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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>
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    <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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      <title>Assessing the spatially heterogeneous transportation impacts of recurrent flooding in the Hampton roads region on auto accessibility</title>
      <link>https://trid.trb.org/View/2622274</link>
      <description><![CDATA[Recurrent flooding has increased rapidly in coastal regions due to sea level rise and climate change. A key metric for evaluating transportation system degradation is accessibility, yet the lack of temporally and spatially disaggregate data means that the impact of recurrent flooding on accessibility—and hence transportation system performance—is not well understood. Using crowdsourced WAZE flood incident data from the Hampton Roads region in Virginia, this study examines changes in the roadway network accessibility for travelers residing in 1,113 traffic analysis zones (TAZs) across five time-of-day periods. Additionally, a social vulnerability index framework is developed to understand the socioeconomic characteristics of TAZs that experience high accessibility reduction under recurrent flooding. Results show that TAZs experience the most accessibility reduction under recurrent flooding during the morning peak period (6 to 9am) with large differences across different zones, ranging from 0 % to 49.6 % for work trips (with population-weighted mean reduction of 1.71 %) and 0 % to 87.9 % for non-work trips (with population-weighted mean reduction of 0.81 %). Furthermore, the social vulnerability analysis showed that zones with higher percentages of lower socio-economic status, unemployed, less educated, and limited English proficiency residents experience greater accessibility reduction for work trips. In contrast to previous studies that aggregate the effects of recurrent flooding across a city, these results demonstrate that there exists large spatial and temporal variation in recurrent flooding’s impacts on accessibility. This study also highlights the need to include social vulnerability analysis in assessing impacts of climate events, to ensure equitable outcomes as investments are made to create resilient transportation infrastructure.]]></description>
      <pubDate>Tue, 02 Dec 2025 09:57:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2622274</guid>
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      <title>Factors Influencing Pedestrian Decisions to Cross Mid-Block and Potential Countermeasures</title>
      <link>https://trid.trb.org/View/2452552</link>
      <description><![CDATA[About 70% of pedestrian fatalities involve mid-block crossings in Virginia. To address this critical safety issue, this research aims to investigate factors influencing pedestrian decisions to cross at mid-block locations and identify potential countermeasures to enhance pedestrian safety. The research team employed a multifaceted approach. A comprehensive literature review was conducted on factors affecting pedestrian crossing demand and choices and countermeasures to enhance pedestrian safety. Field data were collected from 1,150 pedestrians across 35 sites in Hampton Roads, Virginia. Additionally, 540 Virginia residents were involved in a survey to collect information on their crossing choices, human factors, and individual characteristics. A hierarchical negative binomial model, designed to account for potential temporal variations, was developed to estimate hourly pedestrian crossing demand based on the collected field data. Furthermore, a multi-group structural equation model was developed to reveal the decision-making mechanisms behind pedestrian crossing choices using the survey data. Factors such as population, walk ratio, speed limit, number of lanes, sidewalk width, and land use interaction were found to affect crossing demand. Meanwhile, the presence of safety messages, traffic volume, number of lanes, travel time saved by mid-block crossing, and gender influenced crossing choices. Moreover, human factors like safety awareness and delay tolerance, which vary across demographics such as age and gender, were identified as key influences in explaining crossing choices. The crossing demand and choice models developed in this study can guide the identification of countermeasures, which are classified into two categories: 1) encouraging safe crossings at the nearby intersection crosswalk for mid-block locations with low crossing demand but high mid-block crossing probabilities and 2) improving the safety of mid-block crossings for mid-block locations with high crossing demand and high mid-block crossing probabilities. This study recommends integrating quantitative models into pedestrian safety management processes. To facilitate implementation, the researchers developed an Excel-based tool, PedAct, which utilizes the crossing demand and choice models to inform decision-making in safety management, such as identifying locations with pedestrians exposed to high risk, developing countermeasures and evaluating their effectiveness. The PedAct tool has the potential to enhance the Virginia Department of Transportation’s decision-making process in pedestrian safety management, ultimately reducing pedestrian crashes.]]></description>
      <pubDate>Mon, 18 Nov 2024 17:16:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2452552</guid>
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    <item>
      <title>Impacts of household vulnerability on hurricane logistics evacuation under COVID-19: The case of U.S. Hampton Roads</title>
      <link>https://trid.trb.org/View/2196941</link>
      <description><![CDATA[Historical data suggest that when a severe tropical storm or hurricane impacts a community, the vulnerable segment of the population suffers the most severe consequences. With an increased aging population, it is crucial to understand how vulnerability alters evacuation behavior. Emergent variables such as fear of COVID-19 require additional exploration. People afraid of COVID-19 exposure may refuse to evacuate, exposing themselves unnecessarily. Differentiation is critical to evacuation logistics since it is needed to determine what proportion would stay in a local shelter, public or other, rather than evacuating or staying in their home and guide the logistics resource allocation process. This research uses data from a web and phone survey conducted in the Hampton Roads area of U.S. Virginia, with 2,200 valid responses to analyze the influence of social and demographic vulnerability factors and risk perception on evacuation decisions. This research contributes to the existing literature by developing a multinomial order logit model based on vulnerability factors and intended evacuation decisions, including staying at home, looking for a shelter, or leaving the Hampton Roads area. Findings show that race and risk perception are the variables that influence the decision-making process the most. Fear of COVID-19 transmission is also associated with an increased likelihood of leaving homes during evacuation. The variations in findings from previous studies are discussed regarding their implications for logistics emergency managers.]]></description>
      <pubDate>Wed, 28 Jun 2023 16:57:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2196941</guid>
    </item>
    <item>
      <title>Taking the freeway: Inferring evacuee route selection from survey data</title>
      <link>https://trid.trb.org/View/1862557</link>
      <description><![CDATA[Effective evacuation management plans can help reduce the negative impacts of disasters. Understanding evacuee travel behavior is critical for the design of evacuation plans. In this paper, the authors explore which factors contribute to evacuees selecting freeway vs. non-freeway evacuation routes. Freeways are of particular interest due to their ability to evacuate large volumes of traffic. This study used survey data collected for the Hampton Roads region of Virginia. Respondents were asked to provide their preferred route types in the event of a hypothetical Category 4 hurricane evacuation. A mixed (random parameters) logit model was proposed to determine factors that influence evacuees selecting between freeway and non-freeway route. The study found that several factors contribute to evacuees choosing a freeway over other routes. In the descending order of importance (i.e., marginal effects), these factors are: willingness to use the official recommended route, living in a single-family or duplex housing, expected travel time to reach the destination, being employed, and possessing prior evacuation experience. Conversely, a few factors had a negative effect on choosing a freeway. These factors are: willingness to evacuate two days prior to landfall and evacuating to a public shelter or a second home. The findings of this study can help emergency management and transportation agencies design effective traffic control plans to safely evacuate populations during a hurricane.]]></description>
      <pubDate>Tue, 28 Sep 2021 11:30:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/1862557</guid>
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    <item>
      <title>Stakeholder collaboration as a pathway to climate adaptation at coastal ports</title>
      <link>https://trid.trb.org/View/1747492</link>
      <description><![CDATA[In coastal regions of the U.S. maritime transportation system, compelling reasons exist for implementing measures for climate change adaptation. As the effects of climate change amplify the impacts of natural hazards, a critical aspect of the nation’s overall resiliency includes the ability of the maritime and coastal sectors to recover effectively from external shocks and to adapt to changing environmental conditions in order to continue to provide the services the nation relies upon for economic viability and homeland security. This requires adaptation for physical infrastructure as well as organizational, operational, and community elements throughout the maritime transportation system.This paper provides a literature review of port climate adaptation approaches, which highlights the established need and opportunities for collaboration among coastal stakeholders to implement climate adaptation in port communities. The current lack of federal support in the United States for climate adaptation in the maritime sector emphasizes the need for novel methods and approaches to facilitate adaptation at individual port and regional levels. A case study from the port community of Hampton Roads, Virginia provides an example of the time and effort dedicated to stakeholder collaboration to encourage local understanding of climate risks in order to facilitate successful adaptation.]]></description>
      <pubDate>Tue, 10 Nov 2020 09:19:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/1747492</guid>
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    <item>
      <title>Computational Enhancements for the Virginia Department of Transportation’s Regional River Severe Storm Model: Phase II</title>
      <link>https://trid.trb.org/View/1629291</link>
      <description><![CDATA[Climate change is projected to increase the risk of flooding, which can cause severe damage and threaten lives. This increased risk makes it even more important to accurately forecast potential flooding impacts. The report details efforts by the University of Virginia to enhance key aspects of the Virginia Department of Transportation’s (VDOT's) Regional River Severe Storm Model (R²S²) that aims to forecast potential flooding impacts in real-time for transportation infrastructure. This model serves as a planning tool for a large portion of the Hampton Roads District to assist residency administrators in efficiently allocating scarce resources to close roads and to assist first responders in accessing flood prone areas. In this study, researchers first designed and implemented methods to improve the accuracy of R²S² and reassessed the model against the stream data for two different storm events. The calibrated model shows good predictive capability for the majority of the study region, while the easternmost portion of the watershed, which has very flat terrain, remains the most difficult region to model accurately. The final task included automation of the cloud-based system that can provide end-to-end automation of flood warning for bridges and culverts in the region. The system is now available for implementation by VDOT for use during extreme weather events. The study recommends that the Virginia Transportation Research Council (VTRC) brief executives in the Department of Natural Resources on the work accomplished to date on R²S². The briefing should include the capabilities of the current model, its current limitations, and potential modifications that could improve the model. In the spring of 2019, the Governor and the General Assembly determined that coordinated state agency research activities in the areas of climate change, sea level rise, roadway flooding attributable to storm surge, and roadway management strategies in flooding events are desirable. The Department of Natural Resources has been identified as the lead agency for these initiatives; the study’s recommendation reflects that new interagency approach.]]></description>
      <pubDate>Sat, 15 Jun 2019 12:22:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/1629291</guid>
    </item>
    <item>
      <title>An Integrated Dynamic Modeling Approach for Flooding of Coastal Transportation Infrastructure: Assessment of Impacts on Emergency Operations</title>
      <link>https://trid.trb.org/View/1592069</link>
      <description><![CDATA[In this study, the authors develop a framework to couple a hydrodynamic model for storm surge with a hydrodynamic model for inland precipitation-driven flooding as a comprehensive modeling approach for flood modeling in urban coastal communities. This combined model for compound flooding was validated with available data and was applied to simulate flooding during Hurricane Irene (2011). Particular focus was placed on flooding of the area surrounding the Sentara Norfolk General Hospital which houses the only level 1 trauma center in the Hampton Region in southeast Virginia. Different flood level scenarios, based on results of the coupled flood model, were used in a model that solves a set covering problem to compute travel times and optimize the location of ambulance stations in the region such that patients can be transported to the trauma center within a critical time threshold, called the ‘golden hour’. Interaction with stakeholders, including the Hospital and City of Norfolk’s Department of Emergency Management, revealed that that there is lack of a systematic approach to route/position ambulances during flood events. The study area, similar to many coastal communities in the U.S., is under increasing risk of extreme and recurrent flooding due to relative sea level rise and climate change and the framework developed in this project which integrates state of the art in modeling flooding and emergency operations can provide a basis for development of essential predictive tools for city planners and emergency managers.]]></description>
      <pubDate>Fri, 22 Mar 2019 16:16:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/1592069</guid>
    </item>
    <item>
      <title>Evaluation of a reservation-based intersection control algorithm for hurricane evacuation with autonomous vehicles</title>
      <link>https://trid.trb.org/View/1553053</link>
      <description><![CDATA[A reservation-based intersection control algorithm was implemented for hurricane evacuation in a connected and autonomous vehicle environment. The autonomous reservation-based intersection control (AReBIC) algorithm receives and processes reservation requests from approaching vehicles in real-time and routes them free of conflict. The proposed algorithm was implemented in a simulation model of a road network in Hampton Roads, Virginia. The AReBIC algorithm outperformed the next best optimal signal control on all operational measures. The average speeds more than doubled and total delay decreased by 80%. While the total number of conflicts were comparable for AReBIC and optimal signal control, AReBIC traded the more severe crossing conflicts with less severe rear-end conflicts. The same superior performance of AReBIC was realized when transit signal priority was implemented in the study network. While the results are promising, a few practical issues should be addressed prior to any real-world implementation of reservation-based intersection control during evacuation. These issues include, latency and accuracy of communications, accommodation of pedestrians, accounting for incidents, and cybersecurity.]]></description>
      <pubDate>Wed, 10 Oct 2018 16:41:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/1553053</guid>
    </item>
    <item>
      <title>Integrated Ocean, Earth, and Atmospheric Observations for Resilience Planning in Hampton Roads, Virginia</title>
      <link>https://trid.trb.org/View/1509979</link>
      <description><![CDATA[Building flood resilience in coastal communities requires a precise understanding of the temporal and spatial scales of inundation and the ability to detect and predict changes in flooding. In Hampton Roads, the Intergovernmental Pilot Project's Scientific Advisory Committee recommended an integrated network of ocean, earth, and atmospheric data collection from both private and public sector organizations that engage in active scientific monitoring and observing. Since its establishment, the network has grown to include monitoring of water levels, land subsidence, wave measurements, current measurements, and atmospheric conditions. High-resolution land elevation and land cover data sets have also been developed. These products have been incorporated into a number of portals and integrated tools to help support resilience planning. Significant challenges to building the network included establishing consistent data standards across organizations to allow for the integration of the data into multiple, unique products and funding the expansion of the network components. Recommendations to the network development in Hampton Roads include the need to continue to support and expand the publicly available network of sensors; enhance integration between ocean, earth, and atmospheric networks; and improve shallow water bathymetry data used in spatial flooding models.]]></description>
      <pubDate>Thu, 17 May 2018 14:46:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/1509979</guid>
    </item>
    <item>
      <title>StormSense: A New Integrated Network of IoT Water Level Sensors in the Smart Cities of Hampton Roads, VA</title>
      <link>https://trid.trb.org/View/1509978</link>
      <description><![CDATA[Propagation of cost-effective water level sensors powered through the Internet of Things (IoT) has expanded the available offerings of ingestible data streams at the disposal of modern smart cities. StormSense is an IoT-enabled inundation forecasting research initiative and an active participant in the Global City Teams Challenge, seeking to enhance flood preparedness in the smart cities of Hampton Roads, VA, for flooding resulting from storm surge, rain, and tides. In this study, the authors present the results of the new StormSense water level sensors to help establish the “regional resilience monitoring network” noted as a key recommendation from the Intergovernmental Pilot Project. To accomplish this, the Commonwealth Center for Recurrent Flooding Resiliency's Tidewatch tidal forecast system is being used as a starting point to integrate the extant (NOAA) and new (United States Geological Survey [USGS] and StormSense) water level sensors throughout the region and demonstrate replicability of the solution across the cities of Newport News, Norfolk, and Virginia Beach within Hampton Roads, VA. StormSense's network employs a mix of ultrasonic and radar remote sensing technologies to record water levels during 2017 Hurricanes Jose and Maria. These data were used to validate the inundation predictions of a street level hydrodynamic model (5-m resolution), whereas the water levels from the sensors and the model were concomitantly validated by a temporary water level sensor deployed by the USGS in the Hague and crowd-sourced GPS maximum flooding extent observations from the sea level rise app, developed in Norfolk, VA.]]></description>
      <pubDate>Thu, 17 May 2018 14:46:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/1509978</guid>
    </item>
    <item>
      <title>Modeling the Impacts of Sea Level Rise on Storm Surge Inundation in Flood-Prone Urban Areas of Hampton Roads, Virginia</title>
      <link>https://trid.trb.org/View/1509977</link>
      <description><![CDATA[Hampton Roads is a populated area in the United States Mid-Atlantic region that is highly affected by sea level rise (SLR). The transportation infrastructure in the region is increasingly disrupted by storm surge and even minor flooding events. The purpose of this study is to improve understanding of SLR impacts on storm surge flooding in the region. The authors develop a hydrodynamic model to study the vulnerability of several critical flood-prone neighborhoods to storm surge flooding under several SLR projections. The hydrodynamic model is validated for tide prediction, and its performance in storm surge simulation is validated with the water level data from Hurricane Irene (2011). The developed model is then applied to three urban flooding hotspots located in Norfolk, Chesapeake, and the Isle of Wight. The extent, intensity, and duration of storm surge inundation under different SLR scenarios are estimated. Furthermore, the difference between the extent of flooding as predicted by the hydrodynamic model and the “bathtub” approach is highlighted.]]></description>
      <pubDate>Thu, 17 May 2018 14:46:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/1509977</guid>
    </item>
    <item>
      <title>Combining Different Data Sources to Predict Origin-Destinations and Flow Patterns for Trucks in Large Networks</title>
      <link>https://trid.trb.org/View/1505052</link>
      <description><![CDATA[A key aspect of monitoring freight on highways is determining the flow patterns of trucks, which can be achieved by uniquely identifying trucks at specific points along the roads or by tracking individual trucks using technology such as GPS. Both methods require investment in technology. Maintenance cost of equipment is another significant factor in infrastructure-based sensing. Most of the trucks using GPS tracking are owned by private parties that may not be willing to share the data. This research proposes a method capable of tracking trucks by using anonymously collected data from sensors already in place. Data collected from existing vehicle count and classification stations are used. The attributes collected such as length of truck, number of axles, and axle spacing provide valuable information for matching the same truck passing through two stations. The variance in these attributes between different trucks provides a means for re-identifying the same truck. Although there will be measurement errors, measurements from matched trucks still exhibit a distinct pattern in which the difference of measurements between two stations will be less compared to data from non-matched trucks. The feasibility of matching trucks anonymously based on axle data has been demonstrated in previous studies. In this project, previously developed models are enhanced to investigate the value of incorporating travel times provided by private companies into the vehicle re-identification algorithms. Therefore, the source data for this project consists of both private company data and attribute data from vehicle classification sites. For this project, the needed data were collected from two vehicle classification sites along the I-64 corridor in Hampton Roads, Virginia. Per vehicle data from the classification sites include a timestamp, vehicle class, speed, number of axles, axle to axle spacing, and overall length for each vehicle. Trucks crossing upstream and downstream sites are manually identified from the recorded video files so that the results from the vehicle re-identification algorithms can be validated. The re-identification algorithms are applied with different options for incorporating private company travel times. Since the selected I-64 corridor experiences recurrent congestion, the collected datasets include varying levels of traffic conditions. Change in travel time between congested and free-flow conditions significantly affects the performance of the re-identification model. It is found that using a dynamic travel time window informed by the private company data significantly improves the accuracy of the vehicle re-identification results. For some tested cases, the improvement in accuracy is up to 19% when compared to the results from static search windows. Results also show that dynamic search windows provide more robust results against small perturbations in travel time.]]></description>
      <pubDate>Fri, 27 Apr 2018 12:23:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/1505052</guid>
    </item>
    <item>
      <title>Open Toll Lanes in a Connected Vehicle Environment: Development of New Pricing Strategies for a Highly Dynamic and Distributed System – Phase II</title>
      <link>https://trid.trb.org/View/1505076</link>
      <description><![CDATA[This project is focused on investigating and developing alternative tolling options in a connected vehicle environment. In the first part of the project, to investigate future possibilities for open toll lanes in a connected vehicle environment, the research project was split into two research approaches: analytical and simulation. In this phase of the project, behavioral surveys were developed and conducted to gain insights into how people would choose to travel on toll roads when given the opportunity to bid, and whether they have support for new futuristic tolling methods enabled by vehicle-to-infrastructure (V2I) technology. The collected data was further incorporated into a mathematical model. The behavioral surveys were conducted in two parts: online stated preference survey and in-class game. Data from 159 participants residing in mainly Hampton Roads region in Virginia were collected by an online stated preference survey. Analysis showed that there is no outright rejection of the descending price auction tolling among those who are familiar with the current tolling practices. From the in-class game data, the study involved participants viewing videos of several tolling scenarios and placing their bids for using the toll road. Three different travel time savings levels were considered for both auction mechanisms: a sealed-bid auction and a Vickrey auction. The results indicated that there was a difference in respondents’ behavior between the bidding strategies for the two mechanisms, with participants bidding lower in the sealed-bid auction. According to auction theory, this result was expected.]]></description>
      <pubDate>Mon, 16 Apr 2018 11:20:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/1505076</guid>
    </item>
    <item>
      <title>Impact of Climate Change and Sea Level Rise on Stormwater Design
and Reoccurring Flooding Problems in the Hampton Roads Region</title>
      <link>https://trid.trb.org/View/1501746</link>
      <description><![CDATA[The information contained in this report is organized as three separate but related research studies. Collectively, these studies investigate the impact of climate change and sea level rise on transportation infrastructure within portions of the Hampton Roads region of Virginia. The first report “Impact of Sea Level Rise on Roadways in the Hampton Roads Region of Virginia” emphasizes the vulnerability of roadways to sea level rise in Norfolk and Virginia Beach. The second report “Impact of Climate Change on Design Rainfall Events in Hampton Roads, VA” explored various future precipitation scenarios in order to better understand climate change impacts to design rainfall events. The third report “Effect of rain gauge proximity on rain estimation for problematic urban coastal watersheds in Virginia Beach, VA” looks at the question of how rainfall variability within Virginia Beach impacts the ability to accurately measure rainfall using gauging stations. Each subreport is presented independently along with a title page listing the authors of that subreport. The University of Virginia completed the first and third subreports, while Virginia Tech completed the second subreport.]]></description>
      <pubDate>Tue, 20 Mar 2018 17:09:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/1501746</guid>
    </item>
    <item>
      <title>Familiarity and Use of Transportation Network Company (TNC) Services in Virginia</title>
      <link>https://trid.trb.org/View/1494338</link>
      <description><![CDATA[Using survey data from 3004 respondents in Northern Virginia, Richmond, and the Hampton Roads/Tidewater area, this paper identifies factors associated with respondents’ familiarity with transportation network companies (TNCs) and their use frequency. Ordinal logistic regression models were developed to understand the influence of variables related to technology use and comfort, normal travel choices, and socio-demographics and economics. Using a mobile wallet, a cell phone for entertainment, an app for taxi services, or an app for hotel booking/air transport arrangements, living in Northern Virginia, normally using multiple transportation modes for a single trip, higher education levels, and higher household income were associated with increased TNC familiarity and use frequency. Self-identifying as White/Caucasian was also associated with increased TNC use frequency. Increased age was associated with decreasing TNC familiarity and use frequency]]></description>
      <pubDate>Wed, 31 Jan 2018 16:58:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/1494338</guid>
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