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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>Statewide Multimodal Destination Access Methods and Demographic Analysis</title>
      <link>https://rip.trb.org/View/2725639</link>
      <description><![CDATA[The Oregon Department of Transportation (ODOT) does not currently have a consistent, statewide method to evaluate destination access—whether people can reliably and affordably reach essential destinations such as employment, education, health care, and key services. While agency performance measures and analyses focus primarily on infrastructure conditions and system mobility, they do not answer whether investments are improving people’s ability to access what they need for daily life. Without a standardized destination access methodology, ODOT lacks a clear, data-driven basis for monitoring progress, understanding structural access gaps, or using access outcomes to inform investment decisions. 
OBJECTIVES: (1) Establish a standardized, agency-wide methodology for multimodal destination access analysis. ODOT currently performs destination access analysis on an ad hoc basis and does not have a consistent, documented method for statewide or cross-program use. This project will develop and test a unified approach that can be used across business lines for performance reporting, planning, and investment decision-making. (2) Integrate user profile analysis to identify which populations face transportation access gaps—and to what extent. This research will move beyond single-variable demographic assumptions and instead use data-informed definitions of at-risk populations to better understand who experiences structural access barriers and why. (3) Develop a tool for viewing destination accessibility metrics. The tool can be used to view accessibility by mode and destination type by region. The combination of transportation and land use data will enable planners to understand existing accessibility conditions and needs in specific areas.  This tool would be usable for the ODOT Capital Investment Plan (CIP), local transportation system plans, and other programs where access measures offer utility. 
This research fulfills a need for a destination access methodology that supports ODOT policy, planning, and prioritization. With the results of this research, ODOT will be able to answer critical questions about how the transportation system is serving residents. Access metrics can play a critical role in vehicle miles of travel (VMT) per capita and emissions reduction strategies by informing staff on which areas have feasible multimodal access. ]]></description>
      <pubDate>Wed, 08 Jul 2026 16:48:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2725639</guid>
    </item>
    <item>
      <title>Research for the AASHTO Standing Committee on Planning. Task 63. Making NAICS (North American Industrial Classification System) Work for Transportation</title>
      <link>https://rip.trb.org/View/2706284</link>
      <description><![CDATA[Census data historically have been reported using the Standard Industrial Classification (SIC) System. The North American Industrial Classification System (NAICS) was introduced in 1997 to address a need for a new, more rational approach to tallying industrial classes of workers. There are several differences between NAICS and SIC that make a straightforward comparison difficult. NAICS is based on how products and services are created, while SIC focuses on what is created.

The main objective of this report is to analyze the differences between demographic survey data and establishment-based data when they are reported in the same NAICS categories. Specifically, the research focuses on the mismatch between the two databases for the category “Management of Companies and Enterprises”.]]></description>
      <pubDate>Wed, 27 May 2026 15:06:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2706284</guid>
    </item>
    <item>
      <title>Driver to Non-Driver Transitions: Related Health, Mobility and Safety Outcomes</title>
      <link>https://rip.trb.org/View/2671991</link>
      <description><![CDATA[This project involves analyzing the impacts of becoming a non-driver (suddenly or gradually) in Wisconsin and nationally and effects on health, mobility, and safety outcomes. The project will analyze health, quality of life and mobility outcomes for drivers who are no longer able to drive. The researchers will analyze the safety, mobility, and quality of life outcomes for those who have suddenly or gradually become non-drivers. Analysis should focus on adult non-drivers of all ages and demographics, with particular emphasis on adults aging in place and urban versus rural areas. Once the analyses are conducted and complete, the researchers will report findings and provide recommendations for policies that lead to improved outcomes—namely increases in mobility and safety benefits for the entire state. Recommendations will help Wisconsin Department of Transportation (WisDOT) understand how to best offset impacts to mobility for individuals suddenly or gradually transitioning from being drivers to non-drivers.]]></description>
      <pubDate>Wed, 18 Feb 2026 11:39:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2671991</guid>
    </item>
    <item>
      <title>Evaluating the Willingness to Pay for Managed Lanes (MLs)</title>
      <link>https://rip.trb.org/View/2563659</link>
      <description><![CDATA[This research project will investigate users’ willingness to pay to use managed lane (ML) facilities in light of the recent and rapidly shifting demographic trends and develop a better understanding on how recent mobility options, shifts in telework, online shopping adoption, and demographic and societal trends may have affected the preferences and choices toward using ML facilities.]]></description>
      <pubDate>Wed, 11 Jun 2025 13:19:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2563659</guid>
    </item>
    <item>
      <title>The Reverse Side of Online Shopping: Examining Sociodemographic and Built-Environment Determinants of Delivery Returns</title>
      <link>https://rip.trb.org/View/2553166</link>
      <description><![CDATA[The rise of online shopping has led to a significant increase in the return rate for items purchased online (or "delivery returns"). The process of returning items, once a rare occurrence in the traditional retail setting, has become a commonplace aspect of the e-commerce experience. Online purchase return rates (30%) significantly exceed those of physical stores (8.89%). Overall, these high return rates, have substantial financial, logistical, and transportation-related repercussions. From a transportation perspective, the large volume of returns necessitates additional truck trips, leading to increased freight vehicle miles traveled. This trend also results in more truck traffic at residential locations or return points. Despite the acknowledged impacts, this topic remains under-researched, with existing studies focusing on product characteristics, or retailer policies while overlooking consumer-level perspectives. This study aims to bridge this gap by examining how sociodemographic and built-environment factors influence the frequency and channel choice (physical store, mail carrier, Amazon drop-off, home pickup) for returning online purchases. Utilizing the National Household Travel Survey (NHTS) 2022 dataset, the research team analyzes responses on delivery return frequency across four channels. The team employs a multivariate probit ordered-response model to jointly analyze the full product returning behavior spectrum. This approach recognizes that behaviors are multifaceted, involving both the decision to return an item and the choice of a channel, and accounts for the interconnectedness of decision-making processes. The findings provide a foundation for developing targeted strategies to reduce return rates, streamline reverse logistics, manage travel demand, enhance customer satisfaction, and contribute to a more sustainable e-commerce future. ]]></description>
      <pubDate>Thu, 15 May 2025 14:53:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553166</guid>
    </item>
    <item>
      <title>Imputing Socio-Demographics for Mobile Trajectors</title>
      <link>https://rip.trb.org/View/2553157</link>
      <description><![CDATA[Ubiquitous mobile devices have resulted in massive amount of location- and time-stamped traces that can be used to infer people’s mobility patterns for various applications. Unlike household travel survey data that is small but rich (short but wide data), mobile data, is often massive but shallow (long but thin data) whose meanings in terms of people’s travel patterns must be inferred. Not only being massive, it is also longitudinal, or to be precise: the data is continuous. These two key features hold great promises for a wide range of applications that cannot achieved with the traditional household travel survey data. Examples include: just in time or real time policy evaluations, a closed-loop from real time demand forecasting to service provision and then back to demand monitoring, and creation of digital twins for whole-city simulations. This study addresses a critical challenge that needs to be overcome in order to realize the great promises that the big, passively-generated mobile data offers. That is: to impute socio-demographics from the census data with the mobile trajectories generated from the big data. The novelty of the proposed project lies in that the proposed model will explicitly recognize the uncertainty that exists in the linkage between socio-demographics and travel behaviors.]]></description>
      <pubDate>Tue, 13 May 2025 19:27:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553157</guid>
    </item>
    <item>
      <title>Travel Behavior and Financial Impacts of Fare Capping



</title>
      <link>https://rip.trb.org/View/2464324</link>
      <description><![CDATA[Effective fare payment policies and practices are essential to the efficient operation of public transportation services. Transit agencies in the United States and internationally have implemented fare capping since the early 2000s as account-based fare payment technologies and systems became available. Account-based technologies offer numerous benefits, including faster boarding, reduced cash handling, and the ability to provide fare product options, such as fare capping. Currently, limited information is known about the effects of fare capping on transit ridership and revenue. OBJECTIVES: The objectives of this research are to (1) examine the ridership and revenue effects of fare capping for public transit agencies in North America, and (2) provide sketch planning tools that estimate the potential ridership and revenue impacts of fare capping for transit agencies with varying attributes. This research should build on the available research on fare capping and the experiences of domestic and international transit agencies with fare capping programs. 
]]></description>
      <pubDate>Tue, 26 Nov 2024 06:17:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2464324</guid>
    </item>
    <item>
      <title>Effect of Drivers Education on Traffic Safety
</title>
      <link>https://rip.trb.org/View/2437699</link>
      <description><![CDATA[Much of the research on the relationship between young driver training and crash risks has been correlational. What remains unclear is whether driver training has a direct positive impact on reducing crashes among teen drivers. Answering this question will allow planners and policymakers who aspire to enhance teen driving safety to better understand the effectiveness of driver training on safe driving.
In the proposed study, the research team predicts post-licensure traffic crashes among young drivers under 19 years old in Ohio based on whether they received formal driver training before obtaining a driver’s license. The analysis is part of a larger research initiative to study teen driver safety, in collaboration with Children’s Hospital of Philadelphia (CHOP) and the State of Ohio. Data came from a licensing record database maintained by the Ohio Bureau of Motor Vehicles (BMV). The database contains detailed driver demographics, including date of birth, sex, and home address, as well as information on each driver’s interactions with the BMV, including licensing transaction dates and a driver training completion date. In accordance with data privacy agreements between Ohio and CHOP, a data operations team at CHOP will prepare a de-identified dataset that links young driver’s training records and demographics with their crash records and socioeconomic status variables that are associated with their home Census tracts such as median household income. This study is exempt from institutional review board oversight by CHOP owing to use of de-identified data.
Young drivers’ crash risks and whether they choose to take driver training are likely both affected by their safety awareness. Due to the difficulties in measuring safety awareness of young drivers, conventional statistical models such as logistic regression are unable to capture the effect of safety awareness. This shortcoming, known as endogeneity, will lead to inaccurate estimates of the relationship between young driver crash risks and their driver training status. To overcome this issue, the team proposes a two-stage logistic regression modeling framework. In the first stage, the team predicts young drivers’ likelihood of taking driver training using the travel time to the nearest driving school from the drivers’ home Census tracts and the median household income of their home Census tracts. Previous studies from the research team have found that both travel time and home Census tract’s median household income are significant contributors to teens’ likelihood of taking driver training (Dong, Wu, Jensen, et al., 2023; Dong, Wu, Walshe, et al., 2023). In the second stage, the team uses the young driver’s predicted probabilities of taking driver training from the first stage to estimate whether they have been involved in traffic crashes post-licensure. The team hypothesizes that young drivers who took driver training have lower crash risks than those who delayed licensure until 18 years old and forwent driver training.
]]></description>
      <pubDate>Thu, 03 Oct 2024 15:39:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2437699</guid>
    </item>
    <item>
      <title>Development of a High-resolution Statewide Socio-demographic, Land Use and Economic Development Framework for Transportation Planning</title>
      <link>https://rip.trb.org/View/2434093</link>
      <description><![CDATA[The research is geared towards developing a standardized high resolution state-wide socio-demographic, land use and economic development model that will provide stakeholders with a framework analogous to the Florida Standard Urban Transportation Model Structure (FSUTMS) model. The research team, in consultation with the project manager and the Florida Department of Transportation (FDOT) statewide Planning office personnel, will generate a universal template of variables that will be useful for the statewide framework. For the universal template built, the research team will generate socio-economic, land use and economic development variables for a spatial resolution that can be directly employed for local jurisdictions and statewide models. With this overall vision, the specific objectives of the project are as follows: 1. Establish a universal template of socio-demographic, land use and economic indicators useful for the statewide framework for an appropriate spatial resolution to interact with existing transportation planning frameworks. 2. Develop and validate an algorithm to generate socio-demographic, land use and economic indicators employed in transportation planning and economic development analysis using public data sources for a pre-determined base year. 3. Employ the validated algorithm developed to generate future socio-demographic, land use and economic indicators in 5-year increments from 2025 through 2050.]]></description>
      <pubDate>Wed, 25 Sep 2024 07:35:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2434093</guid>
    </item>
    <item>
      <title>Residential Segregation and the Urban Demographic Shift of Pedestrian and Bicyclist Collisions</title>
      <link>https://rip.trb.org/View/2401741</link>
      <description><![CDATA[This project addresses the concerning increase in bicycle and pedestrian fatalities, particularly focusing on the disparities experienced by people of color (POC) in these incidents. It builds upon existing literature suggesting links between race/ethnicity and collision rates, aiming to investigate whether such disparities persist even after controlling for other factors like income and neighborhood characteristics. Data collection includes literature review, demographic data on residential segregation, police enforcement data, and pedestrian and bicyclist crash data from multiple states. Spatial analysis and spatiotemporal statistics will identify emerging hotspots of injury risk and shifts over time or space.]]></description>
      <pubDate>Mon, 08 Jul 2024 14:54:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2401741</guid>
    </item>
    <item>
      <title>State Preference Survey of Pedestrian Street Crossing and Big Data Analysis of Suppressed Pedestrian Trips</title>
      <link>https://rip.trb.org/View/2394416</link>
      <description><![CDATA[The objective of this research is to develop a pedestrian accessibility index that reflects the disutility posed by walking in uncomfortable settings or spending significant time waiting for the opportunity to cross. This index can help the Design Office better understand what designs and conditions result in greater use and attract more individuals to walk versus drive and utilize the designated facilities. This index will then be compared against a review of Replica big data of walking trips and the demographic information of those making those trips and the routes taken. Without this study, FDOT will miss an opportunity to understand which facility design and conditions create the largest benefit in terms of pedestrian use.]]></description>
      <pubDate>Mon, 17 Jun 2024 11:21:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2394416</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>A Granular Characterization of Mobility-Related Air Pollution Exposure Disparity</title>
      <link>https://rip.trb.org/View/2350714</link>
      <description><![CDATA[Air pollution is disproportionately affecting racial minorities and economically-disadvantaged populations. Despite continuous improvement in ambient air quality across the United States, relative exposure disparities among different socioeconomic groups continue to persist, worsening health outcomes and the quality of life of disadvantaged groups. Previous studies have typically measured air pollution exposure based on people’s home locations without considering how individual mobility patterns might influence it. This project quantifies air pollution exposure using big mobility data on individual trips from more than 40 million mobile devices in the contiguous United States for the pre-pandemic year 2019. The research team combines these highly granular mobility data with national air pollution estimates, specifically PM2.5, to first calculate mobility-related exposure in major U.S. cities. In addition, the team links anonymized personal mobility data with their demographics at the census tract level to characterize inequalities in particulate matter (PM2.5) exposure among different racial, ethnic, as well as other demographic groups. Methodologically, this approach explores a new paradigm to assessing short- and long-term individual-level exposure, serving as a reference for cross-sectional and cohort epidemiological studies. The study outcome reveals the spatial heterogeneity of air pollution exposure disparity and how it is linked to street design for cities in the United States. The analysis can inform evidence-based environmental plans and public health strategies to mitigate air pollution’s disproportionate impacts on racial and economically disadvantaged communities.]]></description>
      <pubDate>Mon, 11 Mar 2024 21:35:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2350714</guid>
    </item>
    <item>
      <title>Spatiotemporal Trends in Pedestrian Crashes</title>
      <link>https://rip.trb.org/View/2325688</link>
      <description><![CDATA[Spatiotemporal Trends in Pedestrian Crashes is a study conducted by researchers from the University of Maine (UMaine) and the University of Connecticut (UConn), in collaboration with the Maine and Connecticut Departments of Transportation. The project aims to understand the factors contributing to the rising number of pedestrian fatalities and injuries in the United States, including the 7,388 pedestrian fatalities recorded in 2021—a 13% increase from the previous year.

This research focuses on identifying patterns in pedestrian crashes and the role of community and infrastructure-related factors in crash outcomes. The study will utilize econometrics and machine learning tools to analyze pedestrian crash data in Maine and Connecticut, with particular attention to rural and geographically dispersed areas.

The research plan includes defining the appropriate scale for analysis, collecting relevant data on population, community characteristics, and infrastructure variables, and applying clustering algorithms and statistical models to uncover trends and contributing factors in pedestrian collisions. The findings will support efforts to improve pedestrian safety by addressing key risk factors and identifying priority areas for intervention.
]]></description>
      <pubDate>Mon, 22 Jan 2024 09:42:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2325688</guid>
    </item>
    <item>
      <title>Comprehensive Analysis of Factors Influencing Pedestrian Injury Severity at Intersection and Non-intersection Locations in Connecticut </title>
      <link>https://rip.trb.org/View/2321729</link>
      <description><![CDATA[Pedestrian safety is a growing concern in the United States, with 7,500 fatalities reported in 2022, marking the highest in three decades. Connecticut followed this trend, recording 69 fatalities in the same year. This study examined factors influencing pedestrian injury severity through a multi-level statistical analysis using Connecticut crash data, NHTSA’s VIN decoder, and Canadian Vehicle Specification data.


Crashes were classified into two categories: intersection and non-intersection, and pedestrian injuries were categorized into three: severe (Fatal/K, serious/A), non-severe (Evident/B, Possible/C), or no-injury/property-damage-only (O). Separate multinomial logistic regression models were developed to identify the factors influencing pedestrian injury severity, and binary logistic regression models were developed to compare fatal and serious injuries, providing a deeper analysis of severe injury outcomes.


At non-intersection locations, pedestrian impairment (OR=3.57), driver speeding (2.85), improper crossing (2.84), driver impairment (1.88), and unlighted roadways (1.55) significantly increased the odds of severe injury. At intersections, pedestrian impairment (4.53), speeding (7.40), roadway downgrade (2.04), and unlighted conditions (1.48) were key contributors.
Binary logistic models revealed, at non-intersections, pedestrian age (3% per year), pedestrian impairment (2.03), driver impairment (1.91), and roadway upgradient (3.18) significantly increased the risks of a fatal injury versus a serious injury. At intersections, speeding (7.39) was especially critical, while passive (0.20) and active (0.61) traffic control devices substantially reduced the risk of fatal injury.


The findings provide detailed, context-specific insights to guide pedestrian safety strategies. Reducing pedestrian impairment, enforcing speed control measures, improving roadway lighting, and implementing effective traffic control devices, particularly at intersections, can substantially reduce the likelihood of pedestrian injury severity.]]></description>
      <pubDate>Tue, 16 Jan 2024 12:31:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2321729</guid>
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