<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.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>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Measuring bicycle accessibility within the metro catchment area: An empirical study in Shanghai</title>
      <link>https://trid.trb.org/View/2665443</link>
      <description><![CDATA[Accessibility describes the potential to reach opportunities and is widely used to assess the ease of reaching destinations through urban transport systems. Although much attention has been given to investigate bicycle-accessibility and metro-accessibility methods, extending these methods to model bicycle-metro integration travel at the city scale remains challenging. Based on Hansen’s accessibility model, this study proposes three different models to measure bicycle accessibility within metro catchment areas. In particularly, key factors such as trip purposes, bicycle suitability, total travel time, and traffic demand are incorporated into the accessibility models. These proposed models have been tested and compared using empirical data from Shanghai. Overall, metro stations with multiple interchange lines, cycling-friendly facilities and diverse surrounding activities tend to exhibit higher bicycle accessibility, particularly those located in the city center. For areas with low bicycle accessibility in the city, such as Baoshan Road Station and Anshan Xincun Station, targeted improvement measures can be implemented to enhance bicycle-metro integration and bicycle accessibility.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:34:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665443</guid>
    </item>
    <item>
      <title>Scaling pedestrian crossing analysis to 100 U.S. cities via AI-based segmentation of satellite imagery</title>
      <link>https://trid.trb.org/View/2665441</link>
      <description><![CDATA[Accurately measuring street dimensions is essential to evaluating how their design influences both travel behavior and safety. However, gathering street-level information at city-scale with precision is difficult given the quantity and complexity of urban intersections. To address this challenge in the context of pedestrian crossings — a crucial component of walkability — we introduce a scalable and accurate method for automatically measuring crossing distance at both marked and unmarked crosswalks, applied to America’s 100 largest cities. First, OpenStreetMap coordinates were used to retrieve satellite imagery of intersections throughout each city — totaling roughly three million images. Next, Meta’s Segment Anything Model was trained on a manually labelled subset of these images to differentiate drivable from non-drivable surfaces (i.e., roads vs. sidewalks). Third, all available crossing edges from OpenStreetMap were extracted. Finally, crossing edges were overlaid on the segmented intersection images, and a grow-cut algorithm was applied to connect each edge to its adjacent non-drivable surface (e.g., sidewalk, private property, etc.), thus enabling the calculation of crossing distance. This achieved 93% accuracy in measuring crossing distance, with a median absolute error of 2 feet 3 inches (0.69 meters), when compared to manually verified data for an entire city. Across the 100 largest U.S. cities, median crossing distances ranged from 32 feet to 78 feet (9.8 – 23.8m), with detectable regional patterns. Median crossing distance also displayed a positive relationship with the cities’ year of incorporation, illustrating in a novel way how American city planning increasingly emphasizes wider (and more car-centric) streets. These findings identified opportunities to improve pedestrian safety and increase walkability at multiple scales, from the individual block to the entire city.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:34:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665441</guid>
    </item>
    <item>
      <title>Macroscopic on-street parking inventory modeling: Exploring an open data approach</title>
      <link>https://trid.trb.org/View/2665442</link>
      <description><![CDATA[Parking plays a vital role in shaping land use and transport. Despite occupying significant portions of urban space, detailed data on parking locations and capacities are often unavailable. Recognizing the critical significance of such data for comprehensive transportation modeling and sustainable urban planning, this study presents two statistical models designed to predict the available on-street parking length in urban traffic analysis zones. The first model uses OpenStreetMap (OSM) data as its primary input, while the second is based on official parking inventory data from the city of Berlin. Both models are built using multiple linear regression, with land use and built environment characteristics as independent variables. The models are evaluated by applying them to the city of Munich. This research provides new insights into the spatial distribution of urban on-street parking and offers a practical approach for estimating parking supply to support sustainable urban development strategies.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:34:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665442</guid>
    </item>
    <item>
      <title>Analyzing the parcel delivery pattern in the Global South: The case of Belo Horizonte, Brazil</title>
      <link>https://trid.trb.org/View/2665440</link>
      <description><![CDATA[This study investigates parcel delivery patterns in Belo Horizonte, Brazil, to elucidate the influence of spatial inequalities, urban structure, socioeconomic factors, and retail diversity on delivery demand, employing spatial regression models. The results reveal that income and retail diversity positively impact parcel delivery, while food deserts drive increased reliance on e-commerce due to limited local options. In particular, the distance from the city center negatively affects delivery patterns, highlighting spatial inequities. Areas characterized by social inequalities exhibit greater delivery activity, highlighting e-commerce as a vital alternative where local services are scarce. These findings advocate for integrated urban planning policies that strategically leverage parcel delivery services to achieve more equitable access, address service gaps, and foster delivery expansion in marginalized areas.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:34:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665440</guid>
    </item>
    <item>
      <title>NIMBY effect algorithm for railway construction in functional urban areas: A Polish case study</title>
      <link>https://trid.trb.org/View/2646189</link>
      <description><![CDATA[The limitation of land causes conflicts over how it is used, particularly in functional urban areas. Although Not in My Backyard (NIMBY) effects are often associated with established residential neighborhoods, they may also arise in rapidly developing suburban and peri-urban zones, where transport investments intersect with existing settlements and ongoing land-use change. Due to their specific nature, projects for new railway lines are more likely to receive a negative reception, because they can adversely affect property values, cause buyouts and expropriations, and introduce significant changes to a settlement’s spatial layout. The feasibility of different infrastructure project variants could be affected by varying levels of risk. Therefore, the occurrence of the NIMBY phenomenon necessitates a precise, standardized approach to its assessment. For this reason, the main aim of this study is to develop an algorithm to assess the scale of NIMBY responses for railway construction investments in densely populated Functional Urban Area (FUA) commuting zones, involving multicriteria spatial analysis. The algorithm is tested in the selected Lomza FUA railway construction project located in northeastern Poland. The project involves both the restoration of the railway line and its construction in a new location. This study highlights the analyses of spatial structural change that are crucial for understanding local resistance, since in suburbanizing areas, the intensity of NIMBY is strongly correlated with demographic shifts and patterns of land-use transformation. The Lomza FUA case confirms this.]]></description>
      <pubDate>Fri, 13 Mar 2026 13:44:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646189</guid>
    </item>
    <item>
      <title>Heterogeneity in distance elasticity of active travel to school</title>
      <link>https://trid.trb.org/View/2635944</link>
      <description><![CDATA[Children's active transport to school has multiple health, social, economic, and environmental benefits, and the literature on ways to support children’s active accessibility is vast. The consistent conclusion from this research is that the distance between home and school is a key determinant of whether a child will walk or cycle to and from the school gate. While distance is undoubtedly of central importance to the active school travel puzzle, our understanding of children’s sensitivity to increases in distance remains nascent. How far is too far when it comes to active school access? Using survey data from 6,629 school students in Australia, this paper explores this question through a nuanced focus on the sensitivity of active transport to school (ATS) to changes in trip distance. More specifically, a multinomial logistic regression model is used to analyze the heterogeneity of elasticity to distance, and the nature of the relationship between distance elasticity and land-use and demographic segments. The findings confirm existing understandings that distance and local land use are significant factors associated with ATS. By using a piecewise treatment of distance and estimating point elasticities, the model also shows that mode-choice sensitivity to distance varies across places and populations and is itself non-linear. The turning point of the multinomial logistic regression function with respect to distance is between 1 km and 3 km, indicating that a percentage increase in distance within this range is most likely to deter active school travel. This novel finding provides much-needed clarity to existing understandings of the sensitivity of ATS to distance. Such understandings are central to policy aspirations seeking to design school catchments with active accessibility as a desired outcome.]]></description>
      <pubDate>Wed, 25 Feb 2026 17:00:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635944</guid>
    </item>
    <item>
      <title>Gentrification in motion: Linking urban walkability and connectivity with neighborhood change</title>
      <link>https://trid.trb.org/View/2635943</link>
      <description><![CDATA[This study explores how different aspects of walkability are associated with residential and retail gentrification in U.S. cities. Using data from the American Community Survey (ACS) and the U.S. Environmental Protection Agency’s Smart Locations Dataset (SLD), we examine walkability scores along with their underlying factors including the diversity of amenities, proximity to transit, and intersection density to predict gentrification. The ACS provides detailed demographic, socioeconomic, and housing data at the neighborhood level, enabling analysis of population shifts and economic changes over time. The SLD offers spatial indicators of walkability based on consistent national methodologies, making it a valuable tool for comparing built-environment characteristics across cities. Our findings show that overall walkability and neighborhood amenities are positively associated with both residential and retail gentrification. In addition, higher intersection density is linked to residential gentrification, underscoring the importance of neighborhood connectivity in attracting higher-income residents. These results highlight the complexity of gentrification and the need for more targeted policy interventions that address the various components of walkability and connectivity.]]></description>
      <pubDate>Wed, 25 Feb 2026 17:00:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635943</guid>
    </item>
    <item>
      <title>The application of rational inattention theory in modelling residential location choices: A cross-sectional investigation using a stated preference dataset</title>
      <link>https://trid.trb.org/View/2606529</link>
      <description><![CDATA[The rational inattention theory aims to evaluate instances in which a decision is made in an information-rich environment where consumers cannot process all information due to limited cognitive capacity. In contrast to classical random utility-maximizing models, rational inattention discrete choice models do not assume that decision-makers make choices with complete knowledge of the alternatives. Today’s information technology tools create a decision-making environment in which information is plentiful and easily accessible. Yet, it is cognitively impossible for households to be aware of every aspect of available options. This study uses rational inattention theory to investigate residential location choices in the Greater Toronto Area (GTA) during the COVID-19 pandemic, using an efficient-adaptive stated preference dataset collected in July 2021. The rational inattention theory requires identifying information processing costs and marginal probabilities as decision-makers’ prior beliefs. The empirical model of this paper proposes using the time respondents spend on choice problems to measure their attention span and the latent preferences produced from the efficient-adaptive survey to measure their prior beliefs.]]></description>
      <pubDate>Wed, 15 Oct 2025 12:30:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606529</guid>
    </item>
    <item>
      <title>Exploring the role of the built and natural environment in encouraging active travel for different trip purposes in Montreal</title>
      <link>https://trid.trb.org/View/2601298</link>
      <description><![CDATA[Transportation research has extensively examined the influence of both the built and natural environment on active travel. While most studies assume linear relationships, some evidence indicates that this might not always be the case. This paper addresses this by identifying the nature of the relationship between the built and natural environment (BNE) and active travel (AT) across several trip purposes: school, shopping, work, and leisure trips in Montreal, Canada. The authors also identify areas with low and high potential for active travel. Using Generalized Linear Models with the Tweedie family and including a spatial lag covariate, the authors found that the relationship between BNE and AT is not always linear. In some cases, higher access levels to sidewalks, bike lanes, walkable destinations, and transit stops, constantly increase AT but with cubic or logarithmic relationships. Other variables, such as dwelling density, intersection density, park access, tree coverage, industrial diversity, and proximity to water bodies, also encourage active travel but only up to a certain threshold, beyond which further increases do not increase AT, and in some cases, can lead to a decline, forming an inverted "U" relationship. These relationships vary across trip purposes. Central areas in Montreal show the best potential to support active travel, while the rest of the city displays low levels of support, depending on the trip's purpose. The findings highlight the importance of accounting for non-linear relationships, as improvements in the BNE do not always translate into higher levels of active travel.]]></description>
      <pubDate>Thu, 09 Oct 2025 16:39:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2601298</guid>
    </item>
    <item>
      <title>Inter- and intrajurisdictional commuters in China: How do they differ in socioeconomic, residential and travel characteristics?</title>
      <link>https://trid.trb.org/View/2596484</link>
      <description><![CDATA[Interjurisdictional commuting is increasingly prevalent in China, yet the socioeconomic and mobility disparities between interjurisdictional and intrajurisdictional commuters remain underexamined. This study investigates the socioeconomic, residential, and travel-related differences between interjurisdictional and intrajurisdictional commuters in four cities—Guangzhou, Shenzhen, Foshan, and Dongguan—using cellphone data of 15.2 million users on October 18, 2023. As commuters are not randomly assigned across jurisdictional boundaries, propensity score matching was employed to adjust for differences in workplace characteristics before comparing the two groups. The analysis reveals that interjurisdictional commuters are younger, more likely to be male and migrants, and tend to live in low-rent neighborhoods with poorer access to urban amenities. They also experience significantly longer commuting distances, durations, and higher transport costs compared to their intrajurisdictional counterparts. These disparities reflect not only individual choices but also structural challenges, such as housing affordability gaps, fragmented transport governance, and insufficient regional planning. The study contributes to the spatial mismatch and transport poverty literature by highlighting the regional dimension of commuting inequalities in polycentric urban systems. It underscores the need for integrated transit planning in peripheral cities, and targeted support for long-distance commuters. These findings offer policy-relevant insights for fostering equitable mobility in rapidly urbanizing regions.]]></description>
      <pubDate>Tue, 30 Sep 2025 16:31:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2596484</guid>
    </item>
    <item>
      <title>Grocery shopping behavior in Detroit neighborhoods experiencing disinvestment and decline: An empirically grounded agent-based model of data-scarce communities</title>
      <link>https://trid.trb.org/View/2588369</link>
      <description><![CDATA[This paper presents a data-driven agent-based model that simulates the weekly grocery shopping behavior of disadvantaged consumers in highly segregated lower eastside neighborhoods of Detroit, Michigan. The authors focus on neighborhoods experiencing severe disinvestment to analyze the shopping behavior of residents after all major regional and national supermarket chains abandoned the city. The presented model is unique in that it utilizes detailed shopping behavior data collected to examine travel in marginalized communities, specifically among residents in severe poverty who are often overlooked in the travel behavior literature. The research shows that in extreme socio-economic decline, sociodemographic variables (such as class) can become more relevant than the built environment (land-use mix, density, and street connectivity) in determining access and influencing mobility. After identifying unique groups of household agents, the authors design rules that utilize probability distributions generated from survey responses. The decision-making of agents that emulate households is habitual rather than utility driven. Modeled behavior is designed based on stated preferences, which may contradict premises such as the “shortest distance to the nearest shop” approach, a common assumption in the literature. The authors also report on three what-if scenarios to evaluate how major population changes would affect the results.]]></description>
      <pubDate>Thu, 25 Sep 2025 08:25:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2588369</guid>
    </item>
    <item>
      <title>Housing, travel, and energy spatial-temporal simulation of Riyadh: Impacts of the New Murabba Project</title>
      <link>https://trid.trb.org/View/2588368</link>
      <description><![CDATA[The city of Riyadh in Saudi Arabia envisions rapid growth, from a 2020 population of 7.2 million to one reaching 15 million or more by 2030 (Alhefnawi et al., 2024). A spatial economic and transport model has been developed following well-established approaches to assist in forecasting the expansion of the city, particularly the spatial organization of the population, people's housing, economic activity and employment consumption, and the flows of goods and services on the transportation network. The model, called the Riyadh PECAS model, was used to analyze the housing, travel, energy consumption, and related spatial impacts of a proposed megaproject, the New Murabba, consisting of 104,000 residences, 9,000 hotel rooms, and 4.8 million square meters of other non-residential space, anchored by a 400-meter cube-shaped megastructure with partially open interior space. The model simulates shifts in housing location, housing dwelling type, travel mode choice, travel distance, and energy use (residential and transportation). It predicts some of the details of housing consumption and location choices of Saudis and non-Saudis that provide additional insight. The non-central location of the new development could increase the total quantity of vehicle distance travelled by 6% when compared to a reference simulation where development locations are market-driven. Complementary investment in major public transit infrastructure could lead to a higher mode-split to transit and a corresponding reduction in transportation energy use. Further analysis of the New Murabba and other growth and policy options using the model could help guide Riyadh's growth.]]></description>
      <pubDate>Thu, 25 Sep 2025 08:25:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2588368</guid>
    </item>
    <item>
      <title>Planning beyond the metro: Rural travel behavior and the built environment</title>
      <link>https://trid.trb.org/View/2586778</link>
      <description><![CDATA[Reducing greenhouse gas (GHG) emissions from transportation is particularly challenging in rural communities. People living in rural areas are more vehicle reliant and are also more likely to face barriers to meeting their mobility needs. One approach to reducing vehicle travel without exacerbating mobility challenges is through directing population growth into compact multimodal communities. Despite substantial differences in travel in rural versus urban contexts, very little prior research has evaluated the relationship between travel and the built environment (BE) in rural areas. The authors use spatially detailed travel behavior data to evaluate the relationships between BE factors and sustainable travel behaviors in rural communities in the United States. The authors find that the relationship between travel and the BE differs between rural and urban areas, with local access exhibiting a weaker association with travel behavior in rural communities when compared with urban communities. Conversely, regional access exhibits a larger association with travel behavior in rural communities. The authors also found that the association between personal characteristics and travel behavior was significantly smaller in rural communities. Furthermore, the results suggest that the relationships between travel and the BE may differ across different types of rural communities. Transportation planners and researchers should take note of the different relationship between the BE and travel behavior in urban and rural areas. This research suggests that while relationships between travel and the BE observed in urban-focused research may not hold in rural communities, there does appear to be some ability to influence travel behavior using the built environment.]]></description>
      <pubDate>Wed, 17 Sep 2025 08:28:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2586778</guid>
    </item>
    <item>
      <title>An empirical analysis of transport and land-use integration: The case of public transport and apartment housing in Melbourne, Australia</title>
      <link>https://trid.trb.org/View/2586777</link>
      <description><![CDATA[Despite widespread policy aspirations for integrating high-density housing with public transport, what is achieved in practice is rarely measured. Furthermore, equity in public transport service provision has been given little attention in areas of high-density housing. Using Melbourne as a case study, this research tracks the development of apartment housing against public transport service provision over a 19-year period. Results show that population growth (32%) outstripped modest changes in the number of public transport services provided (5%) in the areas around new apartments, with considerable growth in apartment housing occurring (88%). However, when accounting for the introduction of larger public transport vehicles in the fleet, growth in capacity-adjusted services (35%) was found to slightly exceed population growth at a metropolitan level. Yet at a local level, considerable inequity was found in the number of capacity-adjusted services provided per person across areas and routes. Evidence was also found that apartment development has been strongly attracted to areas already well served by public transport, but with little response in terms of additional services. In the case of Melbourne, it appears that the policy intent of integrating high-density housing with public transport is a one-way mechanism only.]]></description>
      <pubDate>Wed, 17 Sep 2025 08:28:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2586777</guid>
    </item>
    <item>
      <title>A spatio-temporal node-place-ridership model for classifying metro station areas: The case of Shenzhen, China</title>
      <link>https://trid.trb.org/View/2586776</link>
      <description><![CDATA[The node-place model has been widely applied for uncovering the coordination between transit network and land use but overlooks the critical role of ridership and its temporal variations. Focusing on the dynamic nature of urban activities and ridership, this study develops a spatio-temporal node-place-ridership model for evaluating and classifying metro station areas. The extended model emphasizes ridership as a third dimension in addition to the node and place dimensions and focuses on intra-week (weekday versus weekend) and intra-day (day-time versus night-time) temporal variations. Using a case study in Shenzhen, China, results show that ridership is more associated with the place values (i.e., land-use pattern) than with the node values (i.e., network accessibility). The variation in ridership between weekday and weekend is related to non-work activities and land-use types. As for intra-day variation, station areas with a high proportion of commuting ridership face imbalance between node and place values and between job and housing functions. This study highlights the importance of the incorporation of ridership dynamics in understanding the transit and land-use integration and assists urban planners and policymakers in making more informed, flexible, and responsive urban development strategies. The extended model is transferable and valuable for other cities.]]></description>
      <pubDate>Wed, 17 Sep 2025 08:28:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2586776</guid>
    </item>
  </channel>
</rss>