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    <title>Transport Research International Documentation (TRID)</title>
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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>Dangerous ground or peaceful coexistence? Analysing cycling behaviour adaption on shared paths using crowdsourced GPS cycling data</title>
      <link>https://trid.trb.org/View/2706155</link>
      <description><![CDATA[Walking and cycling are increasingly promoted as sustainable and health-enhancing modes, yet rising volumes in shared urban spaces intensify interactions and perceived conflict potential. While numerous studies show that pedestrian–cyclist interactions rarely result in safety-critical incidents, behavioural adaptations, such as speed reduction and crash avoiding manoeuvres, plays a key role in mitigating risk and increasing perceived safety. As most existing research relies on data-intensive methods such as video observations or simulations, this paper explores the potential of large-scale GPS cycling data to analyse conflict potential and behavioural adaptation in areas shared spaces used by pedestrians and cyclists. Considering the pedestrian zone ‘Prague Street’ in Dresden, Germany, as a case study, we combine GPS trajectories from the 2024th CITY CYCLING campaign with a time-based proxy data for pedestrian density. The results show clear behavioural adaptation: during periods of high pedestrian activity, cyclist do not only use other routes, but cycling volumes decrease and average speeds are reduced by approximately 5–8 km/h. The findings confirm established relationships between density, speed, and conflict mitigation, while highlighting both the opportunities and limitations of GPS based approaches. The study demonstrates that such data can serve as a scalable screening tool for assessing behavioural adaptation and potential risk in shared spaces. We further propose a model considering most relevant data to comprehensively analyse risk potential in near future.]]></description>
      <pubDate>Wed, 10 Jun 2026 09:05:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706155</guid>
    </item>
    <item>
      <title>Driver visual attention and in-vehicle touchscreen: the role of short training session</title>
      <link>https://trid.trb.org/View/2706362</link>
      <description><![CDATA[The growing integration of in-vehicle centre stack touchscreens has enhanced driver access to information and control systems but raised significant safety concerns due to increased visual distraction. This study investigates whether a short pre-drive training session can mitigate distraction and improve driver interaction with in-vehicle touchscreen. Using a driving simulator and eye-tracking technology, 60 licensed Norwegian drivers were assigned to trained and untrained groups to compare visual attention patterns during secondary tasks involving touchscreen use. Results showed that while all participants exhibited high visual demand on the touchscreen, trained drivers demonstrated slightly lower fixation counts, shorter durations, and reduced self-transition probabilities within the touchscreen area, suggesting more efficient and potentially safer interactions. However, these differences were not statistically significant, indicating a limited effect of the short training provided. The findings highlight the complexity of the touchscreen interface and potential of pre-drive touchscreen familiarization in improving visual attention.]]></description>
      <pubDate>Wed, 10 Jun 2026 09:05:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706362</guid>
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    <item>
      <title>Personality traits as predictors of driver behaviour: a comprehensive profiling study of Portuguese drivers</title>
      <link>https://trid.trb.org/View/2706361</link>
      <description><![CDATA[Human factors substantially contribute to road crashes. However, assessing their effects is complex due to the influence of individual characteristics such as personality. Therefore, studies examining the relationship between personality traits and driving behaviour are essential. This study aimed to construct driver behaviour profiles based on these relationships within a large sample of Portuguese drivers. A community sample of 747 licensed drivers, aged under 75 and with at least three years of driving experience, completed an online survey. Instruments included the NEO-Five Factor Inventory-20, the Impulsivity and Sensation Seeking Scale, and the 24-item Driver Behaviour Questionnaire (DBQ). Firstly, multiple linear regressions were conducted considering the three driving behaviour dimensions of the DBQ to support the construction of the profiles. Results indicated that neuroticism, agreeableness, extraversion, impulsivity, and sensation seeking predicted infractions and aggressive driving. Neuroticism, conscientiousness, and impulsivity predicted non-intentional errors, while neuroticism, openness, conscientiousness, and impulsivity were associated with lapses. Even after controlling for age and gender, personality traits remained significant predictors. Secondly, four driver behaviour profiles were constructed using two alternative methodologies: an empirical approach and cluster analysis with k-means. Profiles built using the empirical approach resulted in four groups of drivers characterised by more easily identifiable driving behaviours: prudent, regular, distracted/forgetful, and aggressive drivers. The distracted/forgetful group showed a positive relationship to crash involvement. Overall, the study shows that the complex driver behaviour needs to be carefully grouped.]]></description>
      <pubDate>Wed, 10 Jun 2026 09:05:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706361</guid>
    </item>
    <item>
      <title>Seat Belt Use in 2025 – Overall Results</title>
      <link>https://trid.trb.org/View/2709191</link>
      <description><![CDATA[The national estimate of seat belt use by adult front-seat passengers of passenger vehicles in 2025 was 91.3  percent, not statistically different (at the .05 level) from 91.2 percent observed in 2024. This estimate represents  the percentage of occupants who are belted during an average daylight moment.  Figure 1 displays an increasing trend of seat belt use over the 14-year period 2012 to 2025, contrasted with the  percentages of unbelted passenger vehicle occupant fatalities during daytime.1  The 2025 survey identifed three significant changes in seat belt use compared to 2024 – in the Northeast,  Midwest, and West – as shown in Table 1. Seat belt use continued to be higher in the States where vehicles can be  pulled over solely for occupants not using seat belts (“primary law States”) compared to the States with weaker  enforcement laws (“secondary law States”) or no seat belt laws for adults (Figure 2).  Data collection for 2025 occurred in early June, immediately following the Click It or Ticket campaign.  Compared to 2024, the number of occupants observed increased by 2.5 percent.  These results are from the National Occupant Protection Use Survey (NOPUS), the only survey that provides  nationwide probability-based observed data on seat belt use in the United States. Conducted annually by  NHTSA’s National Center for Statistics and Analysis, NOPUS implemented a redesigned sample in 2024. Details  of the redesign are availabe in the Seat Belt Use in 2024 – Overall Results publication (NCSA, 2025), in the  section titled “The 2024 NOPUS Redesign.”]]></description>
      <pubDate>Mon, 08 Jun 2026 08:32:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709191</guid>
    </item>
    <item>
      <title>Driver Electronic Device Use in 2024</title>
      <link>https://trid.trb.org/View/2701042</link>
      <description><![CDATA[The percentage of passenger vehicle drivers holding cellphones to their ears while driving in 2024 decreased to  1.9 percent from the previous year’s level of 2.1 percent (Figure 1 and Table 1); this was not a statistically  significant decrease. The percentage of drivers speaking with visible headsets while driving remained at 0.5  percent as the previous year (Figure 1 and Table 2). Drivers' visible manipulation of handheld devices increased  from 3.0 percent in 2023 to 4.5 percent in 2024 (Figure 1 and Table 3); this was a statistically significant increase.  These results are from the National Occupant Protection Use Survey (NOPUS), the only nationwide probability based observed data on driver electronic device use in the United States. NHTSA’s National Center for Statistics  and Analysis (NCSA) conducts the NOPUS. The percentages in this research note are interpreted as the  percentages of drivers nationwide during an average daylight moment. NOPUS observes three types of driver electronic device use while driving: “holding phones to their ears,”  “speaking with visible headsets on,” and “visibly manipulating handheld devices.” The results of these  observations follow.]]></description>
      <pubDate>Tue, 12 May 2026 09:47:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701042</guid>
    </item>
    <item>
      <title>Assessing how physical and mental fatigue affect driving speed on the Banda Aceh–Medan highway, Indonesia</title>
      <link>https://trid.trb.org/View/2701233</link>
      <description><![CDATA[This study was motivated by the relatively high number of traffic accidents along Banda Aceh–Medan highway, where driver fatigue has been repeatedly cited as the key contributing factor. The research aimed to determine the extent to which physical and mental fatigue influence driving speed behavior on this long-distance route. A quantitative survey was conducted with 400 drivers, both professional and private, using the validated fatigue scales and Structural Equation Modeling for the data analysis. The results indicated that physical fatigue negatively affects speed stability, while mental fatigue significantly increases speed variability. Nevertheless, both physical and mental fatigue explaining 57.8% of the variance in driving speed. These findings highlight the dual cognitive and physiological dimensions of fatigue, emphasizing its role in impaired speed regulation and increased accident risk. This study contributes to existing knowledge by quantifying the distinct effects of physical and mental fatigue on driving performance in a real-world setting, thereby offering empirical support for targeted fatigue management interventions on long-distance routes.]]></description>
      <pubDate>Mon, 11 May 2026 15:23:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701233</guid>
    </item>
    <item>
      <title>Extracting roadway vertical alignment from USGS LiDAR point cloud data using an Artificial Neural Network based method</title>
      <link>https://trid.trb.org/View/2701235</link>
      <description><![CDATA[Vertical grades and vertical curvature significantly influence traffic safety. However, obtaining accurate and large-scale data on roadway vertical alignment remains a major challenge. This paper presents a cost-effective and efficient method for estimating roadway vertical alignment using publicly available aerial LiDAR data provided by the United States Geological Survey. An Artificial Neural Network (ANN) model was proposed to predict whether a LiDAR point belongs to a vertical curve or a tangent segment. Due to the limited availability of actual roadway vertical alignment data and the substantial data requirements of machine learning models, a synthetic training dataset was generated by systematically varying road grades and segment lengths to represent realistic combinations of tangents, crest and sag curves. This approach ensured that the model was exposed to a wide range of geometric configurations and allowed it to learn generalized relationships between vertical alignment features and their corresponding geometric parameters. The model was then independently evaluated by comparing the vertical alignment estimated from the extracted aerial LiDAR data for two-lane two-way rural roadways, Route 152 in New Jersey and Route 299 in California, with their corresponding actual vertical alignment data. In addition, a case study was conducted on another rural two-lane highway in which the model was used to compute safe speeds for each roadway segment. The resulting speeds were then compared with the posted speed limits along the corridor. The satisfactory estimation results of this study indicate that the proposed approach can be used for conducting large-scale analyses to estimate vertical alignment using publicly available LiDAR data.]]></description>
      <pubDate>Mon, 11 May 2026 15:23:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701235</guid>
    </item>
    <item>
      <title>Early Estimate of Motor Vehicle Traffic Fatalities and Fatality Rate in 2025</title>
      <link>https://trid.trb.org/View/2686630</link>
      <description><![CDATA[A statistical projection of traffic fatalities in 2025 shows an estimated 36,640 people died in motor vehicle traffic  crashes, a decrease of about 6.7 percent compared to the 39,254 fatalities reported in 2024, as shown in Table 1.  The fourth quarter of 2025 represents the 15th consecutive quarterly decline in fatalities beginning with the  second quarter of 2022. If realized, the estimated percentage decrease for 2025 would be the fifth largest in the  recorded history of the Fatality Analysis Reporting System (FARS), bringing total fatalities back to the pre-pandemic levels seen in 2019. Preliminary data reported by the Federal Highway Administration (FHWA) shows  that vehicle miles traveled (VMT) in 2025 increased by about 29.8 billion miles, or about a 0.9-percent increase.  Table 1 also shows the fatality rates per 100 million VMT, by quarter and year. In 2025, the fatality rate dropped  to 1.10 fatalities per 100 million VMT, down from the reported rate of 1.19 fatalities per 100 million VMT in  2024, making it the second lowest fatality rate in recorded history (behind only the 1.08 rate observed in 2014).  For the NHTSA regional differences, all 10 NHTSA Regions are projected to have decreases in fatalities and  fatality rate per 100 million VMT in 2025 as compared to 2024. Also, 39 States, the District of Columbia, and  Puerto Rico are projected to have decreases in fatalities. The fatality counts for 2024 and 2025 and the ensuing  percentage change from 2024 to 2025 will be slightly revised when the FARS Final File for 2024 and the FARS  Annual Report File (ARF) for 2025 are made available in early 2027.]]></description>
      <pubDate>Mon, 06 Apr 2026 10:25:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686630</guid>
    </item>
    <item>
      <title>Overview of Motor Vehicle Traffic Crashes In 2024</title>
      <link>https://trid.trb.org/View/2686627</link>
      <description><![CDATA[There were 1,771 fewer people killed in motor vehicle traffic crashes on U.S. roadways during 2024, a 4.3- percent decrease from 41,025 in 2023 to 39,254 in 2024. The fatality rate per 100 million vehicle miles traveled  (VMT) decreased 5.6 percent from 1.26 in 2023 to 1.19 in 2024. It represents the third year-to-year decrease in  both fatalities and fatality rate since 2021. The estimated number of people injured on our roadways decreased in 2024 to 2.42 million, falling 0.8 percent from 2.44 million in 2023. This decrease was not statistically significant. The injury rate per 100 million VMT  decreased 1.3 percent from 75 in 2023 to 74 in 2024.  The estimated number of police-reported traffic crashes increased from 6.14 million in 2023 to 6.18 million in  2024, a 0.7-percent increase which was not statistically significant. VMT for 2024, reported by the Federal  Highway Administration (FHWA), increased 1.5 percent from 3,247 billion in 2023 to 3,294 billion in 2024.]]></description>
      <pubDate>Mon, 06 Apr 2026 10:25:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686627</guid>
    </item>
    <item>
      <title>Distracted Driving in 2024</title>
      <link>https://trid.trb.org/View/2686628</link>
      <description><![CDATA[The National Highway Traffic Safety Administration works to reduce the occurrence of distracted driving and  raise awareness of its dangers. This risky driving behavior poses a danger not only to vehicle occupants but  pedestrians and pedalcyclists as well. Driver distraction is a specific type of driver inattention that occurs when  drivers divert attention from the driving task to focus on some other activity. Discussions regarding distracted  driving often center around cellphones and texting, but distracted driving also includes eating, talking to  passengers, adjusting the radio or climate controls, or adjusting other vehicle controls. A distraction-affected  traffic crash is any traffic crash in which a driver was identified as distracted at the time of the crash.]]></description>
      <pubDate>Mon, 06 Apr 2026 10:25:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686628</guid>
    </item>
    <item>
      <title>To pass or not to pass: a field experiment on human values in interactions with automated vehicles</title>
      <link>https://trid.trb.org/View/2681207</link>
      <description><![CDATA[Vulnerable road users (VRUs), such as pedestrians and cyclists, are at high risk in road traffic, accounting for more than half of all global traffic fatalities. Ensuring safe interactions with highly automated vehicles (AVs) requires understanding and predicting VRUs' behaviour. This study investigated the relevance and predictive role of human values alongside environmental factors in real-world VRU-AV interactions. In a field experiment using a Wizard-of-Oz paradigm, 28 pedestrians and 29 cyclists interacted with an oncoming vehicle in a space-sharing scenario. Human values were assessed both qualitatively and quantitatively, while distance to the vehicle and driving mode (AV vs. manually driven) were manipulated. Results show that numerous human values (e.g. comprehensibility, legal compliance, self-efficacy, relaxedness) were rated as highly relevant, but only values related to relaxed interaction significantly predicted pedestrians' behaviour. Distance predicted interaction behaviour for VRU groups, whereas driving mode had no effect. Overall, the findings highlight the importance of considering both environmental factors and human values. The study demonstrates that values provide a broader perspective for understanding VRU behaviour and informing the design of safe, trustworthy, and acceptable VRU-AV interactions.]]></description>
      <pubDate>Fri, 27 Mar 2026 13:40:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681207</guid>
    </item>
    <item>
      <title>Intelligent Speed Assistance: an on-road evaluation</title>
      <link>https://trid.trb.org/View/2684198</link>
      <description><![CDATA[Although approximately 29% of traffic fatalities involve excessive speed, individual vehicle technology that can reduce speeding has not been widely studied or implemented in the United States (U.S). Starting in 2022, NYC DCAS conducted the largest public pilot of active Intelligent Speed Assistance (ISA) in the U.S., with approximately 400 vehicles equipped with a device that prevents acceleration when the vehicle is traveling faster than a preset threshold over the speed limit (typically 11 mph). Using an “opportunity to speed” framework (i.e., only account for driving time when a driver is traveling at least 5 mph below the speed limit), an analysis of 270 vehicles equipped with ISA showed there was a 64.18% relative decrease in the time driven >11 mph over the posted speed limit following ISA activation compared to before activation. This decrease in time spent speeding was not seen in non-equipped control vehicles. Speeding drive time reduction ranged from ~50% on 25 mph local roads, which have speed safety cameras set to the same enforced speed threshold, to 77% reduction on 50 mph roads. In addition, the impact of ISA on speeding behaviour of habitual speeders in 130 vehicles was similar to that on the primary cohort, indicating active ISA is effective at significantly reducing severe speeding across a wide range of drivers and fleets.]]></description>
      <pubDate>Fri, 27 Mar 2026 13:40:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684198</guid>
    </item>
    <item>
      <title>Supporting success: teaching driving to learners with neurodevelopmental disorders</title>
      <link>https://trid.trb.org/View/2681383</link>
      <description><![CDATA[Driving education can be challenging for individuals with neurodevelopmental disorders (NDDs) due to the various symptoms which accompanies the disorders. However, previous research on NDDs and driving has prioritized cognitive deficits over specific mitigation strategies for driver training. This study aims to explore driving instructors' experiences of teaching individuals with NDDs with the research questions: (i) what challenges do driving instructors experience when working with individuals with NDDs during the process of teaching and learning to drive? and (ii) how do driving instructors address these challenges in terms of teaching methods and strategies? Thirteen certified Swedish driving instructors with experience teaching students with NDDs participated in semi-structured interviews which were analyzed using qualitative content analysis. The findings reveal both cognitive and structural challenges, for example difficulties processing information in various traffic situations and the need for additional resources. The driving instructors emphasized the importance of clear communication and creating a structured and supportive environment. To meet the needs of learners with NDDs, they described using a range of adaptive strategies. These include breaking down tasks into smaller steps, using repetition, giving clear and concrete instructions, and incorporating illustrations and demonstrations to enhance understanding. The results highlight the importance of targeted, individualized support within driver education for learners with NDDs. They also provide practical insights into current teaching approaches and highlight areas for recommended focus. By shedding light on instructional strategies, this study informs both practice and policy, contributing to a more inclusive, effective and accessible driver education system for individuals with NDDs.]]></description>
      <pubDate>Fri, 27 Mar 2026 13:40:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681383</guid>
    </item>
    <item>
      <title>Language matters: an experimental study of language patterns' effects on traffic safety perceptions in Germany</title>
      <link>https://trid.trb.org/View/2679029</link>
      <description><![CDATA[Traffic crashes claim over 1.19 million lives globally each year, yet public support for proven safety measures remains limited. Research suggests that media language patterns may influence public perceptions of traffic violence and policy preferences. This study replicates Goddard et al.'s (2019) experimental design study in the German context, examining how editorial patterns in crash reporting affect responsibility attribution, penalty preferences, and policy support. Using a randomized controlled experiment (N = 1,537), participants read one of three versions of a fictitious news article: status-quo language reflecting common German reporting patterns, agent-focused language avoiding victim-blaming formulations, or agent-focused plus contextual information. Results show that shifting from victim-focused to agent-focused language substantially reduced pedestrian responsibility attribution (from 48.9% to 44.4%) and increased responsibility attributed to the driver (from 43.5% to 48.1%). Adding contextual information enlarged these effects, with driver responsibility attribution reaching 54.8% and pedestrian responsibility attribution dropping to 33.2%. Contextual framing also increased support for structural interventions and reduced support for campaigns appealing to individual behavior. These findings confirm that language patterns in German road traffic collision reporting—including metonymy, passive voice, reflexive verbs, and the lack of context information—systematically shift perceived responsibility toward vulnerable road users. The study demonstrates that more precise language in traffic reporting can increase public support for evidence-based safety policies, suggesting an ethical imperative for improved editorial practices.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:59:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679029</guid>
    </item>
    <item>
      <title>The maximum allowable handlebar disturbance: an indicator for the ex-ante evaluation of cycling fall prevention interventions</title>
      <link>https://trid.trb.org/View/2675142</link>
      <description><![CDATA[Falls due to disturbances are a common cause of serious cycling injuries, yet evaluation approaches to systematically evaluate interventions aimed at improving balance recovery are lacking. Current ex-post evaluations are hindered by sparse crash data, and existing ex-ante approaches often lack generalizability or rely on surrogate measures that are not validated against fall risk. This study introduces the Maximum Allowable Handlebar Disturbance (MAHD), a novel performance indicator that quantifies the largest handlebar disturbance a cyclist can recover from without falling. The MAHD captures the cyclist's resilience to disturbances and provides a direct, interpretable measure of intervention effectiveness. We propose two methods for determining MAHD: (1) controlled treadmill experiments with induced handlebar disturbances and safe fall conditions and (2) simulations using bicycle dynamics and cyclist control models. Together, these methods allow quantitative ex-ante evaluation and systematic comparison of interventions targeting cyclist control, bicycle design, and infrastructure features such as curbs and road shoulders. With further validation, the MAHD offers practical value for researchers, engineers, and policymakers seeking to design safer bicycles, training programs, and road environments and improve evidencebased resource allocation. In the future, this could reduce fall-related cycling injuries.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:59:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675142</guid>
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