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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>Research in Progress (RIP)</title>
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      <title>Causally Informed Forecasting of Event Travel Dynamics for the 2028 Los Angeles Olympics Using Large-Scale Mobility Data - Phase 2</title>
      <link>https://rip.trb.org/View/2732878</link>
      <description><![CDATA[Phase 2 of this project builds on Phase 1 foundational work to develop causal inference frameworks for event-driven travel behavior, and informed by those causal insights, forecasting models for the 2028 Los Angeles Olympic and Paralympic Games. This research can directly inform the work of the White House Task Force on the 2028 Summer Olympics (established by Executive Order 14328) and broader innovations in transportation planning for large-scale events.

In Phase 1 (January–June 2026), the research team is leveraging large-scale smartphone mobility data from approximately 100,000 anonymized mobile devices to characterize travel behavior during historical major events at approximately 20 venue sites where Olympic and Paralympic Games will be held across Los Angeles County. Analyzing mobility data linked to a Points of Interest database, the team is producing descriptive analyses for two key populations: (1) event visitors, characterized by place of origin, lodging location, attendance and post-event lingering duration, and secondary activities; and (2) local residents, for whom disruptions to routine travel are analyzed before, during, and after events. From these analyses the team is constructing origin-destination (O-D) matrices disaggregated by visitor/resident status, time, and event type.

Phase 2 advances this work to develop a causal inference-informed forecasting pipeline through two interconnected research thrusts. In the first stage, the team will use quasi-experimental methods (synthetic control and difference-in differences) to estimate the causal effect of each historical event on mobility outcomes, isolating what changes in mobility were caused by the event vs. confounders including baseline trends and seasonal variation. In the second stage, an invariant prediction framework [1, 2, 3] will identify which event features reliably predict those causal effects across approximately 20 venues and diverse event types; those features then become the inputs to the forecasting model. This design is motivated by a key insight from recent work in causal transfer learning and causal representation learning: prediction performance improves when models rely on features whose relationship to the outcome remains invariant, i.e., stable across different environments, rather than based on correlational patterns that may not generalize. This causal-then-forecast approach is especially well suited to the proposed Olympic Games planning task for three reasons: there is large heterogeneity across training “environments” characterized by different venues, event scales, types, seasons, and audiences simultaneously; observations per environment are limited by modest sampling rates (1-3%) and a finite number of historical events, so restricting to causally stable features can provide performance-improving regularization; and the practical purpose is prospective planning for unprecedented scenarios (e.g., simultaneous events across multiple venues), where causally grounded models can more reliably extrapolate.

Specifically, the first thrust will employ quasi-experimental research designs, including synthetic control and difference-in-differences approaches, to estimate the causal effect of historical major events on mobility outcomes, isolating event-driven behavioral changes from baseline trends, seasonal variation, and other confounders. The second thrust uses these causal estimates to inform the development of forecasting models for predicting mobility behavior to, during, and immediately after major events at venues across Los Angeles County. In a causal transfer and representation learning framework, the causally identified event effects will be used to constrain model architectures by selecting predictive features that are invariant (i.e., that reliably predict the quantified causal effects), across event types, venues, time periods, and other sources of environmental variation. These causally informed forecasting models will generate predictions of travel demand, trip timing, O-D flows, and secondary activity patterns for both event visitors and affected local residents.

Together, these thrusts complete a research pipeline supporting scenario analysis for Olympic transportation planning spanning Phases 1 and 2: descriptive characterization of large-scale historical events, causal inference to isolate event-specific effects, and causally informed forecasting models that predict travel to, during, and after the 2028 Olympic Games. A Technical Advisory Committee (TAC) will be composed of LA Metro Olympic planning staff, mobility data providers, and Crypto.com arena data science staff, to advise on key modeling outputs and insights regarding data inputs. All data handling will comply with USC and Cuebiq privacy standards.
 
]]></description>
      <pubDate>Wed, 22 Jul 2026 17:14:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732878</guid>
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      <title>RES2021-10: Rapid Emergency Evacuation Planning Assessment for Tourist Attractions and Isolated Communities</title>
      <link>https://rip.trb.org/View/1743834</link>
      <description><![CDATA[The purpose of the project is to develop an easy-to-use evacuation planning tool for smaller and less resource-rich communities and municipalities.  A modeling tool dubbed T-REX, or Tennessee Rapid Evacuation MicroSimlation, will be developed and demonstrated using actual locales in Tennessee with fitting scenarios.]]></description>
      <pubDate>Wed, 07 Oct 2020 11:52:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/1743834</guid>
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      <title>Managing the Daily Operations of a Bike Sharing System Using Mobile Stations</title>
      <link>https://rip.trb.org/View/1420236</link>
      <description><![CDATA[Over the last few years, bike-sharing systems have become a cost-effective, environmentally friendly alternatives for public transportation in many cities all over the world. On the one hand, the possibility of renting bicycles for short periods of time has given the users a new alternative for performing their daily commuting trips, a vibrant option of completing touristic loops, and a fast way of traversing a city in a healthy and exciting way. On the other hand, from the perspective of the city, this kind of systems have become a natural platform for reducing traffic and pollution, for promoting healthy habits within its citizens, and overall, for improving the living conditions of the city. In short, with their proven success, bike-sharing systems will continue to shape the landscape many major cities in the years to come.]]></description>
      <pubDate>Wed, 17 Aug 2016 16:01:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/1420236</guid>
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      <title>Sustainable Transportation for Tourism: Indicators &amp; Standards, Green Certification Programs, and Simulation Modeling (Part 1)</title>
      <link>https://rip.trb.org/View/1359768</link>
      <description><![CDATA[Transportation engineers often study patterns for routine daily travel: to work, to school, for social activities and errands. In this project an experienced interdisciplinary team tackles the issue of sustainable transportation in the context of tourism.  The researchers propose a model built on a matrix-based approach to define varying levels of sustainability where "indicators" are organized into a three-fold framework of environmental, social, and economic considerations. A range of "standards" for these indicators is arrayed across the matrix. Researchers are focusing on three types of geography where tourism travel is significant: tourist towns, scenic corridors and national parks. By incorporating indicators and standards a Level of Service (LOS) style metric can be extended to tourist travel.  Researchers are also considering marketing aspects of tourist travel behavior. Provision of more sustainable transportation such as a certified Green Coach is hypothesized to not only affect tourist travel decisions but to also provide public education and community/economic development.]]></description>
      <pubDate>Thu, 02 Jul 2015 01:01:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/1359768</guid>
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