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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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    <item>
      <title>Smart and Cooperative Truck Parking Monitoring and 
Calibration System Empowered by Machine Learning</title>
      <link>https://rip.trb.org/View/2055566</link>
      <description><![CDATA[The primary goal of this research is to create a truck parking monitoring and calibration 
system empowered by machine learning, for the in/out truck parking counting system. Four components will be integrated into the proposed system, including (1) the truck parking lot sensing component, (2) the information collection component, (3) the sensing anomaly detection component, and (4) the cooperative monitoring and calibration component. Counting data can be obtained by the installed sensors, i.e., radars, cameras, and etc., at the entrance and exit at a truck parking lot. A separate video surveillance system will also be installed to collect ground-truth data. Then, the real-time parking occupancy can be calculated, and the anomaly status can be identified based on the comparison with ground-truth records. The next step is to send the collected information, such as real-time or historical ground-truth occupancy sequence and the anomaly detection results, into the cooperative calibration component. Finally, the proposed system can generate the calibrated occupancy result, confidence rate, sensing system status and calibration recommendations. To achieve this goal, the research team has identified four objectives:
(1) propose a real-time truck parking sensing system status anomaly detection 
framework; (2) propose a real-time parking lot occupancy estimation empowered by deep learning; (3) propose a human-machine cooperative monitoring and calibration algorithm 
empowered by sequence classification; and (4) build a live website to visualize the truck parking sensing monitoring and calibration result.]]></description>
      <pubDate>Tue, 01 Nov 2022 15:48:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2055566</guid>
    </item>
    <item>
      <title>Guidebook for Truck Parking Information Management Systems</title>
      <link>https://rip.trb.org/View/1707205</link>
      <description><![CDATA[Truck parking demand is driven by economic demand, safety considerations, and the need for adequate rest for commercial vehicle operators. Recent studies of truck parking demand and capacity constraints reflected a consensus that truck parking demand exceeds the available supply in many public rest areas and private truck stops across the nation. Some state departments of transportation (DOTs) have developed truck information management systems to better address commercial vehicle parking needs by reducing parking search time and providing safer parking options through the collection and dissemination of real-time parking availability using a variety of technologies.
There was a need to (1) explore issues such as planning, design, operations, procurement and selection of technologies, the means and format for reporting information, life-cycle considerations, interoperability, and interagency coordination; and (2) develop a guide that presents rational practices for truck parking information management systems. Such guide should be a resource for state DOTs in implementing information management practices that address commercial vehicle parking needs, thereby reducing the challenges associated with the search for safer parking options.

OBJECTIVE: The objective of this research was to develop a guide of suggested practices for the development, connectivity, and management of truck parking information systems. At a minimum, the guide will address planning, design, operations, procurement and selection of technologies, the means and format for reporting information, life-cycle considerations, and interagency coordination.
]]></description>
      <pubDate>Wed, 20 May 2020 14:57:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/1707205</guid>
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    <item>
      <title>Evaluation of Iowa Truck Parking Information &amp; Management System</title>
      <link>https://rip.trb.org/View/1511806</link>
      <description><![CDATA[Iowa is one of eight participating states of the Mid America Association of State Transportation Officials (MAASTO) region, along with Indiana, Kansas, Kentucky, Michigan, Minnesota, Ohio and Wisconsin that are involved in a multistate truck parking initiative to develop a real time truck parking information management system (TPIMS). This system will collect and provide data to the freight transportation industry to inform drivers of available parking spaces at predetermined locations along certain corridors. The MAASTO's vision is to "strengthen America's freight network by helping commercial truckers make safer, more efficient parking decisions through a user-focused information service that consistently provides timely, reliable parking availability information." The TPIMS will enable remote monitoring and detection of available truck parking spaces at public and private parking facilities on designated, major interstate and highway corridors across the eight-state region.  Although other states in the MAASTO region will disseminate the parking availability information through conventional variable message signs, Iowa DOT has proposed to broadcast the truck parking information through apps, Iowa 511, and in-cab information systems, eliminating the needs for installing and maintaining variable message signs. In 2017 Iowa DOT selected eX2 Technology as the prime contractor to design, build, operate and maintain a real-time TPIMS program along Iowa's 1-80 corridor. The field deployment is expected to take place from February 2018 to the end of September 2018, followed by a 60-day burn-in period from November to December 2018 for testing and validation. The system will go live on January 4, 2019 and remain in operation for 3 years.  The Center for Transportation Research and Education (CTRE) at Iowa State University is tasked to evaluate the performance of the TPIMS, in terms of the accuracy, reliability, and effectiveness.  In particular, during the TPIMS deployment and testing period CTRE will collect relevant data to  evaluate the accuracy of the parking availability data collected by the sensors. After the system goes live, CTRE will develop various performance measures to evaluate the effectiveness of the innovative information dissemination approach through apps, Iowa 511, and in-cab information systems, compared to the conventional variable message sign approach.]]></description>
      <pubDate>Tue, 08 May 2018 16:23:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/1511806</guid>
    </item>
    <item>
      <title>SmartPark: Real-time Parking Availability, Phase II</title>
      <link>https://rip.trb.org/View/1400241</link>
      <description><![CDATA[The objective of this project was to demonstrate a technology for providing real-time information on truck parking availability to truckers on the road. The SmartPark program was prompted by a 2000 National Transportation Safety Board recommendation that the Federal Motor Carrier Safety Administration (FMCSA) create a guide to inform truck drivers about locations and availability of parking. A 2002 study on the adequacy of truck parking by the Federal Highway Administration recommended using Intelligent Transportation Systems (ITS) to provide truckers with real-time information on the location and availability of parking spaces. In 2005, the John A. Volpe National Transportation Systems Center completed a study entitled "ITS and Truck Parking" for FMCSA. FMCSA completed Phase I by field testing a technology, namely, combined Doppler radar and laser scanning/light curtain. The test results from the contractor (independently verified and validated by Volpe) showed that the technology meets three necessary performance requirements. Therefore, a decision was made to proceed to Phase II. Phase II covered information dissemination, reservations, maximization of space, gathering of historical data to make forecasts of availability, and self-sustainability. Phase II of the SmartPark field operations test (FOT) took place at mile markers (MM) 23 and 45 northbound on I-75 in Tennessee. MM 23 is approximately 20 miles north of Chattanooga and MM 45 is halfway between Chattanooga and Knoxville. At both MM 23 and MM 45, there is truck parking. For each of the two truck parking areas, there were two variable message signs providing notice of truck parking availability (available, limited, or full) for a total of four signs. For each truck parking area, one sign was at 1 mile upstream of the truck parking area, and another sign was about 400 feet upstream of the truck parking area. At each of the truck parking areas, there were five spaces that could be reserved on a first-come, first-served basis. In the 6 months of field testing, FMCSA demonstrated and gathered data on the feasibility of the truck parking reservation system, historical utilization of truck parking spaces, and the viability of linking the two truck parking areas together (i.e., were truckers diverted by the variable message signs if one area was filled and the other was not?). A final report for Phase I showing the feasibility of a commercially-available technology (Doppler radar and laser scanning/light curtain) for accurately and reliably determining truck parking space occupancy was accepted in June 2013. The 6-month FOT final report (pending publication) will show whether two truck parking areas can be networked in such a way that trucks can be diverted from a filled area to an unfilled area and show the viability of information dissemination systems for truck parking availability. Other tasks included development of an operations and maintenance manual and training to manage the SmartPark system.
]]></description>
      <pubDate>Fri, 04 Mar 2016 07:30:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/1400241</guid>
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