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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Mobility Hubs: Lessons Learned from Early Adopters</title>
      <link>https://trid.trb.org/View/2608504</link>
      <description><![CDATA[As city and regional officials aspire to promote multimodal transportation, meet environmental sustainability goals, and reduce personal vehicle dependence, mobility hubs are gaining in popularity. Mobility hubs are centralized locations where travelers can conveniently access a growing number of public and private mobility options – including shared bicycles, scooters, and cars, and shared rides delivered by ridehailing and microtransit services. These hubs extend the reach of public transportation networks, safely connect people from one travel mode to another, and make it easier to consider options other than driving alone. Featuring people-focused infrastructure design, these hubs can also serve as focal points for accessing goods and services by centering safety and accessibility for vulnerable travelers, including women, people with disabilities, and BIPOC (black, indigenous, people of color) travelers. This report details lessons learned from mobility hub programs in four geographic areas – Columbus, Ohio; Hamburg, Germany; Minneapolis, Minnesota; and San Diego County, California. Applying lessons learned from these early adopters, I recommend principles to guide FASTLinkDTLA’s approach to the mobility hub program design and development in Los Angeles County. These recommendations include: developing public-private sector champions; piloting multiple hub design and operational models; layering digital platforms onto exceptional physical amenities; conducting public engagement throughout design, testing, and operations; and securing local funding for hub network expansion and operations.]]></description>
      <pubDate>Fri, 07 Nov 2025 11:31:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2608504</guid>
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    <item>
      <title>Measuring the impacts of sidewalks on public transit first mile/last mile accessibility and their association with social and demographic factors</title>
      <link>https://trid.trb.org/View/2590211</link>
      <description><![CDATA[Public transportation offers a sustainable, environment-friendly, and equitable mode of travel, particularly for marginalized and disadvantaged groups. However, the first-mile/last-mile (FMLM) access problem can pose significant challenges to its efficiency and ability to generate accessibility. An incomplete sidewalk network reduces transit accessibility by creating barriers, especially for sidewalk-reliant groups such as people with mobility disabilities, elderly, and school children. In this study, the authors develop new metrics to measure the impacts of sidewalk incompleteness on transit FMLM accessibility. Using data from Columbus, Ohio, USA, a typical mid-sized American city with an incomplete sidewalk network, the authors apply these measures and compare them to the sociodemographic characteristics of the neighborhood surrounding each stop. The authors find that the sidewalk network and high-quality sidewalk network have 45%-50% less spatial coverage around bus stops compared to street networks, respectively. 39% and 49% of bus stops provide access to fewer groceries and healthcare facilities, respectively, when following sidewalk networks. The authors observe that inner-city neighborhoods, despite being less affluent, often have better sidewalk access compared to affluent suburban areas. However, peripheral to city-center and less-affluent neighborhoods exhibit a cumulative burden with the poor sidewalk access that impacts access to essential resources such as groceries and health care. The research provides a direct and comparative measure of FMLM accessibility and suggests a strong linkage of urban morphology with sidewalk accessibility. This study calls for targeted sidewalk improvements and a nuanced understanding of accessibility gaps to promote equitable and efficient public transportation systems.]]></description>
      <pubDate>Wed, 17 Sep 2025 10:55:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2590211</guid>
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    <item>
      <title>Spatial Heterogeneities in Healthcare Visits and their Socioeconomic Determinants: A Comprehensive Analysis Using Traffic Flow Data</title>
      <link>https://trid.trb.org/View/2567030</link>
      <description><![CDATA[Spatial accessibility to healthcare is widely studied across various disciplines, primarily to identify underserved neighborhoods. Whereas most studies focus on the potential travel to healthcare facilities, few consider the actual travel people undertake. Addressing this research gap, our study explores how healthcare visits, the distance traveled, and socioeconomic determinants vary across hospital locations and types. We analyze healthcare visits as origin-to-destination flows to hospitals in Columbus, OH, using GPS-tracked mobility data. We consider three hospital types to account for services offered: general hospitals, specialty hospitals, and outpatient care facilities. We employ a spatially weighted interaction model to identify both global and local influences of distance and socioeconomic factors on healthcare visits. Overall, we observed a significant decline in healthcare visits with increasing distance, whereas socioeconomic influences appeared minor at the global level. However, the disaggregated local models found that the impacts of distance and socioeconomic factors fluctuated considerably across hospital locations and services offered. For instance, hospitals providing diverse and specialized services tended to attract visitors from all socioeconomic backgrounds and greater distances. In contrast, general hospitals and outpatient care facilities, especially suburban ones, exhibited more localized visit patterns with fewer visitors from poorer neighborhoods, whereas a few exclusively serve poorer populations. Additionally, we found that the lack of medical insurance significantly affected healthcare visits. These findings thus underscore the healthcare disparities experienced, enabling practitioners and policy makers to pinpoint the hospital locations that are less accessible to socioeconomically disadvantaged populations, ultimately informing the development of more equity-focused healthcare policies.]]></description>
      <pubDate>Mon, 23 Jun 2025 08:44:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2567030</guid>
    </item>
    <item>
      <title>Inclusive accessibility: Analyzing socio-economic disparities in perceived accessibility</title>
      <link>https://trid.trb.org/View/2487785</link>
      <description><![CDATA[Existing accessibility measures mainly focus on the physical limitations of travel and ignore travelers' perceptions, behavior, and socio-economic differences. By integrating approaches in time geography and travel behavior, this study introduces a bottom-up inclusive accessibility concept that aggregates individual-level travel perceptions across socio-economic groups to evaluate their multimodal access to opportunities. The authors classify accessibility constraints into hard constraints (physical space-time limitations to travel) and soft constraints (perceptual factors influencing travel, such as safety perceptions, comfort, and willingness to travel). The authors categorize travelers into 12 mutually exclusive socio-economic groups from a mobility survey dataset of 477 travelers. The authors apply a support vector regressor-based ensemble algorithm to estimate network-level walking perception scores as soft constraints for each social group. The authors derive group-specific inclusive accessibility measures that consider space-time limitations from transit and sidewalk networks as hard constraints and minimize the group-specific soft constraint to a certain threshold. Finally, The authors demonstrate the effectiveness of group-specific inclusive accessibility by comparing it with the classic access measure. The authors’ study provides scientific evidence on how people of varying socio-economic statuses perceive the same travel environment differently. The authors find that socio-economically disadvantaged communities experience higher mobility barriers and lower accessibility while walking and using transit in Columbus, OH. The authors’ study demonstrates a transition from person- to place-based accessibility measures by sequentially quantifying mobility perceptions for individual travelers and aggregating them by social groups for a large geographic scale, making this approach suitable for equity-oriented need-specific transportation planning.]]></description>
      <pubDate>Tue, 18 Feb 2025 10:56:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2487785</guid>
    </item>
    <item>
      <title>Greening the commute: A case study of demand for employer-sponsored microtransit</title>
      <link>https://trid.trb.org/View/2446791</link>
      <description><![CDATA[Demand-responsive, pooled, app-based transportation services, often known as microtransit, fill a gap in providing public transportation where fixed-route transit services are weak. While prior research mostly focused on public-access microtransit services, little is known about the potential of restricted-access, employer-sponsored services to achieve mode shifts away from driving. This study investigates the possible use of employer-sponsored microtransit service by commuters who currently drive to work, using data from a stated choice experiment conducted at a major medical center in Columbus, Ohio. The results reveal a considerable interest in a hypothetical microtransit commuter service among medical center employees, with on average 29.6% of them shifting from car to microtransit. Overall, relatively few sociodemographic characteristics are found to correlate with interest in employer-sponsored microtransit use, but income, status as a shift worker, and a desire to work while commuting are found to affect choice. Valuations of in-vehicle travel time, flexibility in drop-off/pick-up time, and stop location are calculated and compared to prior results from the transit literature. Such valuations can serve as inputs for optimization models to design microtransit systems. Furthermore, respondents’ potential concerns about a microtransit service and reactions to proposed incentive schemes are analyzed. The study results highlight the value of combining employer-sponsored microtransit implementations with transportation demand management strategies that reduce the attractiveness of commuting by car. The findings suggest that employer-sponsored microtransit represents an opportunity to reduce greenhouse gas emissions and congestion in an industry sector that employs 6.6 million workers in the US.]]></description>
      <pubDate>Fri, 15 Nov 2024 09:47:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2446791</guid>
    </item>
    <item>
      <title>Investigating Alternative Representations of Building Structures in Traffic Noise Modeling</title>
      <link>https://trid.trb.org/View/2440280</link>
      <description><![CDATA[This paper presents the results of an investigation of various traffic noise modeling techniques with the Traffic Noise Model (TNM) 2.5 software package, with specific focus on the representation of traffic noise shielding from residences parallel to the freeway. A comprehensive traffic noise field study was undertaken in a neighborhood adjacent to Interstate 270 in Columbus, Ohio. Data collection activities occurred in three “waves” accounting for the pre-clearing, no barrier, and with barrier conditions. Measured noise levels at the study site indicated that there was some noise shielding effect of the residential structures; this effect was most readily perceptible behind the first row of residences. Separate TNM layouts were developed to represent the neighborhood’s residential structures as either a TNM building row object or as separate TNM barrier objects for each structure. The analysis found that modeling each individual residential structure as a separate TNM noise barrier object (single building façade that is closest to the freeway) produces the most accurate modeled noise levels, as compared to the building row representation or simply omitting the shielding from structures altogether. Based on these findings, it is recommended that traffic noise practitioners consider modeling each residential structure in noise-sensitive areas as TNM noise barrier objects.]]></description>
      <pubDate>Wed, 16 Oct 2024 15:19:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2440280</guid>
    </item>
    <item>
      <title>Spnaf: An R package for analyzing and mapping the hotspots of flow datasets</title>
      <link>https://trid.trb.org/View/2420039</link>
      <description><![CDATA[This paper introduces {spnaf} (spatial network autocorrelation for flows), an R package designed for the hotspot analysis of flow (e.g., human mobility, transportation, and animal movement) datasets based on Berglund and Karlström’s G index. We demonstrate the utility of the {spnaf} package through two example analyses by data forms: 1) bike-sharing trip patterns in Columbus, Ohio, USA, using polygon data, and 2) U.S. airports’ passenger travel patterns, using point data. The {spnaf} is available for download from the Comprehensive R Archive Network (CRAN), which contains a vignette and sample data/code for immediate use. This package addresses limitations in existing spatial analysis packages and emphasizes its efficiency in detecting flow hotspots. It is highly applicable in various urban and geographic data science applications. {spnaf} is still in its early stages and we hope that interested readers can contribute to the development and enhancement of the package.]]></description>
      <pubDate>Mon, 16 Sep 2024 09:00:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2420039</guid>
    </item>
    <item>
      <title>The built environment and the determination of fault in urban pedestrian crashes: Toward a systems-oriented crash investigation</title>
      <link>https://trid.trb.org/View/2379776</link>
      <description><![CDATA[This study identifies built environmental factors that influence the determination of fault in urban pedestrian crashes in the United States, with implications for both safety and equity. Using data from Columbus, Ohio, the authors apply regression modeling, spatial analysis, and case studies, and find pedestrians are more likely to be found at fault on fast, high-volume arterial roads with bus stops. The authors also observe that better provision of crossings leads to more marked intersection crashes, which are less likely to be blamed on pedestrians. In addition, large differences in both the provision of crossings and fault exist between neighborhoods. The authors interpret findings through the lenses of the systems-oriented safety approaches Safe Systems and Vision Zero. The conclusion argues that the designation of individual responsibility for crashes preempts collective responsibility, preventing wider adoption of design interventions as well as systemic changes to the processes that determine the built environment of US roadways.]]></description>
      <pubDate>Tue, 28 May 2024 09:15:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2379776</guid>
    </item>
    <item>
      <title>Evaluating the impacts of supply-demand dynamics and distance decay effects on public transit project assessment: A study of healthcare accessibility and inequalities</title>
      <link>https://trid.trb.org/View/2351070</link>
      <description><![CDATA[Previous studies evaluating the impacts of transport interventions on accessibility to healthcare have largely overlooked competition among patients for limited resources and the tendency to use healthcare located closer to them (i.e., distance decay effects). This study aims to demonstrate how overlooking supply-demand dynamics and distance decay effects can distort the evaluation of a new public transit service's impacts on healthcare accessibility and inequalities. Specifically, using a new bus rapid transit (BRT) service in Columbus, Ohio, USA, as an example, the authors compare three types of measures: 1) cumulative-opportunity, 2) multimodal two-step floating catchment area (2SFCA), and 3) multimodal generalized 2SFCA (G2SFCA) accessibility metrics. To understand the similarities in results obtained from different accessibility measures, they use the Pearson correlation coefficients of standardized accessibility change scores. Further, they leverage an inequality index, the Palma ratios, to examine how inequality assessments can be sensitive to the choice of accessibility measure. The correlation analysis reveals notable differences among the three measures. This indicates that neglecting supply-demand dynamics and distance decay effects can lead to differences in results, potentially distorting transit project evaluations and misleading stakeholders. Moreover, although overall conclusions about inequalities are largely consistent, they observed nuanced and statistically significant differences in Palma ratios when derived from cumulative-opportunity metrics compared to the multimodal 2SFCA and multimodal generalized 2SFCA measures. Their findings underscore the importance of considering supply-demand dynamics and distance decay effects for a more realistic and accurate assessment of new transit service's impacts on healthcare accessibility and inequalities.]]></description>
      <pubDate>Thu, 21 Mar 2024 11:04:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2351070</guid>
    </item>
    <item>
      <title>What determines modal substitution between bike-sharing and public transit? Evidence from Columbus, Ohio during the COVID-19 pandemic</title>
      <link>https://trid.trb.org/View/2259964</link>
      <description><![CDATA[This study explores the relationship between bike-sharing and public transit trips before and after COVID-19 lockdowns, focusing on Columbus, Ohio. Using CoGo Bike Share trip data together with Automatic Passenger Counter (APC) data from the Central Ohio Transit Authority (COTA), the authors first identify bike-share trips and public transit ridership patterns during this period and classify bike-share trips as substitutive and complementary to public transit trips. The authors then employ binary logit models to analyze the determinants of substitutive trips. Some key findings are as follows. First, the COVID-19 outbreak caused decreases in bus ridership while increases in bike-sharing trips. Second, bike-sharing may compete with public transit for short distance trips. Third, bike-sharing trips are likely to substitute public transit within more intensive and congested public transit networks after controlling for various other factors. Fourth, trips made in different neighborhoods and land use reveal significant relationships with modal types. Lastly, lockdowns due to the COVID-19 pandemic led to substitutive trips substantially, particularly on weekdays.]]></description>
      <pubDate>Thu, 22 Feb 2024 16:14:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2259964</guid>
    </item>
    <item>
      <title>Measuring the impacts of disruptions on public transit accessibility and reliability</title>
      <link>https://trid.trb.org/View/2310855</link>
      <description><![CDATA[Public transit systems are facing higher risk of system degradation from external disruptions, affecting their ability to deliver reliable accessibility to transit users. Therefore, resilience, the ability to maintain functions during a disruption, becomes a crucial assessment of public transit systems. In this paper, the authors calculate two space-time prism-based measures with General Transit Feed Specification real-time (GTFS-RT) data: realizable real-time accessibility, a conservative real-time accessibility measure that can be achieved by users subject to delays, and scheduled accessibility, accessibility based on schedule. THey also define accessibility unreliability, the deviation between realizable accessibility and scheduled accessibility, to measure the reliability of delivered accessibility. They use the two measures to gauge the resilience of public transit systems and conduct two case studies of short- and long-term disruptions, namely Ohio State football games and the COVID-19 pandemic, on the Central Ohio Transit Authority (COTA) bus system in Columbus, Ohio. They find there are two peaks of high unreliability before and after each football games, with the stadium as the geographic center of the disruption. The after-game peaks are shorter and more intense than the before-game. They also find COVID-19 had persistent negative impacts on accessibility and reliability: Realizable accessibility universally declined during the pandemic, but only part of cities experienced unreliability increase, primarily in urban perimeters and suburbs. Improved traffic conditions during the pandemic may help to reduce unreliability, but the later service cuts increased unreliability. The two case studies prove the effectiveness of the method to detect system disturbances and provide important guidance for public transit system operation and planning.]]></description>
      <pubDate>Wed, 27 Dec 2023 11:25:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2310855</guid>
    </item>
    <item>
      <title>Using Aggregated Fine Geo-Resolution Vehicle Telemetric Data to Predict Crash Occurrence</title>
      <link>https://trid.trb.org/View/2296672</link>
      <description><![CDATA[Being able to predict motor vehicle crashes which are a major public health concern would greatly improve traffic safety. The prevalence of mobile sensing platforms now allows spatially and temporally rich driving data to be collected relatively easily. Research efforts have been devoted to predicting crashes from such data. This paper seeks in particular to assess the feasibility and performance of using aggregated fine geo-resolution vehicle telemetric data for crash risk prediction. We acquired vehicle telemetric data from Geotab Inc., which recorded the frequency of hard acceleration, hard braking, harsh cornering, and the average magnitude of those harsh events among its registered commercial vehicles for every 150?×?150?m2 roadway segment within Columbus, Ohio between January and April 2018. We aggregated the data, obtained the crash history from the Ohio Police Accident Report, and leveraged three machine learning–based algorithms to predict the crash risk. The results suggest that aggregated vehicle telemetric data could provide acceptable predictions for crash risk at a roadway segment level. Our models’ predictive performances were further improved and maximized by including in the models both vehicle telemetric data and roadway geometric characteristics. Several factors, such as the aggregated count of hard accelerations and the presence of an intersection, were shown to be the factors that potentially made the greatest contribution to crash occurrence. We concluded that vehicle telemetric data could provide complementary and valuable information about crash likelihood monitoring, which may enable the police and city planners to implement proactive safety interventions. Yet the nature of traffic crashes is still complex and multi-dimensional.]]></description>
      <pubDate>Wed, 29 Nov 2023 10:13:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2296672</guid>
    </item>
    <item>
      <title>A Modeling Framework to Quantify Impacts of Mobility Services on Multi-modal Transportation Systems: Methodology and a Case Study in Columbus, OH [supporting dataset]</title>
      <link>https://trid.trb.org/View/2247611</link>
      <description><![CDATA[Abstract of the final report is stated below for reference: This project (1) develops a flexible mobility service model to accommodate different operational policies and strategies leveraging the real-world data (particularly for Columbus, Ohio); (2) develops a holistic multimodal transportation network modeling framework integrating mobility services with existing transportation modes; (3) assesses the system-level impacts of mobility services with different operational policies and strategies on the multimodal transportation network; and (4) simulates future mobility scenarios and analyzes their resulting effects on the system performance.]]></description>
      <pubDate>Tue, 26 Sep 2023 17:04:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2247611</guid>
    </item>
    <item>
      <title>A Modeling Framework to Quantify Impacts of Mobility Services on Multi-modal Transportation Systems: Methodology and a Case Study in Columbus, OH</title>
      <link>https://trid.trb.org/View/2247610</link>
      <description><![CDATA[This project (1) develops a flexible mobility service model to accommodate different operational policies and strategies leveraging the real-world data (particularly for Columbus, Ohio); (2) develops a holistic multimodal transportation network modeling framework integrating mobility services with existing transportation modes; (3) assesses the system-level impacts of mobility services with different operational policies and strategies on the multimodal transportation network; and (4) simulates future mobility scenarios and analyzes their resulting effects on the system performance.]]></description>
      <pubDate>Tue, 26 Sep 2023 17:04:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2247610</guid>
    </item>
    <item>
      <title>Financial status and travel time to driving schools as barriers to obtaining a young driver license in a state with comprehensive young driver licensing policy</title>
      <link>https://trid.trb.org/View/2208442</link>
      <description><![CDATA[The highest lifetime risk for a motor vehicle crash is immediately after the point of licensure, with teen drivers most at risk. Comprehensive teen driver licensing policies that require completion of driver education and behind-the-wheel training along with Graduated Driver Licensing (GDL) are associated with lower young driver crash rates early in licensure. The authors hypothesize that lack of financial resources and travel time to driving schools reduce the likelihood for teens to complete driver training and gain a young driver’s license before age 18. The authors utilize licensing data from the Ohio Bureau of Motor Vehicles on over 35,000 applicants between 15.5 and 25 years old collected between 2017 and 2019. This dataset of driving schools is maintained by the Ohio Department of Public Safety and is linked with Census tract-level socioeconomic data from the U.S. Census. Using logit models, the authors estimate the completion of driver training and license obtainment among young drivers in the Columbus, Ohio metro area. The authors find that young drivers in lower-income Census tracts have a lower likelihood to complete driver training and get licensed before age 18. As travel time to driving schools increases, teens in wealthier Census tracts are more likely to forgo driver training and licensure than teens in lower-income Census tracts. For jurisdictions aspiring to improve safe driving for young drivers, the findings help shape recommendations on policies to enhance access to driver training and licensure especially among teens living in lower-income Census tracts.]]></description>
      <pubDate>Fri, 21 Jul 2023 09:18:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2208442</guid>
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