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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Analysis of Accident Statistics Data Aimed at Mitigating Injuries Caused by Pedal Misapplication Among Elderly People (Second Report)</title>
      <link>https://trid.trb.org/View/2505973</link>
      <description><![CDATA[This research aims to reveal the characteristics of injury levels of drivers who caused pedal misapplication accidents, analyzing drivers’ age groups, drivers’ injury levels, types of accidents, road types, and drivers’ maneuvers. The results indicated that single-vehicle accidents frequently led to fatalities and serious injuries among drivers, with a higher incidence among elderly drivers, and often occurred in parking lots. These accidents commonly involved collisions with houses, walls, guard fences, parked vehicles, and other structures. Furthermore, the incidence of fatalities and serious injuries was higher in cases where collisions with poles or where vehicles fell off.]]></description>
      <pubDate>Tue, 25 Mar 2025 16:57:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2505973</guid>
    </item>
    <item>
      <title>Tree-based approaches to understanding factors influencing crash severity across roadway classes: A Thailand case study</title>
      <link>https://trid.trb.org/View/2434361</link>
      <description><![CDATA[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 the understanding of factors affecting road crash severity and offers valuable insights for improving road safety across various environments.]]></description>
      <pubDate>Thu, 17 Oct 2024 11:00:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2434361</guid>
    </item>
    <item>
      <title>Applying AI to Data Sources to Improve Driver-Pedestrian Interactions at Intersections [supporting dataset]</title>
      <link>https://trid.trb.org/View/2425161</link>
      <description><![CDATA[Tragically, roadway crossings sometimes act as killing fields, especially for pedestrian-vehicle collisions, with intersections accounting for 40% of transportation crashes in the US. This emphasizes the importance of effectively addressing pedestrian safety at intersections to mitigate such incidents. The first chapter of this report incorporates pedestrian safety into the optimization of traffic signals by collecting and linking data from traffic signals (cameras) and analyzing the behaviors of pedestrians and drivers at intersections using Artificial Intelligence techniques, i.e., a decentralized Dyna Q-Learning environment. The results indicate that AI agents may safely prioritize pedestrian service even with longer waiting times or reduce pedestrian delays at the expense of vehicle delay performance. The report’s second chapter explores rare pedestrian crashes at intersections, called “corner cases,” using Fatality Analysis Reporting System (FARS) data and applying text analytics and the K-means unsupervised learning approach. Such crashes are likely to be triggered by a combination of factors, including poor visibility, severe weather, impaired pedestrian or driver behaviors, and dark lighting conditions. The final chapter of the research investigates the determinants of nighttime pedestrian crash injury severity in pedestrian-involved crashes on intersections using the Random Forest algorithm and ordered logit models. The analysis results reveal that alcohol impairment, foggy weather, elderly pedestrians, a speed limit of 50-55 mph, and motorists not yielding to pedestrians are more likely to contribute to severe pedestrian injuries at intersections. The implications of the findings are discussed in each chapter.]]></description>
      <pubDate>Thu, 26 Sep 2024 16:50:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2425161</guid>
    </item>
    <item>
      <title>Applying AI to Data Sources to Improve Driver-Pedestrian Interactions at Intersections</title>
      <link>https://trid.trb.org/View/2425160</link>
      <description><![CDATA[Tragically, roadway crossings sometimes act as killing fields, especially for pedestrian-vehicle collisions, with intersections accounting for 40% of transportation crashes in the US. This emphasizes the importance of effectively addressing pedestrian safety at intersections to mitigate such incidents. The first chapter of this report incorporates pedestrian safety into the optimization of traffic signals by collecting and linking data from traffic signals (cameras) and analyzing the behaviors of pedestrians and drivers at intersections using Artificial Intelligence techniques, i.e., a decentralized Dyna Q-Learning environment. The results indicate that AI agents may safely prioritize pedestrian service even with longer waiting times or reduce pedestrian delays at the expense of vehicle delay performance. The report’s second chapter explores rare pedestrian crashes at intersections, called “corner cases,” using Fatality Analysis Reporting System (FARS) data and applying text analytics and the K-means unsupervised learning approach. Such crashes are likely to be triggered by a combination of factors, including poor visibility, severe weather, impaired pedestrian or driver behaviors, and dark lighting conditions. The final chapter of the research investigates the determinants of nighttime pedestrian crash injury severity in pedestrian-involved crashes on intersections using the Random Forest algorithm and ordered logit models. The analysis results reveal that alcohol impairment, foggy weather, elderly pedestrians, a speed limit of 50-55 mph, and motorists not yielding to pedestrians are more likely to contribute to severe pedestrian injuries at intersections. The implications of the findings are discussed in each chapter.]]></description>
      <pubDate>Thu, 26 Sep 2024 16:50:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2425160</guid>
    </item>
    <item>
      <title>Empirical examination of interdependent relationship between usage of seatbelt restraint system and driver-injury severity of single-vehicle crashes in Thailand using a joint econometric analysis</title>
      <link>https://trid.trb.org/View/2204264</link>
      <description><![CDATA[The paper aims to examine the interdependent relationship between the usage of the seatbelt restraint system and severities of the driver-injury in single-vehicle crashes. This paper developed a comprehensive joint econometric structure − a joint random parameters binary probit-binary probit model − that allows for the simultaneous examination of injury severity of the driver in a crash, and taking into account the fact that seat belt use can be endogenous to the outcomes of driver injury. The developed model is tested using data on drivers-injury severities involved in single-vehicle crashes in Thailand from 2012-2017.ResultsIn terms of the interdependent relationship between seatbelt use status and driver-injury severities, the findings suggest that drivers who do not use seat belts may demonstrate more dangerous or aggressive driving behaviors (such as speeding), subsequently increasing their likelihood of involvement in severe or fatal crashes. Additionally, the result also shows that drivers who are involved in speeding-related crashes are less likely to wear a seatbelt and have a higher risk of sustaining severe and fatal injuries. The findings also reveal that in crashes, drivers who are young, or operating trucks are less likely to be wearing their seat belts. The study also indicates that severe and fatal crashes are associated with factors such as elderly drivers, alcohol involvement, unbelted drivers, fatigue, depressed medians, and barrier medians. Conversely, a crash in a U-turn area, driving a passenger car, pickup truck, or large truck, or colliding with a guardrail reduces the likelihood of severe and fatal injuries. Neglecting the hidden endogenous effect in statistical analyses could result in an overestimation of the impact of seat belt usage on crash-injury outcomes. The findings of this study can provide valuable insights for relevant authorities aiming to improve driver safety.]]></description>
      <pubDate>Fri, 21 Jul 2023 09:18:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2204264</guid>
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    <item>
      <title>Near-term impact of COVID-19 pandemic on seniors’ crash size and severity</title>
      <link>https://trid.trb.org/View/2140252</link>
      <description><![CDATA[Recent research revealed that COVID-19 pandemic was associated with noticeable changes in travel demand, traffic volumes, and traffic safety measures. Despite the reduction of traffic volumes across the US, several recent studies indicated that crash rates increased across different states during COVID-19 pandemic. Although some recent studies have focused on examining the changes in traffic conditions and crash rates before and during the pandemic, not enough research has been conducted to identify risk factors to crash severity. Even the limited research addressing the contributing factors to crash severity were focused on the pool category of drivers and no insight is available regarding older drivers, one of the most vulnerable groups to traffic collision and coronavirus. Moreover, these studies investigated the early impact of the COVID-19 pandemic mostly using up to three months of data. However, near-term and long-term effects of the COVID-19 pandemic are still unknown on traffic collisions. Therefore, this study aims to contribute to the literature by studying the near-term impact of the COVID-19 pandemic on crash size and severity among older drivers. To this end, a relatively large sample of crash data with senior drivers at fault was obtained and analyzed. To identify the main contributing factors affecting crash outcomes, Exploratory Factor Analysis was conducted on a high-dimension data set to identify potential latent factors which were validated through Confirmatory Factor Analysis. After that, Structural Equation Modeling technique was performed to examine the associations among the identified independent latent factors and the dependent variable. Additionally, SEM model identified the impact of the COVID-19 pandemic on seniors’ crash severity. The findings reveal that several latent variables were the significant predictors of crash severity of older drivers including “Driving maneuver & crash location”, “Road features and traffic control devices”, “Driver condition & behavior”, “Road geometric characteristics”, “Crash time and lighting”, and “Road class” latent factors. The binary variable of “Pandemic” was found to be as highly significant as the last four latent factors mentioned above. This means not only were older drivers more likely to be involved in higher crash size with higher severity level during the pandemic period, but also “Pandemic” was a risk factor to seniors as much as “Driver condition & behavior”, “Road geometric characteristics”, “Crash time & lighting”, and “Road class” factors. The results of this study provide useful insights that may improve road safety among senior drivers during pandemic periods like COVID-19.]]></description>
      <pubDate>Tue, 04 Apr 2023 09:36:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2140252</guid>
    </item>
    <item>
      <title>Investigations, observations, analysis of the activity among older drivers: understanding barriers to better act (critical review, theoretical position and research opportunities)</title>
      <link>https://trid.trb.org/View/1491779</link>
      <description><![CDATA[To preserve the independence and quality of life of our seniors, it is essential to reconcile the requirements of security issues with mobility ones. Public action in road safety can be renewed by being sensitized to the issue of security management, i.e. by searching underlying rationalities from the observed behaviors. This requires studying the security in action from senior drivers' activity analysis. Most of the older drivers use their capabilities in order to build an appropriate response to the driving conditions encountered. These coping skills enable them to remain fit to drive. Starting from practice, this approach allows to redefine the "good driving" rules and thus shed new light on the driving assessment issue. In French, the translation of the word 'driving' is a polysemic term. In this 'habilitation to conduct research' dissertation, it will is used in its generic sense. It includes an objectified part (behavior) and a non-objectified part: the reason, the motive, the act(s) of thought that come before, during or after a performance. The research works I have conducted tried to elucidate the roles of cognitive, affective and conative processes to capture the processes' intermediate parts (or links) involved in the adopted behaviors; we based our investigations on the study of intra and inter-subjective processes. This approach allows us to objectivize the origins and sometimes the causes of behaviors. However, to better understand them, an observation approach must be associated. Indeed, through observation, the objective reasons to act are figured-out by building a posteriori arguments based on observed behavior and statements from the individuals. These two approaches are complementary, they allow to explain and to understand behaviors in order, ultimately, to better act. A central concept in the study of adaptation to normal cognitive aging is the cognitive control (ability to respond to stimuli based on the present context, past indices and internal aims). The study of cognitive control during driving allows the description of different forms of adaptation exploring their dimensions, their mechanisms and their determinants. To understand seniors' behaviors, it is also necessary to take into account their situation, their needs, their physical and social environments to better describe their difficulties and the risks they are exposed to. The complementarity of different tools and methods used to tackle this issue is addressed. The research conducted to date help to clarify aims to be reached in order to increase senior safety, to identify priority actions and also to help public authorities to take rational decisions that maximize both the mobility and safety of older drivers. Through examples, we described the adaptive strategies adopted by some older drivers to safe act against disturbances that appear while aging and we also describe how we can identify older drivers who do not adapt optimally. As attention capacities are essential to preserve a safe driving, an expand section on attentional failure while driving is also presented. Within the two research lines, whose main results are detailed below, it is shown that the concept of estimation bias is central to the implementation of driver' behavioral self-regulation. 1. Older drivers' self-regulatory behaviors were explored regarding declared aberrant driving behaviors and perceived abilities . The study suggests that perceived abilities, especially self-assessed driving related processing speed and attentional abilities, play a major role in the decision to self-regulate its own behaviors (avoiding difficult driving situations) and that such self-efficacy beliefs are a stronger predictors of avoidance than driver behavior questionnaire.  The avoidance of difficult driving situations as a behavioral self-regulation option were also compared between young and older drivers : the older drivers reported greater avoidance situations than the younger drivers, more significant correlations were observed between self-reported driving avoidance and both health-related perceptions and objective indicators of cognitive function among them. To explore if older adults spontaneously draw on their monitoring skills to accurately self-regulate their behaviors, we explored drivers' self-regulation within discrepancy reduction framework and the region of proximal learning : we have shown that younger and older drivers were thus equally able to identify their region of proximal learning. In a complementary study, we have also shown that exposing older drivers to a stereotype threat severely impairs their self-regulatory skills; this is at least partly due to exhaustion of the executive resources appearing through working memory overload . These research works were the starting point for a wider reflection on the implementation of more effective training interventions, especially for older drivers that present a cognitive self-assessment bias . These interventions will allow to support behavior change in order to improve the comfort and safety of older drivers. 2. Due to substantial gains in road safety due to speed reduction, safety-belt wear, and the diminution of driving under alcohol influence, the proportion of accidents due to attention failure while driving increases. In a four years project funded by the French research agency (ANR), we worked in a multidisciplinary team (cognitive and mathematical sciences and epidemiology) to clarify the road safety issues and identify avenues for innovative actions to better supervise the driver. A first step was to identify the risk fractions attributable to different types of attention failure in order to better prevent them. A survey was conducted in Bordeaux hospitals. 955 drivers injured in a road accident were interviewed following their admission to the emergency rooms. They were asked to report their activity and the content and intensity of their thoughts in the moments before the accident. Of the 453 drivers related to mind wandering (MW), the contents proved intense or disturbing for 121. These thoughts were significantly more frequent among drivers responsible for the accident. It thus appears that an accident on ten is linked with a driver intrusive thought (Galéra et al., 2012) . Distractions related to events outside the vehicle and driver activity are also associated with responsibility (OR 3.3 and 9.6 respectively). Attributable share of casualties related to external distraction is estimated at 9 % (Bakiri et al., 2013) . These epidemiological findings show that attention failure during driving is a road safety deposit that may reduce the number of casualties on our roads. In a second step, we also tried to understand the influence of various failure of attention on driver behavior. First, three forms of cognitive interference were studied to describe their effects on simulated driving behavior and the treatment of information (studied with the evoked potentials technic). Visuospatial cognitive distractions impacted anticipation (by observing the contingent negative variation, CNV), while verbal distractions impacted the information processing at sensory and cognitive processing steps. The experimental results showed the differential influences of various types of cognitive control (attentional control, emotional control and behavioral inhibition). Experiments conducted on a simulator and on the road showed that: retrospective and prospective thoughts change ocular strategies (eye gaze and increased pupillary diameter; Lemercier et al., 2014) , the increased cognitive effort results in increased heart rate associated with a decrease of its variability and different regulation strategies have been described for the control of cognitive effort while driving (Gabaude et al., 2012) . A complementary study have then shown that the real-time detection of cognitive effort is feasible . A survey through the use of an off-line questionnaire was also conducted to reveal the individual and contextual characteristics of driving in a MW state, to describe behavioral consequences of MW on driving and to determine the characteristics of off-task thoughts while driving (Berthié et al., 2015) . In this research project, the influences of MW on the accident risk and on the driving behavior have been demonstrated, it provides avenues of research and an insight into new original development. The complementarity of the two types of methods to analyze the impact of cognitive distraction on driving activity by exploratory and confirmatory analysis was discussed. Geometry information methods have been used to analyze data applied from the vehicle side to enable the thresholds identification beyond which it is probable that the driver performs driving competing activity (Letelier, 2012) . These criteria are not always sensitive because of the variability of observed behaviors. The psycho-ergonomic analysis of the drivers' activities could help to better describe the various regulation strategies that can be adopted. This approach is required to identify, from the data side, the most sensitive and specific algorithms. This will be the first step towards the development of a driver supervision system, thus contributing to the objectives of reducing road accidents. By comparing different disciplinary perspectives, this project has allowed substantial progress on the subject of attention failure while driving. We began to understand the causes and origins of various attention failure and their consequences on driving. The exploratory analysis of sequential data and supervised learning techniques (data-mining) applied to the data collected on highway are used to search algorithms able to identify a distracted driver. New technological challenges, consisting in the adaptation of driving assistance depending on the driver state, will soon be addressed. The work presented in this dissertation also stress the need to broach health issues in their complexity and not to address the driving ability issue in regard of a unique road safety policy. To take advantage of successful aging, the elderly must evolve with their environment and adapt consequently. The increase of knowledge on this adaptation notion is essential. The knowledge gained on the adaptation to cognitive aging must now be exploited to develop interventions to accompany the older drivers in various stages of adoption of precautionary behavior. In the perspective section outlined in this dissertation, we develop four directions to implement research actions in order to: accompany the public authorities on issues related to the safe mobility of seniors, better prevent negative effects of aging, offer to older users the digital revolution opportunities taking place in the health and transportation domains and lastly to promote a continuum of mobility for our seniors. In future research, it will also be necessary to evaluate the effectiveness of the different actions or interventions proposed, some first ideas in this direction are suggested.]]></description>
      <pubDate>Thu, 14 Dec 2017 15:56:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/1491779</guid>
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    <item>
      <title>Can Driving in the Simulator Diagnose Cognitive Impairments?</title>
      <link>https://trid.trb.org/View/1437812</link>
      <description><![CDATA[There is increasing concern and interest about the association of cognitive impairments and driving performance among the elderly, and several recent studies have identified significant driving performance deficits in cognitively impaired older people, measured by means of changes in driving simulator metrics. In this paper, it is attempted to reverse the question: can driving at the simulator reveal the presence of cognitive impairments? This question has a two-fold interest: first, driving at the simulator may allow for the detection of subtle changes in driving due to cognitive impairments imperceptible in one’s daily routine; and second, driving simulators may have potential of becoming in the future useful tools for the screening of older individuals and assist clinicians both in the medical examination and the advice on whether to continue driving. Data from a large interdisciplinary driving simulator study were analyzed by means of discriminant analysis techniques, in order to classify individuals as healthy or cognitively impaired on the basis of their simulated driving performance. The analysis sample included 86 individuals, out of which 38 patients with Mild Cognitive Impairment (MCI) and 21 patients with Alzheimer’s disease (AD). The results suggest that variables discriminating between healthy and impaired individuals are average speed and headway, lateral position variability, throttle position, reaction time and accident occurrence at incidents. The functions developed correctly classified more than 65% of the individuals, a share that dropped to around 60% when cross-validation analysis was implemented. Overall, although MCI and AD patients had significant shares of misclassified cases, these misclassifications were mostly between the one pathology and the other; very few pathological cases were classified as healthy, and all of these concerned MCI patients. It is indicated that driving at the simulator may under certain conditions assist in the screening for cognitive impairments.]]></description>
      <pubDate>Mon, 30 Jan 2017 09:54:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/1437812</guid>
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    <item>
      <title>Understanding Contributing Factors to Wrong-way Crashes and Evaluating the Effectiveness of Countermeasures in Reducing Wrong-way Crash Risk of Older Drivers</title>
      <link>https://trid.trb.org/View/1414801</link>
      <description><![CDATA[Although relatively infrequent, when Wrong Way Crashes (WWCs) occur they are much more likely to be fatal, and to involve multiple fatalities, compared to other types of highway crashes. Impairment as a result of drug and/or alcohol consumption is a major contributing factor to WWCs. However, older drivers are also at greater risk of being involved in WWCs. The focus of the current project was assess the effectiveness of different countermeasures in preventing Wrong Way Entries (WWEs), a frequent precursor to WWCs, and reducing confusion regarding highway entry points. A driving simulator study asked older drivers (65+) to enter a highway using an entrance ramp on the left while passing an exit ramp on the left that featured various levels of wrong way countermeasures (minimum required signs and pavement markings defined by the MUTCD, minimum plus the addition of a No Left Turn (R3-2) sign before the lip of the exit ramp, and an enhanced countermeasure condition that included additional signs, larger signs, and enhanced pavement markers. The number of WWEs did not statistically differ as a function of countermeasure level, nor did pre-planned analyses of behavioral driving data reveal differences in uncertainty regarding which ramp (entrance or exit) to enter. Exploratory analyses found that a measure of confusion/uncertainty (speed before the exit ramp) did differ significantly between the minimum and enhanced countermeasure conditions, in line with previous simulator findings that enhanced countermeasures can reduce confusion (Boot, Charness, Mitchum, Roque, Stothart, & Barajas, 2015). While providing some support for the benefit of enhanced countermeasures, results also suggest that WWEs are particularly difficult to prevent. Even in the minimum plus and enhanced conditions featuring multiple redundant cues, some older drivers (2) still entered the exit ramp. This research highlights the need to understand not only the best set of cues to prevent WWEs, but the most effective cues to provide further down the exit ramp (e.g., flashing Wrong Way signs, flashing in pavement LED markers) to encourage retreat once a WWE has occurred.]]></description>
      <pubDate>Fri, 01 Jul 2016 11:54:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/1414801</guid>
    </item>
    <item>
      <title>Analyzing fault and severity in pedestrian–motor vehicle accidents in China</title>
      <link>https://trid.trb.org/View/1331039</link>
      <description><![CDATA[The number of pedestrian–motor vehicle accidents and pedestrian deaths in China surged in recent years. However, a large scale empirical research on pedestrian traffic crashes in China is lacking. In this study, the authors identify significant risk factors associated with fault and severity in pedestrian–motor vehicle accidents. Risk factors in several different dimensions, including pedestrian, driver, vehicle, road and environmental factors, are considered. They analyze 6967 pedestrian traffic accident reports for the period 2006–2010 in Guangdong Province, China. These data, obtained from the Guangdong Provincial Security Department, are extracted from the Traffic Management Sector-Specific Incident Case Data Report. Pedestrian traffic crashes have a unique inevitability and particular high risk, due to pedestrians’ fragility, slow movement and lack of lighting equipment. The empirical analysis of the present study has the following policy implications. First, traffic crashes in which pedestrians are at fault are more likely to cause serious injuries or death, suggesting that relevant agencies should pay attention to measures that prevent pedestrians from violating traffic rules. Second, both the attention to elderly pedestrians, male and experienced drivers, the penalty to drunk driving, speeding, driving without a driver's license and other violation behaviors should be strengthened. Third, vehicle safety inspections and safety training sessions for truck drivers should be reinforced. Fourth, improving the road conditions and road lighting at night are important measures in reducing the probability of accident casualties. Fifth, specific road safety campaigns in rural areas, and education programs especially for young children and teens should be developed and promoted. Moreover, the authors reveal a country-specific factor, hukou, which has significant effect on the severity in pedestrian accidents due to the discrepancy in the level of social insurance/security, suggesting that equal social security level among urban and rural people should be set up. In addition, establishing a comprehensive liability distribution system for non-urban areas and roadways will be conducive to both pedestrians’ and drivers’ voluntary compliance with traffic rules.]]></description>
      <pubDate>Mon, 24 Nov 2014 15:49:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1331039</guid>
    </item>
    <item>
      <title>Pattern Recognition and Classification of Fatal Traffic Accidents in Israel: A Neural Network Approach</title>
      <link>https://trid.trb.org/View/1127107</link>
      <description><![CDATA[This article provides a broad picture of fatal traffic accidents in Israel to answer an increasing need of addressing compelling problems, designing preventive measures, and targeting specific population groups with the objective of reducing the number of traffic fatalities. The analysis focuses on 1,793 fatal traffic accidents occurred during the period between 2003 and 2006 and applies Kohonen and feed-forward back-propagation neural networks with the objective of extracting from the data typical patterns and relevant factors. Kohonen neural networks reveal five compelling accident patterns: (1) single-vehicle accidents of young drivers, (2) multiple-vehicle accidents between young drivers, (3) accidents involving motorcyclists or cyclists, (4) accidents where elderly pedestrians crossed in urban areas, and (5) accidents where children and teenagers cross major roads in small urban areas. Feed-forward back-propagation neural networks indicate that sociodemographic characteristics of drivers and victims, accident location, and period of the day are extremely relevant factors. Accident patterns suggest that countermeasures are necessary for identified problems concerning mainly vulnerable road users such as pedestrians, cyclists, motorcyclists and young drivers. A "safe-system" integrating a system approach for the design of countermeasures and a monitoring process of performance indicators might address the priorities highlighted by the neural networks.]]></description>
      <pubDate>Tue, 21 Feb 2012 12:46:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/1127107</guid>
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    <item>
      <title>Makroskopisches Verkehrssicherheitsmodell - Anwendung auf Kinder, Fahranfaenger, Senioren / Macroscopic traffic safety model - applicability to children, inexperienced road users and senior citizens</title>
      <link>https://trid.trb.org/View/944141</link>
      <description><![CDATA[Um Verkehrssicherheitsmassnahmen rechtzeitig zu initiieren, ist es wichtig, Daten zur Entwicklung moeglicher Problemgruppen zu erhalten. Hierzu wird das von der BASt entwickelte makroskopische Verkehrssicherheitsmodell fuer Deutschland im Rahmen des Projekts auf die Gruppen Kinder, Fahranfaenger und Senioren angewendet. Als Ergebnis stehen detaillierte Prognosen zur Entwicklung des Unfallgeschehens von Kindern, Fahranfaengern und Senioren bis 2008 zur Verfuegung. Darueber hinaus werden Moeglichkeiten, die zu erwartende Entwicklung positiv zu beeinflussen, aufgezeigt und quantifiziert. ABSTRACT IN ENGLISH: For a timely introduction of traffic safety measures, it is important to acquire data permitting an upgrading of potentially problematic groups. For this purpose, a macroscopic, German traffic safety model developed by the Federal Highway Research Institute is being applied to the groups comprising children, inexperienced road users and senior citizens. The results obtained in this project until 2008 will allow detailed forecasts of accident scenarios involving these groups. This project is also meant to reveal possibilities of improving and quantifying expected trends.]]></description>
      <pubDate>Thu, 07 Oct 2010 11:13:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/944141</guid>
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    <item>
      <title>Identifying Behaviors and Situations Associated With Increased Crash Risk for Older Drivers</title>
      <link>https://trid.trb.org/View/897748</link>
      <description><![CDATA[This report reviews published literature and analyzes the most recent Fatality Analysis Reporting System (FARS) and National Automotive Sampling System (NASS)/General Estimates System (GES) data to identify specific driving behaviors (performance errors), and combinations of driver, vehicle, and roadway/environmental characteristics associated with increased crash involvement by older drivers. The analyses reveal, in considerable detail, the contemporary (2002−2006) crash experience of older drivers on streets and highways in the United States. The over- and under-involvement of drivers ages 60-69, 70-79, and 80+ in various crash types has been highlighted through tabular summaries, graphs, and accompanying discussion. For subsets of the two-vehicle crash data within each national database, crash involvement ratios based on comparisons of at-fault to not-at-fault drivers within groups of drivers from 20 to 80 and older, segregated in 10-year cohorts, provide further exposure-adjusted estimates of the magnitude of particular risk factors.]]></description>
      <pubDate>Mon, 03 Aug 2009 15:27:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/897748</guid>
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    <item>
      <title>Marginal effect of increasing ageing drivers on injury crashes</title>
      <link>https://trid.trb.org/View/876538</link>
      <description><![CDATA[The safety effects of the aging driving population have been a topic of research interests in health and transportation economics in recent years due to the aging of the baby boomers. This study adds to the current knowledge by examining the marginal effects of changing the driver mix on injury crashes using data from the Canadian Province of Alberta between 1990 and 2004. Results from a Poisson regression model reveal that increasing the number of young and aging drivers will result in an increase in the number of injury crashes whereas increasing the number of middle-aged drivers will result in a reduction. These results are in contrast to those obtained in a previous study on the marginal effects of changing the driver mix on fatal crashes in the Australian State of Queensland and some possible explanations for the differing results are provided.]]></description>
      <pubDate>Tue, 30 Dec 2008 12:36:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/876538</guid>
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      <title>WHAT DO DRIVING ACCIDENT PATTERNS REVEAL ABOUT AGE-RELATED CHANGES IN VISUAL INFORMATION PROCESSING? A COMMENTARY ON: CHARACTERISTICS OF MOTOR VEHICLE CRASHED RELATED TO AGING. IN: MOBILITY AND TRANSPORTATION IN THE ELDERLY</title>
      <link>https://trid.trb.org/View/697266</link>
      <description><![CDATA[This paper discusses previous studies reported where the analysis of automobile crash data in the State of Pennsylvania is consistent wit previous epidemiological studies of the relationship between advancing adult age and driving safety. Most noteworthy among the conclusions of that study are that: (a) older drivers are more likely to die in automobile accidents due to their increasing frailty; and (b) the lack of reliable risk exposure data greatly limits our ability to interpret age-related trends in the accident data base.  The previous study did an excellent job in presenting the case for the above conclusions. Especially interesting is the statistical manipulation of risk exposure by limiting some of the analyses to accidents occurring between 9 a.m. and 4 p.m.-the time of day when most middle-aged persons are at work and when older drivers tend to be over-represented as vehicular occupants and drivers. However, there is another major conclusion that can be drawn that is somewhat understated in the study.  Namely, there is evidence in the data that the causes of accidents among older drivers are shifting away from reckless behaviors such as speeding and alcohol consumption to a new set of causes characterized by limitations in drivers' visual information processing efficiency.]]></description>
      <pubDate>Fri, 02 Apr 2004 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/697266</guid>
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