<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <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>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Prevalence of high-risk behaviors among commercial motor vehicle drivers measured using artificial intelligence for naturalistic data collection</title>
      <link>https://trid.trb.org/View/2617066</link>
      <description><![CDATA[Crashes involving commercial motor vehicles (CMV) result in high rates of injury and fatality, and rates have been increasing, garnering attention as a priority among transportation safety professionals. Major contributors to crash risk and fatalities, such as operator phone use, seatbelt noncompliance, and speeding remain insufficiently understood in terms of their prevalence, hindering the effectiveness of public health outreach and educational initiatives. This study used high-resolution cameras and Artificial Intelligence (AI) processing for naturalistic data collection to measure the prevalence of speeding, seatbelt noncompliance, and handheld phone use among commercial motor vehicle drivers in San Diego County, California. Technologic approaches utilizing AI can greatly expand understanding of the prevalence of these behaviors, allowing for improved opportunities to address this growing problem facing CMV operators, public health, and traffic safety professionals. Using AI technology, radar and infrared cameras mounted on roadside trailers, data were collected continuously over 7-day periods at 16 locations across the county. The prevalence of CMV drivers’ cell phone use, seatbelt noncompliance, and speeding was measured. More than 2,600 h of CMV driving data were collected anonymously across rural and urban locations, including on interstate and county roads, near the US/Mexican border, and on a Native American Reservation. Speeding was defined as exceeding posted speed limits of 55 mph on highways with a maximum speed of 65 mph; we examined both 55 mph (the general CMV speed limit in CA) and 65 mph as cutoffs for speeding. All cell phone and seatbelt violations identified by AI were manually reviewed for accuracy. Temporal associations by time of day, day of the week, and season, as well as roadway characteristics, were used to evaluate the propensity for these behaviors. Data were collected for 160,671 CMVs between April and August 2024. Of these, 17,341 (10.8%) demonstrated at least one risky driving behavior of speeding (65 mph cutoff), cell phone use, or seatbelt noncompliance. The most common risky behavior was speeding, 4.9% (n = 7195), followed by seatbelt noncompliance 4.5% (n = 7,143), and handheld phone use 2.6% (n = 4,241). The prevalence of all three offenses was highest between 6:30 AM and 8:30 AM (rush hour) and on weekends. The prevalence of speeding was 56.4% (n = 90,652) with a cutoff speed limit of 55 mph—the CMV speed limit in California. Technological approaches can inform public understanding of the prevalence of behaviors that contribute to safety-critical mistakes. Offense prevalence was found highest on the lowest vehicular traffic days and times. These naturalistic data can guide safe driving policy, planning and decision-making as well as evaluate the impact of interventions while adhering to state and local privacy laws.]]></description>
      <pubDate>Wed, 19 Nov 2025 17:09:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617066</guid>
    </item>
    <item>
      <title>Special Crash Investigations: Remote Move-Over-Law Crash Investigation; Vehicle: 2016 Dodge Charger Pursuit; Location: Nevada; Crash Date: May 2021</title>
      <link>https://trid.trb.org/View/2292738</link>
      <description><![CDATA[This report documents the remote investigation of a “Move-Over-Law” crash. The crash occurred at night in May 2021 in Nevada. The crash site was designated as a north/south U.S. highway but was oriented east/west at the crash site. Conditions at the time of the crash were dark with artificial lighting and dry with clear visibility. The posted speed limit was 105 kmh (65 mph). The struck vehicle was a 2016 Dodge Charger Pursuit police vehicle, and the striking vehicle was a 2006 Ford Escape. At the time of the crash, the Dodge was unoccupied and stopped, facing west on the right shoulder while police officers investigated a prior crash. The Ford was being driven westbound by a belted 33-year-old female at a police-estimated speed of 89 to 105 kmh (55 to 65 mph). For unknown reasons, the Ford departed the roadway on the right edge and traveled onto the right shoulder where its front plane struck the Dodge’s back plane. Both vehicles came to rest near the impact area. Following the crash, the driver of the Ford refused to submit to field sobriety testing and was subsequently arrested for impairment due to alcohol. No police officers were injured in the crash. The driver of the Ford sustained a police-reported “C” type (claimed) injury but was not transported. Both vehicles were towed due to damage.]]></description>
      <pubDate>Tue, 28 Nov 2023 10:26:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2292738</guid>
    </item>
    <item>
      <title>Assessing the Safety Implication of Alternative Speed Limits in California</title>
      <link>https://trid.trb.org/View/2160674</link>
      <description><![CDATA[This project combined the statewide crash data (Statewide Integrated Traffic Records System [SWITRS]) and traffic data (Performance Measuring System [PeMS]) to develop statistical models to determine the safety impacts of alternative speed limits on California highways. The models examined whether various factors about crashes, including average traffic speed and truck-involvement, correlated with outcomes such as crash severity. The models were then used to test the impact of four alternative speed limit policies (B-E) on the predicted number of fatal crashes and unsafe-speed related crashes in urban and rural areas. The policies were: (A) Existing differential speed policy for cars (65 mph) and trucks (55 mph); (B) Raising the speed limit on interstates for trucks from 55 to 65 mph; (C) Raising the speed limit on interstates from 55 to 75 mph for trucks and 65 to 75 mph for cars; (D) Lowering the existing differential speed on interstates from 55 to 50 mph for trucks and 65 to 60 mph for cars; (E) Raising the existing differential speed on interstates from 55 to 70 mph for trucks and 65 to 80 mph for cars. The policy analysis shows a difference in the predicted number of crashes (fatal, unsafe speed) in and between urban and rural areas. The percentage increase/decrease in predicted fatal crashes in rural areas is lower than urban areas across all policy alternatives.]]></description>
      <pubDate>Sun, 30 Apr 2023 16:52:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2160674</guid>
    </item>
    <item>
      <title>How is traffic safety affected by changes in traffic speeds following speed limit increases? An evaluation with probe vehicle data</title>
      <link>https://trid.trb.org/View/2100425</link>
      <description><![CDATA[Maximum speed limits continue to be an important policy issue. Research has consistently shown speeds to increase following speed limit increases. These increases have also generally coincided with increases in both the frequency and severity of crashes. However, research has been more limited as to the direct relationship between speed and safety. To this end, the present study evaluates the effects of increasing speed limits on the safety of rural limited access freeways while accounting for contemporaneous changes in the speed profiles of these same roadways. The speed limit increases in Michigan occurred between May 1 and June 12 of 2017, when the maximum speed limits were increased from 70 to 75 mph on approximately 600 miles of freeways. The maximum speed limits for trucks were also increased from 60 to 65 mph on all freeways state-wide at this same time. Speed data were obtained for the Michigan rural freeway network through probe vehicles. These data are merged with pertinent roadway data, as well as police-reported crash data at various levels of injury severity. The impacts of the speed limit increase on safety were evaluated by estimating random effects negative binomial models as part of a case-control before-after study design. The results show that the locations where the speed limits were increased experienced a 5% increase in crashes, while a marginal reduction in crashes was observed where speed limits did not increase. Interestingly, mean speeds were found to be negatively associated with crash frequency, while the standard deviation of speed was found to exhibit a positive relationship. Several site-specific characteristics were also found to be strong predictors of crash frequency. The results provide important insights into the nature of the relationship between speed and safety and will help to guide subsequent speed limit policy decisions.]]></description>
      <pubDate>Mon, 23 Jan 2023 12:29:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2100425</guid>
    </item>
    <item>
      <title>Expansion of NASS/CDS for characterizing run-off-road crashes</title>
      <link>https://trid.trb.org/View/1854695</link>
      <description><![CDATA[Run-off-road (ROR) crashes account for one-third of all annual crash fatalities in the US. The National Automotive Sampling System Crashworthiness Data System (NASS/CDS) is a dataset which may be used to understand the nature of ROR crashes. Despite the wealth of coded data available in NASS/CDS, this dataset lacks coded information about the roadside environment and the off-road trajectory of the vehicle. This information would be useful for determining lane departure warning (LDW) benefits, residual safety problems, performance of current safety hardware, lane marking inventory, LDW test procedure development, radius of curvature characterization, and effectiveness of ESC. The purpose of this paper is to demonstrate a methodology for expanding the data available in NASS/CDS to form and validate a specialized road departure database.  Observed, measured, and reconstructed data elements were extracted from NASS/CDS and compiled into the National Cooperative Highway Research Program (NCHRP) 17-43 database. Observed variables were primarily coded from the scene photographs and included information such as the lane markings, and geometry of the roadside cross-section. Additional variables were measured from the scaled scene diagrams including the path of the vehicle, road dimensions, and roadside object positions. The vehicle impact speed and departure speed were reconstructed using the WinSMASH delta-v, roadside object characteristics, and vehicle path. Two studies were conducted to demonstrate the usefulness of the NCHRP 17-43 database in evaluating both vehicle-based and infrastructure-based ROR countermeasures.  The resulting NCHRP 17-43 database includes 1,581 NASS/CDS cases representing 510,154 ROR crashes. Analysis of the database found that drivers which crashed following an overcorrection were younger than drivers which did not overcorrect. This may indicate that inexperienced drivers are more likely to overcorrect when departing the roadway. The 85th percentile impact severity of ROR crashes, which occur on roads with a speed limit greater than 65 mph, is higher than the practical worst-case test conditions for roadside barriers.  The NCHRP 17-43 database contains information extracted from NASS/CDS cases to better understand the nature of ROR crashes, driver behavior in these crashes, and the potential benefits of both vehicle-based and infrastructure-based ROR countermeasures.]]></description>
      <pubDate>Wed, 16 Jun 2021 09:26:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/1854695</guid>
    </item>
    <item>
      <title>Assessing the Impact of Raising Truck Speed Limits on Traffic Safety</title>
      <link>https://trid.trb.org/View/1681787</link>
      <description><![CDATA[This project used statewide crash and traffic data to develop four statistical models to determine the safety impacts of increasing speed limits for trucks and cars on California highways. The models examined whether various factors about crashes—including average traffic speed, involved vehicle type, weather, etc.—correlated with other crash characteristics of particular policy relevance: namely, fatal crash, truck-related crash, speeding-related crash, and crash severity. The fatal crash model was then used to predict the probability of fatal crashes in urban and rural areas under four possible speed limit policies: (A) maintaining the existing speed limits of 65 mph for cars and 55 mph for trucks; (B) increasing each of these by 5 or 10 mph; (C) increasing the current truck speed limit to equal the car speed limit of 65 mph; (D) following policy C and then increasing the uniform 65 mph speed limit by 5 or 10 mph. The obtained probabilities were then used to forecast the number of fatal crashes under these policies. For the cases with speed limit increases (B-D), the corresponding increases in the predicted number of fatal crashes are small in rural areas and are far less than in urban areas. This suggests that increasing the truck speed limit towards 65 mph to a uniform speed limit (Policy C) in rural areas will not likely increase the frequency of fatal crashes. For urban areas, all speed limit increases are likely to increase the number of fatal crashes in comparison to Policy A (the current policy), but the increase in fatal crashes with Policy C (65 mph for trucks and cars) over Policy A is 1%. Policy C, therefore, is considered the best choice in balancing safety and mobility for both rural and urban areas in California.]]></description>
      <pubDate>Fri, 28 Feb 2020 17:10:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/1681787</guid>
    </item>
    <item>
      <title>Predicting the Impact of Changing Speed Limits on Traffic Safety and Mobility on Indiana Freeways</title>
      <link>https://trid.trb.org/View/1679248</link>
      <description><![CDATA[After repeal of the National Maximum Speed Limit Law, states were allowed to set individual speed limits on their interstate roads. Several states opted for a uniform speed limit while others implemented differential speed limits. The current speed limit on Indiana rural freeways limits speed of passenger cars to 70 mph and restricts to 65 mph speed of vehicles with a gross weight of 26,000 pounds or more. Indiana’s speed limit on urban freeways is mostly 55 mph, but varies from 50 mph on certain downtown sections to 65 mph on some suburban sections. Previous studies comparing uniform and differential speed limit settings as to safety and mobility produced inconclusive or conflicting results. This study evaluates the safety and mobility effects of alternative speed limit scenarios on Indiana interstate freeways. Differences in travel time, vehicle operation, and traffic safety were used to compare the speed-limit scenarios. The effect of speed limit was evaluated in hourly periods. The traffic conditions in these periods were classified as uncongested, intermediate, and congested and the speed limit effects were analyzed in relation to these conditions. Rural and urban freeways were analyzed separately and distinct speed models were developed for cars and trucks. Safety was estimated by probability of crash and the conditional probability of crash injury severity. Speed limit was found to affect mobility and safety mostly in non-congested traffic conditions, while no significant effects were found in congested conditions. A limited effect was detected in intermediate traffic conditions on rural freeways. Results indicate that replacing the differential 70/65 mph speed limit on Indiana rural roads with the uniform speed limit of 70 mph may be beneficial for both safety and mobility. Increasing speed limits on urban interstates is confirmed to be beneficial for mobility but detrimental to safety.]]></description>
      <pubDate>Wed, 22 Jan 2020 15:44:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1679248</guid>
    </item>
    <item>
      <title>Safety Evaluation of Change in Posted Speed Limit from 65 to 70 mph on Rural Virginia Interstate System</title>
      <link>https://trid.trb.org/View/1494925</link>
      <description><![CDATA[Effective July 1, 2010, the Virginia Department of Transportation (VDOT) increased the maximum posted speed limit on interstates and similar facilities from 65 to 70?mph, if recommended following an engineering study. As a result, VDOT performed engineering studies on selected rural interstates posted at 65?mph. By November 2010, VDOT had increased the speed limit from 65 to 70?mph for approximately 670 centerline miles of select rural interstates. This paper presents the results of an empirical Bayes before–after study into the safety and operational effects of the speed limit increase. The analysis focused on total, injury, run-off-road, and truck-related crashes. Comparison segments were used to develop annual adjustment factors, account for regional differences, and identify underlying crash trends in the period before the speed limit increase. At the aggregate level, the results indicated no increase in any of the focus crash types after the increase. Focusing on sites without other changes, the increased speed limit did not increase or decrease any of the crash types. The disaggregate analysis provided further insight into the circumstances in which the change in posted speed limit had more and less pronounced impacts; specifically, that segment type (base or interchange) influenced safety: interchange segments observed statistically significant increases in total, run-off-road, and truck-related crashes. The disaggregate analysis also showed that roadway improvements may help to offset the safety impact of increasing the posted speed limit.]]></description>
      <pubDate>Fri, 19 Jan 2018 12:20:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1494925</guid>
    </item>
    <item>
      <title>SPR-4104: Predicting Impact to Traffic Safety and Mobility of Change in Speed Limits for Indiana Freeways</title>
      <link>https://trid.trb.org/View/1421539</link>
      <description><![CDATA[The study will estimate the safety and mobility effects of removing the differential speed limits on rural freeways and raising the speed limits on urban freeways from 55 mph to 60 or 65 mph. It is also possible that the research will confirm the current policy of differential speed limits on rural freeways and the single speed limit on urban freeways as optimal.]]></description>
      <pubDate>Fri, 02 Sep 2016 14:00:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/1421539</guid>
    </item>
    <item>
      <title>Long Term Impact of Differential Speed Limits on Rural Freeways in Idaho</title>
      <link>https://trid.trb.org/View/1339447</link>
      <description><![CDATA[The main focus of this research is to evaluate the long-term operation and safety impact of Differential Speed Limits (DSL) on rural freeways in Idaho. The analysis of speed data covered three periods: period 1: January 1992 - April 1996 (Uniform Speed Limit (USL) of 65 mph); Period 2: April, 1996 - June, 1998 (with a USL of 75 mph); and Period 3: July, 1998 – December, 2011 (with a DSL of 75 mph for passenger cars and 65 mph for commercial truck vehicles). The analysis showed that since the implementation of the DSL policy, Idaho’s speed trends have stabilized with no sizable change. The mean speed for trucks and passenger vehicles are very close to their respective posted speed limits. The 85th percentile speeds have also stabilized at about five mph above the respective speed limits. DSL  implementation also visibly improved the compliance rate of truck speed limit. The considerable reduction in the 85th percentile and the pace speeds for trucks and the improved speed limit compliance rate indicate that the DSL policy favorably impact truck driver behavior by reducing the most extreme truck speeds. Implementation of the DSL policy has contributed to the improved safety conditions on rural freeways in Idaho. Crash rate analysis showed that DSL favorably affects safety. Crash rates for all crash types were highest during the period 1996 to 1998 with a USL of 75 mph. When DSL policy was implemented in 1998, the crash rates decreased considerably and continued to decline since then.]]></description>
      <pubDate>Thu, 26 Feb 2015 09:49:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/1339447</guid>
    </item>
    <item>
      <title>Speed Pattern Analysis in the Proximity of Dynamic Message Signs Using a Driving Simulator</title>
      <link>https://trid.trb.org/View/1289978</link>
      <description><![CDATA[This study aims to find whether dynamic message signs (DMS) have an adverse effect on traffic flow and safety due to traffic slowdown to read the message. Drivers’ speed fluctuations in the proximity of two dynamic message signs with qualitative and quantitative contents on a highway and a freeway are analyzed. Over 100 subjects are recruited to drive on a fairly large and realistic road network developed in a driving simulator. No statistically significant reduction in the speed of the subjects to read the quantitative message in a highway with 55 mph (88.5 km/hr) speed limit was found. In correlation with the speed analysis, majority of the subjects believed their speed reduction was insignificant. However, the average speed decreased by 2.6 mph (4.3 km/hr) to read the quantitative message on a sign mounted on the 65 mph (105 km/hr) freeway. Although DMS accounted to likely impact the speed of fast drivers, they were found to safely operate as traffic management tools.]]></description>
      <pubDate>Tue, 25 Mar 2014 14:25:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/1289978</guid>
    </item>
    <item>
      <title>Implementing Speed Reductions at Specific Interstate Work Zones from 65 MPH to 35 MPH</title>
      <link>https://trid.trb.org/View/1253310</link>
      <description><![CDATA[Interstate preservation projects are commonly conducted at night and often require working in close proximity to ongoing traffic. Vehicle speed and speed variability in work zones is inextricably connected to the work zone design and the selected traffic control devices. To provide guidance on how to effectively and efficiently reduce traffic speeds, the Oregon Department of Transportation conducted a research study to investigate the impact of selected traffic control devices on vehicle speed within highway paving project work zones. The research centered around two case studies on multi-lane paving projects in Oregon. On each case study, the researchers implemented multiple traffic  control devices (portable changeable message signs (PCMS), radar speed display, police officer presence, tubular markers and drums on both sides of travel lane) and evaluated their impact on vehicle speed, construction productivity, cost, and motorist and worker safety. A police officer parked on the site was found to effectively reduce traffic speeds and should be used if available and feasible. The research findings also suggest using a combination of temporary reduced speed limit signs, radar speed monitoring display, and PCMS on both trailers and rollers. Further research is needed to validate the research findings and better identify the advantages of one traffic control device over another.]]></description>
      <pubDate>Mon, 24 Jun 2013 10:57:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/1253310</guid>
    </item>
    <item>
      <title>The safety impact of the 65 mph speed limit: a case study using Alabama accident records</title>
      <link>https://trid.trb.org/View/1179553</link>
      <description><![CDATA[This study investigated the effects of the change of the speed limit on rural Interstate roadways from 55 to 65 mph in Alabama, which took effect on August 1, 1987.  The primary source of data was the accident records, which were compared for several test and control areas as well as one year time periods before and after.  Traffic volume and speed data were also analyzed to properly interpret the results of the accident data analyses. Both average speed and traffic volume increased more on the rural Interstates than on other roadway types.  Significant increases in PDO and injury type accident frequencies were correlated with the speed limit change.  Spillover effects were found on 55 mph Interstates but not on other roadway types.  Several other factors were analyzed, including weather, vehicle type, roadway character, number of vehicles, collision events, and the use of seat belts (A).]]></description>
      <pubDate>Fri, 24 Aug 2012 02:37:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/1179553</guid>
    </item>
    <item>
      <title>The safety impact of the 65 mph speed limit: a case study using Alabama accident records</title>
      <link>https://trid.trb.org/View/1175457</link>
      <description><![CDATA[]]></description>
      <pubDate>Fri, 24 Aug 2012 00:28:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/1175457</guid>
    </item>
    <item>
      <title>Speed Limit Recommendation in Vicinity of Signalized, High-Speed Intersection</title>
      <link>https://trid.trb.org/View/1147993</link>
      <description><![CDATA[The authors evaluated the traffic operations and safety effects of 5 mph and 10 mph speed limit reductions in the vicinity of high-speed, signalized intersections with advance warning flashers (AWF). Traffic operational effects of the reduced speed limits were analyzed for seven high-speed, signalized intersections with AWF using the Quantile regression model and Seemingly Unrelated Regression Estimation (SURE). Change of speed limit from 60 mph to 55 mph did not lead to any statistically significant reduction in 15th, 50th, or 85th percentile. The reduction from 65 mph to 55 mph hour led to a 4.6 mph reduction in 85th percentile speed; also, the speed dispersion based on inter-percentile range between 15th and 85th percentiles was reduced by 1.8 mph. About the mean and standard deviation of speed estimated by SURE, the only statistically significant impact is from the speed limit reduction of 10 mph from 65 mph, which reduced the mean speed of vehicles by 3.8 mph at the significance level of 95%. In the safety effect study, a crash analysis based on 56 approaches from 28 intersections was performed. The 10 mph speed limit reduction from 65 to 55 mph was found to reduce, on an average, 0.4 crashes per approach per year with 90% percent level of confidence while the 5 mph reductions in the dataset was found to reduce, on an average, 0.6 crashes per approach per year with 95% significance level. Also, the studied approaches with 10 mph reduction were found to have a lower probability of possible injury crashes and a higher probability of property damage crashes with a 90% level of confidence. The 5 mph reductions in this dataset did not show any significant effect on reducing crash severity. It was also found that lower speed limits in vicinity of the signalized intersection reduced the probability of fatal and injury crashes.]]></description>
      <pubDate>Tue, 21 Aug 2012 08:50:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/1147993</guid>
    </item>
  </channel>
</rss>