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    <title>Transport Research International Documentation (TRID)</title>
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    <language>en-us</language>
    <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>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Aggregated Mobile Phone Data in Transportation: A Literature Review</title>
      <link>https://trid.trb.org/View/2682772</link>
      <description><![CDATA[Mobile phone data are increasingly considered as a valuable data source for developing innovative mobility applications to support smart city objectives. aggregated mobile phone data, derived from anonymized and aggregated location signals generated by mobile devices, provide density-based insights into how people move and interact within urban environments, while ensuring compliance with privacy regulations. This study provides a literature review analysis of aggregated mobile phone data applications in transportation. The findings demonstrate the usability of aggregated mobile phone data to describe mobility flows. The methodological approach used in previous literature for extracting additional insights are classified to the the four-step transportation model as a conceptual framework. Furthermore, the review highlights the versatility of aggregated mobile phone data, showcasing its use not only for general mobility pattern analysis but also for specific applications, such as event-specific dynamics, traffic management and environmental impact analysis, and public transport planning. The study identifies three key directions for future research: (1) bridging data insights with urban planning policies through practical decision-making applications, (2) addressing the transfer-ability of data extraction methods across different data formats and structures through comparative studies, and (3) expanding the versatility of applications by integrating other complementary data sources.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682772</guid>
    </item>
    <item>
      <title>Machine Learning Approach for Labeling Undetected Planned Trips in Public Transport Operators</title>
      <link>https://trid.trb.org/View/2579232</link>
      <description><![CDATA[Accurate labeling of undetected trips in public transportation is critical, as it directly affects operational efficiency, cost savings, and service quality. Undetected trips refer to scheduled trips that were either not completed or inaccurately recorded by Automatic Vehicle Location (AVL) systems. These discrepancies can disrupt resource allocation, hinder operational planning, and compromise financial accountability. If undetected trips are not properly classified, they can cause significant financial losses, misallocation of resources, and lower customer satisfaction due to unaddressed service issues. This paper presents a machine learning approach to automate the classification of undetected trips in public transit. The model categorizes trips into three types: Operated (successfully completed trips), Lost-Deductible (missed trips within operational limits), and Lost - Non-deductible (missed trips outside operational standards and noncompensable). Automating this process enhances operational efficiency, reduces financial losses, and streamlines claim management. By replacing manual classification with AI-driven automation, transit operators can ensure faster, more accurate trip labeling, ultimately leading to optimized resource use, better decision-making, and higher service standards.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:34:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579232</guid>
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    <item>
      <title>Competitive Analysis of Vehicle-Sharing Systems With Cournot Queueing Games</title>
      <link>https://trid.trb.org/View/2591288</link>
      <description><![CDATA[Product-form closed queueing networks are a useful formalism for analyzing the availability of vehicles within a vehicle sharing system with stochastic user behavior. However, existing models assume that the provider has full control over the system. In reality, competition between providers within a single geographic area is quite common. In this paper, we introduce a non-cooperative game that extends a closed queueing model into a competitive environment, in which players decide the number of jobs that they wish to submit. This can be used to model vehicle sharing systems with multiple providers, in which they receive fares from trips but are responsible for the costs of their own fleet. The core technical results of this paper include conditions that guarantee the existence of a pure Nash equilibrium, and an efficient equilibrium-finding algorithm. We then present a case study, using our model, of a vehicle sharing system in Oslo with multiple providers. In this case study, we find that adding an additional competitor can increase the number of trips by up to 18.9%, and a highly competitive market can increase this by up to 30%.]]></description>
      <pubDate>Mon, 30 Mar 2026 17:10:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591288</guid>
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    <item>
      <title>Integrated public transport index (IPTI): A new measure for identifying public transport services in Indonesia</title>
      <link>https://trid.trb.org/View/2669879</link>
      <description><![CDATA[The low level of integrated public transport services is a significant concern regarding the effectiveness and efficiency of these services. This study aims to develop an assessment of the integrated public transport index (IPTI) for identifying public transport services in Indonesia. The research data is based on a survey of 1050 respondents of airplane passengers from six cities that have air-rail integrated services (ARIS) in Indonesia and 10 expert respondents from regulators, operators, academics, and professionals. The data is explored by using the analytical hierarchy process (AHP) and developing new measures for identifying the assessment of IPTI value. The availability of physical, operational, and institutional integration can be used as a leading indicator to measure the IPTI. Indonesia's public transportation system is physically integrated and has good connectivity, achieving optimal IPTI scores. However, operational and institutional integration needs further attention to ensure the sustainability public transport system.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:21:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669879</guid>
    </item>
    <item>
      <title>Systematic Literature Review on Transport Connectivity and Trade – A Meta-analysis Approach</title>
      <link>https://trid.trb.org/View/2655520</link>
      <description><![CDATA[Previous studies have found many factors have significant impacts on international trade, and the connectivity of international logistics. However, there is no common agreement on which factors are the most important and critical. Connectivity can also be defined in different ways, such as the quality of transport infrastructure and logistics performance. To identify the critical factors that influence transport connectivity and international trade, this study delivers a systematic literature review via meta-analysis. In the analysis results of the model, three crucial factors are identified: the index of economic freedom, the Pacific region, and the infrastructure index. Therefore, this study suggests that government agencies and policymakers should pay special attention to open markets and infrastructure when formulating policies to improve transport connectivity.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655520</guid>
    </item>
    <item>
      <title>Statistical analysis of vehicle usage intensity in a transport company</title>
      <link>https://trid.trb.org/View/2666015</link>
      <description><![CDATA[The article presents the application of selected statistical analyses to monitor the degree of efficiency of the use of a fleet of vehicles in a transport company. The main objective of the work was to present selected statistical tests for a given number of vehicles. 179 vehicles of various types and brands in use. Three groups of vehicles were distinguished in the analysis in terms of the load capacity of the cargo space: small cars, delivery vans and trucks. One of the factors differentiating vehicles within the distinguished groups was their mileage at the beginning of the observation period. Data on the vehicle usage intensity during one year of operation were analyzed. The single-factor statistical analyses used are of a preliminary nature, being an introduction to the issues of multifactor analysis. On the other hand, single-factor analyses can be used in the issues of classification of a heterogeneous the fleet of vehicles in road transport companies. The presented procedure showed the possibility of adapting statistical analyses to the management and forecasting of vehicle use in a transport company from the B2B and B2C sectors.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666015</guid>
    </item>
    <item>
      <title>Statistical analysis of vehicle repair costs in a transport company</title>
      <link>https://trid.trb.org/View/2665988</link>
      <description><![CDATA[Transport companies struggle with various problems, among which the costs of vehicle repairs play a significant role. Due to the changing environment of running a transport company, cost reduction is always important. This article presents an analysis of repair costs in a statistical approach. The data for the analysis were the actual values ​​obtained from one of the courier industry carriers from 2019. 180 vehicles of various types and brands used in a transport company were analyzed. The study used univariate statistical analyses and the chi-square χ2 and Bartlett tests, as well as the nonparametric Kruskal-Wallis test. The possibility of using selected statistical analyses in the scope of the costs of maintaining a fleet of vehicles in a reliable condition was demonstrated.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665988</guid>
    </item>
    <item>
      <title>Possibilities of Using Blockchain Technology in Transportation</title>
      <link>https://trid.trb.org/View/2665958</link>
      <description><![CDATA[Blockchain technology is increasingly becoming more widely known to the professional and lay public and is gradually finding its way for application in various areas of our society. It is not an entirely new technology. Its principle has been known for a long time. However, since 2009, this technology has been experiencing a renaissance, which was mainly driven by developments in the field of crypto technologies, cryptocurrencies and computer science. Blockchain has gradually become a key part of several digital technologies that are now being successfully used and developed. The technology itself has steadily begun to reveal its potential and benefits for a wide range of areas as knowledge has developed. One such promising area with huge potential for the future use of blockchain technology is transportation. The paper will present the application possibilities of blockchain technology in the transportation sector. The aim is to outline the potential applications and future directions of blockchain technology in the transportation sector.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665958</guid>
    </item>
    <item>
      <title>Implementation of Information Security Management System in Transport Company</title>
      <link>https://trid.trb.org/View/2665884</link>
      <description><![CDATA[In today’s era of digitalization and technological advances, the transport and logistics sector is facing new challenges and threats. This paper discusses the issue of information and cyber security, its importance for transport enterprises and the implementation of an Information Security Management System (ISMS). ISMS is a comprehensive process and risk management framework to protect the information assets of enterprises. Based on the literature analysis, the main processes of ISMS processes were identified, and the importance of management and employee involvement was equally highlighted. The results show that an effective ISMS is crucial for managing cyber threats, protecting sensitive data and minimizing the risks associated with digitalization in transportation. The article also highlights the importance of a systematic approach to implementing a functional information security system.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665884</guid>
    </item>
    <item>
      <title>CARMA Platform Version 3.6.0 [supporting software]</title>
      <link>https://trid.trb.org/View/2675031</link>
      <description><![CDATA[The Federal Highway Administration (FHWA) developed the innovative CARMA℠ Platform to encourage collaboration with the goal of improving transportation efficiency and safety. FHWA's interest in advancing Transportation Systems Management and Operations (TSMO) strategies with automated driving technology is focused on how infrastructure can move traffic more efficiently. CARMA enables Automated Driving Systems (ADS) to navigate more safely and efficiently with other vehicles and roadway infrastructure though communication and cooperation. CARMA was designed using open source software (OSS) and is available on GitHub. The unique platform was created to work collaboratively with any vehicle, hardware, or control system. By simplifying software development and providing access to increased functionality and a community of developers, CARMA enables the research and development (R&D) of cooperative automated driving system (CADS) capabilities to support TSMO. CARMA also will develop a concept of operations for new TSMO strategies, such as identifying Traffic Incident Management (TIM) scenarios that provide new strategies for first responder use cases interacting with ADS. This research will accelerate market readiness and the deployment of cooperative automated driving technology, while advancing safety, security, data, and artificial intelligence. Beyond reducing traffic congestion and improving transportation safety, CARMA will support industry collaboration and expand on existing automation capabilities to reduce R&D time and advance cooperative automated driving technology. CARMA promotes collaboration and participation from communities of engineers and researchers to advance the understanding of cooperative automated driving using OSS and agile project management practices. CARMA-platform release version 3.6.0 is comprised of five major enhancements. First, added ADS unobstructed lane change. Second, CTM Move-over law – Upon receiving a request from an emergency vehicle, CARMA Platform plans move over to the adjacent open lane. Third, added Geofence speed, gap control and lane closure. And fourth, added CARMA-cloud integration. Along with the above enhancements, several bug fixes and CI related enhancements are included in this release.]]></description>
      <pubDate>Mon, 16 Mar 2026 08:41:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675031</guid>
    </item>
    <item>
      <title>CARMA Platform Version 3.7.0 [supporting software]</title>
      <link>https://trid.trb.org/View/2675032</link>
      <description><![CDATA[The Federal Highway Administration (FHWA) developed the innovative CARMA℠ Platform to encourage collaboration with the goal of improving transportation efficiency and safety. FHWA's interest in advancing Transportation Systems Management and Operations (TSMO) strategies with automated driving technology is focused on how infrastructure can move traffic more efficiently. CARMA enables Automated Driving Systems (ADS) to navigate more safely and efficiently with other vehicles and roadway infrastructure though communication and cooperation. CARMA was designed using open source software (OSS) and is available on GitHub. The unique platform was created to work collaboratively with any vehicle, hardware, or control system. By simplifying software development and providing access to increased functionality and a community of developers, CARMA enables the research and development (R&D) of cooperative automated driving system (CADS) capabilities to support TSMO. CARMA also will develop a concept of operations for new TSMO strategies, such as identifying Traffic Incident Management (TIM) scenarios that provide new strategies for first responder use cases interacting with ADS. This research will accelerate market readiness and the deployment of cooperative automated driving technology, while advancing safety, security, data, and artificial intelligence. Beyond reducing traffic congestion and improving transportation safety, CARMA will support industry collaboration and expand on existing automation capabilities to reduce R&D time and advance cooperative automated driving technology. CARMA promotes collaboration and participation from communities of engineers and researchers to advance the understanding of cooperative automated driving using OSS and agile project management practices. CARMA-platform release version 3.7.0 is comprised of three major enhancements. First, Unobstructed lane change. Second Cooperative Lane Follow (CLF) - All Predecessor Following (APF) platooning. Third, Cooperative Traffic Management - Speed Advisory. Along with the above enhancements, several bug fixes and CI related enhancements are included in this release.]]></description>
      <pubDate>Mon, 16 Mar 2026 08:41:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675032</guid>
    </item>
    <item>
      <title>CARMA Platform Version 3.7.1 [supporting software]</title>
      <link>https://trid.trb.org/View/2675033</link>
      <description><![CDATA[The Federal Highway Administration (FHWA) developed the innovative CARMA℠ Platform to encourage collaboration with the goal of improving transportation efficiency and safety. FHWA's interest in advancing Transportation Systems Management and Operations (TSMO) strategies with automated driving technology is focused on how infrastructure can move traffic more efficiently. CARMA enables Automated Driving Systems (ADS) to navigate more safely and efficiently with other vehicles and roadway infrastructure though communication and cooperation. CARMA was designed using open source software (OSS) and is available on GitHub. The unique platform was created to work collaboratively with any vehicle, hardware, or control system. By simplifying software development and providing access to increased functionality and a community of developers, CARMA enables the research and development (R&D) of cooperative automated driving system (CADS) capabilities to support TSMO. CARMA also will develop a concept of operations for new TSMO strategies, such as identifying Traffic Incident Management (TIM) scenarios that provide new strategies for first responder use cases interacting with ADS. This research will accelerate market readiness and the deployment of cooperative automated driving technology, while advancing safety, security, data, and artificial intelligence. Beyond reducing traffic congestion and improving transportation safety, CARMA will support industry collaboration and expand on existing automation capabilities to reduce R&D time and advance cooperative automated driving technology. CARMA promotes collaboration and participation from communities of engineers and researchers to advance the understanding of cooperative automated driving using OSS and agile project management practices.]]></description>
      <pubDate>Mon, 16 Mar 2026 08:41:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675033</guid>
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    <item>
      <title>CARMA Platform Version 3.8.0 [supporting software]</title>
      <link>https://trid.trb.org/View/2675034</link>
      <description><![CDATA[The Federal Highway Administration (FHWA) developed the innovative CARMA℠ Platform to encourage collaboration with the goal of improving transportation efficiency and safety. FHWA's interest in advancing Transportation Systems Management and Operations (TSMO) strategies with automated driving technology is focused on how infrastructure can move traffic more efficiently. CARMA enables Automated Driving Systems (ADS) to navigate more safely and efficiently with other vehicles and roadway infrastructure though communication and cooperation. CARMA was designed using open source software (OSS) and is available on GitHub. The unique platform was created to work collaboratively with any vehicle, hardware, or control system. By simplifying software development and providing access to increased functionality and a community of developers, CARMA enables the research and development (R&D) of cooperative automated driving system (CADS) capabilities to support TSMO. CARMA also will develop a concept of operations for new TSMO strategies, such as identifying Traffic Incident Management (TIM) scenarios that provide new strategies for first responder use cases interacting with ADS. This research will accelerate market readiness and the deployment of cooperative automated driving technology, while advancing safety, security, data, and artificial intelligence. Beyond reducing traffic congestion and improving transportation safety, CARMA will support industry collaboration and expand on existing automation capabilities to reduce R&D time and advance cooperative automated driving technology. CARMA promotes collaboration and participation from communities of engineers and researchers to advance the understanding of cooperative automated driving using OSS and agile project management practices. CARMA-platform release version 3.8.0 is comprised of two major enhancements. First, Cooperative Traffic Signaling (CTS), fixed signal transit for Work Zones using a SPaT message a vehicle plans a maneuver to proceed through the intersection as efficiently as possible, or come to a safe stop if needed. Second lane geometry updates affected by a geofence to split and stitch lanelets together to match the geofence requirements. Along with the above enhancements, several bug fixes and CI related enhancements are included in this release.]]></description>
      <pubDate>Mon, 16 Mar 2026 08:41:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675034</guid>
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    <item>
      <title>CARMA Platform Version 3.8.1 [supporting software]</title>
      <link>https://trid.trb.org/View/2675035</link>
      <description><![CDATA[The Federal Highway Administration (FHWA) developed the innovative CARMA℠ Platform to encourage collaboration with the goal of improving transportation efficiency and safety. FHWA's interest in advancing Transportation Systems Management and Operations (TSMO) strategies with automated driving technology is focused on how infrastructure can move traffic more efficiently. CARMA enables Automated Driving Systems (ADS) to navigate more safely and efficiently with other vehicles and roadway infrastructure though communication and cooperation. CARMA was designed using open source software (OSS) and is available on GitHub. The unique platform was created to work collaboratively with any vehicle, hardware, or control system. By simplifying software development and providing access to increased functionality and a community of developers, CARMA enables the research and development (R&D) of cooperative automated driving system (CADS) capabilities to support TSMO. CARMA also will develop a concept of operations for new TSMO strategies, such as identifying Traffic Incident Management (TIM) scenarios that provide new strategies for first responder use cases interacting with ADS. This research will accelerate market readiness and the deployment of cooperative automated driving technology, while advancing safety, security, data, and artificial intelligence. Beyond reducing traffic congestion and improving transportation safety, CARMA will support industry collaboration and expand on existing automation capabilities to reduce R&D time and advance cooperative automated driving technology. CARMA promotes collaboration and participation from communities of engineers and researchers to advance the understanding of cooperative automated driving using OSS and agile project management practices.]]></description>
      <pubDate>Mon, 16 Mar 2026 08:41:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675035</guid>
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    <item>
      <title>CARMA Platform Version 3.8.2 [supporting software]</title>
      <link>https://trid.trb.org/View/2675036</link>
      <description><![CDATA[The Federal Highway Administration (FHWA) developed the innovative CARMA℠ Platform to encourage collaboration with the goal of improving transportation efficiency and safety. FHWA's interest in advancing Transportation Systems Management and Operations (TSMO) strategies with automated driving technology is focused on how infrastructure can move traffic more efficiently. CARMA enables Automated Driving Systems (ADS) to navigate more safely and efficiently with other vehicles and roadway infrastructure though communication and cooperation. CARMA was designed using open source software (OSS) and is available on GitHub. The unique platform was created to work collaboratively with any vehicle, hardware, or control system. By simplifying software development and providing access to increased functionality and a community of developers, CARMA enables the research and development (R&D) of cooperative automated driving system (CADS) capabilities to support TSMO. CARMA also will develop a concept of operations for new TSMO strategies, such as identifying Traffic Incident Management (TIM) scenarios that provide new strategies for first responder use cases interacting with ADS. This research will accelerate market readiness and the deployment of cooperative automated driving technology, while advancing safety, security, data, and artificial intelligence. Beyond reducing traffic congestion and improving transportation safety, CARMA will support industry collaboration and expand on existing automation capabilities to reduce R&D time and advance cooperative automated driving technology. CARMA promotes collaboration and participation from communities of engineers and researchers to advance the understanding of cooperative automated driving using OSS and agile project management practices.]]></description>
      <pubDate>Mon, 16 Mar 2026 08:41:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675036</guid>
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