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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>SPR-4937:  Enhanced Traffic Pattern Knowledge Abstraction and Presentation from 511 Database</title>
      <link>https://rip.trb.org/View/2399821</link>
      <description><![CDATA[This project will develop a system with a user-friendly intelligent interface that will enhance the existing 511 features by developing new features for INDOT and potential users. These new features may include: (1) providing user-adjustable spatial-temporal traffic flow information over a specified period in the display; (2) allowing users to overlay additional parameters of their choice by integrating 511 traffic data and another related database, such as weather and event data; and (3) supporting pattern analysis for the historical data already stored in 511.]]></description>
      <pubDate>Tue, 02 Jul 2024 13:46:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2399821</guid>
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      <title>Driving through Extreme Weather (DEW) Mobile APP: Improving Risk Communication from NWS to Vehicle Drivers</title>
      <link>https://rip.trb.org/View/2341574</link>
      <description><![CDATA[Severe thunderstorms, such as tornadoes, pose a significant threat to the traveling public
and transportation personnel along roadway corridors. For a number of reasons, road travelers cannot receive severe weather warning for their current or down-road locations, which puts them at risk for being impacted by severe weather. In addition, even though the road travelers can receive severe weather warning, they may not take any actions as they do not know what they can do to make them safe. This suggests that extra message
(guidance on where to drive to) is needed by road travelers, besides severe weather warning. Recognizing this need, this project will propose to develop an advanced decision-making framework that can provide the optimized route for vehicle drivers based on big data, in order to facilitate them in taking timely protective actions during extreme weather. To achieve this, a route-optimizing algorithm will be developed to identify an optimized route for vehicle drivers during severe weather, and a mobile app will be developed to facilitate in real-time sending the optimized route to vehicle drivers (risk communication). This product will add a layer of protection to those traveling to ensure that they reach their destination safely. This product can provide end-users when they should get off the highway and which route they should take to get to the nearest storm shelter. Tornadoes will be taken as an example of extreme weather; Missouri will be taken as a pilot location; NWS in Springfield, MO will be taken as our collaborative NWS office; and Missouri Department of Transportation (MODOT) will be brought onboard to develop this mobile app together. The proposed framework and App development procedure can be applied to improve the public safety under other extreme weather conditions and in other locations.
]]></description>
      <pubDate>Mon, 19 Feb 2024 16:41:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2341574</guid>
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    <item>
      <title>Automatic Detection and Understanding of Roadworks</title>
      <link>https://rip.trb.org/View/2087440</link>
      <description><![CDATA[Roadwork zones present a serious impediment to vehicular mobility. Whether new construction or maintenance is taking place, work in road environments cause lower vehicle speeds, congestion, increased risk of rear-end collisions, and more difficult maneuvering. Crowd-sourced navigation systems like Waze warn drivers of roadworks, but those data must be manually entered causing a distraction for the driver. Google maps now automatically shows roadworks, but those data are often slow to update and do not distinguish between active/inactive work zones or specify lane restrictions/changes. In the proposed work, the study team seeks to address these issues by developing computer vision and machine learning methods that will automatically identify and understand (e.g., lane closed and two lanes merge into one lane) road work zones. The calculated information can be shared with other drives and also enable dynamic route planning for navigation systems, driver assist systems, and self-driving cars for efficiently and safely maneuvering through or around road work zones. Moreover, a comprehensive view of road work activity in a region can be constructed from information shared by users. Such a view may prove to be a useful tool for optimizing traffic flow along detour routes (e.g., traffic lights stay green for longer to accommodate the additional volume).]]></description>
      <pubDate>Wed, 21 Dec 2022 11:40:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2087440</guid>
    </item>
    <item>
      <title>Evaluating Detours for a Major Construction Project in the Era of Real-Time Route Guidance  (Project D3)</title>
      <link>https://rip.trb.org/View/1681324</link>
      <description><![CDATA[Major road construction projects can be significant sources of traffic congestion and motorist delays. Maintaining agencies typically attempt to mitigate these impacts by designating detour routes and providing project information to motorists. This information can be conveyed through a variety of media, from traditional static and variable roadway signage placed in the field to electronic media including web sites, radio and television advertisements, call centers, text messaging, and navigation apps. In this era of real-time traffic information and in-vehicle route guidance, it is not clear to what extent this detour information is used and which messaging components are most effective. This study used the Interstate 59/20 reconstruction project in Birmingham, AL to evaluate the detour planning process and the effectiveness of the resulting detour and information strategies. The objective was to develop recommendations and best practices that can be applied to future construction projects and allow transportation agencies to allocate project resources to greatest effect. The evaluation included a review of the transportation modeling process used to project traffic diversions and designate detour routes, a survey of motorists to determine the sources of information they used to choose detour routes during construction, and a study of traffic patterns before, during, and after the project to understand if and how detour patterns changed over the course of the one-year project. The study resulted in recommendations for conducting planning studies for large roadway projects and found that factors such as transit usage assumptions, employer work policies, and roadway capacity assumptions can have significant impacts on model accuracy. The survey found that motorists used a wide variety of information sources when selecting detour routes and that they often modified those routes based on real-time data. The travel time and traffic count analysis found that detour patterns did vary over time as the transportation system reached equilibrium. It also found that actual traffic patterns during the reconstruction project did not always match responses to the motorist survey, suggesting that motorists used designated detour routes initially but adjusted them during the course of the project.

]]></description>
      <pubDate>Thu, 30 Jan 2020 21:05:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/1681324</guid>
    </item>
    <item>
      <title>SPR-4440: An Integrated Critical Information Delivery Platform for Smart Segment Dissemination to Road Users</title>
      <link>https://rip.trb.org/View/1637986</link>
      <description><![CDATA[This proposed INDOT/JTRP FY-2020 research is a collaborative work between TASI from IUPUI and Purdue University, which has the following major deliverables: (1) An integrated database including the baseline mile post database scheme and other data sources. (2) Algorithms that can generate targeted and prioritized alert messages for augmenting the INDOT ITS system. (3) Intelligent and novel traffic information delivery interfaces.
]]></description>
      <pubDate>Mon, 15 Jul 2019 11:20:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/1637986</guid>
    </item>
    <item>
      <title>Real-Time Data-Based Decision Support System for Arterial Traffic Management (Project J2)</title>
      <link>https://rip.trb.org/View/1562217</link>
      <description><![CDATA[Traffic congestion along arterial streets is increasingly becoming a critical issue that needs to be addressed by transportation agencies. Compared to the relatively mature management of freeways, arterial traffic operations and management are lagging behind. To address such a gap, Intelligent Transportation System (ITS) devices, such as traffic detectors, Bluetooth/Wi-Fi readers, and so on, are installed or planned to be installed along a number of arterial streets. The data generated from these devices provide an enriched source for monitoring arterial traffic and estimating performance measures. However, these measures are usually estimated offline to check arterial street performance and to verify the effectiveness of traffic operations. The real-time traffic operations of urban streets still rely more on visual examination of the videos generated from closed circuit television (CCTV) cameras at traffic management centers. To mitigate congestions during incidents or special events, some transportation agencies manually adjust signal timing based on operator's observations of queues. A robust and automated decision support system is needed to help transportation agencies to better manage arterial traffic in real time.
The goal of this project is to develop a real-time data-based decision support system for arterial management. The developed system will not only automatically estimate system performance and identify the traffic state based on data from multiple sources, but also predict the short-term traffic conditions using advanced machine learning techniques. Recommendations will be further provided by the system based on predicted traffic condition as well as agencies’ past operational experience to assist agencies in determining optimal arterial traffic management and control strategies.
]]></description>
      <pubDate>Tue, 09 Oct 2018 09:09:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/1562217</guid>
    </item>
    <item>
      <title>Navigation Guidance for People With Vision Impairment
</title>
      <link>https://rip.trb.org/View/1369996</link>
      <description><![CDATA[The project is intended to accomplish five main objectives in terms of its potential for technical innovation and commercial application in the field of navigation aid for blind or visually impaired persons. The first objective is to provide a navigational aid that can track the location of a blind person anywhere, including areas where a global positioning system (GPS) is not available or not reliable (e.g., indoors, in urban areas with tall buildings, etc.). The second is to look ahead in time and space to plan a route that allows a visually impaired person to get to a destination, then to adaptively update the route based on vision system recognized obstacles that are to be avoided, such as people or construction within the path (a concept know in robotics as Event Horizon). The third is to take gestural input and provide natural route guidance based on tactile stimuli (instead of relying solely on auditory or visual instructions). The fourth objective is to use computer vision techniques to verify that the user has reached the correct destination, as well as to find stairs, elevators (buttons), hallways, and doors in the visual scope to help with navigation. The final objective is to take input from and provide input to intelligent traffic systems (for example, the ability to communicate with drivers to send them alerts when they are getting close to a blind person who is crossing the street).
]]></description>
      <pubDate>Tue, 22 Sep 2015 15:44:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/1369996</guid>
    </item>
    <item>
      <title>Research Program Design---Administration of Highway and Transportation Agencies. Evaluation Guidance for Automated Vehicle Pilot and Demonstration Projects</title>
      <link>https://rip.trb.org/View/1357353</link>
      <description><![CDATA[No summary provided.]]></description>
      <pubDate>Fri, 12 Jun 2015 01:00:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/1357353</guid>
    </item>
    <item>
      <title>Prototype Development of the Open Mode Integrated Transit System</title>
      <link>https://rip.trb.org/View/1236154</link>
      <description><![CDATA[The open mode integrated transit system (OMITS) is proposed to use state of the art technologies in wireless communication, global positioning system (GPS), data exchange and management system to combine availability of public transit system, taxi system, a carpool system, and emergency service system to provide dynamic, efficient, economic, and reliable transportation service in metropolitan areas. A novel device, namely cPhone, will communicate between riders, drivers, and the database server so as to exchange realtime and accurate transit information while serving as a GPS to give routing direction for a driver. A routing and dispatching system will match a driver and riders timely to enable dynamic carpooling, trace and confirm the success or failure of a carpooling match, and provide a consistent algorithm for GPS and the database servers to define the best/shortest route for drivers. A well designed membership management system and user operation system will ensure the security, credibility, and operational reliability of the whole system. The advantages of the OMITS are clear when compared with static transit systems (such as traditional car-pools and scheduled buses and trains) because it allows for dynamic matching of riders with transit options that best suit their needs and incorporates routing information that is adaptable to existing traffic conditions. Furthermore, the OMITS provides benefits over even dynamic ride-share programs because it incorporates multiple modes of transportation thus allowing users to transfer between transit modes when advantageous. In total, the OMITS will integrate a dynamic car-pooling system with public transportation systems and private transportation systems to provide a robust, stable, reliable, and economical solution for the current overloaded and inefficient urban metropolitan transportation system. It will result in new understanding of the critical urban transportation system currently and unsustainably over congested. The success of this system will greatly increase ridership in public and private vehicles, significantly reduce the number of cars in traffic peak time, and thus help to alleviate traffic and parking problems in metropolitan areas. Broad impacts will be produced on gas saving, greenhouse gas emission and transit cost reduction. For demonstration, this proposed project would develop a small prototype system for about 100 residents in northern Bergen County, New Jersey, who are working in New York City. Once the concept of this system is proved, the technology will be immediately transferred to industry partners and transit agencies and be extended to the other parts of New York metropolitan area and other cities.]]></description>
      <pubDate>Thu, 03 Jan 2013 15:42:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/1236154</guid>
    </item>
    <item>
      <title>Development of a Protocol to Assess the Effects of Workload on Older Drivers: A First Step</title>
      <link>https://rip.trb.org/View/1230172</link>
      <description><![CDATA[Before older drivers stop driving, there are transitional periods of restricted driving during which they do not drive on unfamiliar roads or roads that are difficult to drive, presumably of high workload. By knowing the workload associated with a particular route (from historic and existing datasets) and some assessment of the ability of a particular older individual to drive in certain situations (determined using a simulator), drivers could decide which of several routes they should drive, and if they should drive at all. The workload estimates could be a function added in next generation in-vehicle navigation systems or part of the directions calculation in Google maps. Using a driving simulator, this project is intended to answer several questions. How does driving performance degrade with increased workload? How well do the subjective and objective workload estimates from previous University of Michigan Transportation Research Institute (UMTRI) work agree with each other? What are the differences between men and women on these measures?]]></description>
      <pubDate>Thu, 03 Jan 2013 13:55:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/1230172</guid>
    </item>
    <item>
      <title>Smart Dial-a-Ride for Demand-Responsive Transit Operations: Research and Development of a Prototype Dispatch Assistance Tool</title>
      <link>https://rip.trb.org/View/1230039</link>
      <description><![CDATA[This project proposes to investigate and develop the conceptual basis for an efficient system to aid Dial-a-Ride operations in several areas including: (a) taking ride reservations, (b) assigning rides to vehicles, (c) optimizing vehicle routing, and (d) automatically generating reports which characterize system operation and ridership.]]></description>
      <pubDate>Thu, 03 Jan 2013 13:52:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/1230039</guid>
    </item>
    <item>
      <title>Smart Dial-a-Ride for Demand-Responsive Transit: Operations Analysis and Development of a Concept for Improvements</title>
      <link>https://rip.trb.org/View/1230038</link>
      <description><![CDATA[This project proposes to investigate, analyze and develop the conceptual basis for an efficient system to aid Dial-a-Ride operations in several areas including: (a) taking ride reservations, (b) assigning rides to vehicles, (c) optimizing vehicle routing, and (d) automatically generating reports which characterize system operation and ridership.]]></description>
      <pubDate>Thu, 03 Jan 2013 13:52:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/1230038</guid>
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