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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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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Information Design for Public Transportation</title>
      <link>https://rip.trb.org/View/2448892</link>
      <description><![CDATA[This research project explores how effective information design can increase public transportation ridership by making transit systems easier to navigate and understand for a broad range of users. Despite public transportation’s role in improving safety and mobility, gaps remain in how information such as maps, schedules, and updates are communicated, leaving many potential riders unaware of available services. By analyzing how various information types impact users' travel choices, this study aims to create a strategic information dissemination framework to improve public awareness and encourage a shift from private car use to public transit.

Drawing on insights from information design, marketing, and game theory, the project will develop a choice experiment to assess preferences for different transit information formats. This data will inform a model for optimizing information presentation to increase ridership and support safety and service availability goals. Ultimately, the project will provide public transit agencies with actionable strategies for designing effective communication materials to boost transit usage, potentially reducing traffic fatalities and lowering transportation costs for price-sensitive populations.
]]></description>
      <pubDate>Tue, 05 Nov 2024 11:36:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2448892</guid>
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    <item>
      <title>Determining the Effectiveness of Commercial Vehicle Safety Alerts
</title>
      <link>https://rip.trb.org/View/2236968</link>
      <description><![CDATA[Alerting motorists, in particular commercial vehicles, to upcoming hazards such as congestion, incidents, an end of a stopped traffic queue, etc. is very important in the safety and mobility of Ohio's roadway system. While placing physical infrastructure, such as Dynamic Message Signs, along the roadways is a good way to communicate these hazards to motorists, it also faces challenges, such as locations of signs (they can't be everywhere) and their cost (not only for installation, but maintenance as well). Because of these challenges, Ohio Department of Transportation (ODOT) is interested in methods to get information directly into vehicles and specifically into the cabs of commercial vehicles. ODOT currently has a contract with a third-party data vendor, Drivewyze, who ingests congestion and sudden slowdown data to display said data as alerts to commercial vehicles using their in-cab Electronic Logging Devices (ELDs). Through this research project, ODOT would like to identify the effectiveness and benefit/cost of such a system, the penetration rate of these types of alerts to commercial motor vehicles, and if other alternatives exist that should be pursued by the department.

The goal of this research is to determine the effectiveness of Commercial Vehicle Safety Alerts (CVSA).  Objectives include (1) analyzing ODOT's current system with Drivewyze; (2) determining the availability and effectiveness of alternative platforms/systems; (3) investigating the potential for an increase in benefits with an expanded system; and (4) evaluating future methods of improvement based on new technologies and data available. The findings from this research will help ODOT understand the accuracy, reliability and reach of CVSA. This will help decision makers inform ODOT's larger strategic direction for CVAS services.                    ]]></description>
      <pubDate>Mon, 28 Aug 2023 11:40:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2236968</guid>
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    <item>
      <title>Designing In-vehicle Message Delivery for Manual and Highly Automated Driving</title>
      <link>https://rip.trb.org/View/1723541</link>
      <description><![CDATA[With the rapid development of sensor and computing technologies, personal vehicles are now capable of collecting voluminous information on vehicle status and the road environment, as well as making proximity estimates and predicting potential driving events. Recent advances in vehicle automation have envisioned future driving without the need for drivers to attend to the road. With these trends in vehicle technology for the driving task, a shift in information communication is taking place from driver-roadway interaction to driver and in-vehicle display interaction. • Compared to (SAE) Level 2, drivers under Level 3 automation were less accurate in logo identification, likely due to a reduced number of glances to on-road signs. 

Despite decades of research on in-vehicle notification display designs, the majority of studies have concentrated on presenting information related to the driving task, such as display of collision warnings and navigation information. There is little knowledge on how to effectively present information that is secondary to driving but important for a trip, such as notifications of a rest area and local businesses. This information is conventionally presented on a guide or logo sign. Furthermore, existing research on in-vehicle information presentation during highly automated driving has only focused on safety critical messages such collision warnings. These studies do not necessarily generalize to notifications that are trip-related but non-safety critical information, as driver attentional processing could differ depending on the degree of relevance of the notification to the driving task and under various levels of automation. 

This project examined the influence of in-vehicle dynamic message displays of trip-related but non-safety critical information on driver visual behavior and driving performance, as compared with conventional on-road guide or logo sign use, during manual and highly automated driving. To achieve this goal, we first conducted a literature review on the following topics: (1) advances in content and update rates of in-vehicle trip-related messaging, (2) driver interaction with autonomous vehicle technology, (3) driver alertness and information processing, and (4) human factors issues in design of driver notification systems. The research team also performed two empirical studies using the NCSU advanced driving simulator, with the first experiment (E1) examining how drivers respond to messages posing various information loads during manual driving and the second (E2) investigating driver responses to messages when driving with high-level automation. 

Our findings support the use of in-vehicle displays, especially in combination with on-road signage. 

Under manual driving: 
Driver reactions to road hazards were slower when logos were present but the number of collisions did not increase. 
The use of in-vehicle displays produced better vehicle control with comparable workload and visual distraction, as compared to on-road signage. 
Simultaneous in-vehicle and on-road displays showed a benefit on hazard negotiation (fewer collisions). 
Some age differences were observed in driving and visual behaviors, but the evidence does not suggest any particular age-related safety concerns. 
​
When driving with partial automation (level 2): 
Simultaneous in-vehicle and on-road displays led to the highest logo identification accuracy and little impairment of hazard negotiation when logos were present. 
Simultaneous in-vehicle and on-road displays led to shorter single off-road glance durations and mitigated the effect of information load on driver visual processing. Drivers made fewer but longer glances to on-road signage, as compared to in-vehicle displays. 
Older drivers were less accurate in logo identification than young and middle-aged drivers. However, all three age groups showed comparable driving performance, glance durations, and number of glances. 

When driving with conditional automation (level 3):
Compared to (SAE) Level 2, drivers under Level 3 automation were less accurate in logo identification, likely due to a reduced number of glances to on-road signs. ]]></description>
      <pubDate>Thu, 23 Jul 2020 13:07:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/1723541</guid>
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      <title>Training of Pilots and Air Traffic Controllers in Weather-related Decision Making Using Probabilistic Hazard Information Displays</title>
      <link>https://rip.trb.org/View/1532123</link>
      <description><![CDATA[No abstract provided.]]></description>
      <pubDate>Wed, 15 Aug 2018 09:16:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/1532123</guid>
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