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    <title>Research in Progress (RIP)</title>
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    <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>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Advanced Technologies and Data Analytics for Safe, Smart, and Efficient Transportation (ASSET)</title>
      <link>https://rip.trb.org/View/2709572</link>
      <description><![CDATA[This project assists the Massachusetts Department of Transportation (MassDOT) with (A) calibrating safety models for urban and suburban arterial intersections and developing artificial intelligence models for (B) detecting sidewalks and (C) counting multimodal trips.  

There are three main goals:

(A) Calibrate the Safety Performance Functions (SPFs) in Chapter 16.6.4 of the Highway Safety Manual, 2nd Edition (HSM2), along with the associated parameters, for the twelve types of urban and suburban intersections in Massachusetts using the most recent data.

(B) Develop an Artificial Intelligence (AI) model to automate the detection and mapping of sidewalks from publicly available aerial imagery. Also, the model will be used to identify changes in sidewalks using aerial imagery from multiple years.

(C) Leverage AI to automate the counting of pedestrians, active transportation modes (such as bicycles and e-scooters), and site-generated trips from new developments. The results of this task will form the basis for developing AI and/or statistical models to estimate multimodal trip counts required for transportation planning purposes.]]></description>
      <pubDate>Wed, 03 Jun 2026 15:27:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709572</guid>
    </item>
    <item>
      <title>Developing a Data Fusion Tool for Improved Traffic Crash Exposure
Analysis and Modeling</title>
      <link>https://rip.trb.org/View/2663603</link>
      <description><![CDATA[Accurate measurement of exposure is critical for understanding and preventing traffic crashes, as crash frequency is directly related to how much road users are exposed to risk. However, current exposure estimates rely on data sources with complementary but individually insufficient characteristics. Traditional traffic counts and Annual Average Daily Traffic (AADT) offer high accuracy but limited spatial and temporal coverage, while emerging Location-Based Services (LBS) data provide high-resolution mobility patterns but are often biased and less reliable. This fundamental mismatch between accuracy and coverage prevents agencies from developing the complete and reliable exposure estimates needed for effective safety analysis and planning.
This project develops a data fusion tool that integrates traffic counts and AADT, LBS data, and socio-demographically representative survey data from the National Household Travel Survey (NHTS) into a unified measure of exposure. Unlike previous efforts that focused on a single travel mode or low temporal resolution, the proposed framework generates exposure estimates for motor vehicles, pedestrians, bicyclists, and scooters at fine spatial scales (intersection and mid-block) and temporal scales (daily and monthly). The tool is evaluated in Washington, D.C., using three alternative fusion paradigms: Bayesian fusion through hierarchical or state-space modeling, Dempster–Shafer theory for explicit uncertainty representation and accommodation of LBS coverage gaps, and model-based fusion employing structured error modeling with NHTS socio-demographics to correct LBS data bias.
The fusion methods are compared through crash prediction models estimated with fused exposure measures against models using individual data sources, evaluated via pseudo-R², AIC, BIC, and out-of-sample prediction error, with a target improvement of at least 10% in predictive performance. Fused exposure patterns are further validated against Washington, D.C.’s High Injury Network and independent ground-truth count data where available. The final tool is delivered as an open-source Python package with documentation and secure coding practices. Agency outreach, including engagement with D.C. stakeholders managing the High Injury Network, informs tool refinement and supports preparation for future pilot deployment. This research supports USDOT’s Safety priority by generating more accurate and complete multimodal exposure measures that enable better identification of high-risk locations, improved crash prediction, and targeted safety interventions
]]></description>
      <pubDate>Tue, 03 Feb 2026 15:31:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663603</guid>
    </item>
    <item>
      <title>Local Agency Traffic Count Incorporation for ITD Local Road Annual Average Daily Traffic
(AADT) Estimates </title>
      <link>https://rip.trb.org/View/2601428</link>
      <description><![CDATA[This project will develop a process for incorporating local agency Annual Average Daily Traffic (AADT) counts into Idaho Transportation Department's (ITD’s) statewide traffic volume dataset. By integrating locally collected data, the project will improve the accuracy and credibility of AADT estimates across the highway system. The work includes creating a workflow for incorporating local counts and developing a schema within the ArcGIS Roads & Highways extension to manage AADTs. These efforts will ensure that local road estimates are properly calibrated to reflect real-world conditions while strengthening collaboration and data alignment between ITD and its local partners.]]></description>
      <pubDate>Wed, 17 Sep 2025 16:29:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2601428</guid>
    </item>
    <item>
      <title>Replacing the Illinois Traffic Projection Tool</title>
      <link>https://rip.trb.org/View/2593922</link>
      <description><![CDATA[Predicting long-term traffic trends allows transportation agencies to identify and plan transportation projects, helping to reduce congestion and optimize infrastructure development. Researchers will upgrade Illinois’ current traffic projection tool to include traffic count projections up to 25 years in the future as well as allow users to review historical traffic count data on any given roadway. The updated tool will allow for more accurate and streamlined long-range traffic forecasting, reducing the manual effort required to update data and process forecasts.]]></description>
      <pubDate>Thu, 28 Aug 2025 10:05:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593922</guid>
    </item>
    <item>
      <title>2299 Assessing and Enhancing the Traffic Count and HPMS Program</title>
      <link>https://rip.trb.org/View/2434118</link>
      <description><![CDATA[The objective of this project is to assess the adequacy of the Oklahoma Highway Pavement Monitoring System (HPMS) program and enhance it by potentially adding new sites and removing unnecessary locations, and incorporating emerging technologies and data sets to reduce data collection costs. The study will:
• Evaluate whether the current traffic count locations represent the full extent of Oklahoma HPMS roadways.
• Collaborate with local governments and MPOs to investigate the integration of their data into the Oklahoma Department of Transportation (ODOT)’s count program to meet HPMS reporting needs.
• Determine whether continuous count locations are adequate for factoring short-term traffic counts.
• Review emerging technologies and data sources that may be appropriate for Oklahoma HPMS reporting.  
This research project aims to directly support the assessment and improvement of traffic count and HPMS Programs at ODOT. It also seeks to identify emerging traffic data collection technologies and cost-effective data sources suitable for ODOT's HPMS and census reporting purposes. Maximizing the utility of traffic data collection is crucial for meeting HPMS/census reporting needs.

]]></description>
      <pubDate>Wed, 25 Sep 2024 14:39:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2434118</guid>
    </item>
    <item>
      <title>Using Data to Enable Community-Centered Transportation



</title>
      <link>https://rip.trb.org/View/2381731</link>
      <description><![CDATA[As transportation agencies strive to deliver community-centered transportation, practitioners must have reliable data, analytical tools, partners, and resources. Data enables transportation professionals and decision-makers to better understand the diversity of the communities they serve by illustrating community characteristics and needs, and highlights trends that inform transportation planning. Data inputs can include a variety of categories, such as demographic and socioeconomic. Data availability exists on multiple spectra: public to private, primary to tertiary, and freely available to purchased.

Developments in data and analytical techniques are fast evolving and could support transportation agencies in identifying (1) the visions and goals of their communities quantitatively or qualitatively and (2) disparities in access to the transportation that enables community success. As technology increases the number of data sources, there is a corresponding increase in the complexity, clarity, and questions about the fidelity of data for transportation agencies to consider as inputs. Additionally, safeguarding individual privacy and ensuring ethical use of data are paramount as this field develops. 

However, there are knowledge, capacity, and practice gaps in using data to understand communities. Challenges may include data accuracy, assumptions in analyses, accessibility of data, and identifying logical pairings between sources and uses of the data. Research is needed to better understand data opportunities and confront the challenges transportation agencies experience to enable planning for community-centered transportation. 

OBJECTIVE: The objective of this research is to develop a guide and data framework to empower transportation agencies to deliver community-centered transportation.]]></description>
      <pubDate>Tue, 21 May 2024 20:26:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2381731</guid>
    </item>
    <item>
      <title>BikePed Portal: Pedestrian Volume Estimation Based on Push Button Actuations from Signals Data</title>
      <link>https://rip.trb.org/View/2361978</link>
      <description><![CDATA[This project translates research from Oregon DOT's "Active transportation counts from existing on-street signal and detection infrastructure" (SPR 857), into a practical application on BikePed Portal. ]]></description>
      <pubDate>Tue, 02 Apr 2024 13:42:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2361978</guid>
    </item>
    <item>
      <title>Real Time Classification of Vehicle Types and Modes using Image Analysis and Data Fusion</title>
      <link>https://rip.trb.org/View/2353426</link>
      <description><![CDATA[Description: The goal of this project is to conduct a feasibility study on the development of software and selection of hardware that will measure multiple transportation modes and classify vehicles by their Federal Highway Administration (FHWA) classification. The research team will install several combined computer/camera systems to monitor the multi-modal traffic in the proximity of the University of South Carolina campus. This area has multiple transportation users, including pedestrians, mopeds, bicycles, motorcycles, passenger cars, trucks, trains and buses. Along with the video data, additional traffic collection sources such as pneumatic tubes and Bluetooth will be used. Multiple cameras will allow three dimensional data of the environment to be constructed in the software. The video data will be combined with other data using statistical updating methods (Bayesian) to produce final multi-modal traffic information. We will also explore counting traffic in non-typical locations, such as counting the number of pedestrians in/outside of cross walks in the roadway or pedestrians crossing stopped trains.

Intellectual Merit: (1) Image subtractions from successive images will be used to identify objects in the area of interest. (2) A discriminate function based on the object geometry and image texture will be used to classify objects. (3) The development of the object discriminant function as well as utilization of digital image correlation or other video object motion determination approaches will be the major contribution of this research.

Broader Impacts: Broader Impacts: The collection and analysis of integrated multimodal movement of people and goods will provide transportation planners with better quantitative information about the existing system. Beyond providing raw counts, an integrated video based system could provide information about unsafe practices of pedestrians and moped users that could be used to improve safety for these users.]]></description>
      <pubDate>Mon, 25 Mar 2024 15:48:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2353426</guid>
    </item>
    <item>
      <title>Trip Generation for Various Sites</title>
      <link>https://rip.trb.org/View/2325886</link>
      <description><![CDATA[The 11th edition of the ITE Trip Generation Manual is missing several land uses and may have incorrect data for Louisiana's less-urban areas that lack public transportation for other land uses. The objective of this research is to conduct a pilot study in Louisiana that will achieve the following objectives:
(1) How much variability is there in the trip rates among sites and through the week?
(2) Are the trip rates really significantly different from ITE, so as to warrant further determination of rates?
(3) Is there apparent variability with contextual factors (assuming that sites are chosen that cover some variability in contextual factors, which should be a requirement of the design)?
(4) What should be the priority for determining new trip generation rates for use in Louisiana (as evidenced by deviation from ITE and potential impact on traffic planning)?
(5) Can the new smart micro radars provide more accurate counts?
(6) Can third party mobility datasets provide accurate count data for computing trip rates?
]]></description>
      <pubDate>Tue, 23 Jan 2024 08:43:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2325886</guid>
    </item>
    <item>
      <title>Readiness and Feasibility of Automatic Dependent Surveillance-Broadcast (ADS-B) Data for Airport Use Cases</title>
      <link>https://rip.trb.org/View/2007986</link>
      <description><![CDATA[Towered and non-towered airports need accurate daily and annual aircraft operational counts, including aircraft type, for use in tasks like airport planning, environmental analysis, capital improvement program development, funding justification, and staffing. Aircraft operational counts provided by air traffic control towers offer limited details and towers are often staffed for only a portion of the day, possibly missing large swaths of activity. Meanwhile, non-towered airports have limited or no data from which to estimate aircraft operational counts. Airport sponsors rely upon aircraft operational counts to develop products for purposes such as forecasting, budgeting, and project justification.

ACRP Report 129: Evaluating Methods for Counting Aircraft Operations at Non-Towered Airports was published in 2015 and identified options to fill this gap but Automatic Dependent Surveillance-Broadcast (ADS-B) was an emerging technology at that time and was noted for additional research. The FAA mandated that by January 2020, aircraft were required to have ADS-B Out installed when flying in certain airspace and with this inflection point, the industry needs an evaluation as to whether ADS-B is a reliable alternative for aircraft operational counts.  Additionally, the accuracy and efficacy of aircraft operational counts obtained via ADS-B needs to be validated through structured scientific research to better understand the benefits and limitations of various collection technologies and the ability to support different use cases across various airport configurations and user characteristics. 

OBJECTIVE: The objective of this research is to develop a guide and techniques to collect and validate the use of ADS-B data for obtaining aircraft operational counts and identifying aircraft type at U.S. towered and non-towered airports. This research should include, at a minimum: A roadmap for implementing the collection and use of ADS-B data at airports; The state of current readiness and feasibility of ADS-B data for airport use cases in airport operations, planning, and development; Trends and statistics of ADS-B equipage across the general aviation (GA) fleet both in terms of equipped aircraft hulls and as a share of operations by business jets, turboprops, recreational piston aircraft, training piston aircraft, agricultural aircraft, and other relevant groupings; and Coverage and limitations of an ADS-B collection program. 
]]></description>
      <pubDate>Tue, 16 Aug 2022 14:39:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2007986</guid>
    </item>
    <item>
      <title>Leveraging Existing Traffic Signal Assets to Obtain Quality Traffic Counts and Enhance Transportation Monitoring Programs







</title>
      <link>https://rip.trb.org/View/1854166</link>
      <description><![CDATA[State departments of transportation (DOTs), metropolitan planning organizations (MPOs), counties, and other local agencies manage extensive traffic counting programs and have a need to ensure that traffic count data covers a variety of modes of travel, e.g., cycling and walking. These counts support decision-making with the aim of enhancing safety and mobility for the traveling public. There are thousands of existing traffic detection assets throughout the nation that serve traffic management operations. Moreover, other customers of traffic count data such as traffic engineers, traffic monitoring staff, transportation and active transportation planners, and data scientists, as well as non-transportation stakeholders (e.g., those responsible for realty, billboards, economic development, etc.), need to combine traffic count data sets in new ways to support various business processes.

As sensor detection technologies mature in assisting traffic operations and intelligent traffic system (ITS) programs, the providers of traffic count programs recognize the potential benefits of using existing infrastructure and data to supplement their counts. However, the diverse efforts underway are generally not summarized, publicized, or leveraged. Key issues associated with using the data from traffic signal equipment for traditional traffic volume measurement include (a) inconsistency in data quality and format that varies across vendors and technologies; (b) inconsistency in availability of sensors at all intersections as well as approaches to individual intersections; and (c) variable configuration of sensor equipment causing possible gaps in data availability, quality, and storage even though the equipment itself may be capable of counting vehicles, bikes, and pedestrians.

Research was needed to examine whether the data provided by traffic signal assets can provide accurate traffic counts.

The objectives of this research were to (a) determine the feasibility of using existing or enhanced traffic equipment to collect, store, and disseminate data for purposes other than traffic operations, particularly for traffic monitoring programs; (b) determine the suitability of traffic count data from already installed and existing traffic assets for this purpose; and (c) develop effective practices for obtaining and integrating traffic counts from existing traffic assets.
 
 
]]></description>
      <pubDate>Tue, 25 May 2021 15:37:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/1854166</guid>
    </item>
    <item>
      <title>National Accessibility Evaluation Phase II Access Across America</title>
      <link>https://rip.trb.org/View/1722333</link>
      <description><![CDATA[This project is an extension of the National Accessibility Evaluation project, TPF-5(315), This project has two main objectives. First, it will create a new, national Census block-level accessibility dataset that can be used by partners in local transportation system evaluation, performance management, planning, and research efforts. Second, it will produce and publish a series of annual reports describing accessibility to jobs by auto, transit, and biking in metropolitan areas across America. Accessibility Dataset This project will create a national Census-block level dataset describing accessibility to jobs from locations across the county, updated annually. Accessibility calculations will rely on detailed travel time calculations for both driving and transit, which will be implemented using commercially-available GPS-based speed measurements, published transit schedules, and detailed bike and pedestrian networks. Each Access Across America partner will have direct digital access to the accessibility datasets covering the jurisdictions of all partners. Annual Report The annual Access Across America series of annual reports will provide summaries of the detailed accessibility datasets for the 50 largest metropolitan areas across America. These will be released to national and local media outlets and supported by publicity and communications efforts. Partners will be recognized in the report for their sponsorship and support. Optional Goals The accessibility evaluation tools and expertise developed in this project can also support optional goals for interested agencies: 1. Include destinations from local data sources - Local destination datasets from participants can be included in the annual accessibility calculations. Cost: $5,000 2. Accessibility Data Workshop - Researchers can lead an on-site or remote workshop to provide transportation agency staff hands-on experience with accessibility data and training on accessibility concepts. Cost: $5,000. 3. Scenario Evaluation - Using annual accessibility data as a baseline, researchers can develop an accessibility evaluation of highway, transit, bike, or pedestrian scenarios based on planning data from your organization. Cost varies with scenario complexity and objectives.
]]></description>
      <pubDate>Thu, 16 Jul 2020 14:30:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/1722333</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>
    </item>
    <item>
      <title>Census Transportation Data Field Guide for Transportation Applications</title>
      <link>https://rip.trb.org/View/1516162</link>
      <description><![CDATA[The American Consumer Survey (ACS) contains critical data elements that support the analysis of transportation plans, policies, programs and project selection. Changes in the data products over the last 10 years and ongoing staff turnover have left a void of expertise in effectively using this data to support transportation planning. Additionally, there is no centralized resource location where one can go to learn how to use the data for real world applications. This results in wasted time and questionable analysis.

When the ACS was introduced by the Census Bureau as the replacement to the Long Form it brought with it a change in how the data was collected and packaged for public release. This in turn, brought a whole new set of data issues for the transportation analyst to understand and cope with.  Margins of error, privacy protection rules and procedures, imputation, rounding, data suppression, changes to the survey instrument and variables, and period estimates all have stretched the learning curve for the user. Staff from the states, MPOs and transit operators all have struggled with the use and application of the ACS and data products derived from it like the CTPP and Public Use Microdata Sample/Public Use Microdata Area (PUMS/PUMA).

Under NCHRP Project 08-123, “Census Data Field Guide for Transportation Applications,” Cambridge Systematics was asked to develop a field guide for the transportation community on how to best use Census data, including the ACS, CTPP, LODES Employment-Household Dynamics (LEHD), and PUMS/PUMA, to address transportation issues. To support the use of the field guide, the research team developed tools for transportation analysts.

In addition to the field guide that published as NCHRP Research Report 1108, a number of deliverables are available on the TRB website at trb.org by searching for NCHRP Research Report 1108. The deliverables are as follows: Case Studies Notebook; Travel Survey Weighting Spreadsheet 

]]></description>
      <pubDate>Mon, 18 Jun 2018 21:58:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/1516162</guid>
    </item>
    <item>
      <title>Development of Count Transfers Methods for TMAS Data Transfer from Clearinghouses Probably LA</title>
      <link>https://rip.trb.org/View/1513051</link>
      <description><![CDATA[Follow-up to existing work at PSU/NITC with the inclusion of one additional clearinghouse.]]></description>
      <pubDate>Fri, 18 May 2018 13:49:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/1513051</guid>
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