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    <title>Research in Progress (RIP)</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>Research in Progress (RIP)</title>
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      <title>The San José's Mobility Credit Pilot: A Delayed Randomized Control Trial Evaluation</title>
      <link>https://rip.trb.org/View/2691659</link>
      <description><![CDATA[The San Jose Mobility Credit pilot (MCP) tests a new approach that allows individuals the freedom to travel when, where, and how they want to go. The pilot provides MCs that enable individuals to maximize travel while minimizing costs. Interest in these programs is growing throughout the U.S. The research team has experience evaluating similar programs in the U.S. The project will include a delayed longitudinal randomized control trial (RCT) to evaluate the MCP. The design of the 18-month MCP in-person participant recruitment, training, and support by the City of San Jose will support high participation and survey response rates. The study will be the first to use a delayed RCT design with a difference-in-differences (DID) statistical analysis to evaluate an MCP. In general, RCTs are rarely used to test the effectiveness of transportation projects and policies. The proposed study will evaluate the effects of the MCP, not only on individuals’ overall travel freedom, but also on transportation security (e.g., travel speed, time, and reliability), community participation (e.g., church, family, school, and volunteer activities), employment, education, and overall health (which could lead savings in health care costs). Few studies have evaluated the significance of transportation access interventions on these measures. The longer duration of the MCP may allow for a better assessment of evaluation measures. The MCP evaluation will be one of few studies that examine the causal effects (randomized control trial with difference-in-differences analysis) of a transportation intervention on multiple evaluation measures.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:10:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691659</guid>
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      <title>Using Artificial Intelligence to Uncover How Safety Perception Influences Travel Behavior Shifts: Comparative &amp; Longitudinal Analysis for the Future of Autonomous Vehicle, Transit and Ride-hailing Services</title>
      <link>https://rip.trb.org/View/2655700</link>
      <description><![CDATA[Transit agencies and cities are increasingly overwhelmed by large volumes of unstructured data; yet they lack methodical, validated tools to turn safety narratives into operational indicators. This project addresses that gap by measuring and comparing public safety perception for autonomous-vehicle services (robotaxis), public transit, and ride-hailing services. It will assess how these perceptions relate to traveler profiles and mode choice in San Francisco and San Jose over a six-month period. San Francisco as a mature setting where robotaxis may compete with ride-hailing and transit, and San Jose as a newer coming deployment that provides a baseline for comparison and forward-looking extrapolation.
The research team will use artificial intelligence with human-audited classification to analyze public discourse drawn from news-comment threads and social-media posts, for example, discussions of disengagements, curb conflicts, yielding behavior, and interpersonal harm such as unwanted contact, theft, or assault. Validation will include human audit with inter-rater reliability (aiming for Cohen’s kappa of at least 0.60), time- and city-based cross-validation, and an error taxonomy with documented adjustments. The project will deliver (1) a transparent safety-perception taxonomy, (2) traveler-persona profiles linked to safety perceptions, (3) a lightweight dashboard for agencies and cities to explore time, place, and topic trends, and (4) operational and policy frameworks for improvements across all modes, organized into vehicle-level safety measures, station and hub operating practices, reporting and response mechanisms, and rider communication standards. The approach and workflow are replicable and can be extended to additional cities. The innovation lies in a reusable tool bridging research and practice providing concrete, methodical steps to turn qualitative narratives into consistent indicators they can trust. Agencies can adopt it to sort and prioritize incoming signals, rerun it with new data, and compare results across time and places to support day-to-day decisions and longer-term planning.]]></description>
      <pubDate>Mon, 19 Jan 2026 16:09:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655700</guid>
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    <item>
      <title>Building AI and Machine Learning Technologies for Enhancing Transportation Station Area Safety in San Jose, CA</title>
      <link>https://rip.trb.org/View/2431333</link>
      <description><![CDATA[Criminal activities often cluster around transportation hubs like transit stations. While accurate crime prediction tools enhance crime prevention and mobility, few research integrates historical crime data, transportation networks, and hub locations using artificial intelligence (AI). This project develops a machine learning algorithm with the implementation of the software package in Python, leveraging a multi-layered geo-statistical model to predict crimes within transportation systems, enhancing safety and increasing ridership. Unlike past tools focusing solely on historical crime or land use data, this tool combines transportation network insights with hub locations taking advantage of rigorous statistical model. This software package enables local jurisdictions to allocate resources more effectively, plan interventions, and strengthen public safety.]]></description>
      <pubDate>Tue, 17 Sep 2024 16:27:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2431333</guid>
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      <title>Innovations Deserving Exploratory Analysis--The Transit IDEA Program. Bike Love</title>
      <link>https://rip.trb.org/View/1777141</link>
      <description><![CDATA[In this project, Palo Alto Transportation Management Association implemented a new cross-platform mobile app, “Bike Love” that provides daily incentives for verifiable active mode first-mile commute trips to transit and active mode commutes from home to work, up to $600 per year per commuter. Automated travel mode detection identifies bike, e-bike, e-scooter, and e-skateboard trips. Commute trips are verified to stop or start within geofences at 30 Caltrain (commuter rail) stations and two Palo Alto job centers. Incentive dollars are instantly redeemed at local merchants via reloadable Apple Wallet Virtual Visa cards, a new type of payment card. 67% of incentive dollars are spent in Palo Alto, recycling funds back into the local economy. Rapid program growth was achieved by a persuade-to-join-program commute survey and via door-to-door outreach to 800 small businesses. In only eight weeks, the research team’s marketing outreach motivated 106 Palo Alto workers to participate in the project. Commuter retention was 60%, above the team’s 50% retention expectation.  Successful marketing outreach motivated 106 Palo Alto workers to apply to join the Bike Love program. The software team discovered and implemented improved financial technology (fintech), eliminating expected onboarding problems with merchants, and enabling Tap and Pay at all merchants accepting Apple Pay. Travel mode detection was 95% accurate. There were 158 Virtual Visa transactions at 90 different merchants worth a total of $2,355 with an average transaction amount of $14.90. The project was overwhelmed by onboarding issues. Only 33 out of 106 commuters completed the entire seven-step onboarding process. For future software releases, numerous improvements to the onboarding process have been identified. 

 ]]></description>
      <pubDate>Tue, 23 Feb 2021 12:03:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/1777141</guid>
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