<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.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>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>Evaluating Vulnerable Road User Stress During Motor Vehicle Interactions in Urban Traffic Environments</title>
      <link>https://rip.trb.org/View/2625587</link>
      <description><![CDATA[The project aims to understand how interactions with motor vehicles and varying urban infrastructure conditions affect cyclists’ physiological stress responses. It focuses on urban cycling environments where cyclists share space with motor vehicles, considering different infrastructural contexts. The method involves analyzing physiological data (heart rate, heart rate variability, eye tracking), subjective responses, and performance data collected from the naturalistic part of the cycling experiment from the 24UNM03 project to quantify cyclists' stress responses. Using the integrated dataset, statistical models, likely Linear Mixed Effects Models (LMM), will be developed to quantify the relationship between micro-interactions and cyclist stress responses. This modeling approach accounts for both fixed and random effects, allowing examination of how individual variables influence outcomes while considering participant variability. The process aims to identify key stressors linked to infrastructure and vehicle behavior, translating findings into actionable insights for infrastructure design and safety interventions.]]></description>
      <pubDate>Tue, 18 Nov 2025 14:11:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625587</guid>
    </item>
    <item>
      <title>Determine Land Use Patterns, Travel, Regional Development, Population Trends, and Technology Change Impacts on Texas Energy Use and Carbon Emissions</title>
      <link>https://rip.trb.org/View/2593190</link>
      <description><![CDATA[Travel demand modelers and policymakers detailed forecasts of local and regional land use patterns and travel demands, both local and long distance, for freight and passengers, to anticipate Texas' evolving energy demands and their associated costs, emissions, safety, and other quality-of-life implications. To this end, the research team will (1) highlight the various energy, cost, and environmental impacts of different land-development settings across Texas, along with the integrated nature of travel, the built environment, energy, water, health, and natural systems; (2) quantify the infrastructure differences, travel differences, emissions, and energy differences of different land use settings, to accommodate the same number of persons and jobs in different built environments; and (3) use those findings to develop tools for strategic energy- and emissions-related forecasting, reflecting various policy and practice options across Texas settings, including, for example, changes in vehicle and building technologies and incentives, transport fuels and energy policies, zoning practices and building codes, transport system investments and operations, and energy-supply decisions.]]></description>
      <pubDate>Tue, 26 Aug 2025 12:39:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593190</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>The Effect of Urban Infrastructure Change on Movement</title>
      <link>https://rip.trb.org/View/2519203</link>
      <description><![CDATA[COVID is a crisis that is unanticipated both in its occurrence and also its length of impact. In the early days, many office employers implemented work-from-home policies while retail businesses shuttered, leading to deserted downtowns across the country. Yet crisis is also an opportunity, and municipalities and businesses innovated in response to the fears of infection. In particular, many cities changed transportation infrastructure, including permitting sidewalk cafes that accommodated outdoor dining, reallocating street space from travel or parking to outdoor dining, and redesigning streets to accommodate a wide variety of users etc. What are the effects of these urban infrastructure innovations? How well do they draw visitors and support businesses nearby? What are their effects on the region’s traffic patterns? Are there spillover effects spatially? As cities emerge from COVID and re-imagine the future of urban cores, answers to these questions are critical. Though the existing literature has a wealth of knowledge on the built environment effect on travel behavior, they are nearly exclusively at much larger scale (e.g., census tracts) and static (comparing different behavioral patterns between places with different built environment characteristics). There is little to no insight on how block-level urban infrastructure innovations lead to changes in visit patterns as well as nearby businesses. And yet, changes at this scale (block-level) are where local policy changes take place. This proposal is to answer these questions.

This research will leverage a variety of data sources to answer the above questions, including app-based global positioning system (GPS) data, google street view data, business data, and satellite images etc. All data are longitudinal, covering several years from pre- to during and post-COVID (now). More specifically, app-based GPS data will allow the research team to quantify people’s visit patterns as well as traffic flow patterns; Google Street View and satellite images will allow the team to capture changes in urban infrastructure at the block level; and business data will capture business activities over time. The project will develop novel algorithms to clean and explore these data and address issues inherent to their collection, including biases, sparsity, and unrepresentativeness etic. When necessary, data fusion methods integrating different types of data will also be developed. The project will also develop methods and metrics to quantify changes in urban infrastructure. In addition to answering the questions raised above, project deliverables will also include: (1) open-source notebooks that can be used to process the various kinds of data; and (2) visualizations at selected locations to illustrate the changes from before to after.

The study site will be in the City of Seattle, a medium-large city in the Pacific Northwest that has implemented several innovations in urban infrastructure during COVID. Initial sites include Ballard and University District in Seattle. Both have a vibrant business district (though tailoring to different populations), a popular farmers’ market and saw a surge of outdoor restaurants and cafes during the COVID period. In addition to these initial sites, the research team will also screen google street view datasets, satellite images, as well as consult local cities for identification of additional sites in the region. The goal is to have a set of sites with contrasting characteristics in built environment and socio-demographic characteristics.]]></description>
      <pubDate>Sat, 08 Mar 2025 11:33:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2519203</guid>
    </item>
    <item>
      <title>Telemedicine Adoption Before, During, and After COVID-19: The Role of Socioeconomic and Built Environment Variables</title>
      <link>https://rip.trb.org/View/2519198</link>
      <description><![CDATA[In this research, the research team focuses their investigation on the telemedicine adoption preferences of patients/consumers. This comprehensive approach contributes to advancing the existing body of knowledge in five distinct ways. First, the team uses rigorous multivariate econometric models that accommodate multiple sociodemographic and built environment (BE) variables at once rather than simple bivariate correlations of determinant factors with telemedicine adoption. Second, the framework is structured to discern the shifts in the effects of the factors affecting telemedicine adoption between the before- and after-COVID periods. This helps gain a deeper understanding of how socioeconomic and BE variables influenced telemedicine adoption before the pandemic and how the willingness of different segments of society to engage in telemedicine shifted as a result of the pandemic. Third, proposed multivariate model system recognizes that unobserved individual factors (such as technology savviness) that elevate telemedicine adoption before the pandemic may also affect adoption during the pandemic, and collectively influence an individual’s intention to use telemedicine in the post-pandemic period. Not accounting for such intra-individual correlation effects due to unobserved individual-level factors variables will, in general, provide biased estimates of the evolution pattern of telemedicine adoption over time. In this study, the longitudinal data comprises responses from the same individuals across three specific time periods, offering a unique advantage in quantifying the causal effect of the pandemic on telemedicine use. Fourth, the study explores the reasons for using or not using telemedicine in the after-COVID period from the patient’s viewpoint. The team conducts a consumer-focused analysis that provides unique insights into the motivations, preferences, and concerns of different patient segments regarding telemedicine. Specifically, in the after-COVID period, for telemedicine adopters, the team jointly models the reasons for adoption using multivariate binary probit models. Similarly, in the after-COVID period, for non-adopters, the team uses multivariate binary probit models to jointly analyze cited reasons for not adopting telehealth. This can inform healthcare providers, policymakers, and other stakeholders seeking to sustain telemedicine adoption post-COVID. Fifth, the study is the first that the team is aware of in the travel behavior literature that focuses on telemedicine adoption. Earlier studies related to virtual participations have investigated tele-adoption in the context of work, grocery shopping, and non-grocery shopping, but have not considered telemedicine adoption. However, telemedicine adoption can also have transportation ramifications, just as virtual participation in other types of activities can (including individuals potentially appropriating the freed-up time for pursuing other activities). In this regard, the team hopes that their study will open up additional research in studying the travel implications of tele-participation in medical-related activities. This should be of particular interest in the context of medical accessibility for the increasingly aging population of many countries, including the United States.]]></description>
      <pubDate>Sat, 08 Mar 2025 11:26:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2519198</guid>
    </item>
    <item>
      <title>A Dynamic Analysis of the Built Environment-Travel Behavior Relationship Using Three Activity-Travel Surveys in the Austin, Texas Region</title>
      <link>https://rip.trb.org/View/2440264</link>
      <description><![CDATA[The relationship between the built environment (BE) and travel behavior (TB) has long interested scholars and practitioners in transportation, urban planning and design, health, and other fields. The proposed research aims to advance knowledge of the BE-TB relationship by taking a dynamic analysis approach. The study will pool three activity-travel surveys (1998, 2007, and 2017) in Austin, TX, and analyze how variations and changes in TB revealed in the surveys relate to BE variations and changes in the timeframe corresponding to the surveys. The three activity-travel surveys do not provide longitudinal observations, strictly speaking, since the sampled travelers and households differ between surveys. The 20-year timeframe, however, enables the researchers to investigate 1) TB changes over time by groups, which can be defined by socioeconomic, demographic, and geospatial (e.g., neighborhoods) characteristics, and 2) changes in TB attributable to changes in BE. The proposed research is expected to gain new insights into the BE-TB connection and inform effective, BE-based planning and policy interventions to achieve broad goals of efficient and equitable transportation and sustainable cities and regions.]]></description>
      <pubDate>Thu, 10 Oct 2024 16:34:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440264</guid>
    </item>
    <item>
      <title>Identifying TOD-Capable Locations using D Variables: Flipping the Recipe on making the TOD Cake</title>
      <link>https://rip.trb.org/View/2420105</link>
      <description><![CDATA[The transportation-land use field has argued for transit-oriented developments (TODs) due to several benefits such as reduced driving and potential use of travel options with positive health outcomes. However, locations with features that align with TODs are generally undersupplied. In the U.S. context, the production of TODs has been relatively rare. With the intersecting crises of unaffordability and demographic shifts in the U.S. population, there is a need for effective mechanisms to help enable the production of mixed-use communities embedded in active travel and transit networks. Most often, a top-down regulatory process including zoning plus comprehensive or special plans are used to help TODs emerge. Additionally, local government actions are incentivized by private market forces such as real-estate developers who identify markets for TOD-aligned communities. However, these processes are reactionary, while the need is more urgent. Therefore, we argue for flipping this time-consuming and expensive top-down recipe. We contend that using the D variables, documented in the EPA Smart Location Database (US EPA, 2021), can reveal locations that have the ingredients for making TOD-capable, mixed-use places. Our approach should be especially impactful in communities that are focused on creating livable and vibrant neighborhoods. Over time, land use markets evolve to an equilibrium such that some locations become TOD-lite under an existing umbrella of zoning regulations; however, most places even in the same geography do not mature into mixed-use sustainable developments. A deeper investigation could help identify organically emerging TOD-capable locations. Places with a sub-set of features for a TOD could be incentivized to develop into more TOD-aligned locations. In terms of the policy toolkit, a census block group (CBG) with several of the D variables showing high values but missing some key ingredients such as destination accessibility or distance to transit, could be places for strategic transit and active travel investments. Many CBGs might have lower density and diversity but might score high on other D variables. Such locations could be incentivized through mixed use and infill development, without changing anything else on the D-variable ingredient list. In essence, we contend that practitioners need a toolkit to identify sub-markets for transit capable, affordable, mixed-use developments, which must evolve in an urban setting but are often ignored because they do not rise to the level of comprehensive or special plans. Our project will create a toolkit to help identify such TOD-capable locations. Project outputs will include: 1. A practice-ready toolkit to identify TOD-capable locations, at the CBG level, across large Metropolitan Statistical Areas. 2. A replicable framework to engage expert practitioners in local/regional agencies for gathering input for effective TOD implementation. 3. An open-source publicly accessible web app that allows various stakeholders to learn and implement similar ideas in various geographies. 4. A public-facing technical report documenting the research approach, method, and findings. 5. At least one conference presentation and journal manuscript for a scientific audience.]]></description>
      <pubDate>Mon, 26 Aug 2024 14:58:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2420105</guid>
    </item>
    <item>
      <title>A Deep Learning Approach for Detecting Built Environment in Transit-Oriented Developments</title>
      <link>https://rip.trb.org/View/2420210</link>
      <description><![CDATA[Transit-Oriented Developments (TODs) are a pivotal strategy for fostering sustainable urban growth amidst escalating urbanization and population spikes. These developments are strategically designed to combat urban sprawl, significantly reduce the reliance on automobiles, cut greenhouse gas emissions, and create more livable, community-focused urban areas, and help address the housing needs. The core philosophy behind TODs lies in integrating residential, business, and leisure spaces with public transportation systems, thereby promoting a lifestyle that prioritizes walking, cycling, and the use of public transit over automobile use. The effectiveness of TODs in achieving these goals hinges on the built environment’s configuration, which influences sustainable transportation adoption, boosts economic vitality, ensures accessibility, and champions environmental stewardship. However, the acceleration of TODs brings forth a pressing challenge: How to accurately and efficiently measure the built environment in TODs across a large geographical region. To tackle this challenge, this project proposes a novel deep learning framework to automate the detection and categorization of essential built environment elements within TODs. By leveraging the well-established 5D framework—density, diversity, design, destination, and distance to transit—this framework seeks to comprehensively identify and map the components that constitute an effective TOD. To accomplish this, the research project will develop a sophisticated deep learning architecture capable of assimilating multi-sourced datasets with various modalities (e.g., imagery, text, tabular, GIS data). These will include high-resolution aerial imagery to capture urban layouts and green spaces; Google Street-View imagery for a pedestrian-level perspective of the urban landscape; parcel and OpenStreetMap data for detailed insights into land use, infrastructure, and building outlines; General Transit Feed Specification (GTFS) data for a comprehensive overview of public transit networks; and Census data to incorporate demographic insights. This wealth of information will be processed using a blend of cutting-edge machine learning techniques, including but not limited to, pretrained Convolutional Neural Networks (CNNs) and attention-based Transformer models for decoding unstructured data (such as images) and Graph Neural Networks (GNNs) for processing structured data analysis (such as GIS and tabular data). The proposed framework aims to create a nuanced and comprehensive understanding of TODs’ built environments, facilitating the detection of key features like buildings, pedestrian crossings, transit lanes, green spaces, and more. A database created by the PIs will serve as the primary case studies for this research, providing a diverse range of urban contexts for evaluation and validation of the proposed framework. The project outlines several major tasks: data acquisition, preprocessing, development and validation of the deep learning model, applying the validated model to additional TOD locations, hosting educational workshops, and compiling findings into a final report. The proposed approach will be developed, evaluated, and validated by using randomly selected TOD locations across Florida. This research endeavors to equip urban planners, transit authorities, and policymakers with an advanced tool for automatically identifying critical elements of TODs’ built environment, thereby facilitating smarter, more sustainable decision-making nationwide. Outputs will include 1) Publications & conference contributions 2) Database: An open-source database, including various sources of downloadable data for TOD analyses, to disseminate the research outcomes and encourage future research. This database can be used to generate interactive maps, visualizations of the built environment's impact on accessibility within TODs, & will serve as a resource for urban planners, policymakers, researchers, and the public. 3) Methodologies & Technologies: The project will introduce innovative deep learning architectures that can be directly used to automatically detect and assess the built environment elements within TOD. Code repositories and fine-tuned models’ weights will be open sourced to keep transparency and allow for reproducibility for future research. 4) Partnerships: Establish and enhance partnerships with stakeholders from local government, non-profits, & tech companies specializing in geospatial data and AI. ]]></description>
      <pubDate>Mon, 26 Aug 2024 14:51:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2420210</guid>
    </item>
    <item>
      <title>Disability, mode perceptions, and travel behavior: An intersectional study
</title>
      <link>https://rip.trb.org/View/2420068</link>
      <description><![CDATA[Despite the more than three decades since the passage of the Americans with Disabilities
Act (ADA), people with disabilities, which comprise roughly one quarter of the US population,
still face considerable challenges to their mobility and access. They make fewer trips and are more
dependent on others because of deficiencies in pedestrian infrastructure, transit and for-hire vehicles,
and specialized paratransit services. While there is a considerable amount of research that identifies
the breadth of mobility challenges and access barriers, limited research has address how these
mobility challenges influence mode choice for people with disabilities. This project will develop and
administer a web-based survey to an oversample of California residents with disabilities to understand how disability influences mode choice, accounting for perceptions of the built environment and
mode-specific challenges. The project further seeks to understand how intersectional disadvantage
moderates mode choice decisions. The research team anticipates using several analytical methods
to answer the research questions, including descriptive statistics, basic statistical tests of comparison,
and multinomial logistic regression. The research team aims to engage with disability serving
organizations to ensure that the survey reflects real concerns and will provide meaningful data, and
to share results in support of universal access goals that the organizations and public agencies are
pursuing.]]></description>
      <pubDate>Thu, 22 Aug 2024 18:32:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/2420068</guid>
    </item>
    <item>
      <title>The Role of Built Environment Factors in Enhancing Pedestrian and Bicycle Safety: A Comprehensive Analysis and Policy Implications</title>
      <link>https://rip.trb.org/View/2401750</link>
      <description><![CDATA[Despite recent efforts to achieve Vision Zero goals in the US, pedestrian and bicycle safety remains a critical issue that affects individuals and communities. The nearly 7,500 pedestrian fatalities annually and 1,000 bicyclist fatalities in recent years highlight the urgent need to address pedestrian and bicycle safety, particularly in urban, suburban, and rural areas where exposure and crash risks are changing. Importantly, the role of the built environment in such environments is changing, e.g., disadvantaged communities, including low-income neighborhoods and communities of color, can face higher risks of pedestrian and bicycle crashes. This project focuses on enhancing pedestrian and bicycle safety through a detailed analysis of built environment features at the neighborhood level. As part of the Center for Pedestrian and Bicyclist Safety's (CPBS's) priorities on Safety Design, it explores the impact of factors such as street lighting, sidewalk availability, road design, land use type/mix and density, and traffic volumes on pedestrian and bicycle crashes frequency and their severity. With a particular emphasis on safety disparities in disadvantaged communities, this research utilizes a variety of data sources, including police crash reports, census data, land use data, and the Equitable Transportation Community (ETC) data released by the US Department of Transportation. The study will employ both traditional statistical methods and explainable artificial intelligence techniques to analyze data and identify key contributors to crash occurrences and severity during both day and night. Techniques such as negative binomial models, ordered probability models and structural equation modeling will be used to understand the direct and indirect effects of built environment features on safety outcomes. Special attention will be given to the role of these features in disadvantaged communities, aiming to develop targeted interventions to reduce crashes and enhance pedestrian and bicycle safety.]]></description>
      <pubDate>Mon, 08 Jul 2024 14:54:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2401750</guid>
    </item>
    <item>
      <title>Incorporating Public Health Outcomes into the Transportation Planning and Decision-Making Process



</title>
      <link>https://rip.trb.org/View/2381746</link>
      <description><![CDATA[The integration of public health outcomes into transportation planning and decision-making processes is essential to create sustainable, resilient, and healthy communities. Transportation directly impacts public health outcomes, including physical activity levels, safety, and access to everyday destinations. Everyday destinations refer to places associated with access to work, school, recreation, and food. Policies and decisions related to transportation infrastructure, land use, and the built environment can have different impacts on public health outcomes that can lead to health disparities. Evaluating the effectiveness of policy interventions and successful practices for incorporating public health considerations is crucial for improving transportation planning processes.  

Several challenges exist in incorporating public health outcomes into the transportation planning and decision-making processes. Public health agencies and transportation agencies often operate with limited collaboration and coordination. The limited interdisciplinary cooperation poses challenges in aligning public health goals with transportation plans and policies. There is limited documented research on successful collaborative efforts and strategic partnerships between public health and transportation organizations, though the two are closely linked. While some health evaluation tools exist, there is a need for tools that can be readily integrated into transportation decision-making from concept development to post-implementation. This may include tools such as evaluation frameworks, methodologies, comprehensive data, and metrics. 

Transportation decision-makers need research-derived tools to help determine the public health outcomes of transportation projects, inform policies, and help prioritize proposed projects. 

OBJECTIVE: The objective of this research is to develop a guide that details how to (1) integrate public health outcomes into transportation decision-making and resource allocation; and (2) assess positive public health outcomes related to physical activity, mobility options, and access to everyday destinations. The guide shall include an evaluation framework and metrics and be accompanied by a spreadsheet tool. ]]></description>
      <pubDate>Wed, 22 May 2024 14:34:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2381746</guid>
    </item>
    <item>
      <title>Assessing Transportation Infrastructure Segments for Bike Suitability</title>
      <link>https://rip.trb.org/View/2338833</link>
      <description><![CDATA[There is need for research and empirical measurements to assess how the built transportation infrastructure accommodates bike trips in urbanized communities across the US. This project will focus on assessing transportation infrastructure segments for bike suitability using motion and vibration sensors. This effort will supplement the previously completed C2M2 project “Assessing Potential of Bike Share Networks and Active Transportation to Improve Urban Mobility, Physical Activity and Public Health Outcomes in South Carolina”. Data will be collected in Charleston, SC, Columbia, SC, and Lincoln, NE to provide a case study location for exploring insightful relationships that will be informative to other communities. The data will be analyzed to investigate route conditions to better understand how built environment infrastructure is meeting the users’ needs. This research focuses on evaluating the built environment infrastructure by collecting data that will help determine cyclist riding quality. Qualitative, quantitative, and geospatial methods will be used to evaluate cycling paths.]]></description>
      <pubDate>Wed, 14 Feb 2024 17:13:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2338833</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>
    </item>
    <item>
      <title>Exploring the relationship between the built environment, safety, and bus ridership in Connecticut using mixed research methods</title>
      <link>https://rip.trb.org/View/2321640</link>
      <description><![CDATA[This research project aims to analyze and improve public transportation safety and ridership in Connecticut, focusing on the interplay between crime rates, bus stop environments, and ridership patterns. In response to concerns about rising crime and declining transit use, the project seeks to identify environmental factors at bus stops that are associated with crime occurrence, using extensive data analysis and field studies. This interdisciplinary approach combines a literature review, spatial data analysis, and surveys to understand how the built environment shapes transit safety perceptions. The study's findings will inform strategies to enhance bus stop safety and increase ridership. Project outcomes will offer practical insights for transit agencies, contributing to safer and more widely used urban transportation systems.
]]></description>
      <pubDate>Fri, 12 Jan 2024 10:20:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2321640</guid>
    </item>
    <item>
      <title>50 Years of Trends in Station Areas across the United States</title>
      <link>https://rip.trb.org/View/2301339</link>
      <description><![CDATA[Objectives and Importance: Building upon earlier work, including the National Transit Oriented Development (TOD) Database established by the former Center for Transit-Oriented Development / Reconnecting America and other studies led by Dr. Renne in collaboration with Dr. Ewing, Ms. Tolford and others, this study will create a database and examine travel behavior, vehicle ownership, demographics, equity, location affordability, built environment measures, and other related topics for all station areas across the United States. The project will build upon previous work that classifies station areas by TOD typology based on walkability, density, and land use mix. The data created for this project will not only allow for the analysis of trends in station areas over the past 50 years but will also be available on an open-data platform that will serve CETOC and anyone in the public to examine several of US DOT strategic goals including performance indicators towards climate and sustainability and net-zero emissions and climate justice. The platform will also enable a greater level of equity analysis toward wealth creation and expanding access to housing and transportation affordability. The data platform will be a geocoded, layered database that will focus on integrating data for neighborhoods around all fixed transit stations across the nation. Data will be included also for the surrounding metropolitan region to allow for comparison of station areas to control areas outside the stations and to see the influence of the stations well beyond the typical half-mile unit of analysis. The study will also compare the similarities and differences between the concepts of TODs and Transit-Oriented Communities (TOCs) and conduct a literature review to identify what this means from a data collection and analysis perspective. The study will analyze which variables are important to collect and how this may have changed over time since the original TOD databased was published in the 2000s.  

Scope: Update and build a database representing station areas a half-mile from fixed route stations, including about 4,700 stations, representing subway/metro, light rail/tram, commuter rail, intercity passenger rail, bus rapid transit, and ferry stations with available data commuting, vehicle ownership, demographics, equity, location affordability, jobs, and built environment measures from each station resenting 1970, 1980, 1990, 2000, 2010, and 2020. Note that not all stations may have all historical data. The data will then be published on GitHub or a similar platform for public use. 

Method: (1) Using the National Transportation Atlas Database (NTAD) published by the Bureau of Transportation Statistics, develop an updated, geocoded list of all station areas
(2) Utilizing the US Census, EPA Smart Location Database, National Walkability Index, Longitudinal Employer-Household Dynamics data, and other available databases including the outdated National TOD Database, develop data for each census tract within each station, by year. (3) Create a typology for each station area based on built environment measures including walkability, density and potentially other measures to classify each station typology as a TOD, Hybrid, and Transit-Adjacent Development. Also examine station areas based on equity variables including race, ethnicity, and income based on prior studies of gentrification in TODs led by Renne and others on the team and within the literature to identify Equitable TODs (ETODs). (4) Using various station classification measures, analyze longitudinal tends and cross-sectional patterns in station areas.
]]></description>
      <pubDate>Thu, 14 Dec 2023 23:31:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2301339</guid>
    </item>
  </channel>
</rss>