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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Attitude-change-based road safety re-education in Catalonia: Recidivism, severity, and changes in officially recorded sanction profiles</title>
      <link>https://trid.trb.org/View/2773232</link>
      <description><![CDATA[This study examined differences in the frequency, severity, and distribution of officially recorded traffic sanctions before and after attendance at attitude-change-based road safety awareness and re-education courses integrated into the Penalty Point System in Catalonia. A retrospective longitudinal pre–post design was applied to administrative records from the complete 2017 cohort of 14,022 course participants. The main sanction-profile analyses focused on 9,886 drivers with at least one pre-course sanction, accounting for 36,546 pre-course and 23,022 post-course sanctions. Period-by-category differences were examined using Pearson chi-square tests of homogeneity, Cramér’s V, and aggregate log-linear Poisson models. During follow-up, 37.1% of drivers with previous sanctions had no further officially recorded sanction, and the total sanction count in this group was 37.0% lower. Alcohol- and drug-related sanctions showed the largest relative decline (−77.9%), followed by safety-device and driver-related sanctions. Mobile phone-related sanctions declined by 62.0%. Speeding showed a smaller reduction (−17.2%) and represented a larger share of the post-course profile. Significant distributional differences were found for the main offence categories, offence severity, driver-related offence subtypes, and serious speeding subtypes (Cramér’s V = 0.128–.210). Sanctions recorded in average-speed enforcement sections and urban access areas increased despite declines in other speeding categories. These findings show that a single recidivism indicator would conceal meaningful differences in offence type, severity, and speeding context. Because the study was observational, lacked a comparison group, and relied on detected offences, the patterns cannot be attributed causally to course attendance or interpreted as direct evidence of behavioral or attitudinal change.]]></description>
      <pubDate>Wed, 23 Sep 2026 15:55:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2773232</guid>
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    <item>
      <title>Merged Crash Data and DMV Intervention Programs</title>
      <link>https://trid.trb.org/View/2725248</link>
      <description><![CDATA[Risky behaviors are recognized by the Oregon Department of Transportation’s (ODOT) Transportation Safety Action Plan as a contributor to fatal and severe traffic injury. It is the objective of the DMV’s risky driver diversion program to reduce the frequency of crashes caused by repeat traffic offenders by removing them from the roadway until they are deemed safe to return. Until recently, based on research performed by ODOT’s research office and the Oregon Driver & Motor Vehicle Services supported by the National Highway Traffic Safety Administration (NHTSA), it was not possible to robustly evaluate how ODOT’s risky driver diversion program strategies such as the habitual traffic offender (HTO) program, the Oregon driver improvement program (DIP), the driving under the influence of intoxicants (DUII) program, and the at-risk driver program perform in making Oregon streets safely. There is a need to evaluate the effectiveness of these programs to maximize  efforts to improve the safety of road users in Oregon.

This evaluation will confirm which programs are producing positive safety improvements. Policy recommendations for shifting the triggering thresholds for certain diversion programs or modifying their delivery in other ways based on the risk profiles of Oregon drivers, specifically the relationships between citations and crashes experienced in Oregon, will be developed.]]></description>
      <pubDate>Wed, 08 Jul 2026 16:03:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2725248</guid>
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    <item>
      <title>Association between traffic reoffending and type of official points recovery course (partial, full, and court-mandated): Evidence from a three-year follow-up</title>
      <link>https://trid.trb.org/View/2709551</link>
      <description><![CDATA[Traffic recidivism is a major contributor to road accidents, injuries, and fatalities, highlighting the need for effective interventions aimed at reducing risky driving behavior. This study analyses objective records of traffic sanctions and penalty point losses to examine the long-term effectiveness (three-year follow-up) of official driving license points recovery courses within the Spanish Points Penalty System (PPS) in preventing repeat traffic offences. The sample comprised 140 offender drivers who completed one of three official recovery courses: 55 attended a partial recovery course, 35 completed a full recovery course after losing all license points, and 50 participated in a court-mandated recovery course. Objective data on traffic sanctions and penalty point losses were collected for the three years following course completion. Descriptive analyses indicated high overall recidivism. Data provided by the Dirección General de Tráfico (DGT) showed that 71.43% of participants received at least one traffic sanction, and 47.86% lost at least one penalty point during the follow-up period. However, reoffending patterns varied significantly across course types. Drivers attending partial recovery courses showed the highest recidivism, with 83.64% receiving at least one sanction and 72.73% losing penalty points. Participants in full recovery courses also exhibited high sanction rates (80%), although a substantially lower proportion experienced further point loss (28.57%). In contrast, drivers enrolled in court-mandated courses showed lower recidivism rates, with 52% receiving at least one sanction and 34% experiencing point loss. Overall, long-term reoffending was more strongly associated with the type of course attended than with sociodemographic characteristics or substance use. Court-mandated courses were associated with lower levels of sanctions and penalty point losses over the three-year follow-up, potentially reflecting the stronger legal consequences associated with these interventions. Full recovery courses showed patterns consistent with partial behavioral adaptation, with participants tending to shift towards less severe infractions rather than achieving full compliance. Finally, partial recovery courses appeared less effective, possibly due to lower perceived deterrent value or their use as a procedural mechanism for point restoration rather than as a rehabilitative intervention. From this perspective, the higher effectiveness of court-mandated courses may be explained not only by their educational and punitive components, but also by the broader legal consequences experienced by offenders, including community service orders or custodial sentences.]]></description>
      <pubDate>Thu, 18 Jun 2026 16:34:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709551</guid>
    </item>
    <item>
      <title>Behavioural Factors Contributing to Offences Committed by Bus Crews in Sri Lanka's Private Bus Transport Sector</title>
      <link>https://trid.trb.org/View/2659311</link>
      <description><![CDATA[Behavioural issues among bus crews negatively impact passenger satisfaction, safety, and the overall efficiency of the public transport system. Bus crews in the private bus transport sector frequently commit offences, leading to public complaints. This study investigates the behavioural factors contributing to offences committed by both drivers and conductors in inter-provincial private bus transport in Sri Lanka. Using a quantitative research approach, a questionnaire survey was conducted to identify these behavioural factors through exploratory factor analysis. The findings reveal four key factor components: workplace conditions and operational pressures, deficiencies in crew management and training, employee welfare challenges, and stressors and behavioural tendencies. These factors play a significant role in influencing crew behaviour and leading to offences. This study provides valuable guidance for transport policymakers and both government and private sector stakeholders to enhance the quality of bus transportation and implement measures that improve the well-being of bus crews and passengers.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659311</guid>
    </item>
    <item>
      <title>Analysis of duration between crashes among repeatedly crash-involved drivers using alternate unobserved-heterogeneity modeling approaches</title>
      <link>https://trid.trb.org/View/2673274</link>
      <description><![CDATA[Repeatedly crash-involved drivers, due to their short inter-crash durations and high crash frequencies, represent a critical risk to public safety. Identifying the significant factors influencing crash recurrence is therefore of fundamental importance for reducing crash frequency. Using traffic accident records from a city in Inner Mongolia, China, from 2014 to 2023, the authors analyze drivers involved in two or more crashes and examine the factors influencing their inter-crash durations. To address unobserved heterogeneity, two alternate modeling approaches are applied: a correlated random-parameters survival model with mean heterogeneity and a latent-class survival model based on class probability functions. Both models effectively capture multilayered unobserved heterogeneity of the crash data. The correlated random-parameters model identifies heterogeneous effects for spring and novice drivers, with a strong correlation between random parameters (0.746, p < 0.001) significantly influencing crash intervals. Other factors—such as previous PDO crashes, young drivers, truck driving, hilly terrain, median barrier, wet roads, and darkness with lights unlit—exert significant but homogeneous effects. The latent class survival analysis model identifies two distinct latent classes (Latent Class 1 with class membership probability of 0.577 and Latent Class 2 with class membership probability of 0.423) at a 0.001 significance level. And substantial differences are observed in the effects of explanatory variables on accident recurrence intervals across the classes. For instance, previous PDO crashes shorten recurrence durations in Class 1 (−0.789) but lengthen them in Class 2 (0.788); similarly, the wet-road indicator reduces recurrence in Class 1 (−0.815) yet increases it in Class 2 (1.040). Furthermore, estimation results from both heterogeneity-based models indicate that most fixed-effect variables share consistent directional signs, However, the average effect size differs substantially. For example, the effect of the Low visibility indicator is 1.199 in the random-parameters model compared to 0.189 in Class 2 of the latent-class model, while darkness with lights unlit yields −0.526 and −0.250 (Class 2), respectively. By applying two advanced econometric modeling approaches, this study offers a novel theoretical perspective on capturing heterogeneity in inter-crash durations among repeatedly crash-involved drivers. Meanwhile, the key determinants identified through these models provide a robust scientific foundation for developing precision risk-intervention strategies, thereby supporting more targeted behavioral interventions for high-risk driver groups.]]></description>
      <pubDate>Wed, 18 Mar 2026 08:59:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673274</guid>
    </item>
    <item>
      <title>Impaired Driving Tracking System</title>
      <link>https://trid.trb.org/View/2669649</link>
      <description><![CDATA[This report gives a framework for developing and implementing an impaired driving tracking system (IDTS) that supports timely, accurate, and coordinated data sharing among criminal justice and traffic safety for law enforcement, licensing agencies, courts, and other State stakeholders. IDTS monitors impaired driving offenses and recidivism rates. Through a literature review and discussions with stakeholders, 2M Research identified key strategies and compiled a set of best practices for establishing and managing IDTS. States seeking to create or improve their systems may use these best practices to inform planning, design, and implementation. Overcoming common challenges through central coordination, sustained funding, ongoing training, and interagency collaboration is essential for tracking repeat offenders, enhancing resource allocation, and supporting better-informed policy decisions.]]></description>
      <pubDate>Fri, 20 Feb 2026 14:36:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669649</guid>
    </item>
    <item>
      <title>Characteristics Extraction and Increasing Block Fine Modeling for Repeated Speeding Behaviors</title>
      <link>https://trid.trb.org/View/2407627</link>
      <description><![CDATA[To efficiently deter the repeated speeders who are frequently fined but continue to commit the violation, this study attempts to investigate the characteristics of the repeated speeding behaviors and propose an increasing block fine modeling approach. Based on the off-site law enforcement data collected from the Deyang City, three speeding ranges (low (≤20%), medium (20%–50%), and high (≥50%)) were considered, and then the characteristics of repeated speeding behaviors were extracted. After that, the cost-benefit theory was introduced to develop an increasing block fine model by taking into account the speeding range and frequency. Considering the minimum total number of speeding as the goal and the economic cost as the constraint, an optimization model of increasing block fine was developed. The pattern search algorithm was used to solve the developed optimization model to determine the best number of blocks and corresponding fine within each speeding range. Finally, a case study was conducted to validate the performance of the developed model. The results show that for the repeated speeders, the percent of the low speeding behavior is the highest, and the speeding behaviors largely occur during the daytime. Furthermore, within the increasing block fine mechanism, the reduction rates of medium and high speeding behaviors are clearly higher than that of the low speeding behavior. The findings of this study offer a fresh viewpoint on the repeated speeding intervention and enable the speeding enforcement more equitable.]]></description>
      <pubDate>Wed, 27 Aug 2025 08:25:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407627</guid>
    </item>
    <item>
      <title>The influence of past experiences, psychological dysfunction, and problematic mentalities: An investigation into the comprehensive nature of drink-driving deterrence</title>
      <link>https://trid.trb.org/View/2569967</link>
      <description><![CDATA[Despite ongoing efforts to deter drink-driving, it remains a significant contributor to fatal vehicle crashes. This study aimed to investigate the influence of at-risk psychological traits, alcohol-related experiences, and problematic mentalities towards the deterrence of drink-driving. An online survey was shared with a sample of Australians who use alcohol (N = 597), and the responses were analyzed using cluster, comparative, and correlational-based analyses. Two K means cluster analyses were independently conducted, and each exhibited three unique clusters, according to the degree of psychological and experiential risk for future drink driving. Chi-square tests showed that the high-risk psychological group was approximately 10 times more likely to also categorize in the high-risk experiential group. Comparative analyses demonstrated that participants’ mentality towards alcohol and drink-driving significantly differed across the cluster groups, relative to the degree of psychological and experiential risk. Split-file correlations also indicated that the relationship between risk perceptions and perceived drink-driving likelihood was dependent on which cluster group was being examined. Finally, bivariate correlations and structural equation modeling confirmed potential hierarchical relationships between prior experiences with alcohol, psychological traits, alcohol and drink-driving mentalities, and perceived drink-driving likelihood, in descending order. Overall, this study demonstrated that at-risk experiential and psychological factors may determine the level of influence that risk perceptions (and thus deterrence initiatives) have towards offending behavior. The findings also suggested that deterrence of drink driving is multi-faceted and may require additional interventional approaches outside of traditional enforcement initiatives. Relevant authorities may therefore benefit from more collaborative and comprehensive approaches to reduce the prevalence of drink-driving behavior.]]></description>
      <pubDate>Wed, 20 Aug 2025 11:57:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2569967</guid>
    </item>
    <item>
      <title>Recidivism and Crash Risk Among California’s Drug-Involved DUI Offenders</title>
      <link>https://trid.trb.org/View/2566888</link>
      <description><![CDATA[The present study builds upon prior work by Marowitz (1996) by examining the crash and recidivism risk of alcohol-focused versus drug-involved driving under the influence (DUI) offenders. Although drug-involved DUI offenders remain a relatively small proportion of all DUI offenders, this proportion is rising, as is the proportion of impaired-driving fatal crashes involving drugs. Using a cohort of offenders arrested during calendar years 2014 through 2017, comparisons were drawn for the one-year periods pre- and post-arrest. Drug-involved offenders tended to have more problematic driving histories in the year prior to their index offense, as compared to alcohol-focused offenders. Even taking account of these differences in pre-arrest behavior, drug-involved offenders continued to have more problematic driving in the year subsequent to their index offense: they were twice as likely to be involved in a crash, and approximately 30% more likely to recidivate. A particularly powerful predictor of recidivism was whether or not a pre-conviction administrative per se (APS) license suspension – only available in instances where an offender is above the per se blood alcohol concentration (BAC) limit, or refuses a chemical test – was imposed. Offenders convicted of a DUI offense, but against whom no APS license action was initiated, were more than three times as likely to recidivate compared to offenders against whom an APS action was taken.]]></description>
      <pubDate>Thu, 26 Jun 2025 16:15:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2566888</guid>
    </item>
    <item>
      <title>Engineering countermeasures for red-light running: a state-of-the-art review</title>
      <link>https://trid.trb.org/View/2563050</link>
      <description><![CDATA[]]></description>
      <pubDate>Tue, 10 Jun 2025 14:47:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2563050</guid>
    </item>
    <item>
      <title>Driving under the influence: An analysis of second-time offenders and intervention success, Turkey sample</title>
      <link>https://trid.trb.org/View/2507282</link>
      <description><![CDATA[Driving under the influence (DUI) of alcohol is a major public health issue that results in significant mortality, morbidity, and economic costs. Despite various penalties and interventions, DUI remains prevalent. This study examines the demographic characteristics, educational status, and involvement in motor vehicle accidents of second-time DUI offenders, aiming to identify factors influencing the success of educational interventions. Between 2018 and 2023, 151 individuals whose driver’s licenses were suspended for a second DUI offense participated in this study. All participants applied to the Adıyaman Provincial Health Directorate, located in the southeast region of Turkey, to regain their licenses. Data were collected from application documents and digital records during and after the educational program, which included identity information, demographic characteristics, reasons for alcohol consumption, license duration, education level, educational success, frequency of alcohol use, and behavior under the influence. Penalties and traffic accidents in the last 5 years were also recorded. Participants with a high school education and above had a significantly higher success rate in the educational program (P = .03). Those without penalties (P = .001) and those not involved in traffic accidents (P = .006) also showed higher success rates. Despite its limitations, this study shows that second-time DUI offenders who have previous traffic tickets or accidents are less likely to succeed in educational interventions. These findings suggest the need for tailored training programs, extended durations, and personalized evaluations to improve outcomes for these high-risk groups. Future research should explore prospective studies to confirm these results and guide intervention strategies.]]></description>
      <pubDate>Tue, 18 Mar 2025 15:48:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2507282</guid>
    </item>
    <item>
      <title>Understanding high-risk traffic offenders using infringement, debt, and crash data</title>
      <link>https://trid.trb.org/View/2509268</link>
      <description><![CDATA[Recidivist offenders are over-represented in Queensland road crash statistics, and the fact that they are recidivists suggests that they are largely undeterred by current road safety countermeasures. This study used data on traffic offence histories, crash involvement, and debt recovery to examine the profiles of different recidivist traffic offender groups. Results showed that all of the recidivist offender groups have higher rates of crash involvement compared to the general licensed driving population. The results highlight the importance of not treating recidivists as a homogenous group and acknowledging that there is no ‘one size fits all’ solution for repeat traffic offenders. There are opportunities for a more focused and bespoke approach to be taken to traffic offence management and prevention. Novel interventions need to be investigated including using offence profiles to communicate with offenders about potential programs and develop other approaches to behaviour change.]]></description>
      <pubDate>Thu, 13 Feb 2025 09:06:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2509268</guid>
    </item>
    <item>
      <title>Serious motorcycle crashes: does criminal and traffic history predict involvement?</title>
      <link>https://trid.trb.org/View/2509166</link>
      <description><![CDATA[Fatal crashes involving motorcycles account for approximately 27 percent of lives lost on Queensland roads since 2022, despite motorcycles comprising approximately four percent of vehicle registrations. Statistics also demonstrate motorcycles are overrepresented in serious injury and fatal crashes where the rider is at fault. By interrogating data from fatal and all serious injury motorcycle crashes investigated by the Queensland Police Service (QPS) Forensic Crash Units (FCU) for calendar years 2022, 2023 and up to 28 February 2024, this study aimed to examine whether there is a nexus between rider criminal history and traffic history and propensity to be at fault in a crash. The findings show at fault riders are more than four times more likely to have pre-existing criminal history, over four times more likely to have both criminal and traffic history and almost four times more likely to be not appropriately licensed than not at fault riders.]]></description>
      <pubDate>Thu, 13 Feb 2025 09:05:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2509166</guid>
    </item>
    <item>
      <title>Driver’s journey from historical traffic violations to future accidents: A China case based on multilayer complex network approach</title>
      <link>https://trid.trb.org/View/2485537</link>
      <description><![CDATA[Traffic violation records serve as key indicators for predicting drivers’ future accidents. However, beyond statistical correlations, the underlying mechanisms linking historical traffic violations to future accidents remain inadequately understood. This study introduces a research framework to address this gap: Using Propensity Score Matching and an adapted mutual information-based feature selection algorithm to precisely identify correlations and optimal time windows between drivers’ historical traffic violations and future accidents. A multilayer complex network approach was then applied to abstract and model the progression from drivers’ historical traffic violations to subsequent accidents, revealing intrinsic patterns through adapted network analysis metrics and ultimately uncovering underlying mechanisms. Actual data from over 17,000 drivers in Shenzhen, China, spanning the period of 2010 to 2020, was utilized. Results revealed significant heterogeneity among driver subtypes with various driving license types regarding optimal time windows and key traffic violations indicative of future accident risks. A universal “Stable Defect Effect” was identified across all driver subtypes, characterized by persistent driving-related deficiencies resistant to temporal decay and penalties. This effect’s gradual formation and maturation appear to govern the progression from traffic violations to future accidents. In addition, multilayer complex network models demonstrated significant potential in accident risk studies, particularly in providing valuable latent information by overcoming the limitations of accident data samples.]]></description>
      <pubDate>Mon, 27 Jan 2025 15:11:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2485537</guid>
    </item>
    <item>
      <title>Research and Application Analysis of Stepwise Incremental Fine Model for Speeding Behavior</title>
      <link>https://trid.trb.org/View/2407619</link>
      <description><![CDATA[In order to effectively restrict repeated speeding behaviors, this paper first divides the speeding range into three speeding ranges, including low, medium, and high speeding according to the law-enforcement in China. Based on this, the authors further build the basic framework of a stepwise incremental fine model (SIFM) and a stepped incremental fine model based on the principle of stepped pricing with the minimum total number of speeding times as the objective and the economic cost as the constraint. In addition, the response of drivers to changes in the fine amount was examined using the price elasticity theory. The study also analyzes the application of the stepped incremental fine model using Deyang City as an example. The results show that the stepped incremental fine model can effectively target the speeding drivers and their behavioral characteristics, which then ensures the model could be accepted by most speeding recidivists. The setup of the model is scientific and reasonable.]]></description>
      <pubDate>Tue, 31 Dec 2024 09:04:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407619</guid>
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