Elderly Drivers Research Articles

Analyzing Daytime/Nighttime Pedestrian Crash Patterns in Michigan Using Unsupervised Machine Learning Techniques and their Potential as a Decision-Making Tool

Background Data mining applications are becoming increasingly common across various fields. Numerous data mining methodologies have been utilized in pedestrian crash data analysis. However, association and correspondence analyses have yet to be extensively employed in pedestrian safety literature to support decision-making. Material and Methods Road lighting significantly affects pedestrian crashes, highlighting the importance of examining pedestrian crash patterns during daytime and nighttime. This study analyzed pedestrian fatal and injury crashes in Michigan from 2011 to 2021 under varying road lighting conditions. The study identified pedestrian crash patterns using unsupervised machine-learning techniques based on several multidimensional factors. The Association Rules Learning (ARL) technique was used to determine general associations of patterns leading to pedestrian crashes. At the same time, Multiple Correspondence Analysis (MCA) helped understand crash attribute patterns in two notable scenarios: elderly pedestrian crashes during daytime lighting and high-speed midblock crashes during nighttime. These methods revealed key patterns associated with contributing factors to pedestrian crashes in different lighting conditions. Results In the analysis of cloud combinations involving elderly pedestrians during daylight, it was found that: 1) elderly pedestrians were significantly involved in severe injury crashes at two-lane midblock locations with a speed limit between 45 and 65 mph; 2) improper actions by elderly drivers were significantly associated with elderly pedestrians on wide roads (>5 lanes); and 3) the complex interaction between city streets with 3-5 lanes was significantly correlated with drivers 'failed to yield' actions. Additionally, in the analysis of cloud combinations involving nighttime pedestrian crashes at high-speed midblock locations, it was found that 1) a specific age group of alcohol- or drug-involved pedestrians (19-30) was significantly associated with young drivers and improper driver actions; 2) during the winter season, young pedestrians were significantly involved in moderate injury crashes at undivided midblock locations; and 3) elderly drivers and 'failed to yield' actions were highly correlated with 3-5 lane roadways. Discussion The research findings are expected to raise awareness of Michigan pedestrian crash patterns under daytime and nighttime lighting conditions. They will also recommend safety countermeasures for practitioners to enhance pedestrian safety in walkable cities. The proposed methodologies can uncover relationships that need to be well-documented in the existing literature on pedestrian safety. Additionally, these methods can identify valuable relationships without restricting variables to being either dependent or independent, and they can reveal relationships that might be challenging to detect otherwise. The results will also provide decision rules and visualizations that are easy to understand. Conclusion The results showed that the proposed techniques can analyze pedestrian crash data in a way that aligns with understanding pedestrian crash characteristics. Traffic safety administrators could benefit from this methodology as a decision-support tool.

Assessment of age-related features of the condition of heavy-duty truck drivers based on spectral analysis of EEG-ECG and stress tests during mass occupational examinations

The authors studied the physiological characteristics of drivers of heavy-duty coal mine vehicles based on spectral analysis of EEG-ECG and stress tests, which have prognostic value in professional activity. The study aims to assess the functional condition of heavy-duty truck drivers of different age groups based on a comprehensive spectral analysis of EEG-ECG under occupational examinations. During the survey, the authors have studied the spectral parameters of the EEG‒ECG in 300 male drivers aged 25 to 66 years. Specialists have performed EEG registration when placing electrodes in the 10–20 system. The EEG analysis was performed with a preliminary visual analysis followed by the removal of artifacts. The scientists assessed the level of representation of the main alpha rhythm and its spatial distribution. The ECG registration was in the first lead. The duration of EEG-ECG registration is about 15 minutes in a typical sequence of stress tests: baseline, rhythmic photostimulation, hyperventilation with recovery periods. The spectral analysis of the EEG‒ECG was performed automatically on a Neurotravel device (Italy). The authors found lower alpha and delta activity in the initial EEG spectra in older drivers aged 45–66 years, which contributed to the cumulative effect of stress load. Light stress stimulation significantly reduced alpha and delta activity and increased beta EEG activity in both groups, while in the older group the high-frequency range significantly exceeded the low-frequency range. Simultaneously with the changes in the EEG, stress photostimulation increased the energy of the ECG spectra in all frequency ranges. The authors established the appearance of additional stress indicators during light stimulation in older drivers based on the correlation of EEG‒ECG spectra (alpha/delta) and the absence of such changes in the group of young drivers aged 25–35 years. Moreover, the EEG–ECG correlation coefficients and their critical values increased in groups in increments of 6 years — from 35 to 65 years. Elderly drivers, based on neuro-cardiodynamic indicators, were in a state of chronic stress and the resulting risks to health and professional activity, and the identified physiological mechanisms of stress may be at the heart of ways to prevent it. Limitations. The study is limited to the assessment of EEG and ECG indicators in 300 heavy truck drivers. Ethics. The study was performed using non-invasive methods and approved by the Bioethical Committee of the Research Institute of Complex Problems of Hygiene and Occupational Diseases, Protocol No. 5, § 1 dated 12/26/2018, in accordance with the Helsinki Declaration of the World Medical Association "Ethical Principles of conducting medical research with human participation as a subject" as amended in 2013 and the "Rules of Good Clinical Practice" approved by by Order of the Ministry of Health of the Russian Federation No. 200n dated April 1, 2016. All the examined patients signed a voluntary informed consent to participate in the study.

Drivers’ long-term crash risks associated with being ticketed for speeding

Introduction: This research analyzes the relationship between police-issued tickets for speeding and the crash risk of those drivers, in New Zealand, between 2015–2019. Method: The main data are constructed through data-matching license details of crash outcomes with all officer-issued tickets for speeding between 2015–2016 (N = 534,935). The sub-group of drivers that accumulate tickets is compared to a coarsened exact matched set of drivers of the same age. Results: There is a strong relationship between the number of tickets a person has in a two-year period (2015–16) and the likelihood of a crash outcome (2017–2019). However, the accumulation of tickets is not the best predictor of crash likelihood. A combination of the excess in speed and the accumulation of tickets increases the relative odds of a subsequent crash. These results are discussed considering the threshold at which New Zealand criminalizes alcohol-relating offending (notionally 4.2 times the base rate crash risk). The same rate of elevated crash risk exists when a driver has one ticket for being 10 km/h over the speed limit and has another speeding ticket within two years.

Modelling Motor Insurance Claims Using Arma Model And Appropriate Statistical Distribution Of Insurance Companies, Machakos County, Kenya

The prediction of future claim amounts based on past or present claims is a challenging task for insurance companies. According to research carried out by Michael Barth and David L. Eccles (2009); claim costs constitute large proportion of insurers expenses in evaluating the premium growth on the loss ratio of property causality insurers. Various statistical techniques have been employed to analyze claims data and assess its impact on productivity. Autoregressive (AR), Moving Average (MA), and Auto Regressive Moving Average (ARMA) processes are commonly used to model the temporal dependencies and trends in the data. These techniques enable insurance companies to understand the dynamics of claim incidence and their effects on productivity over time (Philip J, 2016). Advanced modeling techniques such as generalized additive models, negative binomial regression, and multinomial logit models have been used to evaluate claims processes, predict claim occurrence, and model claim severity (Renshaw A.E, 1994; Edward. W Frees et al., 2008; Daiane Aparecida Zanetti et al., 2006). These models consider various factors such as driver age, gender, vehicle type, and no claim discount to assess their impact on claim incidence and productivity. Previous studies have overlooked the comprehensive examination of the impact of insurance claims on productivity on insurance companies. Therefore, this research aims to address this gap by investigating the factors associated with insurance motor claims and their effects on the productivity of insurance companies by employing statistical techniques like appropriate probability distribution, incorporation of moving average technique, Autoregressive process and ARMA process, as identified by industry professionals and experts. The results obtained from the real-life data for distinctive 11 years indicated the potential strength of ARMA models to provide insurance company's claim amount prediction that could assist the companies to have better planning. The findings of this study, if adopted by various keynote players in the insurance and regulatory sectors, can be highly beneficial in formulating appropriate policies to enhance the management of motor insurance claims and improve the productivity of insurance companies.

Understanding Elderly Drivers’ Perception of Advanced Driver Assistance Systems: A Systematic Review of Perceived Risks, Trust, Ease of Use, and Usefulness

Background: Elderly drivers often face safety challenges due to age-related declines in cognitive, sensory, and motor functions. Advanced Driver Assistance Systems (ADAS) offer a potential solution by enhancing safety and mobility. Objectives and method: This systematic review investigates the factors influencing the perception and usage of ADAS among elderly drivers, focusing on perceived safety, usefulness, trust, and ease of use. Results: Older adults show a preference for Level 1 ADAS, which they perceive as safer. Although they acknowledge the usefulness of ADAS in supporting their autonomy, skepticism remains regarding higher-level systems, primarily due to concerns about reliability and invasiveness. Trust and ease of use are essential factors influencing their acceptance. The review identifies common themes and barriers to the adoption of these technologies and emphasizes the need for senior-friendly interfaces and targeted training. The findings indicate that addressing these issues can significantly improve the safety and mobility of elderly drivers. The successful adoption of ADAS among older adults depends on balancing safety, control, and ease of use, with gradual and supportive integration fostering greater acceptance and trust. Conclusions: This study outlines practical implications for stakeholders, emphasizing the need for user-friendly ADAS design, public awareness campaigns, government incentives, insurance discounts, and community training to enhance adoption among older drivers.

Knowledge And Practice Regarding Prehospital Care After Road Traffic Accidents Among Three-Wheel Drivers and Associated Factors in Selected Police Areas in Colombo District, Sri Lanka

Introduction: Road traffic injuries are an increasingly significant public health issue worldwide. Knowledge and practice of prehospital care is a crucial responsibility of every citizen of the country. Pre-hospital care is delivered by a civilian first responder, who is usually another driver. The study aims to describe the knowledge and practice of prehospital care after a road traffic injury among three-wheel drivers and associated factors in selected police areas in the Colombo District. Methods: A communitybased descriptive cross-sectional study was carried out in three Colombo Municipality police areas. A sample of 384 full-time three-wheel drivers above the age of 18 years, registered in these police areas were recruited using a simple random sampling method. A structured pre-tested intervieweradministered questionnaire was used for data collection. Descriptive analysis was used to determine the level of knowledge and practices regarding prehospital care. SPSS version 25 was used for data entry and analysis. Significance was taken as p<0.05. Results: Mean age of three-wheel drivers was 40.98 (SD±11.01). Only 31.5% of the drivers had first aid training. The majority (52.9%) of the drivers had good knowledge and 65.2% had poor practice towards prehospital care. Advanced age (>45 years) and having first aid training were significantly associated with good knowledge (p<0.05). Drivers with previous training within the last ten years (p=0.031) and having less hires per day (<10) were significantly associated with better first aid practice (p=0.001). Conclusion: Although knowledge regarding prehospital care was adequate, practice of prehospital care was inadequate. Findings of the study call for ongoing first-aid training for all three-wheel drivers.Keywords: Prehospital care, First aid, Knowledge, Practice, Road traffic accidents

SafeDrive Pro IoT Based Driver Verification System

SafeDrive Pro is an IoT-based Driver Verification System designed to address the rise in vehicular accidents involving underage and unlicensed drivers. By integrating multi-factor authentication, including age verification, facial recognition, and driver license validation, SafeDrive Pro ensures only authorized individuals can operate vehicles. The system aims to improve vehicle security, reduce unauthorized usage, and enhance road safety. The proposed system leverages IoT technology, machine learning for facial recognition, and RFID for license validation. Initial results indicate that the system can effectively prevent unauthorized access, offering a robust approach to reducing road accidents and promoting vehicle security

Amodiaquine ameliorates stress-induced premature cellular senescence via promoting SIRT1-mediated HR repair

DNA damage is considered to be a potentially unifying driver of ageing, and the stalling of DNA damage repair accelerates the cellular senescence. However, augmenting DNA repair has remained a great challenge due to the intricate repair mechanisms specific for multiple types of lesions. Herein, we miniaturized our modified detecting system for homologous recombination (HR) into a 96-well-based platform and performed a high-throughput chemical screen for FDA-approved drugs. We uncovered that amodiaquine could significantly augment HR repair at the noncytotoxic concentration. Further experiments demonstrated that amodiaquine remarkably suppressed stress-induced premature cellular senescence (SIPS), as evidenced by senescence-associated beta-galactosidase (SA-β-gal) staining or senescence‐related markers p21WAF1 and p16ink4a, and the expression of several cytokines. Mechanistic studies revealed that the stimulation of HR repair by amodiaquine might be mostly attributable to the promotion of SIRT1 at the transcriptional level. Additionally, SIRT1 depletion abolished the amodiaquine‐mediated effects on DNA repair and cellular senescence, indicating that amodiaquine delayed the onset of SIPS via a SIRT1-dependent pathway. Taken together, this experimental approach paved the way for the identification of compounds that augment HR activity, which could help to underscore the therapeutic potential of targeting DNA repair for treating aging-related diseases.

Medication Changes Among Older Drivers Involved in Motor Vehicle Crashes

Although older adults may use potentially driver-impairing (PDI) medications that can produce psychomotor impairment, little is known about changes to PDI medication use among older adults from the time before to the time after a motor vehicle crash (MVC). To quantify use of and changes in PDI medications among older adults before and after an MVC. This cohort study used linked Medicare claims and police-reported MVC data on 154 096 person-crashes among 121 846 older drivers. Eligible persons were drivers aged 66 years or older, involved in a police-reported MVC in New Jersey from May 1, 2007, through December 31, 2017, and with continuous enrollment in Medicare fee-for-service Parts A and B for at least 12 months and Part D for at least 120 days prior to the MVC. Data were analyzed from January 2022 to May 2024. Use of benzodiazepines, nonbenzodiazepine hypnotics, opioid analgesics, and other PDI medications in the 120 days before and 120 days after the MVC. Because each person could contribute multiple MVCs during the study period if they met eligibility criteria, the unit of analysis was the number of person-crashes. The proportion of person-crashes after which PDI medications were started, discontinued, or continued was quantified as well. Among 154 096 eligible person-crashes, the mean (SD) age of the drivers was 75.2 (6.7) years at the time of the MVC. Of 121 846 unique persons, 51.6% were women. In 80.0% of the person-crashes, drivers used 1 or more PDI medications before the crash, and in 81.0% of the person-crashes, drivers used 1 or more PDI medications after the crash. Use of benzodiazepines (8.1% before the crash and 8.8% after the crash), nonbenzodiazepine hypnotics (5.9% before the crash and 6.0% after the crash), and opioid analgesics (15.4% before the crash and 17.5% after the crash) was slightly higher after the MVC. After the MVC, drivers in 2.1% of person-crashes started benzodiazepines and 1.4% stopped benzodiazepines, drivers in 1.2% of person-crashes started nonbenzodiazepine hypnotics and 1.2% stopped nonbenzodiazepine hypnotics, and drivers in 8.4% of person-crashes started opioid analgesics and 6.3% stopped opioid analgesics. This cohort study suggests that most older drivers involved in MVCs did not use fewer PDI medications after crashes than before crashes. Qualitative research of perceived risks vs benefits of PDI medications is necessary to understand the reasons why MVCs do not appear to motivate clinicians to deprescribe PDI medications as a strategy to avert potential harms, including additional MVCs.

Somatic and mental symptoms associated with dysglycaemia, diabetes-related complications and mental conditions in people with diabetes: Assessments in daily life using continuous glucose monitoring and ecological momentary assessment.

To analyse the potential drivers (glucose level, complications, diabetes type, gender, age and mental health) of diabetes symptoms using continuous glucose monitoring (CGM) and ecological momentary assessment. Participants used a smartphone application to rate 25 diabetes symptoms in their daily lives over 8 days. These symptoms were grouped into four blocks so that each symptom was rated six times on 2 days (noon, afternoon and evening). The symptom ratings were associated with the glucose levels for the previous 2 hours, measured with CGM. Linear mixed-effects models were used, allowing for nested random effects and the conduct of N = 1 analysis of individual associations. In total, 192 individuals with type 1 diabetes and 179 with type 2 diabetes completed 6380 app check-ins. Four symptoms showed a significant negative association with glucose values, indicating higher ratings at lower glucose (speech difficulties, P = .003; coordination problems, P = .00005; confusion, P = .049; and food cravings, P = .0003). Four symptoms showed a significant positive association with glucose values, indicating higher scores at higher glucose (thirst, P = .0001; urination, P = .0003; taste disturbances, P = .021; and itching, P = .0120). There were also significant positive associations between microangiopathy and eight symptoms. Elevated depression and diabetes distress were associated with higher symptom scores. N = 1 analysis showed highly idiosyncratic associations between symptom reports and glucose levels. The N = 1 analysis facilitated the creation of personalized symptom profiles related to glucose levels with consideration of factors such as complications, gender, body mass index, depression and diabetes distress. This approach can enhance precision monitoring for diabetes symptoms in precision medicine.

Targeted apoptosis of senescent cells by valproic acid alleviates therapy-induced cellular senescence and lung aging

BackgroundAccumulation of senescent cells in tissues and their downstream effect programs have emerged as key drivers of aging and age-associated pathologies. Recent progresses in senotherapeutics indicated that either selectively killing senescent cells with senolytics or suppressing the senescence-associated secretory phenotype (SASP) secretion using senomorphics contributes to extending of the healthy lifespan and alleviating numerous age-related disorders in mice. PurposeHowever, the potential side-effects and long-term cytotoxicity of the above novel compounds have not yet been determined. Therefore, it seems to be more efficient to explore new senotherapeutical functions from approved drugs. MethodsThe effects of valproic acid (VPA), a derivative of valine, in cellular senescence were evaluated by senescence-associated β galactosidase (SA-β-Gal) staining, flow cytometry and western blot (WB). The cell viability was tested using CCK-8 kits. Cell apoptosis was detected by Annexin V-EGFP/PI apoptosis detection kit. Cell autophagy was checked using GFP-RFP-LC3 ratiometric plasmid. The roles of VPA in lung aging were investigated by in vivo experiments using H&E and Masson staining, WB, as well as electronic microscope strategies. ResultsHere we identified VPA was able to induce an over-accumulation of reactive oxygen species (ROS) (>1.5 times increasing) and apoptosis (>2 times increasing) of senescent cells. Mechanistically, VPA activated the phospholipid modifying enzyme membrane-bound O-acyltransferase domain-containing protein 1 (MBOAT1), which was repressed during senescence, then promoted mitochondrial autophagy and apoptosis. In addition, VPA was also found to alleviate therapy induced abnormal mitochondria and lung aging phenotype (>1.5 times decreasing of lung fibrosis markers and >2.5 times increasing of naïve/memory CD4+ or CD8+ T cells) in vivo. ConclusionTaken together, our study demonstrated that VPA was able to selectively kill senescent cells both in vitro and in vivo, and thus shedding light on new functions and novel potential application of VPA in anti-aging and anti-age-associated diseases.

Roles of chromatin and genome instability in cellular senescence and their relevance to ageing and related diseases.

Ageing is a complex biological process in which a gradual decline in physiological fitness increases susceptibility to diseases such as neurodegenerative disorders and cancer. Cellular senescence, a state of irreversible cell-growth arrest accompanied by functional deterioration, has emerged as a pivotal driver of ageing. In this Review, we discuss how heterochromatin loss, telomere attrition and DNA damage contribute to cellular senescence, ageing and age-related diseases by eliciting genome instability, innate immunity and inflammation. We also discuss how emerging therapeutic strategies could restore heterochromatin stability, maintain telomere integrity and boost the DNA repair capacity, and thus counteract cellular senescence and ageing-associated pathologies. Finally, we outline current research challenges and future directions aimed at better comprehending and delaying ageing.

Tree-based approaches to understanding factors influencing crash severity across roadway classes: A Thailand case study

Existing studies often overlook the nuanced differences between various road classifications and their respective crash dynamics, hindering the development of targeted interventions to mitigate crash severity. To address this gap, this study investigates factors influencing the likelihood of fatality in road crashes across highways, collector roads, and local roads in Thailand using crash data from 2015 to 2021. Highways connect regions with high-speed traffic and large volumes, collector roads link smaller communities with lower traffic density but allow higher speeds, and local roads primarily pass through villages, with narrow pathways, two traffic lanes, and frequent motorcycle use. The study employs machine learning methodologies utilizing tree-based algorithms, including Decision Trees, Random Forest, Gradient Boosting, AdaBoost, Extra Trees, XGBoost, LightGBM, and CatBoost. The XGBoost model delivered superior performance for highways, while Gradient Boosting slightly outperformed XGBoost for local and collector roads. Both models consistently achieved a test accuracy of 0.70, with precision between 0.66 and 0.67, recall ranging from 0.59 to 0.61, and F1-scores from 0.58 to 0.61. The AUC values also consistently ranged from 0.59 to 0.61. SHAP values reveal key factors influencing fatality risk across road types, including speeding, gender disparities, driving under the influence of alcohol, inadequate lighting, and elderly drivers. Specific concerns include reversing on highways, collisions in poorly lit areas on collector roads, and helmet non-use on local roads. The findings support policy recommendations to address speeding, target male and older drivers, prevent reversing incidents, enhance lighting, and promote helmet use. This research deepens our understanding of factors affecting road crash severity and offers valuable insights for improving road safety across various environments.

Protection of the genome and the central exome by peripheral non-coding DNA against DNA damage in health, ageing and age-related diseases.

DNA in eukaryotic genomes is under constant assault from both exogenous and endogenous sources, leading to DNA damage, which is considered a major molecular driver of ageing. Fortunately, the genome and the central exome are safeguarded against these attacks by abundant peripheral non-coding DNA. Non-coding DNA codes for small non-coding RNAs that inactivate foreign nucleic acids in the cytoplasm and physically blocks these attacks in the nucleus. Damage to non-coding DNA produced during such blockage is removed in the form of extrachromosomal circular DNA (eccDNA) through nucleic pore complexes. Consequently, non-coding DNA serves as a line of defence for the exome against DNA damage. The total amount of non-coding DNA/heterochromatin declines with age, resulting in a decrease in both physical blockage and eccDNA exclusion, and thus an increase in the accumulation of DNA damage in the nucleus during ageing and in age-related diseases. Here, we summarize recent evidence supporting a protective role of non-coding DNA in healthy and pathological states and argue that DNA damage is the proximate cause of ageing and age-related genetic diseases. Strategies aimed at strengthening the protective role of non-coding DNA/heterochromatin could potentially offer better systematic protection for the dynamic genome and the exome against diverse assaults, reduce the burden of DNA damage to the exome, and thus slow ageing, counteract age-related genetic diseases and promote a healthier life for individuals.

Exploration of the Characteristics of Elderly-Driver-Involved Single-Vehicle Hit-Fixed-Object Crashes in Pennsylvania, USA

With the acceleration of population aging, the elderly driving safety issue is increasingly prominent. Method: With the crash data of Pennsylvania from 2010 to 2019, this study exclusively discusses features of single-vehicle hit-fixed-object crashes (SVHFOCs), one of the most common and deadliest crash types for elderly drivers. Results: Firstly, we demonstrate that elderly drivers are much more likely to be injured and killed than young drivers in SVHFOCs by checking crash consequences. The descriptive analysis indicates that elderly drivers have very different crash features from young drivers. They are found to drive with more caution in many aspects, such as more low-speed local travels, fewer illegal behaviors, fewer nighttime travels, etc. Then, a logistic regression model is built to find the factors significantly influencing the severity of SVHFOCs from driver, vehicle, roadway, and environment. The estimation results indicate that female sex, not wearing a seatbelt, DUI, rural area, and SUV involvement tend to be associated with more severe SVHFOCs. Additionally, illumination, weather, and road type could also significantly affect crash severity. Especially, SVHFOCs in adverse weather, in dark conditions, and at intersections are found to be less severe, which implies that elderly drivers might drive more carefully in complex environments. Practical Applications: These findings are expected to provide new insights for agencies in formulating customized measures to prevent elderly drivers from being involved in SVHFOCs.

An Intelligent System for Determining Driver Anxiety Level: A Comparison Study of Two Fuzzy-Based Models

While driving, stress and frustration can affect safe driving and pose the risk of causing traffic accidents. Therefore, it is important to control the driver’s anxiety level in order to improve the driving experience. In this paper, we propose and implement an intelligent system based on fuzzy logic (FL) for deciding the driver’s anxiety level (DAL). In order to investigate the effects of the considered parameters and compare the evaluation results, we implement two models: DAL Model 1 (DALM1) and DAL Model 2 (DALM2). The input parameters of DALM1 include driving experience (DE), in-car environment conditions (IECs), and driver age (DA), while for DALM2, we add a new parameter called the accident anxiety state (AAS). For both models, the output parameter is DAL. We carried out many simulations and compared the results of DALM1 and DALM2. The evaluation results show that the DAL is very good for drivers’ ages between 30 to 50 years old. However, when the driver’s age is below 30 or above 50, DAL tends to decline. With an increase in DE and IECs, the DAL value is decreased. But when the AAS is increased, the DAL is increased. DALM2 is more complex because the rule base is larger than DALM1, but it makes a better decision of DAL value.

Identifying the reciprocal causation between hit-and-run behavior and crash injury severity

Objective Hit-and-run behavior is believed to exacerbate the injury severity of traffic crashes due to the delayed emergency response for the victims. However, several previous studies indicated the opposite finding that hit-and-run crashes were associated with less severe injuries. The relevant studies mainly identified the statistical associations between hit-and-run behavior and injury severity without revealing causation between them. To this end, the study aims to explore the reciprocal causation between the two variables. Method The two-stage probit model with endogenous regressors is employed to identify the reciprocal causation between hit-and-run behavior and crash injury severity for single- and two-vehicle crashes, respectively, with the use of crash data extracted from the Crash Report Sampling System and Fatality Analysis Reporting System (2016-2019). Results The results indicate that 1) for both single- and two-vehicle crashes, the fleeing behavior can significantly increase the injury severity of the victims in the crashes while the severe injury of the victims has a negative impact on the propensity of such behavior, 2) the propensity of hit-and-run behavior is influenced by various instrumental variables such as driver age, gender, alcohol involvement, weekday, area type, and light condition, and 3) crash injury severity is significantly related to the victim age, gender, and vehicle damage. Conclusions There is a reciprocal causation between hit-and-run behavior and injury severity in traffic crashes. The analytical results can provide a reasonable explanation for the counterintuitive finding on hit-an-run crashes and help mitigate the injury severity.

Investigating factors affecting injury severity of single-vehicle run-off-road crashes

This study aimed to identify and investigate the contributing factors influencing injury severity in single-vehicle run-off-road (ROR) crashes, which are known for their high severity. The primary objective was to analyze and compare the impact of these factors across three distinct vehicle classes: passenger cars, sport utility vehicles (SUVs), and pickups. A mixed logit model with heterogeneity in mean and variance was developed to analyze the injury severity outcomes in ROR crashes for the three vehicle classes. The model accounted for the potential variations in the impact of contributing factors across different vehicle types. The study revealed several significant variables consistently influencing injury severity across all three vehicle classes. These included driver age, alcohol or drug usage, seatbelt utilization, airbag deployment, higher travel speeds, and the vehicle model year post-2010. Notably, as driver age increased, the impact on changes in injury severity outcomes was more pronounced for pickup drivers compared to those operating passenger cars and SUVs. Among the common findings was the highly effective role of seatbelt usage in mitigating injury severity in ROR crashes. Additionally, passenger cars were associated with increased injury severity, particularly at relatively higher travel speeds exceeding 75 mph when contrasted with SUVs and pickups traveling between 61 and 75 mph. The study highlights the importance of considering vehicle class-specific factors in analyzing injury severity in ROR crashes. Recommendations include further in-depth investigations into distinct factors contributing to injury severity within each vehicle class and utilizing more extensive crash datasets to gain additional insights for enhancing road safety.

PREDICTING THE CHARACTERISTICS OF DRIVER’S LICENSE APPLICANTS AT SATPAS POLRESTA MANOKWARI USING HIERARCHICAL MULTIPLE REGRESSION

This research aims to predict the characteristics of driver's license (SIM) applicants at the SATPAS (Driver’s License Issuance Unit) Polresta Manokwari using Hierarchical Multiple Regression analysis. The study explores six key variables: gender, age, occupation, city, type of driver's license, and type of application, as predictors of applicant characteristics. The analysis was conducted using SPSS version 29, with data collected from the population of driver's license applicants since 2023. Three models were tested, with Model 3 being identified as the best predictor, explaining 7% of the variance in applicant characteristics (R² = 0.070). This model incorporates the variables of age, occupation, city, and type of application, while gender and driver's license class were found to have no significant individual impact. The partial t-test results show that age, occupation, city, and type of application significantly influence applicant characteristics, with negative regression coefficients indicating that an increase in these variables leads to a decrease in the predicted characteristics of SIM applicants. The study highlights practical implications for SATPAS, suggesting that service processes could be improved by considering demographic factors such as age and occupation in order to optimize resource allocation and reduce service complexity. However, the study has several limitations. The use of secondary data limits the completeness and accuracy of the analysis, and the limited number of variables results in a narrow interpretation of the factors influencing SIM applicants. Additionally, the model explains only a small portion of the variance in applicant characteristics, suggesting that other unmeasured factors, such as education level or driving experience, may play a more significant role. Furthermore, the findings are not generalizable to other regions, as local conditions may impact license application patterns. Future research should address these limitations by collecting primary data, expanding the range of variables, employing more sophisticated analytical methods, and exploring other regions. This would provide a more comprehensive understanding of the factors affecting driver's license applicants and contribute to enhancing the quality of SIM issuance services in Indonesia. Keywords: Driver's License, Prediction, Hierarchical Multiple Regression, Applicant Characteristics

Mangrove forest regeneration age map and drivers of restoration success in Gulf Cooperation Council countries from satellite imagery

Mangrove forests are found across the Gulf Cooperation Council (GCC) region despite challenging environmental extremes, including highly variable temperatures and hypersalinity. Understanding the biophysical and anthropogenic factors that influence mangrove forest growth is key to locate suitable areas for regeneration and afforestation activities. The main objectives of this study were to develop a mangrove forest regeneration age map that represents the age of all the existing secondary mangroves in the past 37 years (1986–2023). Long-term Landsat satellite imagery, the random forest classification algorithm, and logistic regression analyses were used to identify the existing secondary mangroves and determine the underlying drivers that contribute to the successful afforestation of mangroves in the region. Our results showed that only around 8.5% of secondary mangrove forests in the GCC region were older than 30 years, with mangroves younger than 5 years being the most abundant age class (41.3%). Saudi Arabia and Oman have the highest percentages of young mangroves, while relatively older secondary mangrove forests were most common in Bahrain, Qatar, and UAE. The current trends in overall mangrove area show that the UAE and Saudi Arabia have the largest total mangrove area among the GCC countries, followed by Qatar, Oman, Bahrain, and Kuwait. The results of the stepwise logistic regression show that the main drivers that influence mangrove regeneration are lower elevation, lower slope, higher available soil moisture, lower average temperatures, higher precipitation, greater proximity to freshwater sources, lower population density and greater distance from agricultural and urban areas. Our results aim to offer support to decision-making in selecting optimal areas for new planting initiatives in the region.