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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>Exploring Compound Heat Exposure of Pendulum Residents within Mobility Network: A Case of Chongqing</title>
      <link>https://trid.trb.org/View/2699092</link>
      <description><![CDATA[Pendulum residents face compound heat exposure across day and night because their rigid commuting routines trap them in consistently hot environments. Traditional residence-based assessments often misestimate this risk, as they overlook the multidirectional nature of daily movements. This study addresses that gap by applying a job–housing mobility perspective that incorporates both home and workplace conditions, using Chongqing as the case study. Mobile phone signaling data were used to identify pendulum residents and map their multidirectional mobility patterns. Most of these residents are concentrated in the urban centers within the inner ring road, forming a tightly connected network of intra- and inter-cluster flows. In contrast, those living outside the inner ring road tend to have more localized, intra-cluster movements. We simulated hourly Mean Radiant Temperature (MRT) and calculated heat exposure by combining MRT with the duration of stay at each location. The results show that over 20% of pendulum residents experience compound exposure—high heat at work during the day and at home at night. Logit regression further reveals that short-range, intra-cluster mobility and movement within periphery areas are key drivers of compound exposure. Demographic factors such as being young or female, along with neighborhood characteristics like employment in industrial areas and limited public transportation access, also elevate the likelihood of experiencing this exposure. Together, constrained mobility and heightened vulnerability contribute substantially to the risk. The research framework is particularly applicable to large, polycentric cities with complex commuting patterns, where multidirectional mobility shapes uneven heat exposure across space and time. These findings highlight the need for targeted heat-mitigation strategies, such as improving mobility options, providing accessible cooling facilities, and protecting vulnerable groups.]]></description>
      <pubDate>Mon, 03 Aug 2026 09:10:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2699092</guid>
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    <item>
      <title>Timing matters: shifts in traffic intensity impact the risk of wildlife-vehicle collisions</title>
      <link>https://trid.trb.org/View/2721009</link>
      <description><![CDATA[Wildlife–vehicle collisions (WVC) result from the interaction between traffic and wildlife. While traffic is structured by clock time, wildlife activity is governed by daylight, leading to seasonal variation in their temporal overlap. Using WVC data from Germany and hourly traffic estimates, we examined how daylight-saving time (DST) alter this overlap and affect collision risk. We found that DST-induced changes in traffic intensity significantly influenced WVC rates, however, residual variation suggested deviations from a strictly proportional response across situations. In spring, the shift of traffic into dawn was associated with higher-than-expected WVC rates, a pattern consistent with seasonal increases in wildlife movement. Variation in WVC responses was more pronounced at dawn, indicating that factors beyond traffic intensity may contribute to observed patterns. DST effects on WVC, therefore, differ from patterns observed in other regions, emphasizing the importance of considering species ecology and regional conditions when informing management decisions.]]></description>
      <pubDate>Mon, 27 Jul 2026 09:46:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2721009</guid>
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    <item>
      <title>NLR’s FleetDNA Speed Distributions for EPA MOVES</title>
      <link>https://trid.trb.org/View/2714195</link>
      <description><![CDATA[The U.S. Environmental Protection Agency (EPA) MOtor Vehicle Emission Simulator (MOVES) models mobile sources of air toxics at small and large scales across the Unites States. Vehicle speed distributions based on the type of vehicle, type of road traveled, and hour of the day are inputs into MOVES. In conjunction with vehicle miles traveled (VMT) inputs, they are used to estimate total operation time and select the relevant driving cycles from which running operating modes and emissions are estimated. In an ongoing research effort, the National Laboratory of the Rockies (NLR) partnered with the EPA to provide default average speed data for heavy-duty vehicles based on NLR's FleetDNA database and the Bourns College of Engineering - Center for Environmental Research and Technology (CE-CERT) data set from the University of California, Riverside.]]></description>
      <pubDate>Mon, 22 Jun 2026 07:23:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714195</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>
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    <item>
      <title>Feasibility of MALSR and Runway Lighting for ILS Approaches for Helicopters</title>
      <link>https://trid.trb.org/View/2693804</link>
      <description><![CDATA[The Federal Aviation Administration Office of Aviation Research and Development Airport and Aircraft Safety R&D Division (ATO-P), William J. Hughes Technical Center Flight Program Group (ACB-870), Simulation and Analysis Group (ACB-330), and support contractors performed a human-in-the-loop Copter Instrument Landing System (ILS) study in a Level D Sikorsky 76 simulator at Flight Safety International, West Palm Beach, Florida, March 27-April 1, 2004. The purpose of the Copter ILS study was to evaluate the adequacy of airport lighting to support helicopter approaches with reduced minima. Specifically, the study investigated whether a Medium Intensity Approach Lighting System with Runway Alignment Indicator Lights, runway markings, and runway edge lights, only, would be adequate for helicopter pilots to perform an instrument approach to 100-ft decision height above the ground with visibility averaging to 1/4 mile during three time-of-day conditions (day, night, and dusk) and successfully land the helicopter using a prescribed approach and landing technique. The majority of participant feedback and performance data, based on a sample of 14 pilots, supported the feasibility of the Copter ILS procedure in terms of lighting and visual cue adequacy and the two-person crew landing technique. Some pilots did point out that the addition of runway centerline lighting would be helpful. They all agreed a two-person crew would be essential to the safety of the procedure. Pilots visually acquired the airport environment, in most cases, before the 100-ft decision height. Approach and landing performance for the most part was in accordance with the recommended parameters. Time of day appeared to have some impact on performance and opinion about the lighting and visual cue adequacy for the simulated Copter ILS approaches. Due to the small sample size in the study, the collection of more data is desirable to augment and validate the results of this simulation.]]></description>
      <pubDate>Sun, 26 Apr 2026 17:38:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2693804</guid>
    </item>
    <item>
      <title>Understanding the Configurations of Street Networks and Their Effect on the Quality of Urban Morphology and Pedestrian Flow in Baghdad, Iraq</title>
      <link>https://trid.trb.org/View/2652058</link>
      <description><![CDATA[This article offers an insight into the impact of street networks, how they affect the quality of urban morphology, and their impact on pedestrian flow in two neighborhoods in Baghdad, Iraq. Using multiple centrality assessment as a systematic method to determine betweenness centrality (a metric value of street quantity), 12 streets within two different neighborhoods were surveyed. An ethnographic technique was adopted in conducting site observations over three different periods in a day when pedestrian density was studied to determine both movement and activity. Subsequently, the correlation between street centrality and pedestrian intensity was calculated and compared for the two selected neighborhoods. The results indicate that a hybrid pattern accommodated social interaction, action, and intensities with a high level of betweenness, which differed from the modern pattern with its low level of betweenness. This indicates that street patterns and their centrality values play a significant role in shaping street life and fostering social interaction. Thus, adopting an integrated and comprehensive approach to planning by considering environmental conditions at different levels (micro, meso, macro, and local) and applying street centrality can significantly promote urban life.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652058</guid>
    </item>
    <item>
      <title>Using segmented regression to estimate the optimal illuminance for pedestrian reassurance</title>
      <link>https://trid.trb.org/View/2689789</link>
      <description><![CDATA[CIE 115:2010 gives guidance for the design of road lighting that feeds into the design standards of many nations. There is, however, an emerging awareness that the guidance in CIE 115:2010 needs amendment, and this is being targeted through rapid and long-term revisions. One aim of the rapid revision is to provide an empirical basis for the guidance, with that to be founded in what is already known rather than waiting for further research to be conducted. For P-class lighting, applied to situations where pedestrians are the target user, it has been suggested that pedestrian reassurance (the feeling of safety) is a suitable basis for establishing optimal lighting conditions. This article analyses the relationship between measures of pedestrian reassurance (after-dark evaluations and the difference between daylight and after-dark evaluations) and the conventional measures of lighting in the P class (mean, minimum and uniformity of horizontal illuminance) using the results from previous field studies investigating this. For these data, the day-dark difference provided a better association with illuminance than did after-dark ratings. For the day-dark difference, the association with mean illuminance was better than that with minimum illuminance or uniformity. Segmented regression suggested an optimum mean illuminance of 8.76?lx. To fit within the existing P classes, this could be rounded down to class P3 (mean illuminance?=?7.5?lx).]]></description>
      <pubDate>Mon, 20 Apr 2026 09:22:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689789</guid>
    </item>
    <item>
      <title>Development of a Fast Method to Analyze Patterns in Airport Noise</title>
      <link>https://trid.trb.org/View/2685585</link>
      <description><![CDATA[Traditional airport noise modeling is limited in its ability to analyze large quantities of flight tracks due to high computation time. As a result, yearly noise reports are often limited to modeling flights from a single “representative day,” which lacks detail arising from the natural dispersion of flight tracks and variety in airport operations occurring throughout an entire year of operations. A framework for processing actual flight data and applying an existing, fast noise approximation is presented. Tens of thousands of flights can be analyzed in a matter of hours, allowing for a data-comprehensive approach to calculating noise metrics. Method results are cross-validated against the Aviation Environmental Design Tool (AEDT) on a single-event basis and an existing aggregate result on a multi-event basis. Results for a variety of metrics are presented based on data sourced from Boston Logan International Airport in 2016. Day-Night-Level (DNL) is calculated on a yearly, daily, and hourly basis, highlighting the variability in noise patterns depending on evolving airport runway configuration. N60 is calculated as a supplemental metric on a daily basis.]]></description>
      <pubDate>Thu, 09 Apr 2026 13:41:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685585</guid>
    </item>
    <item>
      <title>Effect of chronotype on e-bike riders’ alertness: Evidence based on behavior and fNIRS</title>
      <link>https://trid.trb.org/View/2659473</link>
      <description><![CDATA[Physiology, personality, and cognitive ability are all associated with safe behavior in traffic. Chronotype, an individual’s inclination to feel most alert at particular time of day, is an underexplored factor that may also affect safety. This study explores the effects of chronotype on e-bike riders’ alertness from the perspectives of behavior and hemodynamic response to cortex activation.  A total of 64 Chinese riders, 34 morning-type and 30 evening-type, were recruited based on their score on the Morningness-Eveningness Questionnaire. A mixed experimental 2 (chronotype: morning-type group vs. evening-type group) * 2 (test time: a.m. vs. p.m.) design was conducted, with riders’ performance on an alertness task while engaged and cerebral cortex activation serving as outcome measures.  Morning-type riders had a faster response than evening-type riders when testing in the morning and evening-type riders reacted faster in the afternoon. More broadly, all riders showed greater accuracy on the alertness task in the morning. Hemodynamic response to cortex activation varied across riders and test times, with significant differences in activation of multiple channels in the primary visual cortex (PVC). Specifically, the morning-type riders showed positive activation in the morning and negative activation in the afternoon, while the evening-type riders showed negative activation at both testing times, with higher activation in the morning. The alertness of the riders demonstrates the synchronicity of brain – behavior, the activation state of some channels (CH1, CH3, CH5, CH8) in the PFC region was significantly correlated with riders’ alertness behavior.  Chronotype affects e-bike riders’ alertness, as evidenced both by behavioral and brain activation pattern outcomes. The impact varies depending on riding time, with morning-type riders exhibiting better riding alertness in the morning and evening-type riders showing advantages in the afternoon.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:43:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659473</guid>
    </item>
    <item>
      <title>Validation of Streetlight Insight® 2021 Vehicle Volume Metrics in Maine</title>
      <link>https://trid.trb.org/View/2669636</link>
      <description><![CDATA[The University of Maine's Margaret Chase Smith Policy center undertook a validation effort on behalf of Maine Department of Transportation (MaineDOT) to better understand the accuracy of StreetLight  Insight's (StL's) vehicle volume metrics for monthly, daily, and hourly time periods. We examine the impact of two characteristics of the traffic counter location on the accuracy and precision: the annual average daily traffic (AADT) and MaineDOT's factor group classification. The factor groups were created to classify roads with high and low seasonal variability of traffic volume due to tourism. We also present a preliminary analysis of StL's turning movement counts (TMC). We employ similar statistical methodologies to those published by StL and the FHWA (Federal Highway Administration) to validated StL's AADT metrics. We find that the accuracy of the monthly average daily traffic (MADT) and DOW (MADT for days of the week) estimates are very similar to the AADT estimates and should therefore be considered sufficiently accurate in most cases. The use of StL's MADT estimates for low-volume roads (under 5,000 AADT) will require judgment on the part of MaineDOT's staff and consultants before use in transportation planning. The Levene's test of equality of variance finds that the variance of StL metrics is different between Factor Group I & II, meaning that Factor Group two is significantly affecting the precision of the MADT, whereas the AADT Range does not significantly change the variance. In the analysis of the Day of the Week (DOW estimates), there is a significant difference in accuracy between weekdays (Mon-Fri) and weekends (Sat-Sun). The analysis of the Hour of the Day (HOD) estimates found that StL underestimates traffic volume in the morning, and increasingly overestimates traffic in the afternoon and evening. Using the average difference of short-term counts of 1.8% (as a percent of intersection traffic), 62.5% of turning movements could be considered within an acceptable error margin.]]></description>
      <pubDate>Mon, 02 Mar 2026 13:24:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669636</guid>
    </item>
    <item>
      <title>Understanding Driving Behaviors and Traffic Crashes among University Commuter Drivers</title>
      <link>https://trid.trb.org/View/2562246</link>
      <description><![CDATA[The fast expansion and urbanization of the Najran region of Saudi Arabia have made road safety a serious concern. The main goal is to provide a thorough analysis of the dynamics of traffic safety in the Najran area, with an emphasis on understanding the driving behaviors among university commuter drivers. A survey was conducted within the campus of Najran University to gather responses from students, faculty, and staff members, enabling subsequent analysis of the responses. Statistical techniques were employed to study the cumulative number of crashes, the relative distribution of crash types, and the total number of affected individuals, and to find risk factors. The survey gathered over 600 responses; however, only 199 responses involving vehicle crashes were included in the analysis. During the study period, survey analysis revealed a cumulative toll of 199 vehicle accidents, including 38 injury crashes and 7 fatalities. Based on the relative proportions of crash severity, 19% resulted in injuries, 4% in fatalities, and 77% were crashes causing property damage only. The statistical analysis results revealed that 5 factors out of 20 were significant in increasing the likelihood of vehicle crashes. Drivers involved in vehicle crashes were significantly affected by the time of day, driver factors, type of crash, presence of passengers, and seat belt usage. This study undertakes a comprehensive analysis of road safety dynamics in the Najran region, providing foundational insights that policymakers, practitioners, and researchers can use to make evidence-based decisions.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562246</guid>
    </item>
    <item>
      <title>Assessing Rideshare Satisfaction among a University Community</title>
      <link>https://trid.trb.org/View/2562183</link>
      <description><![CDATA[On-demand mobile applications for rideshare services are a relatively new transportation phenomenon that encourages the reallocation of resources on a peer-to-peer basis. This shared economy promotes efficiency and sustainability by maximizing the use of existing resources and minimizing fuel consumption, congestion, and transportation inequity; however, customer satisfaction and retention must be assessed to fully realize these benefits. Prior research explores ride ratings through the lens of customer biases, but the literature lacks an evaluation of the interplay between customer satisfaction and various trip characteristics. This study analyzed the service criteria of rideshare trips taken both to and from a university in Arlington, Texas, between 2021 and 2022 and developed a random forest model to evaluate the impact of various trip characteristics on ride ratings and predict the likelihood of the ratings being high. The findings revealed that ride distance, duration, month, and day of the week most significantly impacted university rideshare ratings. Partial dependence plots were also developed to enhance the model’s interpretability, and managerial implications and policy strategies to improve customer satisfaction and encourage feedback are discussed. The findings offer valuable implications for rideshare service providers, policymakers, and transportation professionals.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562183</guid>
    </item>
    <item>
      <title>Predicting Rideshare Ratings with Trip Characteristics</title>
      <link>https://trid.trb.org/View/2562181</link>
      <description><![CDATA[Rideshare services have the potential to positively impact traffic congestion, safety, emissions, and energy consumption, as they minimize mobility obstacles and transportation inequity for the disabled, rural residents, and other transportation disadvantaged groups. Prior research has extensively explored the benefits of this mobility trend; however, it has not fully addressed the importance of trip characteristics to ride ratings as an indicator of customer satisfaction. This study uses a treed regression analysis on rideshare data collected from Via, an operation in Arlington, Texas, from 2021 to 2022, to gain a better understanding of rider satisfaction. A predictive model was developed to identify the ride characteristics that have the greatest impact on ratings, and the analysis showed that three primary factors positively impact a trip’s rating: having a ride pass, travelling early in the morning, and the origination and destination points. The findings also demonstrated the interplay of various trip characteristics and how they may concurrently maximize the likelihood of a ride receiving a high rating. This study will benefit rideshare service providers and transportation professionals by providing them with insights into the factors that will improve customers’ rideshare experiences.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562181</guid>
    </item>
    <item>
      <title>Reducing Time-of-Day Traffic Signal Controller Transitions through Collective Offset Adjustments</title>
      <link>https://trid.trb.org/View/2632990</link>
      <description><![CDATA[Time-of-day transitions are a critical process traffic signal controllers use to ensure coordination when switching between timing plans. There are multiple factors that can influence the time it takes to go through transition. This paper investigates the optimization of traffic signal transitions. The goal is to identify an optimal collective offset adjustment that will be able to reduce the total transition times across coordinated intersections. A mathematical framework was demonstrated to estimate transition times, which was then validated through simulations using the virtual traffic signal controllers. The numerical results from this paper identified an optimal collective adjustment to existing offsets at four coordinated intersections in Tuscaloosa, AL—Hillcrest, Patriot, Bobby Miller, and Mae Hinton. This set of offset was implemented over a 3-day period, and the resulting transition times were compared to a prior baseline, leading to a total transition time reduction from 1,039 to 371 s—a 64.3% reduction as confirmed using automated traffic signal performance measure data. Statistical analysis using Welch’s t-test confirmed the significance of this reduction, with a p-value of 0.0002. The study also highlights the adaptability of the proposed methodology, which can be applied to a wide range of traffic signal controllers while maintaining any relative offsets between coordinated intersections that have been designed by an engineer. These findings contribute to improving traffic flow and reducing delays during transitions, offering a practical solution for traffic management systems.]]></description>
      <pubDate>Wed, 18 Feb 2026 13:22:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2632990</guid>
    </item>
    <item>
      <title>The design of a time-of-use tariff with a demand charge for residential electric vehicle charging posts</title>
      <link>https://trid.trb.org/View/2665732</link>
      <description><![CDATA[Time-of-use tariffs have been widely adopted to manage the charging demand of electric vehicles within residential communities. However, the growing penetration of EVs has led to challenges, particularly over-response during off-peak periods. Currently, residential consumers in China are served by the grid company at a privileged price, which excludes network tariffs. In early 2024, the National Development and Reform Commission of China issued Guideline No. 1721, requiring the implementation of ”a differentiated pricing mechanism for the charging demand”. Consequently, while traditional household demand remains at the favorable price, innovative tariffs tailored for residential charging posts are being encouraged to address the increasing charging demand. Drawing on international experiences in tariff design, this study proposes a time-of-use tariff with a demand charge (ToU-D) for EV charging. Under this scheme, each EV owner reserves a monthly charging capacity and pays for the consumed energy according to a ToU tariff, but is subject to a penalty energy price for the charging profile above the reserved capacity. A mixed-integer bilevel optimization model is developed, where the upper level represents the grid company aiming to minimize wholesale electricity purchase costs subject to a regulated profit rate, while the lower level represents residential consumers aiming to minimize their electricity bills. To demonstrate the model’s adaptability to unbundled retail market settings, it is extended to consider network tariffs of different structures. The bilevel model is solved by transforming into a single-level one using a heuristic method that minimizes the duality gap of the lower-level problem, due to the existence of binary variables at the lower level. An empirical analysis based on realistic data reveals that the proposed tariff not only mitigates over-response issues and generates substantial economic benefits for both the grid company and EV owners overall, but also indicates that less flexible EV owners may face increased charging costs.]]></description>
      <pubDate>Fri, 06 Feb 2026 08:47:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665732</guid>
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