence of the Driver Profile on the Autonomy of Electric Vehicles

In response to the growing pressure from civil society on the automotive industry and governments regarding the sustainability and reduction of environmental impacts of vehicular fleets, there has been a substantial increase in the application and development of technologies applied to vehicular electrification. Among the efforts to massify electric vehicles there are aspects of their reduced autonomy compared to combustion vehicles. The studies concerning vehicle aspects that influence vehicle autonomy become imperative so that the best driving practices and energy management strategies are mapped and known. This work aimed to evaluate the influence of the driver profile on the autonomy of electric vehicles. Data were obtained through a device connected to the OBDII port and collected significant information for vehicle parameters evaluation such as speed, battery level, distance covered, autonomy, and so on. The results obtained, albeit preliminary, indicate that the profile of the driver influences the autonomy of electric vehicles.

Assessment of the nutritional profile through the 24-hour record in truckers in the central-southern Brazilian region: a prospective observational cross-sectional study

Introduction: Truck drivers are of great importance to the Brazilian economy. The exorbitant workload and long periods away from home are causes of the adoption of unhealthy eating habits, such as the intake of foods that are sources of lipids with high caloric value. Extensive travel contributes to a sedentary lifestyle. This causes health damage and comorbidities such as diabetes, obesity, hypertension, stress, and depression. Based on these considerations, the present study evaluates the nutritional profile of truck drivers. Objective: It was to evaluate the food quality of truck drivers in the Center-South Geoeconomic Region of the country, comparing the portions of macronutrients consumed with those recommended by the Brazilian Food Pyramid. Methods: Observational and cross-sectional study. The consumption of macronutrients by 100 truck drivers from the states of São Paulo, Minas Gerais, Mato Grosso do Sul and Goiás was qualitatively evaluated. The research used 24-hour food recalls, completed online, via Google Forms, between June and September 2020. Statistical analysis was obtained by calculating the Wilcoxon's non-parametric test to compare the medians of ingested portions and the ideas. Results: The Wilcoxon test identified significant differences between the number of servings consumed by truck drivers and the recommended ideal with regard to fruits, vegetables/vegetables, oils/fats, meats/eggs, sweets/sugars, milk, cheese and derivatives (p<0.0001). Only the portions of carbohydrates were in accordance with the nutritional recommendations, that is, six portions a day (ns). In the class of sugars and sweets, consumption four times higher than recommended was observed. In the meat and eggs category, consumption was double the ideal. In oils and fats, triple the recommended value. The consumption of milk and dairy products is three times less than ideal, such as vegetables. Finally, the average value of fruit portions eaten by truck drivers was zero, while the recommended value is three. Conclusion: Among the food groups analyzed, carbohydrates were the only group in which consumption was adequate. There was a significant excess of sweets and fats intake by truck drivers, when compared to the ideal amount indicated by the food pyramid. The intake of milk and dairy products, vegetables and legumes was deficient, three times lower than recommended. Fruits counted as a median intake value of zero servings.

Vehicle Rental System

Application based Vehicle Booking deals with an internet application system designed for booking vehicles as per the wants of the shoppers at their convenience. The vehicle company as all their bookings are now managed via vehicle booking application. The vehicle booking service provided to customers they need to login the app with individual login id/phone number and password. This brings together the vehicle operators/admin and therefore the customers. Admin give the customer satisfaction the utmost priority then give sample options to book vehicle by entering details like their journey date and time, origin, pick-up point, destination and therefore the drop-off point they have to achieve. This admin deals with creating an application regarding vehicle booking and checking the provision of vehicles. The admin of the applying have a stuffed with authority to watch and allocate the driving force. For this application we are going to store some model names, their registration no, available vehicles, rent rate on the idea of per day, each vehicle driver profiles for security purpose and therefore the amount to be deposited accordingly. Machine Learning Concepts are been used for finding nearest locations of vehicles.

A Study Examining the Effects of Driver Profile and Route Characteristics on Vehicle Performance and Tailpipe Emissions under Virtual Real Driving Scenarios

A Study Examining the Effects of Driver Profile and Route Characteristics on Vehicle Performance and Tailpipe Emissions under Virtual Real Driving Scenarios