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    <title>Research in Progress (RIP)</title>
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    <language>en-us</language>
    <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>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Misinformation Detection for Safe Transportation Systems</title>
      <link>https://rip.trb.org/View/2548501</link>
      <description><![CDATA[This project is focused on developing a methodology for the detection of misinformation regarding transportation systems on social
media. A novel dataset combining Twitter and traffic sensor data will be constructed and shared with the broader community. This will be the first large-scale public dataset for research on this topic. A comprehensive characterization of the dataset, correlating
traffic events and tweets will be performed. One key question to be investigated is whether misinformation related to transportation follows similar patterns to misinformation in other domains, such as vaccines and politics. Results from the characterization will be applied to identify potential fake tweets related to traffic events. This analysis will be combined with a manual inspection to generate a set of labeled tweets that will be used for training semi-supervised methods for misinformation detection related to transportation. The proposed approach will be compared against alternatives from the literature. The main findings will be summarized in the project report and in at least one publication. Software, datasets, and metadata produced through the project will be made publicly available.]]></description>
      <pubDate>Tue, 29 Apr 2025 16:51:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2548501</guid>
    </item>
    <item>
      <title>Future Travel Foresight Catalyst: A Unique Approach to Exploring the Intersection of Transformative Technologies and Future Travel Behavior and Demand</title>
      <link>https://rip.trb.org/View/2440044</link>
      <description><![CDATA[A growing number of advanced technologies -- including AI, automation, robotics, spatial computing, quantum technologies, and more -- have the potential to revolutionize future travel behavior and demand. However successfully navigating the intersection between travel, emerging technologies, and a changing society, will demand bold new ideas and insights. Building on and integrating with the work of the ASU Future of Being Human initiative, the Future Travel Foresight Catalyst project will utilize a blended model of research, engagement, thought leadership, and knowledge mobilization, to catalyze innovative and integrated thinking at scale around future travel behavior and demand. The project will combine futures methodologies with cutting edge use of media platforms such as podcasts, articles, videos, and more, to engage across diverse communities and stimulate new and transformative thinking around future travel behavior and demand. 

The Future Travel Foresight Catalyst project explicitly responds to a growing recognition that siloed approaches to research, development, dissemination, learning/education, and engagement, are inadequate to ensure ground-breaking and responsive advances within increasingly complex sociotechnical ecosystems. These form a landscape that is dominated by fast moving innovation, radical entrepreneurship at the interstices between ideas and capabilities, rapidly shifting social norms and expectations, and governance and regulatory tools and frameworks that are struggling to play catchup with reality. To better understand the future of travel behavior and demand there is an urgent need for initiatives that intentionally blur the lines between research, development, and knowledge mobilization, in order to catalyze new thinking and ideas while revealing novel and potentially transforming pathways forward. Such initiatives need to be unconstrained by conventional disciplines, while facilitating knowledge discovery and mobilization within areas of expertise that are substantially enhanced through exposure to ideas from other areas.

By partnering with initiatives that include the ASU Future of Being Human initiative and the ASU Risk Innovation Nexus, and by drawing on leading expertise at the forefront of responsive and innovative knowledge mobilization, the Future Travel Foresight Catalyst project will uniquely extend understanding around the future of travel behavior and demand through exploring and expanding on emerging ideas across a number of public-facing platforms and modalities.

Deliverables from the project will include a foresight catalyst network within TBD and that extends to key external partners; public-facing knowledge mobilization media platforms; at least 10 public facing outputs.]]></description>
      <pubDate>Thu, 10 Oct 2024 17:25:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440044</guid>
    </item>
    <item>
      <title>Guidelines for Selecting Communication Channels to Deliver Traffic Safety Messaging



</title>
      <link>https://rip.trb.org/View/1905553</link>
      <description><![CDATA[BTSCRP Research Report 17: Selecting Communication Channels to Deliver Traffic Safety Messaging: A Guide provides guidelines for delivering effective behavioral traffic safety messaging, including information on how delivery methods impact various audiences.

Behavioral traffic safety messaging is intended to inform, persuade, and motivate specific groups of road users to modify their risky behaviors with the goal of improving road safety and reducing the number of traffic-related injuries and fatalities. To reach specific road users and influence unsafe behaviors, behavioral traffic safety messages must be developed for and adapted to the needs of specific audiences in multiple locations.

SHSOs are routinely contacted by media companies that offer various ways to share traffic safety messaging with the motoring public. Common strategies include print, broadcast, digital, out-of-home, social, experiential, and partner cobranding. Little is known about the effectiveness of such messaging, whether one form is better than others, or which audiences might be most impacted by each form of messaging.

Under BTSCRP Project BTS-22, “Guidelines for Selecting Communication Channels to Deliver Traffic Safety Messaging,” Virginia Polytechnic Institute and State University was asked to develop guidelines on delivering effective behavioral traffic safety messaging and how different delivery methods impact various audiences. To support this deliverable, the research team was asked to assess a variety of traditional and innovative behavioral traffic safety campaigns to identify noteworthy practices and cost-effective approaches, and to prepare case examples.

In addition to this report, the following deliverables are available on the National Academies Press website (nap.nationalacademies.org) by searching for BTSCRP Research Report 17: Selecting Communication Channels to Deliver Traffic Safety Messaging: A Guide: campaign matrix (Excel file); conduct of research report documenting the entire research effort; technical memorandum on implementation of research findings; and technical memorandum on research limitations.
]]></description>
      <pubDate>Tue, 25 Jan 2022 15:04:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/1905553</guid>
    </item>
    <item>
      <title>Synthesis of Information Related to Highway Practices. Topic 53-03. Practices Leveraging Social Media Data for Emergency Preparedness and Response</title>
      <link>https://rip.trb.org/View/1853026</link>
      <description><![CDATA[Emergencies are often unpredictable, unique, and hard to track. Timely response to emergencies on highways is a critical issue faced by state departments of transportation (DOTs). State DOTs have been developing emergency response protocols and procedures. The popularity of social media provides an unprecedented opportunity for state DOTs to obtain information. Social media data provides vital spatial and temporal information before, during, and after emergencies and the use of social media is popular in emergency management for its high accessibility and effectiveness. Many DOTs have undergone technology renovations and have started using social media data for rapid emergency situation detection, damage assessment, and evacuation plan propagation. However, there is a lack of documentation of DOT practices of using social media and corresponding data under different emergency scenarios.

The objective of this synthesis is to document current state DOT practices that leverage social media data for emergency preparedness, response, and recovery.]]></description>
      <pubDate>Tue, 18 May 2021 20:08:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/1853026</guid>
    </item>
    <item>
      <title>Real-time Transportation Social Media Analytics using Pulse (Pulse-T)</title>
      <link>https://rip.trb.org/View/1746166</link>
      <description><![CDATA[As city planners and transportation system planners consider changes and upgrades to transportation systems and infrastructure, they require models that accurately reflect communities’ needs. Planners need access to advanced activity-travel demand analysis models that are responsive and sensitive to emerging transportation technologies; models are needed that not only provide insights into communities’ current travel demands and behaviors, but also help understand people’s attitudes and expectations toward a change — or a proposed change — in a community’s transportation infrastructure or transportation options. For example, using this system public sentiment can be tracked when accidents involving autonomous vehicles occur or when transportation milestones are achieved in this field. However, the data on which the current models rely has limitations that prevent planners and policymakers from tapping into residents’ attitudes and perceptions widely, across the population and across time. Current models utilize surveys or opinion polls and yield a regimented set of responses to fixed questions. Moreover, the surveys reach a relatively small number of self-selecting individuals. They measure attitudes or self-reports of behavior at a single point in time, and to update them with new research topics or at new points in time is laborious and expensive. And, while some research requires datasets that extend over time, other, critical research requires real-time data that allows gauging current community sentiment around a topic. In this project the research team builds the Pulse-T, which will exponentially expand the access of TOMNET researchers and other organizations to an up-to-date, filtered dataset of public opinion and discussions around virtually any transportation research area. Researchers and organizations will have user perceptions on transport demand at their fingertips, enabling them to take appropriate measures and actions and undertake planning projects much more effectively than is possible today.
]]></description>
      <pubDate>Wed, 21 Oct 2020 20:35:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/1746166</guid>
    </item>
    <item>
      <title>Utilizing Social Media Data for Estimating Transit Performance Metrics in a Pre- and Post-COVID-19 World</title>
      <link>https://rip.trb.org/View/1740570</link>
      <description><![CDATA[This project aims to assess the perception of public transit service from the standpoint of customer. Using publicly available social media posts and analyzing the sentiment over time, the service perception will be quantified. In addition, the perception of public transit can be determined by delay, station environment, etc. or perception of public health safety around the time of pandemics such as COVID-19. In this study, the research team will also study the public perception of transit service before and after the lockdown due to COVID-19 and draw insights on determining factors that drive customer perception of public transit.]]></description>
      <pubDate>Wed, 23 Sep 2020 21:59:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/1740570</guid>
    </item>
    <item>
      <title>Synthesis of Information Related to Transit Practices. Topic SB-33. Uses of Social Media in Public Transportation</title>
      <link>https://rip.trb.org/View/1708342</link>
      <description><![CDATA[This synthesis topic will update Synthesis 99 and will again explore the use of social media among transit agencies as well as document innovative practices in the United States and Canada. 
]]></description>
      <pubDate>Tue, 26 May 2020 16:17:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/1708342</guid>
    </item>
    <item>
      <title>Using Social Media Data to Enhance Up-to-date Origin-Destination Demand Monitoring </title>
      <link>https://rip.trb.org/View/1489958</link>
      <description><![CDATA[This proposal aims to investigate the following problems: (P1) How to derive individual activity locations (ODs) from social media? Social media is usually a sparse sample of human spatial-temporal space and advanced data analytic methods are needed to derive the complete patterns using longitudinal data. Geo-tagged social media data can be categorized into activity locations and traveling locations. The research team aims to extract activity locations which can be further used to derive OD locations. (P2) How to infer individual travel behavior from longitudinal social media data? Social media data is generally sparse. Also people may post while they are moving. Therefore, conventional global positioning system (GPS)-based study may not be appropriate for answering this question. The team must figure out people's social media usage behavior, and further understand the discrepancies between social media sampling and household survey sampling. (P3) How to use trends in individual travel behavior to enhance longitudinal travel demand monitoring? To address this problem, the team first builds a model to classify travelers based on their social media usage or travel behavior pattern. Then the team will use social demographics information to map individual behavior to travel OD demand based on demographic information found when solving P1 and user classification results when solving P2.
]]></description>
      <pubDate>Tue, 28 Nov 2017 19:30:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/1489958</guid>
    </item>
    <item>
      <title>Inferring High‑Resolution Individual’s Activity and Trip Purposes with the Fusion of Social Media, Land Use and Connected Vehicle Trajectories</title>
      <link>https://rip.trb.org/View/1457186</link>
      <description><![CDATA[Inferring Individual’s activity and trip purposes is critical for transportation and travel behavior. State-of-Art trip purpose inference is conducted by geographic information systems (GIS) and land use data. However, there exist two major challenges: (1) how to identify accurate trip purposes in a high business density area with various possibilities of activities. (2) how to recognize high-resolution activities, which are much more than typical trip purposes (home, work, recreation, personal business, education, etc.) in existing literature.
Nowadays, the thriving growth of social media platforms, such as Twitter and Facebook, provides a new opportunity to extract crowdsourced data. Transportation authorities have also begun to identify social media data as another data source for transportation informatics. The advantage of social media is that passive activity information as well as time and location can be retrieved in real time with relatively small building and maintenance costs.
The objective of this project is, as a first attempt, to prove the concept that social media, combined with existing land use data and Connected Vehicle (CV) trajectories, can infer individual’s high-resolution activity and trip purposes information. In order to accomplish this goal, a 15-month project is defined in this proposal with a multidisciplinary team assembled with two principal investigators (PIs) from transportation engineering and computer science, respectively.
To accomplish the objective, first, the study will conduct a comprehensive literature review of previous studies in social media analytics and trip purpose inferences. Second, the research will develop machine learning models to retrieve travel related tweets and label geo information for tweets without geo-tags. Third, the project team will leverage a keyword-search approach to identify major public events and people’s gathering for public activities. Fourth, individual’s activities will be derived by deep learning and topic modeling. Fifth, the trip segments will be derived from Connected Vehicle trajectories and will be labeled with activities from both social media and land use data using topic modeling and WordNet, a popular lexical database. The proposed models will be finally evaluated with recently released 2 month CV data from 3000 equipped vehicles in Michigan CV safety pilot, the land use data, and the corresponding Tweeter data.
With the consideration of emerging social media and CV technologies, this research closely aligns with University Transportation Research Center's (UTRC’s) Focus Area 4: “System modernization through the implementation of advanced and information technologies”, together with Focus Area 7: “Promoting livable and sustainable communities through the quality of life improvements and diverse transportation development”. This work will also contribute to U.S. Department of Transportation (USDOT) strategic goals of “Economic Competitiveness” and “Quality of Life in Communities”. In the near future, the project team will work closely with the New York City Department of Transportation (NYCDOT) and New York Metropolitan Transportation Council (NYMTC), and implement developed models in newly established NYC CV test bed. The results will be disseminated to transportation authorities through webinars or workshops for workforce training. Also, the project team will develop K-12 hands-on projects in PI He’s National Summer Transportation Institute (NSTI), funded by the Federal Highway Administration (FHWA) in the last consecutive three years since 2013.]]></description>
      <pubDate>Fri, 24 Feb 2017 14:02:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/1457186</guid>
    </item>
    <item>
      <title>Reducing Incident-Induced Emissions and Energy Use in Transportation: Use of Social Media Feeds as an Incident Management Support Tool</title>
      <link>https://rip.trb.org/View/1446625</link>
      <description><![CDATA[By 2020, traffic delay is froecasted to cost 8.4 million hours for society and result in a fuel waste of 4.5 billion gallons in the United States (U.S.).  Besides the wasted time and fuel, incidents also cause local pollution (due to higher levels of emissions), and injuries/fatalities.  Roadway accidents are responsible for the majority of this high toll a 57.9%.  If an incident is not cleared in a timely fashion, the queue back-up due to incidents can further block nearby ramps or intersections, causing additional delays.  Early incident detection is also reported to save lives by increasing the survival probability of an injury accident victim.

In order to reduce these negative impacts, government agencies invest in Intelligent Transportation Systems (ITS) infrastructure (such as traffic sensors and cameras) for better management of traffic, including roadway incidents.  ITS infrastructure includes and array of information collection systems to share real-time data with integrated traffic control systems and advisory alerts designed to manage traffic, detect incidents, and provide travelers with current route information.  A wide range of statistical inference methods and algorithms as well as commercialized products are suggested for efficient and accurate detection.  However these technologies heavily depend on input from ITS infrastructure, i.e. magnetic loop detectors, Bluetooth readers, traffic cameras, etc.  Consequently, such sensor dependent incident management (IM) strategies come with substantial infrastructure and maintenance costs.  Incident detection methods with lower costs can yield a very high cost-benefit ratio.  Use of social media feeds to detect traffic indidents is one such approach which does not require any infrastructure investment, yet has shown to exhibit a strong potential for effectiveness.]]></description>
      <pubDate>Tue, 24 Jan 2017 11:30:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/1446625</guid>
    </item>
    <item>
      <title>Adaptive Vehicle Routing for Evacuation under Uncertainty</title>
      <link>https://rip.trb.org/View/1420237</link>
      <description><![CDATA[The objective of this research project is to deliver a real-time, adaptive evacuation system for cascading events (e.g., hurricanes, following flooding, and following aftershocks). To realize this goal, the principal investigator (PI) will synthesize the existing information (e.g., flood inundation scenarios, human mobility information such as location-based social media data, U.S. Census Bureau data, past disaster statistics, and relevant weather and land conditions); establish theoretically proven experimental models; simulate evacuation plans using the synthesized data sets and models; and conceptualize and present a new approach. The adaptive evacuation transportation-planning model is anticipated to contribute to substantial improvement in understanding of natural or man-made hazards and mitigation of their effects. The target disaster type is flooding induced by hurricane and the target region is the New York City area, selected considering the potentially affected population size and the high frequency of hurricane occurrence in that region.]]></description>
      <pubDate>Wed, 17 Aug 2016 16:02:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/1420237</guid>
    </item>
    <item>
      <title>SEGMENT: Applicability of an Existing Segmentation Technique to TDM Social Marketing Campaigns in the United States</title>
      <link>https://rip.trb.org/View/1420034</link>
      <description><![CDATA[Social marketing seeks to develop and integrate marketing concepts with other approaches to influence behaviors that benefit individuals and communities for the greater social good (International Social Marketing Association, 2013). Social marketing is a useful transportation demand management (TDM) planning approach to promote travel behavior change, and integrates several distinguishing features which set it apart from other popular behavior change planning approaches, such as education and mass media campaigns. These features include a focus on socially beneficial behavior change, a strong consumer orientation, the use of audience segmentation techniques and the selection of target audiences, the use of marketing’s conceptual framework (marketing mix and exchange theory), the recognition of competition, and continual marketing research. The purpose of the proposed study is to explore a consumer market segmentation technique (SEGMENT) successfully used in Europe for its applicability to social marketing campaigns in Florida. The SEGMENT project has developed a replicable and transferable market segmentation model to be used by all of the EU’s 27 member states when designing social marketing campaigns to persuade people to change their travel behavior and adopt more energy-efficient forms of transport (Intelligent Energy Europe, 2015). The SEGMENT project analyzed over 10,000 comprehensive attitudinal surveys containing over 100 questions to generate eight main attitudinal segments useful for the design of mobility social marketing campaigns; additional analysis produced eighteen ‘golden questions’ representing the smallest number of survey questions required to reproduce the eight market segments (Intelligent Energy Europe, 2015). The proposed study will replicate the SEGMENT methodology to determine whether the ‘golden questions’ accurately segment markets in Florida. Individuals will be surveyed using the long list of questions and discriminate analysis will be applied to identify the most powerful questions between the segments. Major contributions of this project will be the validation of a successful existing segmentation technique for applicability in the State of Florida, which will maximize the impact of TDM social marketing campaigns on changing travel behavior.]]></description>
      <pubDate>Tue, 16 Aug 2016 10:16:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/1420034</guid>
    </item>
    <item>
      <title>The Socialization of Travel: The Effects of Traveler Social Networks on Resiliency in Traffic Networks</title>
      <link>https://rip.trb.org/View/1419893</link>
      <description><![CDATA[This working paper serves as a starting point for a broader long-term research program on the socialization of travel in an information era. The focus is on the role social networks play in motivating resiliency in transportation infrastructures and their interdependence with other infrastructures. The concept of behavioral resiliency in traffic networks is characterized and further investigated as an outcome of social networks, their connectivity, strength of relations and key members. Two infrastructures providing context are transportation and social infrastructures, which have witnessed rapid socialization, both increasingly intertwining with each other. One motivating factor for this interrelatedness is the market penetration of mobile technologies and apps, such as smart phones, allowing travelers to share information across wide spatial and temporal geographies. A second motivating factor is the emergence of demand-responsive mobility services, such as Uber and bike shares, that rely on advances in technology, changing demographics, and evolving residential and commuting patterns. Both factors provide backdrop for this paper, underscoring the importance of social infrastructures that facilitate social networks, which in turn govern travel decisions in infrastructures with these mobility services.

Serving as an exploratory analysis, this paper will conduct a series of agent-based simulation experiments that explore the resiliency of travelers as a function of travelers’ social networks with varying attributes (i.e. size, connectivity, etc.) and communication mechanisms among members. The decision context will be departure time and route choices in a traffic network, similar to those conducted in past studies (Chen and Mahmassani 2004, 2006; Mahmassani et al. 1982). This paper will make two main contributions to the academic literature. First this paper will examine resiliency from an individual travel decision standpoint, broadening its characterization in the existing literature which has been largely dominated by a systems or network perspective on resiliency. More specifically, given that users face a network perturbation, in this paper resiliency refers to the recover in their decisions to pre-perturbation experiences. More resilient travelers will select alternatives or options that minimize the effects from the perturbation. The specific context here is traffic where travelers make decisions on departure time, route and travel mode. The second contribution of this work is to characterize the relationships between social network and resiliency in transportation infrastructures. A central hypothesis is that social networks matter for ensuring resiliency. From an individual user standpoint, the resiliency of decisions in the aftermath of a perturbation likely depends on the social networks present, which facilitate the sharing of experiences, observations and possible courses of action. While, broadly speaking, the impact of social networks on resiliency from a systems and individual user perspective is expected, the exact mechanisms, such as the interactions between social network members are unknown.]]></description>
      <pubDate>Fri, 12 Aug 2016 16:55:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/1419893</guid>
    </item>
    <item>
      <title>Synthesis of Information Related to Airport Practices. Topic S04-18. Uses of Social Media to Inform Emergency Responders During an Airport Emergency</title>
      <link>https://rip.trb.org/View/1377071</link>
      <description><![CDATA[No summary provided.]]></description>
      <pubDate>Wed, 09 Dec 2015 11:34:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/1377071</guid>
    </item>
    <item>
      <title>Big Data for Safety Monitoring, Assessment, and Improvement</title>
      <link>https://rip.trb.org/View/1364443</link>
      <description><![CDATA[The objective of this Major Research Initiative (MRI) is to develop innovative programs to monitor, assess, and improve safety using Big Data. The MRI will provide a framework and a roadmap that can be used to plan, design, operate, and maintain data intensive safety systems, i.e., systems that can enhance safety by using historical and real-time data from several sources. By developing knowledge, tools, and products based on Big Data, the MRI will contribute to potential improvements in safety. The MRI focuses on safety data from a diverse set of sources that include radars, video, loop detectors, Bluetooth devices, global positioning system (GPS) devices, social media, and road weather information system (RWIS), merged with conventional data sources such as roadway parameters, roadside elements, land use, and planning data.]]></description>
      <pubDate>Sat, 08 Aug 2015 01:01:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/1364443</guid>
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