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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>Vehicle Classification Technologies for Toll Collection</title>
      <link>https://rip.trb.org/View/1948960</link>
      <description><![CDATA[Toll facilities (roads, bridges and tunnels) are used primarily for revenue generation to repay for
long-term debt issued to finance construction, capacity expansion, operations and maintenance
of these facilities. Tolls are one form of a broad concept known as road pricing. In addition to
revenue generation, road pricing is used for other reasons including transportation demand
management to reduce peak hour travel and the recurring traffic congestion on some corridors.
Tolling technologies have evolved rapidly in the past two decades and today offer many solutions
for toll collection. Traditional (mostly cash) tolling has been gradually replaced by electronic toll
collection (ETC) that enables users to go through toll lanes without stopping. In addition to
improving traffic safety and enhancing the efficient use of the existing infrastructure, ETC results
in reduction in toll collection costs.
The three most important components of ETC are user account identification, vehicle
classification (where vehicles are charged differently according to class), and determination of the
distance traveled. Most of the tolling agencies in Region-6 (and most of the U.S.) determine the
user fee based on the number of axles. The latter is often identified using induction loop sensors
buried in the pavement and energized by low-voltage electrical currents that produce
electromagnetic fields above the roadway. Vehicles traveling through these fields produce digital
signatures that are used to identify the number of axles. The loop sensors used by many tolling
authorities in the U.S. are manufactured by TransCore, Inc. and are known as “Intelligent Vehicle
Identification System (IVIS).
The IVIS sensors have high accuracy rate in classifying vehicles. However, loop detectors
present several problems including the intrusive nature of their installation and maintenance
(disruptive lane closures), high failure rate, sensitivity to rebar in concrete pavements, and their
undermining of the structural health of the surrounding pavement. Loop failure can result
because of several reasons including cracks across saw cuts, broken loop or lead-in wires, and
sealant failure.
The aim of this study is to provide tolling authorities in Region-6 with detailed analysis of the
fitness of various non-pavement-intrusive vehicle classification technologies (imaging, radar,
Lidar, thermal profiling, etc.) under different roadway, traffic, and environmental conditions to
inform decision-makers of the accuracy, performance, and lifecycle-cost of these technologies.
Toll facilities have multiple lane configurations including highways with multiple lanes at highway
speeds and ramps at reduced speed. The classification technology must be accurate at high
speed, low speed, single lane facilities, multiple lane facilities, and all kinds of weather. The
study involves in-depth review of available non-pavement-intrusive vehicle classification
technologies; gathering and analyzing data on the performance and cost of available
technologies from manufacturers and toll facility operators; and making presentations to the
tolling industry in Region-6.]]></description>
      <pubDate>Mon, 09 May 2022 06:49:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/1948960</guid>
    </item>
    <item>
      <title>Price Discovery for Strategic Compensation of Toll Road
Operators to Relieve State Maintenance Impacts</title>
      <link>https://rip.trb.org/View/1686398</link>
      <description><![CDATA[1. Document how state DOTs currently work with toll road
operators, including publicprivate partnership (P3)
concessionaires and public toll authorities, to mitigate the
impacts of major maintenance and other planned and
unplanned facility outages. 2. Examine whether and how
much states could further mitigate the adverse impacts of
scheduled major maintenance and unanticipated facility
closures by cooperating with the operators of nearby facilities
operated by toll road operators. 3. Examine how much states
could benefit by designing major maintenance programs that,
by using such mitigation measures, allow larger scale and
more efficient major maintenance strategies. 4. Estimate the
feasibility and cost of such cooperation to the state DOT and
the toll road operators and devise potential strategies to enable
such cooperation]]></description>
      <pubDate>Wed, 12 Feb 2020 15:18:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/1686398</guid>
    </item>
    <item>
      <title>Electric Toll Collection Interoperability Testing</title>
      <link>https://rip.trb.org/View/1513416</link>
      <description><![CDATA[No abstract provided.]]></description>
      <pubDate>Wed, 23 May 2018 09:46:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/1513416</guid>
    </item>
    <item>
      <title>Efficient Utilization of The Existing ITS System and The Viability of A Proactive Traffic Management System for The Orlando-Orange County Expressway Authority System</title>
      <link>https://rip.trb.org/View/1474333</link>
      <description><![CDATA[There are a wide range of vehicle detection devices in use than ever before on freeways and expressways, starting from the popular inductive loops and magnetometers to videos and radar-based detectors. The Central Florida Expressway System utilizes Automatic Vehicle Identification (AVI) system for Electronic Toll Collection (ETC) as well as for the provision of real-time information to motorists within the ATIS. Data are gathered using AVI tag readers that are installed for the purpose of toll collection and additional tag readers installed solely for the purpose of estimating travel times. The advent of the new ETC systems changed the way toll roads are designed and operated. ETC systems have the ability to easily support other value-added services on the same technology platform. These services might include but not be limited to fleet management systems, emergency response services, congestion pricing pay-as-you-drive insurance services and navigation capabilities. The aspect of tolling (a distance-base, a flat-rate or a congestion-base) and the type of facility and access (freeway, expressway or conventional road) play an important role in the structure and the spacing of the tag readers. The main objective of this research is to investigate the viability of using the AVI traffic data in the identification of freeway real-time "hot-spots" in a proactive traffic management framework. Guidelines will be provided to adapt the existing structure of the AVI system (e.g. locations, spacing, and archiving system) to provide more useful data.]]></description>
      <pubDate>Thu, 13 Jul 2017 01:01:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/1474333</guid>
    </item>
    <item>
      <title>Electronic Fare Transaction Data</title>
      <link>https://rip.trb.org/View/1350433</link>
      <description><![CDATA[This project will: dramatically increase the availability of data describing transit use and broaden the use of transit data in a variety of activities that Washington State Department of Transportation (WSDOT) funds and supports; improve WSDOT's ability to plan and deliver community based multimodal solutions in corridors; provide data to evaluate the effectiveness of WSDOT's transportation demand management activities and make better use of limited funding.]]></description>
      <pubDate>Wed, 15 Apr 2015 01:00:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/1350433</guid>
    </item>
    <item>
      <title>Development of a New Connected Eco-Driving Technology at Signalized Intersections with Adaptive Signal</title>
      <link>https://rip.trb.org/View/1346331</link>
      <description><![CDATA[The advances of wireless communication and information technology have enabled the technological foundation and provided an unprecedented data-rich environment known as "big data". One emerging transformative technological initiative is Connected Vehicle, which aims to enable networked wireless communications among vehicles, infrastructure and passengers' personal devices. The proposed research aims to develop a new connected vehicle technology that enables eco-driving of vehicles at signalized intersections where adaptive control is instrumented. The work capitalizes on the emerging advanced technologies including Connected Vehicle, Adaptive Traffic Signal Control, and Big Data Analytics. The outcome includes smoother vehicle movement trajectories, reduced fuel consumption and green-house gas emissions, hence system-wide better mobility, efficiency and environmental benefits. The proposed work is extremely timely and significantly different from other on-going connected vehicle research, in that it aims to integrate the developed technology with New York City's real-time adaptive control system, applying big-data analytics on the already available big traffic data. Mostly notably, New York City's big traffic data environment include millions of records of per-trip travel times from 8 million daily commuters, volumes and occupancies from a wireless sensor network, and detailed historical and real-time controller status data for more than 10,000 ASTC controllers. One of the team members, namely, KLD is the developer of New York City's adaptive control system. This enables the proposed work as an innovative solution providing practical and workable contributions to New York's transportation community.  The proposed research involves developing the following methodologies and evaluating them using microscopic traffic simulation:  * Data fusion of real-time large-scale multi-source traffic, vehicle and environmental data. The data includes traffic conditions, network-wide signal operational status, real-time adaptive signal timing information, registered Transit Priority Preemption Request, vehicle dynamics and engine economy data. The sources of the data include ITS roadway sensors, Electronic Toll Collection (ETC) tag readers, connected vehicle equipment's and central adaptive signal control systems at Traffic Management Center.  * Big Data Analytics to synthetize the data and evaluate traffic and environmental parameters and develop operational strategies for individual vehicles at signalized intersections, focusing on smoother vehicle trajectories, and reducing real-time fuel consumption and emissions.  * Connected Eco-Driving. By virtue of V2I and V2V, real-time adaptive signal timing data (and relevant transit signal priority request, if any) from the central TMC are synthesized with vehicles mechanical dynamics and engine-economy status. These data are analyzed to generate customized driving advice to drivers so that they can adjust their driving behavior for a smoother movement trajectory, save fuel and reduce emissions, while clearing the intersection safely and efficiently.  * Test the methodologies through rigorous microscopic traffic simulation, explore the feasibility of a commercializable system prototype, and outline steps to the implementation of such a prototype.]]></description>
      <pubDate>Tue, 17 Mar 2015 01:00:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/1346331</guid>
    </item>
    <item>
      <title>AR: Bluetooth Travel Time System</title>
      <link>https://rip.trb.org/View/1234301</link>
      <description><![CDATA[Many districts have projects in the pipeline to install generate travel time from re-identifying toll tags. There toll tag readers coat $50k installed and require a few years through the PS&amp;E process. Bluetooth readers cost $500 each, have a much higher hit rate, and don't have privacy concerns. There is considerable savings to be recouped by deploying bluetooth readers instead of toll tag readers.]]></description>
      <pubDate>Thu, 03 Jan 2013 15:10:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/1234301</guid>
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