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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>Coastal storm-induced flooding risk of the New York City subway amid climate change</title>
      <link>https://trid.trb.org/View/2611318</link>
      <description><![CDATA[Coastal areas face worsening storm-induced flooding due to climate change, threatening critical below-ground infrastructure like subway systems, as seen from Hurricane Sandy’s catastrophic impact on New York City (NYC)’s subway system in 2012. Stakeholders must urgently address these risks to protect infrastructure and assets. Simulating future flood scenarios is crucial for estimating flood risk and damage efficiently and for identifying reliable and effective protective measures. This article uses the GIS-based Subdivision-Redistribution (GISSR) methodology, a high-speed, physics-based flood estimation tool, that is now extended to model subway system flooding and associated economic impacts. It identifies flooded tunnels and stations and quantifies indirect economic losses from subway inoperability using an input–output model. The model is validated against observed subway flooding during Hurricane Sandy. Each analysis, covering both above- and below-ground flooding in Lower Manhattan, takes less than 90 s on a single 4-core Intel Core i7-13620H CPU machine. Scenario analyses were conducted with NYC stakeholders, incorporating sea level rise projections and various protective measures. Results, benchmarked against NYC’s ongoing resiliency projects, demonstrate the effectiveness of adaptation/protective strategies, particularly when subway system-specific and coastal measures are combined, highlighting the model’s value as a practical guide for stakeholders.]]></description>
      <pubDate>Tue, 28 Oct 2025 13:42:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611318</guid>
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    <item>
      <title>Detecting Traffic Anomalies During Extreme Events via a Temporal Self-Expressive Model</title>
      <link>https://trid.trb.org/View/2441892</link>
      <description><![CDATA[Motivated by rapid urbanization and increasing natural hazards, this study aims to develop a data-driven method for detecting urban traffic anomalies during extreme events. Past experiences have shown that abnormal traffic patterns caused by extreme events can disrupt traffic in a large portion of the road network. Timely and reliable traffic monitoring for detection of such anomalies is crucial for congestion mitigation and successful emergency operation plans. An effective traffic monitoring system should detect disruptions at both network and local levels. However, the existing methods are not capable of addressing this need. This study proposes a temporal self-expressive network monitoring method to achieve this purpose. The proposed method first utilizes a temporal self-expressive model to uncover the dynamic interdependencies between local zones of the traffic network. Next, a statistical monitoring method detects network-wide anomalies based on regular traffic interdependencies. Finally, the method identifies the zones most affected by the anomalous event. The authors applied the proposed method to the road network of Manhattan in New York City to evaluate its performance during Hurricane Sandy. The outcomes confirmed that the temporal self-expressive model, augmented with statistical monitoring tools, could accurately detect anomalous traffic at both network and zone levels.]]></description>
      <pubDate>Mon, 30 Dec 2024 09:57:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2441892</guid>
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    <item>
      <title>Multilevel Monitoring System for Road Networks: Anomaly Detection at the Network and Road-Segment Levels</title>
      <link>https://trid.trb.org/View/2379750</link>
      <description><![CDATA[This study introduces a novel multilevel disruption detection method for road networks. The proposed monitoring and disruption detection method can detect disruptions at both the network and road-segment levels simultaneously. The monitoring process begins with a short-term prediction of hourly traffic speed on each road segment of the network using long short-term memory (LSTM) artificial neural networks. The prediction errors on each road segment at each timestep are used as a proxy to detect disruptions. Network-level disruptions are detected using a multivariate cumulative sum (MCUSUM) control chart. Local disruptions at a road-segment level of granularity are detected by decomposing the monitoring statistic of the MCUSUM control chart that follows a quadratic form using the correlation-maximization (corr-max) transformation. The proposed method was applied to the road network of Manhattan in New York City to examine its performance in detecting disruptions caused by Hurricane Sandy in 2012. The outcomes indicated that the proposed method could detect disruptions precisely at both network and road-segment levels. Whereas existing solutions can either monitor the entire network as a whole or focus on one or a limited number of road segments, the proposed method in this study can recognize if the entire network has been disrupted and also can recognize the road segments that are experiencing unusual traffic patterns. The outcomes of this study set the stage for transportation agencies and decision makers to design adaptive traffic management systems using real-time disruption detection at the network and road-segment levels.]]></description>
      <pubDate>Thu, 11 Jul 2024 13:53:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2379750</guid>
    </item>
    <item>
      <title>Agent-Based Evacuation Modeling in the Context of Non-Recurring Events</title>
      <link>https://trid.trb.org/View/2237752</link>
      <description><![CDATA[Evacuation is an important measure in emergency planning, and agent-based simulation model is an effective tool to test the reasonableness of the evacuation plans. This study simulates the evacuation behavior of New York City residents based on real data from Hurricane Sandy by using the mesoscopic multi-intelligence simulation tool MATSim. It aims to estimate road capacity loss. In this study, negative binomial and log-normal distributions are applied to model accident frequency and duration. Subsequently, observations of non-recurring events are generated from the above distributions by Monte Carlo simulations, and the loss of capacity caused by non-recurring events is modeled and dynamically incorporated into the simulation network according to the observations. In addition, the NYBPM V2.1 model is also used to calibrate the road network capacity and free speed of MATSim resulting in a more realistic and reliable simulation model. The results show that the data obtained from the calibrated road network model are more consistent with Manhattan taxi travel times.]]></description>
      <pubDate>Tue, 30 Jan 2024 09:23:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2237752</guid>
    </item>
    <item>
      <title>A location-inventory-routing model for distributing emergency supplies</title>
      <link>https://trid.trb.org/View/2186205</link>
      <description><![CDATA[Dynamic last-mile distribution of emergency supplies to affected people in post-disaster situations is a key emergency response task, which however is seldom addressed in the literature. To fill this gap, the authors present a path-based location-inventory-routing model for developing a dynamic last-mile emergency supply distribution plan, including a local distribution center, points of distribution, and demand points. The model can be applied to evolving post-hurricane situations, where candidate point of distribution sites and demands dynamically change. Both cost and equity are incorporated into the planning goal to ensure that distribution of emergency supplies is cost-efficient and fair. With a case study of the previously flooded Red Hook neighborhood in New York City during Hurricane Sandy, they show that their model outperforms different comparison models significantly. Moreover, they obtain managerial insights regarding location, inventory, and routing flexibility, which could be leveraged for post-hurricane distribution practice.]]></description>
      <pubDate>Wed, 28 Jun 2023 16:57:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2186205</guid>
    </item>
    <item>
      <title>FTA Made Progress in Providing Hurricane Sandy Funds but Weaknesses in Tracking and Reporting Reduce Transparency Into Their Use</title>
      <link>https://trid.trb.org/View/1875856</link>
      <description><![CDATA[After Hurricane Sandy caused widespread damage to transportation infrastructure in October 2012, the Disaster Relief Appropriations Act (DRAA) designated $10.9 billion for the Federal Transit Administration’s (FTA) new Public Transportation Emergency Relief Program. The Office of Inspector General (OIG) assessed (1) FTA’s progress in allocating, obligating, and disbursing its Hurricane Sandy funding and (2) any weaknesses in these processes that they identified. OIG found that through December 31, 2020, FTA allocated and obligated approximately $10 billion—most of its Hurricane Sandy appropriation—but only disbursed about $4.3 billion. The pace was influenced by a number of factors, including but not limited to project construction planning and execution and the complexity of competitive resilience projects. As a result, over 8 years after the storm, more than half of the funds remain to be spent. OIG also found that FTA inconsistently tracks and reports Hurricane Sandy funding data or does not fully comply with Federal guidance. The Agency has allocation data in a variety of sources, but—as FTA does not have procedures to accurately communicate allocated amounts over time—the data from these sources do not align. Thus, FTA cannot use these data to determine whether obligation amounts for specific recipients and purposes stayed within the allocated amounts in FTA’s official documentation. Finally, FTA has not complied with a directive from the Office of Management and Budget to make DRAA obligation data readily identifiable on the USAspending.gov website. Overall, the weaknesses OIG identified reduced transparency for internal users, decision makers, and the public into FTA’s use of Hurricane Sandy funds. OIG made two recommendations to improve FTA’s tracking and reporting on its use of Hurricane Sandy funds. FTA concurred with both recommendations and proposed appropriate actions and completion dates. Accordingly, OIG considers both recommendations as resolved but open pending completion of the planned actions.]]></description>
      <pubDate>Wed, 22 Sep 2021 17:09:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/1875856</guid>
    </item>
    <item>
      <title>Network Modeling of Hurricane Evacuation Using Data-Driven Demand and Incident-Induced Capacity Loss Models</title>
      <link>https://trid.trb.org/View/1878407</link>
      <description><![CDATA[The development of a hurricane evacuation simulation model is a crucial task in emergency management and planning. Two major issues affect the reliability of an evacuation model: one is estimations of evacuation traffic based on socioeconomic characteristics, and the other is capacity change and its influence on evacuation outcome due to traffic incidents in the context of hurricanes. Both issues can impact the effectiveness of emergency planning in terms of evacuation order issuance, and evacuation route planning. The proposed research aims to investigate the demand and supply modeling in the context of hurricane evacuations. This methodology created three scenarios for the New York City (NYC) metropolitan area, including one base and two evacuation scenarios with different levels of traffic demand and capacity uncertainty. Observed volume data prior to Hurricane Sandy is collected to model the response curve of the model, and the empirical incident data under actual evacuation conditions are analyzed and modeled. Then, the modeled incidents are incorporated into the planning model modified for evacuation. Simulation results are sampled and compared with observed sensor-based travel times as well as O-D-based trip times of NYC taxi data. The results show that the introduction of incident frequency and duration models can significantly improve the performance of the evacuation model. The results of this approach imply the importance of traffic incident consideration for hurricane evacuation simulation.]]></description>
      <pubDate>Mon, 20 Sep 2021 14:52:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/1878407</guid>
    </item>
    <item>
      <title>Household Evacuation Planning and Preparation for Future Hurricanes: Role of Utility Service Disruptions</title>
      <link>https://trid.trb.org/View/1855934</link>
      <description><![CDATA[We analyzed data from a survey administered to 1,212 respondents living in superstorm Hurricane Sandy-affected areas. We estimated the effect of having experienced hurricane-induced disruptions to utility services, such as electricity, water, gas, phone service, and public transportation, on having an evacuation plan. Around 39% of respondents reported having an evacuation plan in case a hurricane affects their neighborhood this year. Respondents who had experienced disruptions to electricity supply had an approximately 11 percentage-point higher likelihood of having an evacuation plan than those who had experienced no such disruptions. Respondents who had experienced monetary losses from Hurricane Sandy had around a five percentage-point higher likelihood of having an evacuation plan compared with those who had not. Among control variables, prior evacuation, distance to the coastline, residence in a flood zone, concern about the impacts of future natural disaster events, had window protection, and household members being disabled, each had an association with residents’ future evacuation planning and hurricane preparedness. In light of these findings, we discuss the policy implications of our findings for improving disaster management in hurricane-prone areas.]]></description>
      <pubDate>Wed, 02 Jun 2021 09:21:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/1855934</guid>
    </item>
    <item>
      <title>Assessing the Impact of Transportation Diversity on Postdisaster Intraurban Mobility</title>
      <link>https://trid.trb.org/View/1752045</link>
      <description><![CDATA[Transportation infrastructure enables mobility in urban communities and impacts the functionality of other infrastructure and services. Natural hazards can cause failures in a transportation system, which can affect mobility and other economic activities in a community. Diversity is recognized as an important factor of resilience in transportation infrastructure, but empirical work linking the two is limited. In this study, the impact of transportation diversity on mobility in New York City after Hurricane Sandy is explored. Transportation diversity, defined as the availability and distribution of modes in a community, is measured by employing a recently developed approach at the zip code level using transportation system GIS data. The geotagged Twitter data of one month before and after Hurricane Sandy in New York City are used to understand mobility patterns before and after this extreme event. The primary locations of individuals are found, and their mobility patterns are subsequently determined by measuring travel distance, the radius of gyration, and mobility entropy one week and one month before and after the hurricane. Individuals are grouped in quartiles based on the transportation diversity of their primary locations, which is determined by two different methods that overcome the lack of transportation system information in call detail records and social media data. The results indicate that the distance, radius, and entropy of all individuals significantly decreased after Sandy. The comparison of the significance of change in mobility metrics by diversity quartiles revealed that one-week distance and radius metrics in low transportation diversity quartiles were changed and statistically significant. Thus, individuals with primary locations in zip codes with higher transportation diversity generally had a higher maintained distance and radius one week after the hurricane. The comparison of diversity quartiles in the one-month analysis showed that the radius of low transportation diversity quartiles was impacted more than high transportation diversity quartiles and was statistically significant. Further, the comparison of results of one week and one month after the hurricane indicated that distance, radius of gyration, and entropy improved after a month as the transportation system was recovering. The findings establish an empirical link between transportation diversity and intraurban mobility in the wake of natural disasters such as Hurricane Sandy and confirm that transportation diversity influences individual postdisaster mobility. In addition, the results contribute to the mobility resilience literature by deepening understanding of the underlying drivers of changes in human mobility following extreme events. Further, the adopted approach supports pinpointing areas with low transportation diversity, which can enable more targeted infrastructure and urban resilience management.]]></description>
      <pubDate>Mon, 21 Dec 2020 13:48:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/1752045</guid>
    </item>
    <item>
      <title>Empirical Analysis of Impacts of Post-Disaster Human Mobility Patterns on the Resilience of Transportation Networks</title>
      <link>https://trid.trb.org/View/1750194</link>
      <description><![CDATA[Resilient transportation networks are a critical component of urban societies during and after a disaster, necessary for emergency services, rescue operations, and access to major population and activity centers. Previous studies observed that beside physical damages to transportation infrastructure, post-disaster unusual traffic patterns may lead to gridlock, congestion, and failures in transportation networks. However, little is known about analyzing the impacts of post-disaster unusual human mobility and traffic patterns on the performance of transportation networks. The objective of this study is to empirically and statistically examine two hypotheses: 1) post-disaster unusual traffic patterns perturb the topology of transportation networks; and 2) perturbed topological features of the transportation network affect the network performance. Historical records of taxi GPS traces in New York City were used to examine the two hypotheses on the New York City road network after Hurricane Sandy in 2012. The results of the statistical process control using the exponentially weighted moving average control chart show that post-disaster unusual traffic patterns perturbed the topology of the New York City transportation network and significantly shifted the network betweenness index from its usual variations. The outcomes of the Granger causality test confirm the second hypothesis and indicate that the deviated betweenness index resulted from the perturbed network topology is statistically associated with the network closeness index indicating that the perturbed topological features of the network affect its performance. The outcomes of this study will help decision makers empirically analyze impacts of post-disaster unusual traffic patterns on performance and resiliency of transportation networks.]]></description>
      <pubDate>Thu, 17 Dec 2020 09:55:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/1750194</guid>
    </item>
    <item>
      <title>Port stakeholder perceptions of Sandy impacts: a case study of Red Hook, New York</title>
      <link>https://trid.trb.org/View/1747491</link>
      <description><![CDATA[Understanding the impacts of coastal storm hazards on all maritime port system stakeholders (e.g. operators, tenants, clients, workers, communities, governments) is essential to comprehensive climate change resilience planning. While direct damages and indirect impacts are quantifiable through economic data and modeling, qualitative data on the intangible consequences of storms are necessary to explicate interdependencies between stakeholders as well as conditions that substantially affect response and recovery capacities. This case study explores Hurricane Sandy storm impacts using evidence solicited from stakeholder representatives and extracted from contemporaneous and technical accounts of storm impacts on the port system at Red Hook Container Terminal, Brooklyn, New York, USA. Results highlight the wide range of direct damages, indirect costs, and intangible consequences impacting stakeholders across institutional boundaries and requiring coordination for recovery, providing insight into stakeholder relationships and dependencies in the post-disaster response and recovery process that are often not fully accounted for in current vulnerability assessment and response planning methodologies.]]></description>
      <pubDate>Tue, 10 Nov 2020 09:19:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/1747491</guid>
    </item>
    <item>
      <title>Exploring network properties of social media interactions and activities during Hurricane Sandy</title>
      <link>https://trid.trb.org/View/1714714</link>
      <description><![CDATA[In this study, the authors analyze Twitter data to understand information spreading activities of social media users during Hurricane Sandy. They create multiple subgraphs of Twitter users based on activity levels and analyze such network properties. The authors observe that user information sharing activity follows a power-law distribution suggesting the existence of few highly active nodes in disseminating information compared to many other nodes. The authors also observe close enough connected components and isolates at all levels of activity, and networks become less transitive, but more assortative for larger subgraphs. The authors also analyze the association between user activities and characteristics that may influence user behavior to spread information during a crisis. Users who are centrally placed in the network, less eccentric and have higher degrees, they are more active in spreading information. The authors' analyses provide insights on how to exploit user characteristics and network properties to spread targeted information in major disasters.]]></description>
      <pubDate>Mon, 13 Jul 2020 10:33:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/1714714</guid>
    </item>
    <item>
      <title>Joint modeling of evacuation departure and travel times in hurricanes</title>
      <link>https://trid.trb.org/View/1667089</link>
      <description><![CDATA[Hurricanes are costly natural disasters periodically faced by households in coastal and to some extent, inland areas. A detailed understanding of evacuation behavior is fundamental to the development of efficient emergency plans. Once a household decides to evacuate, a key behavioral issue is the time at which individuals depart to reach their destination. An accurate estimation of evacuation departure time is useful to predict evacuation demand over time and develop effective evacuation strategies. In addition, the time it takes for evacuees to reach their preferred destinations is important. A holistic understanding of the factors that affect travel time is useful to emergency officials in controlling road traffic and helps in preventing adverse conditions like traffic jams. Past studies suggest that departure time and travel time can be related. Hence, an important question arises whether there is an interdependence between evacuation departure time and travel time? Does departing close to the landfall increases the possibility of traveling short distances? Are people more likely to depart early when destined to longer distances? In this study, we present a model to jointly estimate departure and travel times during hurricane evacuations. Empirical results underscore the importance of accommodating an inter-relationship among these dimensions of evacuation behavior. This paper also attempts to empirically investigate the influence of social ties of individuals on joint estimation of evacuation departure and travel times. Survey data from Hurricane Sandy is used for computing empirical results. Results indicate significant role of social networks in addition to other key factors on evacuation departure and travel times during hurricanes.]]></description>
      <pubDate>Sat, 30 Nov 2019 15:23:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/1667089</guid>
    </item>
    <item>
      <title>FTA’s Limited Oversight of Grantees’ Compliance With Insurance Requirements Puts Federal Funds and Hurricane Sandy Insurance Proceeds at Risk</title>
      <link>https://trid.trb.org/View/1666125</link>
      <description><![CDATA[After Hurricane Sandy hit in October 2012, the Federal Transit Administration (FTA) awarded approximately $5.03 billion in grant funding to 14 grantees through 2017 for response, recovery, and rebuilding projects. The U.S. Department of Transportation Office of Inspector General (OIG) prior audits supporting oversight of this funding found that FTA established formal reporting and tracking procedures for grantees’ receipt of insurance proceeds to help prevent the Agency from funding project expenses for which a recipient already received insurance proceeds. However, OIG could not assess implementation of these oversight procedures at the time, because grantees faced years of ongoing monitoring before reaching settlements with the insurance companies. Now that grantees have begun to receive insurance settlements and develop plans for applying them, OIG initiated this audit to assess FTA’s oversight of its Hurricane Sandy grantees’ compliance with insurance requirements. Specifically, OIG assessed FTA’s oversight of Hurricane Sandy recovery grantees’ compliance with requirements for (1) carrying required insurance, (2) reporting on insurance proceeds, and (3) applying insurance proceeds. OIG found that FTA has not verified that grantees have required flood insurance for Hurricane Sandy damages and its other Federal transit investments. This is in part because FTA relies on grantees to self-certify that they have the requisite insurance coverage, does not require the grantees to produce the necessary data to support their certifications, and lacks procedures to confirm that grantees carry flood insurance when required. As a result, FTA cannot conclusively determine whether its grantees are eligible for the full amount of funding they received for Hurricane Sandy grants or a portion of the billions in Federal transit investments it funds annually. Further, FTA lacks procedures to follow up with grantees that do not submit Insurance Proceeds Reports, which may diminish its ability to eliminate duplication between Federal funds and insurance proceeds, as well as to ensure proceeds are properly allocated. Lastly, FTA has failed to hold Hurricane Sandy grantees accountable for timely or completely applying their over $1 billion in insurance proceeds, in some cases years after they received them. Consequently, OIG found over $982.8 million in insurance proceeds could be put to better use. OIG made eight recommendations to improve FTA’s oversight of its Hurricane Sandy grantees’ compliance with insurance requirements. FTA concurred with three, partially concurred with two, and did not concur with three. In response, OIG requested that FTA clarify and reconsider its actions.]]></description>
      <pubDate>Mon, 25 Nov 2019 11:54:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/1666125</guid>
    </item>
    <item>
      <title>Observing transient behavior during Hurricane Sandy through passively collected data</title>
      <link>https://trid.trb.org/View/1658731</link>
      <description><![CDATA[Transients, such as tourists or business travelers, often fly into large cities and subsequently have to rely on public transportation for movement within the city. It is difficult to observe transient behavior in the event of a disaster through traditional survey methods. Thus, transient behavior has not been understood as well as resident behavior, which presents a challenge to emergency management. This study used taxi trip records that are passively collected year-round in New York City (NYC) to observe transient behavior during Hurricane Sandy (2012). The linkage between transients and taxi trips was built upon the fact that many transients choose hotels as accommodation and depend on taxis during their travel in NYC. This study extracted and analyzed taxi trips originating from hotels shortly before and after the hurricane landfall, which cover transient outbound activities in a week. In the pre-landfall period, it was found that transients who evacuated out of Manhattan were more likely to leave hotels about 18–24 h before hurricane landfall; and those who stayed made more shopping trips within Manhattan. In the post-landfall period, transients were more likely to relocate on the second day after hurricane landfall; some transients headed to local airports once limited service was resumed. This is believed to be the first study to use passively collected data to observe transient travel behavior in the event of a hurricane as well as on normal days.]]></description>
      <pubDate>Thu, 14 Nov 2019 09:30:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/1658731</guid>
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