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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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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Unveiling the Dynamics of Human Mobility in Response to Wildfire-Induced Air Quality Degradation: An Examination of the 2019 Kincade Fire</title>
      <link>https://trid.trb.org/View/2509388</link>
      <description><![CDATA[tRising wildfire frequency and intensity are exacerbating air pollution, significantly impacting human mobility, particularly among vulnerable populations like low-income, elderly, and minority groups. This study, set in Sonoma County, California during the 2019 Kincade Fire, examines how particulate matter 2.5 (PM2.5) affects mobility patterns in communities not under direct evacuation orders. Analyzing daily home-dwell time, travel distance, and resident outflow, the authors note variations in how different groups respond to changing PM2.5 levels. Their findings reveal a direct relationship between PM2.5 concentration and mobility changes: higher PM2.5 levels are linked to increased travel distances and reduced home-dwell times, suggesting an adaptive response to air quality degradation. A notable decrease in weekend outflows indicates heightened air quality concerns affecting mobility. Through counterfactual analysis, they isolate the effects of PM2.5 on mobility patterns, and the results suggest that deteriorating air quality correlates with reduced outflow ratios. Their findings emphasize the need for targeted policy interventions to help vulnerable populations enhance their resilience to air quality deterioration during such events.]]></description>
      <pubDate>Fri, 07 Mar 2025 15:07:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2509388</guid>
    </item>
    <item>
      <title>Situational-aware multi-graph convolutional recurrent network (SA-MGCRN) for travel demand forecasting during wildfires</title>
      <link>https://trid.trb.org/View/2429443</link>
      <description><![CDATA[Natural hazards, such as wildfires, pose a significant threat to communities worldwide. Real-time forecasting of travel demand during wildfire evacuations is crucial for emergency managers and transportation planners to make timely and better-informed decisions. However, few studies focus on accurate travel demand forecasting in large-scale emergency evacuations. To tackle this research gap, the study develops a new methodological framework for modeling highly granular spatiotemporal trip generation in wildfire evacuations by using (a) large-scale GPS data generated by mobile devices and (b) state-of-the-art AI technologies. Based on the travel demand inferred from the GPS data, the authors develop a new deep learning model, i.e., Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN), along with a model updating scheme to achieve real-time forecasting of travel demand during wildfire evacuations. The proposed methodological framework is tested using a real-world case study: the 2019 Kincade Fire in Sonoma County, CA. The results show that SA-MGCRN significantly outperforms all the selected state-of-the-art benchmarks in terms of prediction performance. The authors' finding suggests that the most important model components of SA-MGCRN are weekend indicator, population change, evacuation order/warning information, and proximity to fire, which are consistent with behavioral theories and empirical findings. SA-MGCRN can be directly used in future wildfire events to assist real-time decision-making and emergency management.]]></description>
      <pubDate>Mon, 30 Sep 2024 17:21:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2429443</guid>
    </item>
    <item>
      <title>Destination unknown: Examining wildfire evacuee trips using GPS data</title>
      <link>https://trid.trb.org/View/2369007</link>
      <description><![CDATA[Effective wildfire evacuation planning requires understanding where evacuees are likely to travel and temporarily reside. Detailed information on evacuee destinations is a valuable input into critical decisions related to evacuation traffic control, shelter assignment, and return-entry planning. To improve their understanding of where evacuees stay, the authors analyzed GPS data generated by mobile devices from the 2019 Kincade Fire evacuation in Sonoma County, California. A new algorithm was developed to infer evacuee stops and destinations from mobile device GPS data. Evacuees were placed into distinct subgroups associated with their warning and departure context (e.g., self-evacuee, warned evacuee, ordered evacuee, shadow evacuee). The results reveal a surprising diversity of destinations not captured and mapped in prior studies. Analysis of the sub-groups showed marked differences in destination types and travel distances. The results of this study can aid in planning for future wildfires by providing emergency managers with detailed findings on empirical evacuee destination distributions.]]></description>
      <pubDate>Fri, 10 May 2024 16:51:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2369007</guid>
    </item>
    <item>
      <title>Wildfire evacuation decision modeling using GPS data</title>
      <link>https://trid.trb.org/View/2046920</link>
      <description><![CDATA[The threat of wildfires is increasing at an alarming rate due to climate change and the expansion of the wildland-urban interface. It is critical to improve understanding of people’s evacuation decisions during wildfire emergencies. Therefore, this study proposes a novel methodology to model evacuation rates using large-scale GPS data generated by mobile devices. The authors first overlay socio-demographic and built environment attributes—aggregated at the census-block-group-level—to the inferred home locations of the mobile device users. The authors then develop a linear regression model to examine how the socio-demographic and built environment variables affect evacuation rates across census block groups. The authors apply the GPS data (44.2 million signal records from over 5,000 devices) collected during the 2019 Kincade Fire in Sonoma County, California to evaluate the proposed methodology. The results of the model are generally consistent with findings of a prior survey of the same fire event. The authors also include factors in the model that are rarely measured through previous methods and find several built environment factors such as distance to the fire, land parcel size, and residing in a high fire risk area to be correlated with evacuation rates. Another notable finding is that people living in urban block groups, block groups with a higher median age, and block groups with a higher average level of educational attainment are more likely to evacuate. This research shows that the use of GPS data is a valuable complement to existing methods in wildfire evacuation research, and provides new insights to improve evacuation planning.]]></description>
      <pubDate>Mon, 21 Nov 2022 16:19:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2046920</guid>
    </item>
    <item>
      <title>Modeling evacuation decisions in the 2019 Kincade fire in California</title>
      <link>https://trid.trb.org/View/1889207</link>
      <description><![CDATA[Communities around the world are increasingly exposed to larger and more intense wildfires. A common method that officials use to protect community members from harm is evacuation. Data on how people behave during wildfires is critical when planning for evacuation and deciding when and how to evacuate entire communities during an event. Using a similar method to the 2016 Chimney Tops 2 Fire study, an online survey was conducted with households impacted by the 2019 Kincade fire in Sonoma County, California. The survey measured pre-event and event-based factors to 1) predict household perceptions and evacuation decisions and 2) compare results across fire events. Regression analysis identified the factors that influenced risk perception at the time of evacuation decision, i.e., pre-fire perceptions of safety, household makeup (of adults, pets, and livestock), income, education and threat assessment. Logistic regression analyses found that risk perception, length of residence, household makeup, income, education, evacuation order, fire cues, pre-fire perceptions of the safety, and homeownership influenced evacuation decisions. These results differ widely from the 2016 fire due to differences in fire conditions and experiences across populations. Results from this work bring the field closer to a generalized theory of human behavior during wildfire evacuation and improve community-wide evacuation planning and real-time decision-making.]]></description>
      <pubDate>Thu, 18 Nov 2021 12:12:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/1889207</guid>
    </item>
    <item>
      <title>Reaching into the rescue realm</title>
      <link>https://trid.trb.org/View/984823</link>
      <description><![CDATA[]]></description>
      <pubDate>Wed, 10 Nov 2010 12:13:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/984823</guid>
    </item>
    <item>
      <title>Volunteer Wheels: A Voluntary Paratransit Program</title>
      <link>https://trid.trb.org/View/809308</link>
      <description><![CDATA[This paper describes how, since the advent of the automobile, people of the world have become more and more mobile. The automobile has certainly revolutionized our culture and enhanced our freedom of mobility.  Most of us enjoy our cars and don’t think about the day when we are no longer capable of driving them.  Consider for a moment what your plight would be if for one reason or another you couldn’t drive and also couldn’t ride the bus.  This paper describes Volunteer Wheels, a service of the Volunteer Center of Sonoma County, California.  The volunteer center is committed to volunteerism and its initial intent was to demonstrate that a primarily volunteer program could meet the needs of specialized transportation and do so less expensively than if entirely paid drivers, schedulers and dispatchers were employed.  Volunteer Wheels provides 35,000 demand-responsive advance reservation trips a year to over 2,000 different clients nationwide.  The paper describes the three essential criteria for starting a Volunteer Wheels Program.  There must be a parent agency or government entity willing to sponsor the program that has an unyielding commitment to volunteerism with the knowledge of good principles of volunteer recruitment and management.  There must also be a sense of civic pride and community participation among the population and there must be a financial base.]]></description>
      <pubDate>Wed, 30 May 2007 15:05:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/809308</guid>
    </item>
    <item>
      <title>Factors Influencing Support for Local Transportation Sales Tax Measures</title>
      <link>https://trid.trb.org/View/805621</link>
      <description><![CDATA[Growth of local transportation sales taxes (LTSTs), most approved in local elections, has led to a gradual shift of the financial base for transportation projects away from user fees and toward broader-based taxes. This study explores the relationship between voter support for LTSTs and the social, political, and geographic characteristics of the voters.  Regression models were constructed to analyze this relationship using precinct-level voting data and census demographic data for three local transportation sales tax elections in Sonoma County, California. In addition, the relationship between the outcomes of the three measures was explored to better understand which transportation projects might have garnered more support for the successful measure. Findings show that the closer voters lived to the transportation projects to be funded, the greater their support. Higher incomes were also positively related to support, controlling for other variables. Political leanings were found to affect support, with the direction of the effect dependent upon the project list in each measure’s expenditure plan. At least one of the measures appears to have benefited greatly from its multi-modal expenditure plan.   Since this study was limited in scale, it is suggested that similar research in communities throughout the country will provide a deeper understanding of nationwide trends.]]></description>
      <pubDate>Fri, 30 Mar 2007 06:59:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/805621</guid>
    </item>
    <item>
      <title>REGIONAL TRANSPORTATION HOT SPOT FORUM : MARIN/SONOMA 101 CORRIDOR</title>
      <link>https://trid.trb.org/View/663551</link>
      <description><![CDATA[This publication is an edited version of a regional transportation forum conducted on April 11,2002 in which several representatives from key transportation-related agencies and community organizations in Marin County and Sonoma County (California). Focus was on discussing the entire Highway 101 corridor in Marin County and 10 miles in Sonoma County and the numerous possible actions that could provide alternatives and relieve congestion. The forum concluded with a set of recommendations relating to focus, partnership, and funding.]]></description>
      <pubDate>Tue, 02 Sep 2003 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/663551</guid>
    </item>
    <item>
      <title>SONOMA SR 101 VARIABLE PRICING STUDY.</title>
      <link>https://trid.trb.org/View/581992</link>
      <description><![CDATA[No abstract provided.]]></description>
      <pubDate>Thu, 29 Apr 1999 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/581992</guid>
    </item>
    <item>
      <title>PILOT STUDY OF SOLANO AND SONOMA COUNTIES LAND USE AND DEVELOPMENT POLICY ALTERNATIVES.</title>
      <link>https://trid.trb.org/View/550630</link>
      <description><![CDATA[No abstract provided.]]></description>
      <pubDate>Fri, 16 Jun 1995 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/550630</guid>
    </item>
    <item>
      <title>101 CORRIDOR STUDY: STRATEGIC TRANSPORTATION PLAN</title>
      <link>https://trid.trb.org/View/338198</link>
      <description><![CDATA[No abstract provided.]]></description>
      <pubDate>Thu, 28 Feb 1991 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/338198</guid>
    </item>
    <item>
      <title>OCCIDENTAL ROAD: CASE STUDY OF A HIGHWAY IN A SENSITIVE ENVIRONMENT</title>
      <link>https://trid.trb.org/View/339517</link>
      <description><![CDATA[No abstract provided.]]></description>
      <pubDate>Thu, 28 Feb 1991 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/339517</guid>
    </item>
    <item>
      <title>SCT'S EXPERIENCE WITH AUTOMATED FLEET MANAGEMENT</title>
      <link>https://trid.trb.org/View/243106</link>
      <description><![CDATA[No abstract provided.]]></description>
      <pubDate>Mon, 29 Feb 1988 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/243106</guid>
    </item>
    <item>
      <title>SONOMA COUNTY GENERAL PLAN: CIRCULATION AND TRANSIT ELEMENT</title>
      <link>https://trid.trb.org/View/234912</link>
      <description><![CDATA[No abstract provided.]]></description>
      <pubDate>Mon, 31 Aug 1987 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/234912</guid>
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