Search Results (49)

Thailand’s Western New Year and Songkran festivals witness a surge in traffic crashes due to increased travel volume. This study explores risk factors influencing crash injury severity during these holidays (2017–2019). Crash data is analyzed using both the Random Parameters Ordered Logit Model with Means Heterogeneity (RPOLHM) and machine learning techniques (Multilayer Perceptron (MLP), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Random Forest). Crash severity is categorized as property damage only (PDO), minor injury, and severe/fatal injury. The results reveal that factors like motorcycle or pedestrian involvement, adverse weather, speeding, drunk driving, fatigue, nighttime conditions, improper overtaking, and urban location all significantly increase the risk of severe/fatal crashes. Notably, the XGBoost model outperforms both RPOLHM and other machine learning methods, achieving a validation accuracy of 83.8%. While machine learning approaches demonstrate superior predictive capability, RPOLHM provides interpretable coefficient estimates and marginal effects essential for understanding causal mechanisms. This complementarity suggests that concurrent application of both paradigms offers comprehensive insights: machine learning for prediction-oriented objectives and econometric models for policy formulation. These findings provide valuable guidance for policymakers, highway engineers, and researchers to develop targeted road safety interventions during these high-risk periods. Full article

This paper addresses two safety issues regarding smart vehicles: that of intoxicated drivers (one of the most common causes for car accidents) and that of theft. More specifically, it presents the design and implementation of an intelligent system based on the Arduino-Mega2560 board. The issue of intoxicated drivers is addressed by using an MQ3 alcohol sensor that is capable of sensing the driver’s breath and a relay that immobilizes the vehicle if it detects alcohol above the permissible limit. Regarding theft, there are two safety layers: the first layer uses a fingerprint sensor which would not let the vehicle move unless the user is authenticated, while the second layer includes a GPS module that collects the information about the vehicle’s location and, through an incorporated GSM module, transmits the location data to an Internet-of-Things (IoT) server. The main contribution of the proposed system is that it treats two essential safety-security issues (drunk driving and theft) at the same time with the additional merits of low-cost implementation and easy placement and use within a vehicle. Full article

In Hungary, the consumption of any alcoholic beverage before driving is illegal. A person is considered drunk if they have a blood alcohol concentration of 0.5 g per liter or more. The situation regarding drug use is also disappointing. This research analyses these effects on transport and their “outcome” by evaluating analyses based on police data, driver training data, and experimental data. The research aims to further raise awareness of the public health importance of this problem through a case–control study. Descriptive and correlational, statistical calculations were performed with a significance value of p < 0.05. Between 2019 and 2023, there were 10–13.000 drunk driving offenses and 1.000–1.300 drunk-driving accidents on the roads each year, most of which occurred in the capital and caused minor injuries. The results will be used to discover synergies to improve road safety. Full article

Global road safety reports identify human factors as the leading causes of traffic accidents, particularly behaviors such as speeding, drunk driving, and driver distraction, emphasizing the need for autonomous driving technologies to enhance transport safety. This research aims to provide a practical model for the development of autonomous driving systems as part of an autonomous transportation system for inter-building passenger mobility, intended to enable safe and efficient short-distance transport between buildings in semi-open environments such as university campuses. This work presents a fully integrated autonomous platform combining LiDAR, cameras, and IMU sensors for mapping, perception, localization, and control within a drive-by-wire framework, achieving superior coordination in driving, braking, and obstacle avoidance and validated under real campus conditions. The electric golf cart prototype achieved centimeter-level mapping accuracy (0.32 m), precise localization (0.08 m), and 2D object detection with an mAP value exceeding 70%, demonstrating accurate perception and positioning under real-world conditions. These results confirm its reliable performance and suitability for practical autonomous operation. Field tests showed that the vehicle maintained appropriate speeds and path curvature while performing effective obstacle avoidance. The findings highlight the system’s potential to improve safety and reliability in short-distance autonomous mobility while supporting scalable smart mobility development. Full article

This study aims to decrease the number of drunk drivers, a significant social problem. Traditional methods to measure alcohol intake include blood alcohol concentration (BAC) and breath alcohol concentration (BrAC) tests. While BAC testing requires blood samples and is impractical, BrAC testing is commonly used in drunk driving enforcement. In this study, the multiple biological signals of electrocardiogram (ECG), photoplethysmogram (PPG), and electrodermal activity (EDA) were collected non-invasively and with minimal driver restraint in a driving simulator. Data were collected from 10 participants for approximately 10 min at BrAC levels of 0.00%, 0.03%, and 0.08%, which align with the latest Korean drunk driving standards. The collected data underwent frequency filtering and were segmented into 30 s intervals with a 10 s overlap to extract heart rate variability (HRV) and pulse arrival time (PAT). Using more than 10 machine learning algorithms, the classification accuracy reached 88%. The results indicate that it is possible to classify a driver’s level of intoxication using only non-invasive biological signals within a short period of about 30 s, potentially aiding in the prevention of drunk driving. Full article

The paper applies the prototype willingness model (PWM) and incorporates components of the theory of planned behavior (TPB), along with deterrence factors, to understand the behavioral intentions, willingness, and recidivism behaviors of individuals penalized for drunk driving. It explores psychological and social factors influencing repeat offenses, focusing on attitudes, subjective norms, prototypes, and deterrence. The PWM outlines two pathways—reasoned (based on intentions) and social reactive (based on willingness). The model helps predict risky behaviors like drunk driving. Thirteen hypotheses are proposed in this study to examine how various factors, such as attitudes, subjective norms, and deterrence, influence willingness, intentions, and behavior. Surveys were conducted among individuals attending road safety classes after being penalized for drunk driving. A total of 1156 individuals participated in the survey, with 855 valid responses collected. The results indicate that behavioral willingness had a stronger impact on recidivism than intention. On the other hand, subjective norms did not significantly affect the intent to reoffend, but attitudes, deterrence, and PBC did. The findings suggest that focusing on behavioral willingness, deterrence, and educational interventions could help reduce repeat drunk driving offenses. The paper offers insights for policymakers to improve prevention strategies, by focusing on the psychological motivators of repeat offenders. Full article

Background: Drugged driving is associated with an increased risk of road accidents worldwide. In Italy, driving under the influence (DUI) of alcohol and drugs is a reason for driving disqualification or revocation of the driving license. Drivers charged with driving under the influence of alcohol and drugs must attend a Local Medical Commission (LMC) to undergo mandatory examinations to regain the suspended license. Our study mainly aims to report on the analysis performed on hair samples collected from 7560 drivers who had their licenses suspended for drugged or drunk driving between January 2019 and June 2024. Methods: A rapid, sensitive, and selective method for the determination of ethyl glucuronide in hair by UPLC/MS-MS was developed and fully validated. Results: The most frequently detected substances were cocaine (ecgonine methyl ester, norcocaine, and benzoylecgonine) and cannabinoids (Δ9-tetrahydrocannabinol, cannabidiol, and cannabinol), followed by opiates (codeine, morphine, and 6-MAM), methadone (EDDP), and amphetamines (amphetamine, methamphetamine, MDA, MDMA, and MDEA). To perform a more in-depth analysis, we also compared hair color with the drug classes that tested positive. The results showed a significant prevalence of dark hair that tested positive for one or more substances, followed by gray/white hair and light hair. Conclusions: Our study provides an interesting and alarming insight into drug exposure in the general population with serious public health threats, discussing the main aspects of hair matrix analysis and focusing on its advantages and reliability in the interpretation of results. Full article

Drugged driving, the act of driving a vehicle under the influence of illicit drugs, by adolescents is a serious public health concern. Many factors contribute to this risk behavior, but much less is known regarding the role of parenting behaviors in this phenomenon. The purpose of this study was to examine specific parenting behaviors and their influence among a nationally representative sample of adolescents. Pooled data from the 2016–2019 National Survey on Drug Use and Health (NSDUH) among 17,520 adolescents ages 16–17 years old were analyzed. Differences were found in specific parenting behaviors and adolescent drugged/drunk driving, with parents not checking homework and not telling their children they are proud of them being the most influential. Findings from the present study may inform drugged driving prevention programs for parents and adolescents and enhance road safety interventions. Full article

Alcohol acts as a central nervous system depressant and falls under the category of psychoactive drugs. It has the potential to impair vital bodily functions, including cognitive alertness, muscle coordination, and induce fatigue. Taking the wheel after consuming alcohol can lead to delayed responses in emergency situations and increases the likelihood of collisions with obstacles or suddenly appearing objects. Statistically, drivers under the influence of alcohol are seven times more likely to cause accidents compared to sober individuals. Various techniques and methods for alcohol measurement have been developed. The widely used breathalyzer, which requires direct contact with the mouth, raises concerns about hygiene. Methods like chromatography require skilled examiners, while semiconductor sensors exhibit instability in sensitivity over measurement time and has a short lifespan, posing structural challenges. Non-dispersive infrared analyzers face structural limitations, and in-vehicle air detection methods are susceptible to external influences, necessitating periodic calibration. Despite existing research and technologies, there remain several limitations, including sensitivity to external factors such as temperature, humidity, hygiene consideration, and the requirement for periodic calibration. Hence, there is a demand for a novel technology that can address these shortcomings. This study delved into the near-infrared wavelength range to investigate optimal wavelengths for non-invasively measuring blood alcohol concentration. Furthermore, we conducted an analysis of the optical characteristics of biological substances, integrated these data into a mathematical model, and demonstrated that alcohol concentration can be accurately sensed using the first-order modeling equation at the optimal wavelength. The goal is to minimize user infection and hygiene issues through a non-destructive and non-invasive method, while applying a compact spectrometer sensor suitable for button-type ignition devices in vehicles. Anticipated applications of this study encompass diverse industrial sectors, including the development of non-invasive ignition button-based alcohol prevention systems, surgeon’s alcohol consumption status in the operating room, screening heavy equipment operators for alcohol use, and detecting alcohol use in close proximity to hazardous machinery within factories. Full article

Freight transportation, dominated by trucks, is an integral part of trade and production in the USA. Given the prevalence of large truck crashes, a comprehensive investigation is imperative to ascertain the underlying causes. This study analyzed 2017–2021 Texas crash data to identify factors impacting large truck crash rates and injury severity and to locate high-risk zones for severe incidents. Logistic regression models and bivariate analysis were utilized to assess the impacts of various crash-related variables individually and collectively. Heat maps and hotspot analysis were employed to pinpoint areas with a high frequency of both minor and severe large truck crashes. The findings of the investigation highlighted night-time no-passing zones and marked lanes as primary road traffic control, highway or FM roads, a higher posted road speed limit, dark lighting conditions, male and older drivers, and curved road alignment as prominent contributing factors to large truck crashes. Furthermore, in cases where the large truck driver was determined not to be at fault, the likelihood of severe collisions significantly increased. The study’s findings urge policymakers to prioritize infrastructure improvements like dual left-turn lanes and extended exit ramps while advocating for wider adoption of safety technologies like lane departure warnings and autonomous emergency braking. Additionally, public awareness campaigns aimed at reducing distracted driving and drunk driving, particularly among truck drivers, could significantly reduce crashes. By implementing these targeted solutions, we can create safer roads for everyone in Texas. Full article

Positive Youth Development (PYD) emerged as a holistic and strength-based perspective that focuses on the fact that young people may have the internal and external resources for healthy and successful development through five dimensions (5Cs) that empower them: Perceived Competence, Confidence, Character, Connection, and Caring. The aim of this study was to examine the relationship between the overall PYD factor, the 5Cs, and risk behaviors, in addition to analyzing gender differences. This study showed the results of a cross-sectional study of 1044 emerging adults from 11 Spanish universities in 2021. Data collection was performed by applying an online self-report measure. The results showed that the Character was protective against substance abuse, mainly in women, while the connection was related to the participation of betting money and online betting in men. Caring was protective against money bets in the men’s sample. However, controversial results were found regarding Perceived competence, which had a positive association with substance abuse, money bets, and drunk driving. It seems that high levels of Perceived competence, rather than objective competence, were associated with engagement in various risk behaviors. Concerning gender differences, men showed more risky behaviors than women. A partial mediation model pointed out that lower character and higher perceived competence in men partly explained the higher presence of risky behavior compared to women. These results underline the need to promote PYD within the university context to prevent risky behaviors by addressing gender differences and the separate role of the 5Cs. Full article

In this research, 3356 alcohol-related traffic crashes were obtained from blood-alcohol test reports in Tianjin, China. Population density, intersection density, road density, and alcohol outlet densities, including retail density, entertainment density, restaurant density, company density, hotel density, and residential density, were extracted from 2114 traffic analysis zones (TAZs). After a spatial autocorrelation test, the multiple linear regression model (MLR), geographically weighted Poisson regression model (GWPR), and semi-parametric geographically weighted Poisson regression model (SGWPR) were utilized to explore the spatial effects of the aforementioned variables on drunk-driving crash density. The result shows that the SGWPR model based on the adaptive Gaussian function had the smallest AICc value and the best-fitting accuracy. The residential density and the intersection density are global variables, and the others are local variables that have different influences in different regions. Furthermore, we found that the influence of local variables in the economic–technological development area shows significantly different characteristics compared with other districts. Thus, a comprehensive consideration of spatial heterogeneity would be able to improve the effectiveness of the programs formulated to decrease drunk driving crashes. Full article

Traffic accidents present significant risks to human life, leading to a high number of fatalities and injuries. According to the World Health Organization’s 2022 worldwide status report on road safety, there were 27,582 deaths linked to traffic-related events, including 4448 fatalities at the collision scenes. Drunk driving is one of the leading causes contributing to the rising count of deadly accidents. Current methods to assess driver alcohol consumption are vulnerable to network risks, such as data corruption, identity theft, and man-in-the-middle attacks. In addition, these systems are subject to security restrictions that have been largely overlooked in earlier research focused on driver information. This study intends to develop a platform that combines the Internet of Things (IoT) with blockchain technology in order to address these concerns and improve the security of user data. In this work, we present a device- and blockchain-based dashboard solution for a centralized police monitoring account. The equipment is responsible for determining the driver’s impairment level by monitoring the driver’s blood alcohol concentration (BAC) and the stability of the vehicle. At predetermined times, integrated blockchain transactions are executed, transmitting data straight to the central police account. This eliminates the need for a central server, ensuring the immutability of data and the existence of blockchain transactions that are independent of any central authority. Our system delivers scalability, compatibility, and faster execution times by adopting this approach. Through comparative research, we have identified a significant increase in the need for security measures in relevant scenarios, highlighting the importance of our suggested model. Full article

The movement of a motorcycle is one of the critical factors that influences the stability of the ride. It has been established that the gait patterns of drunk and sober people are distinct. However, drunk motorcycle (MC) drivers’ balance has not been investigated as a predictor of intoxication. This paper characterized and used MC and head movements, such as pitch and roll, to predict intoxication while riding. Two separate experiments were conducted to monitor MC and head movement. Male participants were recruited between the ages of 23 and 50 to participate in the study. Participants used alcohol intoxication goggles (AIG) to simulate blood alcohol content (BAC) while driving on a straight path. Placebo goggles were used for control. Results showed that pitch and roll amplitudes of the MC could distinguish drivers wearing placebo and AIGs, as well as the pitch and roll frequency of the head. Deep learning can be used to predict the intoxication of MC riders. The predictive accuracy of the algorithm shows a viable opportunity for the use of movement to monitor drunk riders on the road. Full article

With the rapid development of shared cars, to reduce the phenomenon of drunk driving in shared cars, we have studied the onboard drunk driving rapid detection electronic nose system suitable for shared cars. To accurately judge whether the driver is drunk while driving in the presence of interfering gases such as passenger exhalation and the volatile smell containing alcohol, this paper proposes a two-step drunk driving detection frame for shared cars that first judges whether someone in the car is drunk and then judges whether the driver is drunk. To reduce the cost and volume of the electronic nose, the sensor array was optimized based on the random forest algorithm. To find the optimal sampling time, we processed the original data by time slicing. Finally, using the two-step framework proposed by us, the accuracy of the first step and the second step of driver drunk driving detection reached 99.44% and 100%, respectively, with a sampling time of 5 s. After algorithm optimization, only 9 of the 21 sensors were left. This paper presents a practical electronic nose system for the detection of drunk driving in shared cars. Full article