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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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    <item>
      <title>Evolution of Mode Choice: Examining the Relationship Between Telecommuting and Transit Use</title>
      <link>https://trid.trb.org/View/2702855</link>
      <description><![CDATA[This project aims to quantify the impacts of telecommuting on transit use. Data for this analysis is derived from the 2019 and 2023 editions of the Puget Sound Regional Council (PSRC) household travel survey and a joint model of telecommuting and transit use frequency is estimated to understand the nature of the relationship in the pre- and post-pandemic periods. The findings reveal a U-shaped relationship between telecommuting and transit use. Lower transit frequency was observed at both the highest and lowest levels of telecommuting, while higher transit frequency was associated with medium or hybrid levels of telecommuting. This pattern became even more pronounced in 2023. Computations of average treatment effects show that transitioning from medium-level (hybrid) telecommuting to non-telecommuting resulted in a 21 percent decrease in transit use in 2019, and a steeper 35 percent decrease in 2023. Similarly, moving from hybrid to frequent telecommuting led to a six percent reduction in transit use in 2019, and a larger nine percent reduction in 2023. These findings suggest that the loss in transit ridership in the post-pandemic era is likely to persist and that compelling workers to return to the workplace full-time is unlikely to yield significant gains unless transit agencies find innovative ways to attract non-telecommuters (full commuters) back to transit. Instead, embracing a hybrid work modality while providing incentives to promote transit use may yield greater benefits.]]></description>
      <pubDate>Thu, 14 May 2026 15:50:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2702855</guid>
    </item>
    <item>
      <title>Adapting travel behavior to extreme heat: Public transportation evidence from Seattle’s heatwave</title>
      <link>https://trid.trb.org/View/2692634</link>
      <description><![CDATA[Urban heatwaves can constrain residents’ mobility and access to heat relief, particularly for transit-dependent populations. Using ORCA smart-card data from the Puget Sound (Seattle) region, we examine travel responses to the August 14–17, 2023 heatwave through matched four-day Monday-Thursday windows. Among riders observed both before and after the event, 47.7% had no recorded boardings during the heatwave window. Many riders recorded no midday (10:00-13:59) boardings during the heatwave, and typical boarding times shifted slightly earlier. Spatial responses were heterogeneous, combining small median changes in travel range (radius of gyration) with marked reorganization in riders’ boarding-stop portfolios. Using a stop-anchored, time-matched index of nearby indoor cooling opportunities around observed boarding stops, we find that average user-day exposure was broadly stable across phases, with limited evidence of systematic reallocation toward the highest-supply stops during the heatwave.]]></description>
      <pubDate>Mon, 20 Apr 2026 09:25:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692634</guid>
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    <item>
      <title>Assessing the vulnerability of U.S. energy infrastructure to dual source flood hazards: A spatial and population exposure analysis</title>
      <link>https://trid.trb.org/View/2657282</link>
      <description><![CDATA[Flood risk to U.S. energy infrastructure (EI) is shaped by both inland and coastal processes, yet most national assessments rely on a single hazard dataset. To address this limitation, we combine FEMA Special Flood Hazard Areas (SFHA; 1% annual chance) with NOAA's coastal composite (storm surge, high-tide flooding, sea-level rise, and related layers) to develop a unified view of exposure. Using a national EI inventory (n = 21,988), we conduct spatial overlays, county-level Getis-Ord Gi* clustering, subtype analyses, and a 5-mile population proximity assessment. Nationwide, 3174 facilities (14.4%) are classified as flood-exposed in the combined dataset, compared with 9.2% when using FEMA alone and 9.5% when using NOAA alone; 925 facilities (4.2%) are exposed in both, while FEMA-only (1096) and NOAA-only (1153) contribute roughly equally. Exposure varies by sector, with petrochemical and petroleum facilities exhibiting the highest rates. Subtypes such as petroleum ports, LNG terminals, and hydropower plants stand out as particularly exposed. Within NOAA's footprint, agreement between sources is high (87.8%), but NOAA-only facilities far exceed FEMA-only facilities, highlighting additional coastal exposure beyond regulatory SFHAs. Hotspot analysis reveals complementary geographies: NOAA emphasizes a continuous coastal belt (TX–LA Gulf, Florida, Mid-Atlantic, NY–NJ harbor, Puget Sound), while FEMA emphasizes inland regions along the Mississippi and Ohio–Tennessee corridors. Population exposure is substantial, with over 52 million people living within 5 miles of FEMA SFHA electric power facilities and 37 million near petroleum assets, underscoring the societal stakes of infrastructure disruption. To our knowledge, this is the first national-scale study to integrate FEMA and NOAA hazard datasets for EI exposure, providing a more comprehensive basis for resilience planning and focusing attention on the sectors and communities at most significant risk.]]></description>
      <pubDate>Mon, 13 Apr 2026 09:40:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2657282</guid>
    </item>
    <item>
      <title>Puget Sound Maritime Operations: An Alaskan lifeline</title>
      <link>https://trid.trb.org/View/2640665</link>
      <description><![CDATA[Seattle, strategically located on the Puget Sound, plays an indispensable role in transporting essential goods and services from the continental United States to Alaska. In 2014, the ports of Seattle and Tacoma joined forces to unify the management of marine cargo facilities and business to strengthen the Puget Sound gateway and attract more marine cargo and jobs to the region. The result was the Northwest Seaport Alliance (NWSA), which is the fourth largest container gateway in North America, It merges all marine cargo operations while managing 80% of all trade between Alaska and continental America. Puget Sound’s maritime support sector also includes ship and boat construction, repair and maintenance, marine-related goods, materials, and equipment suppliers, and manufacturing. Key routes and services promote containerized and bulk shipping of essential supplies. The connectivity facilitated by Puget Sound ensures that Alaska’s economy is robust, efficient, and well-integrated with national and international markets.]]></description>
      <pubDate>Tue, 16 Dec 2025 11:34:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640665</guid>
    </item>
    <item>
      <title>COVID &amp; telecommuting-induced changes in individual activity and travel patterns: Evidence from the Puget Sound Region</title>
      <link>https://trid.trb.org/View/2598102</link>
      <description><![CDATA[One enduring effect of the COVID-19 pandemic has been the popularity of telecommuting: To this day, 23% of the salaried workers continue to work from home, according to the U.S. Bureau of Labor Statistics. Using three waves of the household travel survey data from 2017, 2019 and 2021 in the Puget Sound Region, WA, this study examines how telecommuting, which also means the removal of the workplace as an anchor point from one’s daily activity and travel pattern, affects the generation and rescheduling of maintenance and discretionary trips that are previously conducted around home and workplaces. The associated consequences including changes in modes of transportation used and vehicle miles traveled (VMT) are also investigated. The authors found that though telecommuting resulted in reduced number of trips and VMT in general, there is a significant increase in the number of maintenance and discretionary trips. Additionally, telecommuters exhibited less complex trip chaining behavior, characterized by simpler tours with shorter trips, fewer stops, and lower mode diversity compared to non-telecommuters. Spatially, telecommuters conducted maintenance and discretionary trips closer to home; temporally, and the departure times of these trips are more spread out with emerging peaks such as late morning, and mid-day. These results have significant policy and modeling implications relating to transportation service provision, local economy, and travel demand forecasting models.]]></description>
      <pubDate>Fri, 26 Sep 2025 10:23:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598102</guid>
    </item>
    <item>
      <title>Understanding access to restaurants through personas: A latent class approach integrating preferences and travel behavior</title>
      <link>https://trid.trb.org/View/2587173</link>
      <description><![CDATA[Access to food plays a key role in one’s health and well-being. Past studies on food access have primarily focused on grocery stores. Eating out, however, consumes on average 5.1% of Americans’ disposable income. Eating out is also a key activity that serves multiple purposes: social occasions, opportunity for networking and simply refueling and relaxation. Because eating out combines activity and travel, it is important to understand how individuals’ restaurant preferences and travel behavior may be bundled together to form different personas and how each persona may be related to the built environment and socioeconomic demographics. This study uses data from the Puget Sound Regional Council 2017–2019 Household Travel Survey to perform Latent Class Analysis (LCA). LCA can uncover similar subpopulations, allowing for separation of demographics and behavior during the clustering process. The study finds four personas: Convenient Eaters, Lunch Breakers, Restaurant Explorers and Fast Food Enthusiasts. Restaurant-related travel behavior is significantly impacted by the number of children in a household, vehicle access and age, though it is not constrained for those who live in a “food desert”. Differences in travel time, frequency of restaurant trips and meal time are observed between personas. The findings of this study suggest that interventions toward healthy eating could be tailored to the specific needs of each persona. Personas display specific preferences toward cuisine and affordability in addition to travel-based motivations. For example, Convenient Eaters, who consume a moderate amount of fast food, could be encouraged to choose healthier options near their home, work, or other often-frequented locations. Fast Food Enthusiasts who solely eat out at fast food and are lower-income could be supported by both educational programs and government subsidies. Overall, the approach used in this study could be adopted for interventions to curate personalized recommendations for healthier eating and sustainable travel behaviors.]]></description>
      <pubDate>Wed, 17 Sep 2025 10:55:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2587173</guid>
    </item>
    <item>
      <title>A novel pattern recognition technique to characterize multi-day shopping and entertainment trip activities</title>
      <link>https://trid.trb.org/View/2536277</link>
      <description><![CDATA[The driving force behind individuals’ travel behavior is closely linked to the need to engage in various activities, such as working, shopping, and entertainment. While the importance of shopping and entertainment activities is well-documented in activity-based modeling research, there is no existing literature specifically addressing different shopping activities and entertainment trips over long time periods, such as an entire week, with a granular level of investigation. This study introduces a novel framework using comprehensive pattern recognition modeling, aiming to identify households’ level weekly shopping and entertainment trip activity patterns and to identify their representative patterns. Utilizing data from the 2019 Puget Sound Regional Household Travel Survey, the one-week activity patterns are split into 336 30-minute intervals. Each interval is comprised of information on trip activity types, duration, and start time. Pattern complexity of activity sequences in the dataset is recognized using the two-staged clustering process involving affinity propagation (AP) and k-means algorithms, which results in six unique clusters of homogeneous weekly activity patterns. These clusters exhibit a heterogeneous diversity in the temporal distribution of trip activity durations and significant differences in a variety of sociodemographic variables. Moreover, using sequence alignment techniques, we identified the representative trip activity pattern of the households in each cluster. Notably, younger individuals tend to shop on weekends, while older adults (age 65+) maintain consistent daily shopping habits. Households with higher incomes and vehicle access typically shop midweek, whereas a significant portion of high-income households without vehicles opt for Monday shopping. This comprehensive analysis highlights the intricate relationship between recreational travel behavior and sociodemographic factors, shedding light on nuanced patterns of activity engagement over extended time periods.]]></description>
      <pubDate>Wed, 14 May 2025 13:09:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2536277</guid>
    </item>
    <item>
      <title>Autonomous Electric Ferries (Autoferry) - Low Carbon Connectors - II</title>
      <link>https://trid.trb.org/View/2548807</link>
      <description><![CDATA[Island and peninsula communities in the Puget Sound are often isolated from regional cities like Seattle and Tacoma due to large transit time by road. Regional ferry systems are large and expensive to operate, limiting the number of service times and access points. Most of the ferries only operate between larger regional towns and major cities, isolating smaller communities that often lack bus services as well. Autonomous electric ferries offer a unique low-carbon option to better connect rural communities in the region. In recent years, the Washington state ferry system has struggled with staffing and maintenance of older diesel ferry systems. For example, the residents of Anderson Island and Ketron Island in the south Puget Sound region are served by one ferry that connects them to the mainland. For Ketron Island, the ferry runs only 4 times per day and was out of service completely for several days recently while the ferry was being repaired. This project will focus on addressing key technical objectives with the autonomous ferries, including (1) autonomous docking procedures, (2) optimization of possible routes for weather and tidal events, and (3) building deeper partnerships with commercialization partners. Our methods will include literature review, weather and tidal data collection, design and prototyping, and testing.]]></description>
      <pubDate>Thu, 01 May 2025 15:30:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2548807</guid>
    </item>
    <item>
      <title>Modeling ride-hailing and carsharing adoption &amp; use patterns: deciphering the substitutive and complementary impacts of built environment, transit accessibility, &amp; active travel</title>
      <link>https://trid.trb.org/View/2495287</link>
      <description><![CDATA[The rise of shared mobility services, including carsharing and ride-hailing, has transformative impacts on transportation systems. The authors present a behavioral framework to jointly model individuals’ carsharing and ride-hailing use with a focus on deciphering the substitutive vs. complementary roles of the built environment, transit accessibility, and active travel. Based on a sample of over 3,200 individuals from the 2019 Puget Sound Travel Survey, detailed travel behavior data are spatially integrated with neighborhood-level objectively assessed built environment and transit accessibility data. Joint heterogeneity-based multivariate ordered discrete choice models are specified to simultaneously account for random (unobserved) and systematic (observed) heterogeneity. The use patterns of carsharing and ride-hailing services exhibited positive dependence. Reflecting complementary impacts, neighborhood walkability, urban compactness, pedestrian-oriented urban design, and transit accessibility exhibited positive associations with individuals’ carsharing and ride-hailing use. Active travel behaviors (walking, biking, and transit use) also exhibited synergistic relationships with carsharing and ride-hailing use. While transit accessibility and active travel independently complement shared mobility services, the findings indicate that the interaction between the two could replace ride-hailing services. In particular, more physically active individuals (i.e., those engaging in greater active travel) may be choosing ride-hailing not out of preference but out of necessity due to lower transit accessibility around their home neighborhoods. Results suggest a mix of complementary vs. substitutive impacts, as opposed to the assumption of dichotomized (complementary or substitutive) impacts. Significant random and systematic heterogeneity in the behavioral, environmental, and demographic determinants of shared mobility services was revealed. The authors discuss the relevance and implications of the new findings considering scenario planning and travel demand modeling needs.]]></description>
      <pubDate>Tue, 25 Mar 2025 16:57:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2495287</guid>
    </item>
    <item>
      <title>Autonomous Electric Ferries (Autoferry) - Low Carbon Connectors - I </title>
      <link>https://trid.trb.org/View/2508973</link>
      <description><![CDATA[Island and peninsula communities in Puget Sound are often isolated from regional cities like Seattle and Tacoma due to an extensive transit time by road. Regional ferry systems are large and expensive to operate, limiting the number of service times and access points. Most of the ferries only operate between larger regional towns and major cities, isolating smaller communities that often lack bus services as well.
Autonomous electric ferries offer a unique low-carbon option to better connect rural communities in the region. For example, the residents of Anderson Island and Ketron Island in the south Puget Sound region are served by one ferry that connects them to the mainland. For Ketron Island, the ferry runs only four times per day and was out of service entirely for several days recently while the ferry was repaired. On the nearby peninsula, the community of Longbranch is accessible by road, but travel requires more than an hour by car for residents to reach services. Several tribal communities are included in these isolated areas.
An autonomous passenger ferry would operate like an elevator, where passengers on one side press a button to summon the ferry. While the ferry is not in use, it charges at the dock in preparation for the call. The ferry could transport passengers and bikes, creating a new alternative to traditional car-centric communities. The Seattle-Tacoma area has 23.8 million passengers on ferries per year, which could be shifted to low-carbon electric ferries over time.
]]></description>
      <pubDate>Wed, 12 Feb 2025 15:35:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2508973</guid>
    </item>
    <item>
      <title>Predicting Curb Side Parking Availability for Commercial Vehicle Loading Zones</title>
      <link>https://trid.trb.org/View/2452571</link>
      <description><![CDATA[Commercial fleet management and operations pose distinct challenges compared to regular passenger vehicles. These challenges stem from the varying sizes, shapes, and parking demands of commercial vehicles, requiring specific curbside accommodations. Despite extensive research on smart-parking management for personal vehicles, there has been limited focus on improving parking outcomes for urban freight systems. To address this gap, we have developed a framework that utilizes sensors installed in parking areas to collect occupancy information. This framework predicts parking space availability for commercial vehicles in 10-minute intervals. The current states and the predictions are relayed to the drivers in near real-time through a web-based interface, empowering them to find suitable parking spaces and reducing search time. Our framework incorporates a suite of machine-learning models for predicting curbside parking availability based on real-time sensor data from commercial vehicle loading zones. We evaluated these models in a busy commercial district in the Seattle area, focusing on prediction accuracy and real-world performance. Our study concludes that, for practical use, the convolutional neural network (CNN) model outperforms other architectures, including Spatial Temporal Graph Convolutional Networks (ST-GCN) and Transformer.]]></description>
      <pubDate>Wed, 11 Dec 2024 10:39:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2452571</guid>
    </item>
    <item>
      <title>Generating online freight delivery demand during COVID-19 using limited data</title>
      <link>https://trid.trb.org/View/2447362</link>
      <description><![CDATA[Urban freight data analysis is crucial for informed decision-making, resource allocation, and optimizing routes, leading to efficient and sustainable freight operations in cities. Driven in part by the COVID-19 pandemic, the pace of online purchases for at-home delivery has accelerated significantly. However, responding to this development has been challenging given the lack of public data. The existing data may be infrequent because of survey participant non-responses. This data paucity renders conventional predictive models unreliable. The authors address this shortcoming by developing algorithms for data imputation and replication for future urban freight demand given limited ground truth online freight delivery data. The authors' generic framework is capable of taking in repeated cross-sectional surveys and replicating frequent samples from them. In this paper, the authors' case study is focused on Puget Sound Regional Council (PSRC) household travel survey (HTS) data restricted to the Seattle–Tacoma–Bellevue, WA Metropolitan Statistical Area (MSA). The authors show how to impute the missing online freight deliveries in the authors' travel survey dataset from ground truth values by making a similarity-based matching between the samples of missing and available online delivery volumes. Empirical and theoretical analyses both demonstrate high veracity of imputation where the estimated freight delivery volumes closely resemble the ground truth values. Utilizing the obtained similarity-based matching, the authors show how to generate data across future and past travel survey datasets with an emphasis on maintaining some consistent trends across the datasets. This work furthers existing methods in demand estimation for goods deliveries by maximizing available scarce data to generate reasonable estimates that could facilitate policy decisions.]]></description>
      <pubDate>Fri, 15 Nov 2024 16:05:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2447362</guid>
    </item>
    <item>
      <title>No Longer Seat-Less in Seattle: The Role of Coordinated Transportation and Land Use Planning in Sustaining Transit Ridership through the Pandemic Recovery Period</title>
      <link>https://trid.trb.org/View/2425456</link>
      <description><![CDATA[Transit ridership had been decreasing in major cities across the United States prior to the Covid-19 pandemic, with Seattle as a notable exception. I examine the relationship between travel behavior and Seattle’s land use planning program in conjunction with transit improvements. I use econometric methods to analyze multiple waves of the Puget Sound Regional Council (PSRC) Household Travel Survey from 2014 to 2021. Living in one of Seattle’s Urban Villages is significantly associated with a higher likelihood of taking transit. This relationship holds during the pandemic time period and when controlling for self-selection.]]></description>
      <pubDate>Wed, 16 Oct 2024 11:10:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2425456</guid>
    </item>
    <item>
      <title>Incorporating Mobility on Demand into Public Transit in Suburban Areas: A Comparative Evaluation of Cost-Effectiveness</title>
      <link>https://trid.trb.org/View/2431682</link>
      <description><![CDATA[Traditional fixed-route transit services are inefficient in low-density areas due to limited and dispersed service demand. Many transit agencies look for effective alternatives to provide adequate transportation services in these areas, especially by leveraging mobile ICT-enabled new mobility services. This study evaluates the cost-effectiveness of transit incorporating mobility-on-demand (TIMOD) compared to fixed-route bus transit, driving alone, and commercial ridehailing services in suburban areas. It develops a comprehensive analytical framework to evaluate the cost-effectiveness of TIMOD and other alternatives from a societal perspective, considering differences in built environments. The analysis accounts for travelers’ monetary and time costs, service providers’ operating costs, and environmental externalities. Using real-world data from the Metro Flex program in the Seattle region and estimates based on simulation, the study compares the economic cost of Metro Flex trips with equivalent trips made using other travel modes in two different suburban areas. The results indicate that, in the study areas, Metro Flex trips have a total generalized cost for travelers that is higher than driving alone but lower than fixed-route transit and ride-hailing trips. Adding service operation and emission abatement costs, Metro Flex becomes less cost-effective than all the alternatives due to high operating costs and a higher proportion of deadheading, however, the difference is slight in comparison to fixed-route transit. The findings also show that areas with higher density and more transit services result in lower operation costs per rider for the transit agency. Incorporating equity into the cost-effectiveness analysis shows that Metro Flex has a more equitable distribution of travel cost than fixed-route transit, but riders with high median income will have larger reductions in their travel time cost using Metro Flex compared to fixed-route transit. The study highlights the potential benefits and tradeoffs of providing TIMOD services in suburban areas, shedding light on the conditions under which such services are economically competitive.]]></description>
      <pubDate>Thu, 26 Sep 2024 16:50:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2431682</guid>
    </item>
    <item>
      <title>Impacts of teleworking and online shopping on travel: a tour-based analysis</title>
      <link>https://trid.trb.org/View/2319897</link>
      <description><![CDATA[Large-scale adoption of telemobility, such as teleworking and online shopping, has affected travel patterns significantly. The impacts of teleworking and online shopping on travel have been studied separately and with trip-level analyses, thereby ignoring tour complexity, trip chaining, and activity scheduling. We aim to address this gap by investigating the interactions between online shopping, teleworking, and travel at a tour level, considering trip chaining and the importance of the activities involved. We classify tours into mandatory (e.g., travel for work, school), maintenance (e.g., travel for grocery shopping, appointments, errands), and discretionary (e.g., travel for non-grocery shopping, leisure, religious activities) tours according to the primary activity purpose. We then estimate a structural equation model using a one-week activity-travel diary from the 2019 Puget Sound Regional Travel Study. The results indicate that teleworking reduced mandatory and maintenance tours while increasing online shopping. Mandatory tours were negatively associated with both maintenance tours and online shopping, whereas the number of maintenance tours was positively associated with the number of discretionary tours. We did not find a statistically significant relationship between online shopping, maintenance tours, and discretionary tours. Overall, this study offers new insights into the effect of teleworking and online shopping on travel, with potential implications for travel demand modeling and management, as well as for the design of travel surveys that take such virtual activities into account.]]></description>
      <pubDate>Wed, 17 Jan 2024 16:54:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2319897</guid>
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