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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>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Distraction at the Crossroads: Analyzing Pedestrian Behavior and Distraction Prevalence at Signalized Intersections in the District of Columbia</title>
      <link>https://trid.trb.org/View/2562196</link>
      <description><![CDATA[This study examines pedestrian distraction patterns at signalized intersections in Washington, DC, through a comprehensive analysis of survey data and video observations. The research employs multinomial logistic regression analysis of survey response and the Apriori algorithm for video data analysis at 15 representative intersections during peak hours. Initial survey analysis revealed an uneven distribution among distraction levels, with the balanced model showing strong performance in identifying non-distracted individuals. The model identified electronic device usage, noise cancellation technology, and walking in groups as significant predictors of multiple distractions, while older pedestrians were less likely to be distracted. Video analysis through association rule mining revealed location-specific variations in distraction patterns, particularly during afternoon periods when social interaction strongly predicted device usage. These findings provide evidence-based insights for urban planners, suggesting that effective safety interventions should consider both social dynamics and location-specific characteristics at signalized intersections.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562196</guid>
    </item>
    <item>
      <title>A Cost-Effective Vehicle-Probe-Based Signal Management Technology with Microscopic Simulation Evaluation</title>
      <link>https://trid.trb.org/View/2562160</link>
      <description><![CDATA[Most existing signal control systems utilize fixed-location sensors (e.g., loop detectors or roadside cameras), which are constrained by limited spatial coverage and have relatively high installation and maintenance costs. As an alternative method for vehicle detection and traffic management at signalized intersections, it is possible to use vehicle-probe data, collected from smartphones, navigational aids, GNSS receivers, and other types of mobile devices. In this paper, we propose a cost-effective vehicle-probe-based signal management technology to leverage the widely available probe data for traffic signal control. To demonstrate its effectiveness, we then develop a microscopic simulation to compare its performance to a state-of-the-practice system. A candidate “unbalanced” intersection is identified in the City of Riverside and replicated in a microscopic simulation. The performance of the vehicle-probe-based optimized signal control plan is evaluated under the calibrated traffic demand. The simulation results demonstrated that it significantly reduces the length of the eastbound peak-hour queue from 800 m to under 50 m. The average travel time was reduced by 69% for the eastbound and 40% for the westbound, while maintaining a similar level in the northbound and southbound. The vehicles’ emissions were decreased by 32%–52% due to the mitigation of congestion in the major westbound-eastbound direction, resulting in fewer vehicles being halted in queues. Overall, the proposed vehicle-probe-based signal management technology demonstrated significant potential in enhancing traffic efficiency and reducing environmental impact. It provides a low-cost, scalable, and sustainable solution that can be potentially applied to both fixed-time and adaptive signalized intersections.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562160</guid>
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    <item>
      <title>A Survey on Perceptions of Smartwatch Haptic Feedback for Enhancing Automated Vehicle Takeover Decisions</title>
      <link>https://trid.trb.org/View/2480024</link>
      <description><![CDATA[The imperfections in the driving automation system have challenged older adults because the takeover process is cognitively and physically demanding. Due to the wrist being more vibration-sensitive, the haptic display on the smartwatch could be a good option to warn the driver. However, the preference between two vibrotactile patterns, dynamic patterns (vibrating sequences at different locations on the smartwatch) and static patterns (vibrating at certain locations on the smartwatch), is still unclear. Therefore, this study examined the effects of vibrotactile patterns between younger (mean age = 30.97) and older adults (mean age = 69.45) using a national survey. Three hundred forty respondents’ data were collected. The results showed that static patterns received higher usefulness and satisfaction scores than dynamic patterns. However, no age differences were found. These findings provide a potential guide for the next-generation takeover warning system on wrist-wearable devices in the automated system.]]></description>
      <pubDate>Thu, 06 Feb 2025 10:49:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2480024</guid>
    </item>
    <item>
      <title>Effects of integrated takeover request warning with personal portable device on takeover time and post-takeover performance in level 3 driving automation</title>
      <link>https://trid.trb.org/View/2438091</link>
      <description><![CDATA[Level 3 driving automation defined by the Society of Automotive Engineers (SAE) requests human drivers to drive manually when the vehicle cannot perform the driving task. In this regard, researchers have studied the integrated takeover request (TOR) which provides visual and auditory TOR warning in both vehicle interface (e.g., dashboard, windshield (head-up display) and personal portable device (PPD) (e.g., cell phone, tablet). However, these studies neither used auditory TOR warning in PPD nor examined the effect of use of headphone on takeover. Thus, this study evaluates the effects of the integrated TOR with the use of headphones on the takeover time and the post-takeover performance. The behavior of 60 drivers was observed in the driving simulator experiment. During the experiment, the drivers watched a video on a tablet in automated driving, received the TOR warning, and manually drove in the lane change and pullover scenarios. The survey was also conducted to ask drivers’ experience and preference for TOR warning. The integrated TOR significantly reduced the takeover time compared to the conventional TOR which provides the TOR warning in vehicle interface only. The integrated TOR also improved the post-takeover performance as indicated by more stable steering operation and safer driving behavior after TOR warning. However, the use of headphones did not significantly reduce the takeover time or improve the post-takeover performance for the integrated TOR. The participants generally perceived higher subjective comfort and safety level with the integrated TOR than the conventional TOR. The integrated TOR with auditory warning in PPD can significantly reduce the takeover time and improve the post-takeover performance in both urgent and less urgent conditions. The integrated TOR with auditory warning in PPD can be applied to SAE Level 3 driving automation for safe transition from automated to manual driving.]]></description>
      <pubDate>Fri, 18 Oct 2024 14:11:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2438091</guid>
    </item>
    <item>
      <title>Emerging Data Streams for Pavement (Asset) Health Monitoring and Management</title>
      <link>https://trid.trb.org/View/2156184</link>
      <description><![CDATA[Over the last two decades, accelerating technological changes are changing the way transportation assets are managed. Innovations, such as smart cities, smart infrastructure, automated vehicles, multifunctionality, and high-tech construction are redefining the transportation profession and offer a myriad of challenges and opportunities.

These disrupting trends offer unique opportunities to adjust the way we conceive, design, construct, and manage the infrastructure of the future.  This pooled fund will focus on exploring the use of the new stream of data produced by these innovations to better evaluate  and manage pavement assets. 

Examples of the technologies to be considered have been presented in several recent Transport Research Board committees and other professional meetings.

OBJECTIVE: The main objective of the pooled-fund program of research is to identify, test and evaluate emerging big data stream that may enhance the process that is used to evaluate the performance and manage pavement assets. The technologies considered will include at a minimum, vehicle response data collected by connected and automated vehicles, smart infrastructure sensors (e.g., internet of things), mobile devices and e-construction and Building Information Models (BIM) technologies (e.g., digital twins).]]></description>
      <pubDate>Wed, 19 Apr 2023 18:44:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2156184</guid>
    </item>
    <item>
      <title>Pedestrian mode identification, classification and characterization by tracking mobile data</title>
      <link>https://trid.trb.org/View/2118582</link>
      <description><![CDATA[In recent years, with the emergence of personal mobility (PM) and the importance of eco-friendly modes, the role of pedestrian has increased. However, studies on pedestrian, especially methods for determining pedestrian volume, are very limited. Therefore, in this research, the authors study algorithms for detecting pedestrians based on mobile data and GPS base station information, which depends on the actual user location. To identify the travel modes, including pedestrian group, the key variables are travel speed, travel time, travel distance, and departure time. In addition, the key variable for categorizing pedestrian group into main and access modes is whether or not to go dwell location (destination) and to use transportation vehicles. The results of pedestrian as main mode and access mode are based on a spatio-temporal distribution, and the ratios of the two pedestrian mode types are compared and verified using household traffic survey data.]]></description>
      <pubDate>Thu, 23 Mar 2023 16:58:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2118582</guid>
    </item>
    <item>
      <title>A Framework to Assess Pedestrian Exposure Using Personal Device Data</title>
      <link>https://trid.trb.org/View/2052490</link>
      <description><![CDATA[Capturing pedestrian exposure is important to assess the likelihood of a pedestrian-vehicle crash. In this study, we show how data collected on pedestrians using personal electronic devices can provide insights on exposure. This paper presents a framework for capturing exposure using spatial pedestrian movements based on GPS coordinates collected from accelerometers, de?ned as walking bouts. The process includes extracting and cleaning the walking bouts and then merging other environmental factors. A zero-in?ated negative binomial model is used to show how the data can be used to predict the likelihood of walking bouts at the intersection level. This information can be used by engineers, designers and planners in roadway designs to enhance pedestrian safety.]]></description>
      <pubDate>Mon, 21 Nov 2022 16:21:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2052490</guid>
    </item>
    <item>
      <title>An analysis of the role of residential location on the relationships between time spent online and non-mandatory activity-travel time use over time</title>
      <link>https://trid.trb.org/View/1976395</link>
      <description><![CDATA[Although the associations between the use of information and communications technologies (ICT) and individuals' daily travel and activity patterns have been extensively investigated for several decades, few studies have examined the amount of time spent using ICT, its implications on activity-travel behavior, and how such ICT-travel relationships may vary over time and according to residential location. This study takes a quasi-longitudinal perspective to explore how the amount of time individuals spend on the Internet for personal or non-work purposes correlates to their activity-travel for non-mandatory maintenance and leisure purposes, as well as how such associations evolve over a decade. More importantly, it examines how the role of people's residential locations in determining the associations between time spent on the Internet and travel has changed over time. The authors' approach utilizes two datasets from two major cross-sectional surveys in Scotland: the 2005/06 Scottish Household Survey (SHS) and the 2015 Integrated Multimedia City Data (iMCD) Survey, which were similarly structured and developed. To accommodate the multiple discreteness characterizing activity-travel choice and duration, the multiple discrete-continuous extreme value (MDCEV) model was employed to capture the Internet–travel relationships for the full sample and the urban, town, and rural sub-samples in both 2005/06 and 2015. Their findings suggest that use of the Internet for personal purposes increasingly tends to discourage rather than facilitate physical activity and travel for non-mandatory purposes over time, especially for those who spend high levels of time on the Internet (over ten hours per week). However, such Internet–travel relationships are generally weaker among people living in remote areas than those living in urban areas. While the relationships regarding maintenance activity purposes are significant for almost all levels of Internet users among the urbanites in both years, they were not significantly found at all among rural residents in either 2005/06 or 2015.]]></description>
      <pubDate>Thu, 21 Jul 2022 11:30:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/1976395</guid>
    </item>
    <item>
      <title>Synthesis of Information Related to Highway Practices. Topic 54-04. Mobile Devices as a Tool for Digitalized Project Documentation and Inspection</title>
      <link>https://trid.trb.org/View/1953241</link>
      <description><![CDATA[Consumer-grade mobile devices, including smartphones, tablets, peripheral devices, and Rovers, are increasingly used as innovative tools in construction project delivery, documentation, and inspection. Advances in camera technology combined with increased accuracy in geolocation, graphical displays, and LiDAR abilities provide a powerful construction technology that is also widely accessible and used by most construction professionals on jobsites. Mobile device applications that can be used by construction professionals include digitized documents; geolocation of data; augmented reality with engineering precision; capturing 3D images of as-built conditions through built-in LiDAR cameras; object recognition through AI; viewing of 3D models; access to inspection history; connecting QA results to the model; data storage, object recognition, and feature extraction methods; and improved communications.

The objective of this synthesis was to document state DOT practices for using mobile devices to support digitized project delivery, documentation, and inspection. Information for this study was gathered through a literature review, a survey of state DOTs, and follow-up interviews with selected DOTs. Case examples of five state DOTs provide additional information on using mobile devices to support digitized project documentation and inspection.

Dr. Hala Nassereddine and her colleagues at the University of Kentucky, Lexington, Kentucky, collected and synthesized the information and wrote the report. The members of the topic panel are acknowledged on page iv. This synthesis is an immediately useful document that records state DOT practices on the use of mobile devices to support digitized project documentation and inspection that were acceptable within the limitations of the knowledge available at the time of its preparation. As progress in research and practice continues, new knowledge will be added to that now at hand. The Synthesis was published as NCHRP Synthesis 635.]]></description>
      <pubDate>Tue, 17 May 2022 10:12:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/1953241</guid>
    </item>
    <item>
      <title>Examining the Implications of Legislation and Enforcement on Electronic Device Use While Driving</title>
      <link>https://trid.trb.org/View/1894353</link>
      <description><![CDATA[Distracted driving is a complex and ever increasing risk to public safety on roadways. Drivers’ use of electronic devices significantly diverts human attention away from the driving task. The law enforcement community faces significant challenges as electronic device use has expanded beyond simply texting, and legislation regulating electronic device use while driving is inconsistent in content and implementation. For example, many states currently prohibit texting while driving, but don't address other functions of portable and in-vehicle electronic devices. The effectiveness of current distracted driving legislation, such as primary handheld bans and texting bans, is unknown. This confusion may lead to the continued perception among drivers that it is acceptable to use electronic devices while driving. Therefore, there is a need to systematically examine relevant existing legislation and enforcement practices.
 
 
The objectives of this research were to (1) examine the essential components of current state and provincial legislation (e.g., language, penalties, sanctions) used to address distracted driving while using electronic devices; (2) evaluate the benefits and impediments associated with enacting, enforcing, and adjudicating texting and hands-free legislation; and (3) develop model legislation to deter distracted driving while using electronic devices.
 


]]></description>
      <pubDate>Tue, 30 Nov 2021 11:04:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/1894353</guid>
    </item>
    <item>
      <title>Children’s street crossing performance when auditory information about traffic is lacking</title>
      <link>https://trid.trb.org/View/1765672</link>
      <description><![CDATA[The current study examined the impact on children’s street crossing behaviors of not having auditory-based information about traffic when crossing streets. 	 	Using a fully-immersive virtual reality system, numerous indices of children’s street crossing behaviors were measured both when they had auditory-based information about traffic and when this was lacking. 	 	The lack of traffic sounds did not influence the inter-vehicle gap size that children crossed into but it did result in slower initiations and, ultimately, more high-risk outcomes (close calls and hits). 	 	Traffic sounds significantly contribute to enhance children’s safety when crossing streets. Cars with reduced sounds (e.g., electric) and anything that interferes with children accessing auditory-based traffic information (e.g., wearing headphones) could increase their risk of pedestrian injury.]]></description>
      <pubDate>Tue, 23 Mar 2021 11:13:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/1765672</guid>
    </item>
    <item>
      <title>A big-data driven approach to analyzing and modeling human mobility trend under non-pharmaceutical interventions during COVID-19 pandemic</title>
      <link>https://trid.trb.org/View/1764587</link>
      <description><![CDATA[During the unprecedented coronavirus disease 2019 (COVID-19) challenge, non-pharmaceutical interventions became a widely adopted strategy to limit physical movements and interactions to mitigate virus transmissions. For situational awareness and decision-support, quickly available yet accurate big-data analytics about human mobility and social distancing is invaluable to agencies and decision-makers. This paper presents a big-data-driven analytical framework that ingests terabytes of data on a daily basis and quantitatively assesses the human mobility trend during COVID-19. Using mobile device location data of over 150 million monthly active samples in the United States (U.S.), the study successfully measures human mobility with three main metrics at the county level: daily average number of trips per person; daily average person-miles traveled; and daily percentage of residents staying home. A set of generalized additive mixed models is employed to disentangle the policy effect on human mobility from other confounding effects including virus effect, socio-demographic effect, weather effect, industry effect, and spatiotemporal autocorrelation. Results reveal the policy plays a limited, time-decreasing, and region-specific effect on human movement. The stay-at-home orders only contribute to a 3.5%-7.9% decrease in human mobility, while the reopening guidelines lead to a 1.6%-5.2% mobility increase. Results also indicate a reasonable spatial heterogeneity among the U.S. counties, wherein the number of confirmed COVID-19 cases, income levels, industry structure, age and racial distribution play important roles. The data informatics generated by the framework are made available to the public for a timely understanding of mobility trends and policy effects, as well as for time-sensitive decision support to further contain the spread of the virus.]]></description>
      <pubDate>Tue, 23 Mar 2021 11:13:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1764587</guid>
    </item>
    <item>
      <title>Application of tablet-based cognitive tasks to predict unsafe drivers in older adults</title>
      <link>https://trid.trb.org/View/1694161</link>
      <description><![CDATA[Due to aging and medication interferences, a wide range of motor, sensory, and cognitive skills that are imperative for driving are affected in older adults. Though on-road tests are most indicative of driving ability, they are costly, stressful, time-consuming, and risky. Application of tablet-based cognitive tasks is investigated in identifying unsafe drivers in a population of healthy and at-risk for driving older adults. 	 	Forty-nine older adult participants aged 54 to 81 (M = 78.08, SD = 9.78) that were screened by their physicians as “at-risk for driving impairment”, and forty-eight control participants aged 54 to 81 years (M = 65.85, SD = 6.93) completed an on-road driving test designed specifically to evaluate cognitive decline related to driving, and a set of tablet-based cognitive tasks (composed of reaction speed, decision making, memory, and bi-manual perceptual-motor tasks) that measured the cognitive skills needed during driving. Accuracy and reliability of predicting unsafe drivers based on the cognitive tasks were investigated using different trichotomous classifiers (class outputs: safe, unsafe, undefined). 	 	Trichotomous naive Bayes demonstrated the highest overall accuracy performance of 73%, a sensitivity of 69%, and a specificity of 75%. The rate of misclassified unsafe drivers was 19%, and the rate of misclassified safe drivers was 8%. 	 	High accuracy and reliable prediction of unsafe drivers using cognitive-only tasks in a sample of older adults population demonstrate the efficacy of a widely available screening tool that can be applied in other cognitively impaired populations such as drug users.]]></description>
      <pubDate>Fri, 15 May 2020 11:07:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/1694161</guid>
    </item>
    <item>
      <title>An IoT Oriented Development Framework for Prototyping User Experience on Vehicle</title>
      <link>https://trid.trb.org/View/1640589</link>
      <description><![CDATA[Many organizations have tried to establish novel communication style among passengers using on-board electronic devices related with modern automotive systems that consists of various hardware and software. Although several robotic middle-wares have been proposed to manage and develop such systems, it is too difficult for artists to implement user experiences. To overcome the problem, the authors introduce a practical framework inspired by building blocks of Internet of Things, which consists of three elements based on IPv4 communication type related with the blocks. Applying an example implementation to several prototyping products on vehicles, suitability and feasibility of the authors' framework are practically confirmed.]]></description>
      <pubDate>Tue, 22 Oct 2019 14:42:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/1640589</guid>
    </item>
    <item>
      <title>Understanding Driver Distractions in Fatal Crashes: An Exploratory Empirical Analysis</title>
      <link>https://trid.trb.org/View/1590494</link>
      <description><![CDATA[Driver distraction has become a significant problem in transportation safety. As more portable wireless devices and driver assistance and entertainment systems become available to drivers, the sources of distraction are increasing. Based on the results of different studies in the literature review, this paper categorizes different distraction enablers into six subcategories according to their fundamental characteristics and how they would affect a driver's likelihood of engaging in non-driving related activities. The review also discusses the characteristics and influence of external and internal distractions. The objective of this study is to examine the effect of different distraction sources in fatal crashes with the consideration of a driver's age and sex. Tukey test, chi-square test of independence, Nemenyi post-hoc test, and Marascuilo procedure have been used to investigate the top distraction sources, the trend of distraction-affected fatal crashes, the effect of different distractions on drives in different age groups, and their influence on female and male drivers. It was found that inner cognitive inferences accounted for the greatest proportion of driver engagement in distractions. Young drivers show a larger probability of being distracted by in-vehicle technology-related devices/objects. Within the group of young drivers, female drivers showed a higher probability than their male counterparts of engaging in distracted driving caused by in-vehicle technology-related devices. Among six subcategories of distractions, drivers older than 80 years old were found to be most likely affected by inner cognitive interferences.]]></description>
      <pubDate>Mon, 01 Apr 2019 10:15:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/1590494</guid>
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