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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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    <item>
      <title>Snow Plow Performance Measures in Non-RWIS Locations</title>
      <link>https://rip.trb.org/View/2262826</link>
      <description><![CDATA[The objectives of this project are to (1) develop an Artificial Intelligence (AI) model and integrated weather condition retrieval mechanism to comprehensively evaluate snow cover conditions of road surfaces based on existing roadside CCTV cameras in non-RWS locations, (2) produce easily understandable snow cover information on road surfaces for public sharing.]]></description>
      <pubDate>Fri, 06 Oct 2023 13:40:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2262826</guid>
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      <title>SPR-4738: Origin-Destination Counts in Weaving Area Utilizing Existing Field Data</title>
      <link>https://rip.trb.org/View/1998997</link>
      <description><![CDATA[Vehicle weaving describes the trajectories of vehicles that change lanes in areas between ramp merge and diverge junctions. Especially during heavy traffic, vehicle weaving will slow traffic, cause congestion, and cause a higher possibility of crashes. The primary goal of this proposed research is to develop an efficient and effective method to quantitatively determine the vehicle weaving at specific highway locations using CCTV cameras. The
outcome of this research will provide INDOT with an effective tool to improve the design of vehicle weaving areas for reduced traffic congestion and enhanced safety.]]></description>
      <pubDate>Tue, 26 Jul 2022 14:12:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/1998997</guid>
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    <item>
      <title>SPR-4638: Integration of the Lane-specific Traffic Data Generated from Real-time CCTV Videos into INDOT's Traffic Management System</title>
      <link>https://rip.trb.org/View/1862594</link>
      <description><![CDATA[This project aims to integrate TASI-generated real-time lane-level traffic
status data into Indiana Department of Transportation (INDOT) traffic management systems to enable INDOT to utilize this valuable information. TASI will collaborate with INDOT users to specify and satisfy dissemination and improvement requirements based on INDOT's needs.]]></description>
      <pubDate>Tue, 29 Jun 2021 11:42:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/1862594</guid>
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      <title>SPR-4436: Road Condition Detection and Classification from Existing CCTV Feed</title>
      <link>https://rip.trb.org/View/1644248</link>
      <description><![CDATA[TASI is working with INDOT on an automatic incident detection based on the video stream from CCTV cameras along interstate highways to develop software that can automatically identify the roads and lanes on the video, the vehicle moving direction, and traffic flow rate. ]]></description>
      <pubDate>Tue, 06 Aug 2019 14:26:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/1644248</guid>
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    <item>
      <title>Crash Modification Factors (CMFs) for Intelligent Transportation System (ITS) Applications</title>
      <link>https://rip.trb.org/View/1628621</link>
      <description><![CDATA[It is generally understood that Intelligent Transportation System (ITS) applications, such as variable/dynamic/changeable message signs, closed-circuit television (CCTV) cameras, traffic monitoring stations, ramp meters, and Road Weather Information Systems (RWIS), help manage traffic and improve incident response, thus enhancing roadway safety. However, actual data, specifically crash reduction data resulting from ITS applications, are very limited. Research is needed to develop crash modification factors (CMFs) for the various, typically deployed ITS applications.

OBJECTIVE: The objective of this research was to address a long-standing deficiency in safety related ITS data by developing (1) CMFs for commonly deployed ITS applications, independently and as a part of systems and (2) case studies of safety benefit/cost ratio calculations for such ITS applications. The CMFs shall be suitable for use by safety professionals in their analyses and shall meet at least a CMF Clearinghouse four-star quality rating.
 ]]></description>
      <pubDate>Thu, 06 Jun 2019 19:41:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/1628621</guid>
    </item>
    <item>
      <title>Automating Wrong Way Detection Using Existing CCTV Cameras</title>
      <link>https://rip.trb.org/View/1601823</link>
      <description><![CDATA[Wrong way driving has been defined as “vehicular movement along a travel lane in a direction opposing the legal flow of traffic”. Although wrong way driving crashes occur infrequently accounting for almost 3 percent of all crashes, they have a very high likelihood of resulting in fatal or serious injury crashes. Vaswani found that wrong-way crashes had 27 times higher fatality rates as compared to any other kind of crashes occurring on controlled-access highways in Virginia. Another study, from Michigan, found that 22% of all wrong-way crashes resulted in a fatality, as compared to, only 0.3% percent of all highway crashes resulted in fatality for the same time frame. The main causes of wrong way driving constitute of: (a) Alcohol- In an analysis conducted on FARS database, from 2002-2009, it was found that 60% of wrong way crash drivers had some indications of alcohol involvement; (b) Age- Driver over age of 70 constitute nearly 15 percent of wrong way drivers as compared to only 3 percent of the right way drivers involved in wrong way crashes; (c) Poorly marked ramps- The primary origin was for controlled-access highway was found to be the exit-ramps; (d)	Time of day- Disproportionate amount of the wrong-way fatalities happen during night time.

The causes associated with wrong way crashes tend to make them spatially concentrated to particular stretches of roads, thus making it important to identify and monitor such high-risk locations. FHWA Highway Safety Improvement Program recommends generating wrong way monitoring warrants based on total collision and fatal collision rates. If either total wrong-way collision are greater than 0.5 event per mile per year, or a fatal wrong-way collision rate of 0.12 per mile per year and at least 3 wrong-way collisions are recorded for a five-year period, the monitoring warrants are met. This approach is reactive and needs the crash history to develop for a period of at least 3 to 5 years. In this research, the research team proposes to use a pro-active technology that count the number of wrong-way drivers that are detected using image based technology. The proposed solution is discussed in detail in subsequent sections.
]]></description>
      <pubDate>Wed, 24 Apr 2019 14:45:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/1601823</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>SPR-4305: Development of Automated Incident Detection System Using Existing ATMS CCTV</title>
      <link>https://rip.trb.org/View/1530346</link>
      <description><![CDATA[The main objective of this research is to develop an automated incident detection system using existing ATMS CCTV data. The proposed system will perform: (1) traffic flow estimation, (2) vehicle type recognition, and (3) incident detection (big speed change of a set of vehicles to other set of vehicles) based on the INDOT CCTV video feeds. The deliverables by the end of this project are: (1) a database that will be built to store the incident data, and (2) a user-friendly map-based interface to provide the incident locations automatically.]]></description>
      <pubDate>Mon, 06 Aug 2018 14:57:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/1530346</guid>
    </item>
    <item>
      <title>Camera System for Traffic Incident Management PI</title>
      <link>https://rip.trb.org/View/1440842</link>
      <description><![CDATA[Traffic Management Centers (TMCs) would be able to manage and do post-incident analysis of an incident better if they have access to a live feed of the incident response as it is occurring. Certain sections of the highways do not have any monitoring systems or could not use existing monitoring systems. The problem is how to get live video feed into TMCs.  Some portions of the highway cannot be seen by existing California Department of Transportation (Caltrans) closed circuit television (CCTV) cameras. When an incident occurs in one of these areas, the TMCs cannot see the incident or how it is being managed. Where Caltrans does have CCTV cameras, in most cases, the video feeds from the cameras are being posted to the Internet.  The video feeds from individual CCTV cameras cannot easily be taken off of the Internet. In the case of injuries and fatalities, Caltrans does not want the personal information (license plate, make and model of car, images of the victim) related to the victims available to the general public, so the CCTV cameras are turned away from the incident.  Cameras on Maintenance vehicles can provide the needed live video to better manage the incident and perform after action analysis.]]></description>
      <pubDate>Wed, 28 Dec 2016 11:39:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/1440842</guid>
    </item>
    <item>
      <title>Automated Video Incident Detection (AVID) System</title>
      <link>https://rip.trb.org/View/1440837</link>
      <description><![CDATA[Detection and verification of incidents on major freeways is one of the most critical functions for incident response.  Studies show that every seven minute delay in detection results in one additional mile of queue in the system.  Therefore, early detection and verification of an incident results in less congestion and faster restoration of traffic flow.  One of the methods that Transportation Management Centers (TMCs) use to verify an incident is to manually monitor Closed Circuit Television (CCTV) cameras once the incident is identified.  The cameras are typically in idle mode over 95% of the time, performing no functions when not being used for incident verification.  The Department lacks an automated method to detect incidents, so existing CCTV cameras are only used passively for manual verification after the TMC is informed of an incident. Currently, TMC operators use CCTV cameras to verify incidents upon either receiving calls from commuters, monitoring California Highway Patrol (CHP) logs and news media, or viewing the Advanced Transportation Management System (ATMS) map, which presents freeway vehicle speeds through the vehicle detection systems that feed information to the TMC.  Once informed of an incident, they monitor the CCTV cameras to verify it prior to taking appropriate action in responding to and removing the incident.  Therefore, time is lost during the period between the operators being informed of the incident and manually identifying its location using the existing CCTV cameras.  This time loss will add a few minutes to the verification time of an incident, resulting in additional total delay. If the Department had a method to automatically detect incidents using existing CCTV cameras, which are currently only used for manual incident verification, TMC operators could detect and respond to incidents more rapidly.]]></description>
      <pubDate>Wed, 28 Dec 2016 11:39:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/1440837</guid>
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