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    <title>Transport Research International Documentation (TRID)</title>
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    <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>
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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>Observational Study of Cell Phone and Texting Use Among California Drivers 2012 and Comparison to 2011 Data</title>
      <link>https://trid.trb.org/View/2635338</link>
      <description><![CDATA[This methodological report describes survey research and data collection methods employed for the second Observational Survey of Cell Phone and Texting Use among California Drivers study conducted in 2012. This study was conducted by Ewald & Wasserman Research Consultants (E&W) on behalf of the California Office of Traffic Safety and the Safe Transportation Research and Education Center at University of California at Berkeley. The survey’s goal was to obtain a statewide statistically representative observational sample of California’s cell phone use behaviors, focusing on mobile device use and compare it to 2011 survey data. Vehicle drivers were observed at controlled intersections, such as traffic lights and stop signs, using a protocol similar to the National Occupancy Protection Use Study methodology published by the National Highway Traffic Safety Administration. The sample frame included a total of 5,664 vehicle observations from 129 sites. The total percentage of distracted driving by electronic devices (holding a phone to the ear, manipulating a hand-held electronic device while driving, or talking on a hand-held device) observed increased to 6.2% in 2012 from 4.2% in 2011. California’s baseline level of cell phone use and driving will be a critical metric over the years as traffic safety stakeholders mobilize to conduct high visibility enforcement campaigns, explore new policies, expand educational programs, and engineer countermeasures to increase safety on the roads.]]></description>
      <pubDate>Mon, 23 Feb 2026 16:30:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635338</guid>
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    <item>
      <title>Multitasking While Walking: How Green Noise and Texting Alter Gait</title>
      <link>https://trid.trb.org/View/2647414</link>
      <description><![CDATA[This study examines the combined effects of green noise and texting on gait to understand how multitasking and auditory stimuli affect walking stability. Green noise, associated with relaxation, was the environmental stimulus; texting the cognitive distractor. We hypothesized texting would reduce cadence and stride length, with green noise either stabilizing or disrupting gait. Ten participants walked on a treadmill under five conditions: normal walking (NW), no texting/sound (WNTNS), sound only (WNTS), texting only (WTNS), and texting with sound (WTS). Gait parameters—step length, step width, single support time, and terminal double support time—were analyzed. Step length decreased in WNTS (p?=?.0106) and WTS (p?=?.0115) versus NW; step width increased in WTNS (p?<?.0001). Single support time was prolonged in WTS; terminal double support time increased in all conditions vs. NW (p?=?.0435). Cognitive and environmental distractions alter gait, potentially increasing fall risk. Further research should explore how green noise and texting influence walking stability.]]></description>
      <pubDate>Mon, 26 Jan 2026 08:41:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647414</guid>
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    <item>
      <title>Avoidance behavior and movement characteristics of pedestrians under mobile phone distraction</title>
      <link>https://trid.trb.org/View/2643822</link>
      <description><![CDATA[Smartphone use while walking has become increasingly prevalent, which significantly affects pedestrian safety and increases the risk of traffic accidents, especially in avoidance scenarios. Therefore, to reveal the avoidance mechanism and gait features under mobile phone use distractions, a series of walking experiments under varying distraction conditions including no phone use, text-browsing, message-sending, and video-watching were conducted. Multiple types of data such as physiological signals including the electrocardiogram (ECG) and electrodermal activity (EDA), and high-precision motion trajectories were synchronously obtained. To characterize the entire avoidance process, three critical positions including the start point, maximum point and end point are identified in this paper. The results show that distracted pedestrians initiate evasive maneuvers when they are close to obstacles, whereas non-distracted pedestrians prefer to avoid obstacles in advance. Moreover, the right-side avoidance strategy is adopted by most pedestrians under different distraction levels. Physiological signal indicates that distracted pedestrians experience elevated levels of psychological stress. Besides, the walking speed is significantly decreased in distracted circumstances. The gait analysis demonstrates that distracted pedestrians exhibit shorter step length, larger step width and longer step time during the avoidance phase. Furthermore, the real-time messaging task consumes large cognitive resources of pedestrians, which has the most pronounced impact on pedestrian safety. The study elucidates how the use of smartphones affects the avoidance mechanism of pedestrians, which can help develop intervention measures and safety strategies to reduce the risk of distracted walking in public places.]]></description>
      <pubDate>Thu, 15 Jan 2026 14:31:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643822</guid>
    </item>
    <item>
      <title>Use of mobile phone data to measure behavioral response to SMS evacuation alerts</title>
      <link>https://trid.trb.org/View/2630771</link>
      <description><![CDATA[This study examines behavioral responses after mobile phone evacuation alerts during the February 2024 wildfires in Valparaíso, Chile. Using anonymized mobile network data from 580,000 devices, the authors analyze population movement following emergency SMS notifications. Results reveal three key patterns: (1) initial alerts trigger immediate evacuation responses with connectivity dropping by 80% within 1.5 h, while subsequent messages show diminishing effects; (2) substantial evacuation also occurs in non-warned areas, indicating potential transportation congestion; (3) socioeconomic disparities exist in evacuation timing, with high-income areas evacuating faster and showing less differentiation between warned and non-warned locations. Statistical modeling demonstrates socioeconomic variations in both evacuation decision rates and recovery patterns. These findings inform emergency communication strategies for climate-driven disasters, highlighting the need for targeted alerts, socioeconomically calibrated messaging, and staged evacuation procedures to enhance public safety during crises.]]></description>
      <pubDate>Mon, 29 Dec 2025 09:35:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2630771</guid>
    </item>
    <item>
      <title>Teen Texting While Driving in Association With All-Driver and Young-Driver Cellphone Laws</title>
      <link>https://trid.trb.org/View/2582335</link>
      <description><![CDATA[Cellphone-related driver distraction claimed 402 lives, over 26,000 injuries, and $10 billion in economic costs including medical costs in 2022 in the United States. Young drivers exhibit a disproportionately higher prevalence of cellphone use while driving and are over-represented in traffic injuries. The authors assessed the impact of all-driver and young-driver cellphone laws on teen drivers' texting behaviors. Participants were 110,193 high school students from the 2013, 2015, 2017, and 2019 state Youth Risk Behavior Survey across 42 states. The authors utilized survey-weighted modified Poisson regression with robust standard errors to estimate the associations. Approximately 54% of high school student drivers reported texting while driving monthly. The presence of both an all-driver texting law and a young-driver cellphone law was not associated with less texting while driving, compared to states without such laws. However, an all-driver comprehensive handheld ban, without a young-driver cellphone law, was associated with a 26% lower prevalence of texting while driving (adjusted prevalence ratio: 0.74, 95% confidence interval: 0.68-0.80) compared to states with an all-driver texting law and a young-driver cellphone law. In contrast, a young-driver cellphone law without an all-driver cellphone law was not associated with less texting (adjusted prevalence ratio: 1.04, 95% confidence interval: 0.98-1.11). Young-driver cellphone laws showed limited effectiveness in reducing texting while driving, while all-driver handheld cellphone bans were effective. The findings underscore the safety benefits of implementing all-driver handheld bans in the 22 states that have yet to enact such legislation.]]></description>
      <pubDate>Fri, 08 Aug 2025 15:29:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582335</guid>
    </item>
    <item>
      <title>Texting, talking, trawling and tunes: Risk perceptions, phone dependence and different types of phone use while driving behaviors</title>
      <link>https://trid.trb.org/View/2551100</link>
      <description><![CDATA[The aim of this study was to investigate the links between specific types of mobile phone use while driving (MPUWD) behaviors (i.e., reading, writing, talking and using media on a hand-held phone) and risk perceptions (i.e., the perceived risk of legal and non-legal sanctions), as well as to understand the influence that a psychological dependence on phone use might have toward these relationships. Using a variety of recruitment initiatives, a total of 821 drivers completed an online questionnaire about their phone use behaviors, MPUWD behaviors, and MPUWD risk perceptions. Participants were eligible if they were aged 18 years or over, held a Queensland (Australia) driver’s license, and owned a mobile phone device. First, a repeated measures ANOVA showed that participants engaged in and perceived each of the four MPUWD behaviors differently. Of note, writing MPUWD behaviors were engaged in the least frequently and perceived as the riskiest, while using media was engaged in the most frequently and perceived as the least risky. Second, cross-tabulations and concurrent chi-square tests showed that there were significant differences in the MPUWD behaviors of pre-defined “likely dependent” phone users, compared to “likely non-dependent” users. In fact, likely dependent users were up to three times more likely to engage in MPUWD behaviors frequently compared to the latter group. Finally, structural equation modeling indicated that likely phone dependency directly predicted risk perceptions, and indirectly predicted MPUWD behaviors via the effects they had toward risk perceptions. The findings of this study have suggested people hold unique perceptions about different types of MPUWD behaviors. It is also suggested that problematic phone use may play a role in not only the engagement in different types of MPUWD, but also the effectiveness of road safety countermeasures aimed at reducing this behavior.]]></description>
      <pubDate>Wed, 11 Jun 2025 10:53:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2551100</guid>
    </item>
    <item>
      <title>Perceived vs. actual multitasking abilities: Predicting texting while driving efficacy and behavior from overconfidence</title>
      <link>https://trid.trb.org/View/2540259</link>
      <description><![CDATA[Whereas numerous studies have reported drivers’ overconfidence in their driving ability, this study examines overconfidence in one’s multitasking abilities operationalized as overestimation (perception relative to one’s actual performance) and overplacement (perception relative to others’ abilities) as predictors of texting while driving (TWD). This study also examines TWD self-efficacy as an explanatory mechanism for the relationship between overconfidence and TWD. A sample of 611 undergraduate students (34 % male, mean age of 19.52 years) from a southwestern US university completed an online task-switching paradigm to assess their multitasking ability and multiple self-report measures of TWD-related constructs. TWD was also measured using phone application data. Results indicated that overconfidence (both overestimation and overplacement) was more strongly related to TWD self-efficacy than self-efficacy to resist TWD. TWD self-efficacy explained the relationships between overconfidence and TWD. Additionally, TWD self-efficacy predicted self-reported and actual TWD above and beyond self-efficacy to resist TWD and vice versa. Actual multitasking ability was not significantly related to actual or self-reported TWD. Overall, these findings provide evidence for the influence of overconfidence in multitasking and two forms of self-efficacy on TWD. Implications as well as future directions for research are discussed.]]></description>
      <pubDate>Wed, 28 May 2025 16:23:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2540259</guid>
    </item>
    <item>
      <title>Youth Distracted Driving Survey: National Results</title>
      <link>https://trid.trb.org/View/2539981</link>
      <description><![CDATA[This document presents the results of a survey, related to cell phone use and distracted driving, of 1,217 young U.S. drivers. Survey questions addressed the frequency of risky behaviors such as texting while driving, phone calls while driving, and social media use while driving. Respondents were also asked about their perception of the danger associated with these risky behaviors. Additional questions focused on seatbelt use, speeding, and driving while impaired. The average age of survey respondents was 16 years, with 50% of the respondents having learner permits and only 19% with full licenses. Of the 1,124 respondents who answered the question regarding phone use while driving, 398 (35%) had used their phone while driving in the past 30 days.]]></description>
      <pubDate>Mon, 28 Apr 2025 08:48:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2539981</guid>
    </item>
    <item>
      <title>Text-aided Group Decision-making Process Observation Method (x-GDP): a novel methodology for observing the joint decision-making process of travel choices</title>
      <link>https://trid.trb.org/View/2521833</link>
      <description><![CDATA[Joint travel decisions, particularly related to social activities remain poorly explained in traditional behavioral models. A key reason for this is the lack of empirical data, and the difficulties associated with collecting such data in the first place. To address this problem, we propose Text-aided Group Decision-making Process Observation Method (x-GDP), a novel survey methodology to collect data on joint activities from all members of a given clique. More specifically, on a Zoom-moderated experiment, participant cliques are asked to coordinate an activity (or set of activities) using a group chat interface. The experiment requires not only the coordination but the execution of the planned activity, thus guaranteeing a real discussion that takes into consideration the preferences and constraints of clique members. Through this method we are able to observe not only the outcome of the choice process, but also the decision-making process itself in a quasi-naturalistic manner, including the alternatives that compose the choice set, individual and clique characteristics that might affect the choice process, as well as the discussion behind the choice via texts. In this paper we introduce the results of an x-GDP survey implementation focusing on joint eating-out activities in the Greater Tokyo Area. Preliminary data analysis clearly illustrates the heterogeneity of the choice processes among groups and how members’ spatiotemporal constraints, individual and relational characteristics, as well as the bargaining process affect choice outcomes. Given the unique characteristics of the collected data, we discuss how x-GDP can be used to (i) identify and categorize group decision-making patterns, (ii) model group decisions explicitly considering the decision-making process, (iii) estimate joint accessibility measures and (iv) analyze choice set generation processes.]]></description>
      <pubDate>Wed, 19 Mar 2025 16:58:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2521833</guid>
    </item>
    <item>
      <title>Effectiveness of unconventional anti-texting messages displayed on dynamic message signs</title>
      <link>https://trid.trb.org/View/2464705</link>
      <description><![CDATA[Dynamic message signs (DMS) are widely adopted to display traffic and safety information. This study investigated the effectiveness of behavioral safety messages, including 10 unconventional anti-texting messages used on DMS. Feedback on the usability and effectiveness of various messages was collected from 120 drivers in an online survey. The influence of ten unconventional anti-texting DMS messages on driver performance was observed using driving simulator experiments with sixty participants. 89% of the respondents reported reading the signs, while 85% thought about the relevance of displayed messages. During the simulator experiment, drivers responded to a significantly lesser number of text messages after observing the anti-texting DMS messages, compared to the segments without any DMS. Both self-perceived opinions and driver performance metrics indicated a positive influence of displaying safety messages on DMS. Group comparisons across age and gender showed that some messages were more effective than others among specific driver groups.]]></description>
      <pubDate>Mon, 27 Jan 2025 15:39:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2464705</guid>
    </item>
    <item>
      <title>State-of-Knowledge on Distracted Driving Due to Portable Electronic Device Use: 2008 – 2022 Update</title>
      <link>https://trid.trb.org/View/2485284</link>
      <description><![CDATA[The National Highway Traffic Safety Administration recognizes that safe driving requires the driver’s attention to be fully on the driving task. The last comprehensive NHTSA-sponsored state-of-knowledge (SOK) report on distracted driving was conducted over 15 years ago. The intervening years have seen a marked growth in the quantity and types of distraction sources and activities. The increase comes from advances in portable electronic device (PED) technology, the many ways drivers interact with them, and advances in measuring these interactions as well as the many ways PEDs affect driver behavior and safety. PED use involves any device easily carried into and out of a vehicle that a driver can use while driving, whether directly with the device independently or through the vehicle’s interface. The latest data from NHTSA’s National Center for Statistics and Analysis (NCSA) showed that 8% of all drivers involved in fatal crashes in 2021 were reported as distracted at the time of their crashes. The latest cost estimate of societal harm caused by distraction-involved crashes, estimated for 2019, was $395 billion. This same report estimates the percentage of all motor vehicle crashes in 2019 attributable to distraction to be 29%, and the percentage of crashes caused by cellphone distraction to be 6.1%. This Traffic Tech identifies what is known about driver distraction behavior due to PED use from 2008 through September 2022.]]></description>
      <pubDate>Thu, 09 Jan 2025 14:35:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2485284</guid>
    </item>
    <item>
      <title>State-of-Knowledge on Distracted Driving Due to Portable Electronic Device Use: 2008 – 2022 Update</title>
      <link>https://trid.trb.org/View/2485283</link>
      <description><![CDATA[This report updates what is known about behavioral aspects of driver distraction due to portable electronic device (PED) use since publication of a 2008 NHTSA state-of-knowledge (SOK) report. This update is a reference for highway safety stakeholders by benchmarking the state of driver PED-use problems from 2008 through September 2022 as defined by four primary focus areas, with each focus area covered in a separate chapter. Two introductory chapters cover the background and purpose of the SOK, key terminology, and research methodologies illuminating each defined problem area. Chapter 3 focuses on driver PED use, estimates of distracted driving prevalence, related characteristics, and driver motivations for driving distracted. Chapter 4 centers on effects of distraction on driver behavior and performance, focusing on driver attention and aspects of performance such as lane position, headway, speed, and reaction time. Chapter 5 involves distracted driving’s effects on safety, describing crash prevalence, characteristics, and risk. Chapter 6 describes behavioral countermeasures for reducing driver distraction.]]></description>
      <pubDate>Thu, 09 Jan 2025 14:35:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2485283</guid>
    </item>
    <item>
      <title>The effects of death awareness and reactance on texting-and-driving prevention</title>
      <link>https://trid.trb.org/View/2307200</link>
      <description><![CDATA[This article reports on a study that considered the influence of death awareness and psychological reactance on the effectiveness of safety messages about texting and driving.  The authors based their study design on the terror management health model and the theory of psychological reactance.  The study used the idea of preventing texting-while-driving with a cohort of young adults (n = 208; age range 18-31 years; 57% female), a group at particular risk for mortality from traffic crashes. A four-item measure (threatened my freedom to choose, tried to make a decision for me, tried to manipulate, tried to pressure; Dillard & Shen, 2005) was used to capture threat-to-freedom perceptions.  A six-item index was used to measure participants’ attitudes toward the texting-and-driving prevention (good, wise, favor-able, positive, desirable, necessary; Dillard & Shen, 2005). The authors found that mortality salience influenced attitudes toward texting-and-driving prevention and behavioral intentions to reduce unsafe driving practices.  The authors also tested the effectiveness of direct messages that include some freedom-limiting concepts; these were found to be more effective than anticipated, based on earlier research findings.]]></description>
      <pubDate>Fri, 15 Nov 2024 09:40:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2307200</guid>
    </item>
    <item>
      <title>Impact of Texting-Induced Distraction on Driving Behavior Based on Field Operation Tests</title>
      <link>https://trid.trb.org/View/2431215</link>
      <description><![CDATA[Driver distraction has proven to be among the top contributory factors in road crashes worldwide. The involvement of drivers in secondary activities, such as phone usage, conversation with co-passengers, reaching for objects, eating, and so forth, have been shown to be significant causes of in-vehicle driver distraction. Text messaging using mobile phones while driving can be detrimental to the primary driving task as it invokes manual, visual, and cognitive distractions. Numerous studies in the past have analyzed the effect of texting on driver distraction using driving simulators. This study followed a field operation test–based approach to simulate real road conditions and evaluate the impact of texting on driving behavior. The study was conducted at the connected autonomous vehicle testbed at the Indian Institute of Technology Hyderabad, using an instrumented test vehicle. Data on gaze parameters and vehicle kinematics were recorded using eye-tracker glasses and a high-end global positioning system (GPS) data logger. Thirty-one participants drove on the pre-defined path at the testbed under baseline and texting conditions. The findings demonstrated that the texting task significantly affected fixation count, mean speed, and lateral acceleration. The texting task led to a reduction in mean fixation counts, mean speed, and mean lateral acceleration by 44.37%, 34%, and 46.72%, respectively. The age and driving experience of the participants had a significant effect on their behavior. The results indicate the critical potential of texting-based driver distraction and suggest the enforcement of stringent rules for texting while driving to create a safer road environment.]]></description>
      <pubDate>Tue, 17 Sep 2024 13:43:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2431215</guid>
    </item>
    <item>
      <title>Text and Voice Message Distraction Detection: A Machine Learning Approach Using Vehicle Trajectory Data</title>
      <link>https://trid.trb.org/View/2397673</link>
      <description><![CDATA[Cellphone usage is often considered to be one of the major causes of distracted driving. Similarly, voice messaging is identified as a potential cause of distracted driving but has received limited attention in the literature. Thus, this study aims to develop supervised machine learning (ML) methods to detect distracted driving events caused by texting and voice messaging using vehicle trajectory data. Vehicle trajectory data was collected from 92 participants who drove a simulated network of the Baltimore metropolitan area using a driving simulator. Different key variables were extracted from the data to construct the features for developing the ML methods, including speed, brake usage, throttle, steering velocity, brake light, and offset from the road center. Several methods, including the support vector machine, k-nearest neighbor, decision tree, neural network, and adaptive boosting (AdaBoost), were examined on the data to achieve the best model. In addition, several metrics were used to assess the performance of the ML models, such as accuracy, sensitivity, precision, the Matthews correlation coefficient, and the area under the receiver operating characteristic curve (AUC-ROC). The results indicated that the AdaBoost algorithm had the best yields, with an accuracy of 74.67% and AUC-ROC of 82.5% on the independent test set. The findings of this study can be directly leveraged to develop in-vehicle driver warning systems to alert drivers with respect to distracted behavior, which lead to the reduction of distracted driving events and an improvement in traffic safety.]]></description>
      <pubDate>Wed, 26 Jun 2024 14:12:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2397673</guid>
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