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    <title>Research in Progress (RIP)</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>
    <image>
      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Real-time Pedestrian Safety and Risk Exposure using Real-time Vehicle Activity and Fleet Composition</title>
      <link>https://rip.trb.org/View/2669652</link>
      <description><![CDATA[Pedestrians face significant a risk crossing roadways as they interact with vehicle traffic (more than 7,000 pedestrians were killed in traffic crashes in 2023).  New tools that assess risk exposure can improve the safety of routes delivered by navigation apps.  In 2025, the research team generated a complete-paths pedestrian network for Downtown Atlanta and inspected the condition of all sidewalk surfaces.  In 2024, Georgia Tech researchers also began collecting very consistent vehicle images using portable high-resolution video cameras positioned on Interstate overpasses (more than one-million vehicle images) for the State Road and Tollway Authority.  A large subset of vehicle images were coded by vehicle make-and-model and used in a prior research project to develop machine-vision artificial intelligence (AI) models to generate fleet composition profiles, for use in energy and safety research.  

In this new project, the researchers will integrate traffic operations data and assess pedestrian exposure to high traffic volumes, high vehicle speeds, and turn movements that cross pedestrian paths.  The team will enhance SidewalkSim and G-MAP (www.its.dot.gov/research-areas/ITS4US/), models that find the “shortest path” (i.e., lowest impedance path) for pedestrian trips between any origin-destination, by integrating traffic exposure into routing impedance factors.  This will allow the apps to route pedestrians around high-risk-exposure crossings.  The team will also map and integrate pedestrian safety countermeasures (bollards, barriers, pedestrian fences, extended crossing times, leading pedestrian intervals, no-crossing zones, no-right-turn-on-red, etc.) in the study area, so that these countermeasures can also be used in impedance-based pedestrian routing along safer paths.  The project culminates by integrating traffic conditions, fleet composition, and risk exposure into SidewalkSim and G-MAP pedestrian routing app and demonstrating the system in downtown Atlanta.  


Another finding from past research was that the resulting machine vision models are so fast, they can run in real-time.  In this new project, the research team will further refine the AI models so that they can be used in edge-computing, processing vehicle fleet composition in the field, without transmitting video data to a data center.  In this project, the team aims to design and package an efficient portable computing system with a high-end graphics card that can operate under year-round Atlanta outdoor temperature and humidity conditions, balancing system performance with power-draw and heating/cooling requirements.  This equipment research (downsizing, enclosure design, heat dissipation, power consideration, etc.) and machine vision model implementation may lead to patentable inventions or licensable software.  If successful equipment deployments are afforded patent protection, the team will work with Georgia Tech’s commercialization office (commercialization.gatech.edu) to develop license agreements for the manufacture of equipment and deployment of portable edge-computing systems and/or will create a GT Create-X business startup.  If the USPTO rejects the patent claims, the team will release equipment specifications, software code, and technology transfer reports under open-source licensing that will allow state DOTs and their consultants to implement the systems.  ]]></description>
      <pubDate>Sun, 15 Feb 2026 16:27:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669652</guid>
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    <item>
      <title>Pedestrian Level of Traffic Stress (PLTS) Validation for Pedestrians with a range of Ages and Abilities</title>
      <link>https://rip.trb.org/View/2625597</link>
      <description><![CDATA[Pedestrian Level of Traffic Stress (PLTS) is a safety tool that can be used to map the most and least pedestrian-friendly parts of an entire roadway network, recommend comfortable walking routes, help prioritize locations for infrastructure improvements, and evaluate project- and system-level changes in pedestrian accommodations over time. However, existing methods of evaluating pedestrian traffic stress (Landis et al. 2001, Chu & Baltes 2001, Raad & Burke 2018) are not standardized and often require many inputs that are impractical for agencies to apply. During the first two years of CPBS grants, the UW-Milwaukee research team attempted to address these issues by creating and testing a new Pedestrian Level of Traffic Stress (PLTS) method. The PLTS provides ratings from 1 to 4 (lowest to highest stress) to assess how pedestrians are likely to feel around vehicular traffic when crossing or traveling along specific roadway segments. The PLTS ratings are based on look-up tables with a relatively small number of inputs (e.g., number of lanes, traffic volume, speed limit, sidewalks and buffers, pedestrian crossing facilities, curb ramps), many of which are readily available in agency data. The PLTS builds on other recent table-based PLTS methods (Oregon Department of Transportation 2020, Washington State Department of Transportation 2020, Montgomery County 2020, Richardson 2023), but it is the first PLTS that the research team is aware of to undergo rigorous validation testing. During the Year 2 project, applied the PLTS method to different types of roadway corridors in three case study communities and compared our PLTS ratings with stress levels reported by online survey respondents. Still, online survey videos and pictures do not fully reproduce conditions that pedestrians experience on actual roadways. Further, the online format with videos and pictures was not accessible to people with visual disabilities, and our sample only captured a small number of older adults and people with other types of disabilities. Therefore, the Year 3 research will build on our current work by comparing the research team's PLTS ratings with PLTS ratings gathered from pedestrians who are older adults or who have sensory or mobility limitations at a series of real-time PLTS data collection events in three communities. This feedback will also inform whether (and if so, which) additional variables should be incorporated into PLTS ratings to better account for suitability among these groups.  The goal is to establish a validated, practical PLTS method that agencies across the country can use to estimate roadway segment and crossing suitability for pedestrians in various contexts, ultimately leading to safer and more enjoyable walking and rolling conditions. This Year 3 CPBS project will help improve understanding of PLTS for pedestrians with a wider range of ages and abilities.]]></description>
      <pubDate>Mon, 17 Nov 2025 14:33:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625597</guid>
    </item>
    <item>
      <title>Bike Infrastructure Planning Based On Mobile-Sourced Data and Anticipated Route Shifts</title>
      <link>https://rip.trb.org/View/2606556</link>
      <description><![CDATA[This project will develop tools to answer critical questions towards supporting bike lanes: if we build a new bike lane, how many bikers will use and benefit from it? How does the addition of bike lanes on specific roads affect transportation accessibility? Where should we build bike lanes to best improve equitable accessibility? In the past, it has been difficult to answer these questions due to limited data on biking travel patterns, but the recent NS616 MnDOT project that estimated bike volumes from mobile-sourced data has created new opportunities to quantify bike lane benefits.]]></description>
      <pubDate>Fri, 03 Oct 2025 14:54:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606556</guid>
    </item>
    <item>
      <title>Correlation of Laboratory Three-Wheel Polisher Revolutions with Roadway Accumulated-Traffic to Evaluate Laboratory Polishing of Pavements</title>
      <link>https://rip.trb.org/View/2593938</link>
      <description><![CDATA[Prior to conducting laboratory dynamic friction tests (DFTs), a three-wheel polisher is used to simulate the polishing effect of traffic on asphalt pavements. Limited research has looked at how the number of revolutions completed by a three-wheel polisher correlates with real-world cumulative traffic volume. Establishing correlations between the number of three-wheel polisher revolutions and real-world traffic volume will clarify how aggregate polishing affects pavement friction.]]></description>
      <pubDate>Thu, 28 Aug 2025 11:32:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593938</guid>
    </item>
    <item>
      <title>Experimental Approaches for Integrating External Factors into Pathway for Planning (P4P) Traffic Volume Forecasting</title>
      <link>https://rip.trb.org/View/2573011</link>
      <description><![CDATA[Traffic volume forecasts are critical for effective transportation planning, helping to assess future roadway demand and allocate resources efficiently.  Virginia’s Department of Transportation (VDOT) uses the Pathway for Planning (P4P) web-based application to provide historical and forecasted traffic volume data.  However, P4P’s current trend-based approach, which relies on linear regression, may either under- or over-predict future traffic volumes due to the lack of consideration for socioeconomic changes.

The purpose of this study is to evaluate alternative methods for forecasting annual traffic volumes by incorporating external socioeconomic factors such as population growth, employment trends, and a forecast change in personal income from reliable sources.  This research focuses on annual average daily traffic volumes (AADT) only and excludes daily, seasonal, and project induced traffic variations. The research will include a literature review, a survey focused on segment-level traffic forecasting, an exploration of available external data, and the development of a pilot forecast model in VDOT Fredericksburg District as a case study.  Statistical analyses will be conducted to ensure region-specific predictions and to evaluate the effectiveness of alternative models for enhancing the P4P platform.  The goal of this work is to incorporate socioeconomic factors into what are currently trend-based traffic volume forecasts.  If this modification improves forecast accuracy, it may also improve infrastructure planning and policy decisions across Virginia.
 
]]></description>
      <pubDate>Thu, 10 Jul 2025 08:17:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2573011</guid>
    </item>
    <item>
      <title>Truck Permits: Managing Increasing Loads and Mitigating Infrastructure Damage to Balance Freight Mobility</title>
      <link>https://rip.trb.org/View/2472700</link>
      <description><![CDATA[Non-reducible truck permits, essential for freight mobility, pose significant challenges to infrastructure integrity, contributing to accelerated fatigue, increased maintenance costs, and safety hazards. This study quantifies the scope and distribution of permit loads across Massachusetts, evaluates their impact on bridges and highways, and verifies their alignment with current regulations and industry standards. The research will integrate data on truck permits, freight volumes, and infrastructure conditions to develop data-informed recommendations for mitigating adverse effects. Outcomes include optimized permit management strategies, improved infrastructure durability, and expanded access to reliable transportation, aligning with US DOT priorities in safety and system performance.
]]></description>
      <pubDate>Mon, 09 Dec 2024 10:27:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2472700</guid>
    </item>
    <item>
      <title>Roundabouts, J-Turn, etc. - Understanding their Economic Impacts</title>
      <link>https://rip.trb.org/View/2414044</link>
      <description><![CDATA[This research aims to investigate the economic impact and benefits of J-Turn intersections and roundabout treatments on Minnesota roads. While these treatments are increasingly preferred for their operational and safety advantages, they often face resistance from community residents and nearby businesses. The study's primary focus is to demonstrate the economic benefits of J-Turns and roundabouts to the public, local businesses, and the community as a whole. The research will employ a multi-stage approach to comprehensively assess these benefits. In the initial stage, interviews with business owners will be conducted to gauge their sentiments regarding roundabouts and their effects on business performance. Subsequently, the study will analyze traffic counts (AADT) on roads leading to roundabouts and J-Turn treatments before and after installation to determine any changes in traffic flow. Finally, an economic impact assessment will be conducted by analyzing sales tax data for census tracts near these treatments. Perceived economic benefits will also be calculated by examining reductions in fatal and injury crashes due to the treatments.]]></description>
      <pubDate>Wed, 07 Aug 2024 17:23:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2414044</guid>
    </item>
    <item>
      <title>Traffic Monitoring and Traffic Data</title>
      <link>https://rip.trb.org/View/2071563</link>
      <description><![CDATA[The Traffic Monitoring and Traffic Data program develops efficient and cost-effective methods to collect traffic data. The focus includes effort on (1) delivering big data analytics to derive annual average daily traffic (AADT) through passive data, (2) collecting and processing monthly traffic volume data, (3) generating the monthly Traffic Volume Trend (TVT) report, (4) updating and debugging the current intelligent transportation (IT) data processing codes.]]></description>
      <pubDate>Mon, 28 Nov 2022 14:20:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2071563</guid>
    </item>
    <item>
      <title>Evaluate the Benefits of Increasing Clear Zone at Higher Speed/Traffic Volume/Crash Locations</title>
      <link>https://rip.trb.org/View/1957084</link>
      <description><![CDATA[The current edition of the American Association of State Highway and Transportation Officials (AASHTO) Roadside Design Guide (RDG) provides guidance on developing standards and policies for determining the widths of clear zones along roadways based on design speed, traffic volume, roadside slope, and road curvature. By providing clear zones, transportation agencies can increase the likelihood that a roadway departure results in a safe recovery and mitigates the severity of crashes that occur. The RDG provides only a general approximation of the needed clear zone distance. Clear zone recommendations can be extrapolated for design speeds greater than the maximum ranges shown in the RDG, fourth edition, corresponding to 65 to 70 mph (100 to 110 km/h) and for average daily traffic (ADT) greater than 6,000 vehicles/day or more. However, it is unclear if extrapolated values are optimized for speeds greater than 70 mph (110 km/h) or for roads with ADTs significantly higher than 6,000 vehicle/day at all speeds.

Two of the key factors in assessing risk are design speed and traffic volumes. In some locations in the United States, posted speed limits (PSLs) have been increased to 80 mph (129 km/h) or more, and there are many segments of highway in which the ADT is greater than 50,000 vehicles/day. Limited data has been collected to evaluate the effectiveness of clear zone recommendations not consistent with the existing ranges shown in the RDG. Clear zone values should be based on actual risks. Therefore, there is a need to analyze crash data and other factors (i.e., human and environmental factors) to determine if revisions to the RDG clear zones are warranted to accommodate increased design speeds, increased traffic volumes, and other factors that may contribute to higher crash frequencies. Results of this research will help guide future editions of the AASHTO RDG, Manual for Assessing Safety Hardware (MASH), and other traffic safety documents.

OBJECTIVES: The research objectives are to (1) identify factors influencing clear zone values and (2) recommend clear zone values corresponding with design speeds and traffic volumes in excess of thresholds recommended in the AASHTO RDG.]]></description>
      <pubDate>Thu, 26 May 2022 17:03:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/1957084</guid>
    </item>
    <item>
      <title>Safety Performance for Active Transportation Modes Using Exposure Models</title>
      <link>https://rip.trb.org/View/1854199</link>
      <description><![CDATA[State departments of transportation (DOTs) are committed to modally integrated and well-functioning transportation systems and providing safe, accessible, and reliable systems for all users, including those who walk, bike, or use mobility assisted devices. However, for local and state DOTs that operate these systems, constrained financial and human resources have made it rather difficult to maintain their current assets in good repair, let alone build new infrastructure. These constraints often result in the prioritization of motorized vehicle projects over those advancing the use of active transportation modes. Active transportation facilities also often lose out because of the unavailability of adequate information and decision-making tools needed to assess the potential safety performance tradeoffs when evaluating alternatives, including walking and biking facilities. 
To support all modes and improve the transportation system’s safety and equity, it is critical to determine the potential use of the system by those who bike, walk, or use mobility assistive devices (devices such as motorized scooters and bikes). However, counts of people walking and biking are often unavailable or collected in a manner that is unusable for crash prediction or for comparing the needs of all users to support location-based (e.g., segments and intersections) equitable decisions. Exposure models for active transportation can potentially help circumvent this problem to allow DOTs to consider all modes of transportation in their project planning and development process in a fair and equitable manner.
Exposure models enable state DOTs to incorporate predictive information into their decision-making process when volume data for those who walk or bike is not available or when the DOTs do not have funds to collect data at every potential project location. Even when volume data is collected for some specific project locations, DOTs may not be able to sustain that collection on a consistent, regular basis due to financial constraints. This non-inclusion has a particularly negative impact on lower-income areas where significant gaps for active transportation modes already exist. Exposure models have the potential to address this deficiency. The multivariate nature of this approach would allow for the necessary planning, design, and operational considerations upfront in different combinations and contexts. The information provided by the models regarding potential safety performance can be used in the decision-making process during project planning and development for active transportation modes.
Research is needed on how the use of exposure models can help advance a decision-making framework that considers how best to achieve an integrated multimodal approach on public roadway system. It will also help supplement other ongoing predictive modeling and systemic tools developments for active transportation. Furthermore, the results of this research could also be used by others to develop additional safety performance functions (SPF) that could supplement models being developed under the NCHRP Project 17-84, Pedestrian and Bicycle Safety Performance Functions for the Highway Safety Manual, with additional functional classes and contexts.
The objectives of this research are to: 
(1) Advance the predictive safety performance methodologies for pedestrians, bicyclists, and those using mobility-assistive devices (such as motorized scooters and bikes) using exposure estimates and prediction models. Develop models and predictive methods for use by state and local DOTs of all sizes to determine potential exposure to help evaluate the likely safety performance at a given location.
(2) Develop guidance and resources to support the implementation of the developed methodologies that can be used to inform multimodal decision-making in different design and land use contexts and also for different modal priorities. ]]></description>
      <pubDate>Thu, 27 May 2021 20:07:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/1854199</guid>
    </item>
    <item>
      <title>A Guide for the Development and Use of Truck Traffic Forecasts in Design</title>
      <link>https://rip.trb.org/View/1854172</link>
      <description><![CDATA[Transportation planning and design require different levels of detail with respect to forecasting travel behavior, demand, and use. Whereas many planning decisions can be supported by typical outputs of a four-step travel demand model, these same outputs are insufficiently precise to support many of the decisions being made during detailed project development and design.
 
Truck traffic forecasting is often conducted as a post process of the data results from a travel demand model or is conducted using commodity flow or other economic and statistical models. Yet, a variety of specific decisions regarding the placement, quantity, length, and geometry of facilities to support a specific volume and type of truck traffic would benefit from more specific data, methods, and techniques for using truck traffic forecasts in project design.
 
Among the limitations of typical travel forecasting models are the ability to predict the specific volumes, weights, and movements of trucks on highways. Truck traffic imposes specific design requirements to accommodate their unique weights and configurations. Transportation agencies and freight distributors need to assess truck travel in different contexts, including but not limited to long-haul goods transportation, local, and last-mile freight deliveries.
 
State departments of transportation (DOTs) and modeling professionals are responding to these challenges by creating more accurate and responsive models and model applications, particularly as the research and development of modeling methods and techniques continue to advance. Nevertheless, the transportation industry is not uniform with respect to its technical knowledge, capabilities, budgets, or other resources needed to develop and apply sophisticated models and decision tools to support project design decision-making. While some state DOTs have the resources to supplement in-house staff or hire outside experts to conduct model runs and analyses, many others simply do not have that capacity.
 
OBJECTIVE: The objective of this research is to develop a guide to assist state DOTs and other agencies in the selection and use of forecasting models, applications, procedures, tools, and techniques needed to support project design.
 
At a minimum, the research team shall: 1. Identify and evaluate the range of existing and emerging technical approaches, data sources, models, model applications, and tools available to generate and apply truck traffic forecasts to support project design; 2. Identify gaps and needs for improvement in those applications, tools, and techniques within the context of design decision-making; 3. Compare (quantitatively) the accuracy of new methods to traditional methods; 4. Provide user-friendly instructions on the appropriate selection and use of specific model applications, tools, or techniques during project design; and 5. Refer to the truck classifications 5-13 provided in the Federal Highway Administration Traffic Monitoring Guide, updated October, 2016.]]></description>
      <pubDate>Tue, 25 May 2021 15:45:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/1854172</guid>
    </item>
    <item>
      <title>Determining Segment and Network Traffic Volumes from Video Data Obtained from Transit Buses in Regular Service: Developments and Evaluation of Approaches for Ongoing Use Across Urban Networks</title>
      <link>https://rip.trb.org/View/1758051</link>
      <description><![CDATA[Transit agencies around the world are increasingly mounting video cameras inside and outside their buses for liability, safety, and security reasons. Some of the cameras provide fields of view that allow observation of vehicles traveling on the surrounding roadways. Such video imagery could conceivably be used to estimate traffic volumes on roadway segments traversed by the transit buses. Transit buses are attractive platforms for acquiring the information that leads to traffic volume estimates, since a fleet of transit buses collectively covers most major surface streets in an urban area and the buses regularly and repeatedly cover the same roadway segments, which would allow for multiple, independent estimates of roadway segment flows across days and by time of day. Since the video cameras are already installed for other purposes, the costs of estimating traffic flows from video obtained from transit buses in regular service would be minimal. Therefore, traffic flows could be estimated with much greater geographic coverage, with much greater frequency, and with much lower cost than is presently available from existing traffic volume observation methods.]]></description>
      <pubDate>Wed, 16 Dec 2020 14:26:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/1758051</guid>
    </item>
    <item>
      <title>Evaluating Permitted/Protected Versus Protected Left Turn Signals in Louisiana</title>
      <link>https://rip.trb.org/View/1745653</link>
      <description><![CDATA[The primary objective of this project is to study the safety and operation of existing signalized intersections (protected only versus permitted/protected left turns versus permitted only but with left turn lanes) along with their geometric features, as described in the Department of Transportation and Development (DOTD) Traffic Signal Manual, with the view to develop guidance on when it is appropriate to install each signal type. The research will answer whether or not the signal type affects the intersection control delay as well as if the signal type affects crash type and frequency. It will also investigate which geometric features significantly impact on the choice of signal type and if flow characteristics (traffic volumes) influence crash characteristics, and ultimately the choice of signal type. Finally, the research will explore when it is most appropriate to install a specific signal type considering operation and safety concerns. ]]></description>
      <pubDate>Thu, 15 Oct 2020 16:16:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/1745653</guid>
    </item>
    <item>
      <title>RES2020-12: Evaluating the Performance of Inverted Pavements in Tennessee</title>
      <link>https://rip.trb.org/View/1716733</link>
      <description><![CDATA[Inverted pavement is an unconventional type of flexible pavement structure. In this pavement structure, an
unbound aggregate base (UAB) with a low initial modulus is sandwiched (layered) between two stiffer layers, a
thinner asphalt concrete layer (AC) and a cement-treated base layer (CTB). This type of pavement structure has
been a potential alternative to the conventional flexible pavement structure due to its cost-efficient usage of asphalt,
comparable performance and durability based on past studies. However, field investigations of inverted pavement
have not been widely conducted and are very limited in the USA. Therefore, the objective of this study is to present
a comprehensive investigation of the inverted pavement system including field and laboratory works. In this study,
the effect of nonlinear stress-dependent property of unbound aggregates on both the inverted and conventional
flexible pavement structures was first investigated. Second, through field investigation in Vulcan pavement, a
comparison study between the inverted and conventional pavements was conducted under the same traffic level
and environmental conditions. In addition, the nondestructive pavement testing method – falling weight
deflectometer (FWD) was applied to evaluate the structural conditions of the inverted pavement, contributing to
the effective maintenance and preservation of pavements. Finally, the accelerated pavement testing (APT) method
was used to evaluate the rutting performance of a full-scale inverted pavement constructed on the UT (University
of Tennessee) campus. Based on the results of the comprehensive investigation of both field (full-scale) and
numerical simulations, the inverted pavement structure can be regarded as an alternative to the conventional
flexible pavement. 
]]></description>
      <pubDate>Fri, 26 Jun 2020 18:17:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/1716733</guid>
    </item>
    <item>
      <title>Methods for Assigning Short-Duration Traffic Volume Counts to Adjustment Factor Groups for Estimating AADT</title>
      <link>https://rip.trb.org/View/1707201</link>
      <description><![CDATA[Annual average daily traffic (AADT) which represents traffic on a typical day of the year is used by transportation agencies for reporting requirements, allocating resources, informing decision-making, and supporting various agency functions. Transportation agencies use different methods to derive AADT estimates from short-duration counts of traffic data from permanent and portable traffic counting equipment installed at selected locations. Commonly used methods for estimating AADT do not adequately address how short-duration counts should be assigned to adjustment factor groups. Also, there are concerns about the inherent errors in these methods, their applicability to roadways with insufficient traffic data, and the accuracy of the derived AADT estimates. There was a need to improve existing methods and develop new methods for functional classes of roadway where insufficient continuous counting exists to improve accuracy of AADT estimates. These methods will help transportation agencies improve the quality of traffic information and support the decisions regarding capital investment programs and budgets as well as design and maintenance programs. OBJECTIVE: The objective of this research was to develop rational methods for assigning short-duration traffic volume counts to adjustment factor groups for estimating AADT (the research was to consider all functional classes of roadways and traffic volumes).
 

 
]]></description>
      <pubDate>Wed, 20 May 2020 15:21:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/1707201</guid>
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