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    <title>Transport Research International Documentation (TRID)</title>
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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>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>The Chicago Freeway Traffic and Incident Management Program [video]</title>
      <link>https://trid.trb.org/View/2719327</link>
      <description><![CDATA[This video compilation includes a history and description of the freeways in the Chicago Metropolitan area with emphasis on the automated management of traffic congestion. The first video includes historic photographs. The first video includes information related to the Illinois Department of Transportation (IDOT) traffic surveillance system, automatic incident detection, computerized traffic reports, emergency traffic patrol (Minutemen), and ramp metering. The second video introduces how computerized traffic surveillance is conducted by the IDOT traffic systems center (TSC). The third video focuses on the role of the IDOT District 1 Communications Center.]]></description>
      <pubDate>Tue, 30 Jun 2026 08:51:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2719327</guid>
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    <item>
      <title>Implementing Human-AI Collaboration for Enhancing TMC Freeway Operations</title>
      <link>https://trid.trb.org/View/2709250</link>
      <description><![CDATA[The rapid proliferation of artificial intelligence (AI) is fundamentally transforming real-time operations and decision-making across industries, including transportation. Human operators, while skilled and experienced, are inherently limited in their ability to process and interpret vast streams of data, especially under time pressure and uncertainty. These limitations can lead to overconfidence in judgment, susceptibility to cognitive biases, and challenges in maintaining situational awareness during complex or high-stress events. In contrast, AI excels at analyzing large datasets, identifying patterns, and providing objective, data-driven decision support, making it a powerful tool for augmenting human capabilities.

The concept of Intelligence Augmentation (IA) centers on leveraging AI not to replace human decision makers, but to enhance and amplify their reasoning, problem solving, and decision-making. IA emphasizes collaborative partnership, where AI systems handle computationally intensive tasks and humans contribute strategic oversight, contextual understanding, and ethical judgment. This approach preserves human agency while unlocking new levels of operational performance.

Traffic Management Centers (TMCs) serve as the central command hubs for monitoring and managing regional transportation networks, including freeways. TMCs rely on a diverse workforce to monitor, detect, and manage traffic incidents, congestion, and emergencies. As transportation systems become more complex and data-rich, the opportunity to integrate AI into TMC operations grows. AI can support TMC staff by automating routine analysis, predicting incidents, optimizing response strategies, and enabling proactive management of traffic flows. However, realizing these benefits requires a thoughtful framework for human-AI collaboration that addresses technical, organizational, and human factors.

The objective of this research is to develop a comprehensive technical guide for state departments of transportation (DOTs) and other transportation agencies to effectively incorporate human–AI collaboration into TMC freeway operations. This guide will provide actionable strategies, best practices, and implementation pathways to optimize decision-making, operational efficiency, and safety through the integration of AI technologies alongside human expertise.]]></description>
      <pubDate>Tue, 02 Jun 2026 11:32:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709250</guid>
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    <item>
      <title>Monitoring production pressure in socio-technical systems: The case of Belgian railroads</title>
      <link>https://trid.trb.org/View/2680161</link>
      <description><![CDATA[The pursuit of efficiency in Socio-Technical Systems (STSs), where people and technology are interacting to achieve shared goals under dynamic, high-pressure conditions, often places strain on resources. Many transportation settings exemplify an STS, where infrastructures, technologies, and operators are tightly interdependent. In such settings, the drive for efficiency can give rise to Production Pressure (PrP); the tension between performance demands and the capacity to meet them without compromising safety. Left unmanaged, PrP leads to workarounds, cognitive overload, or unsafe practices. The authors present an initial step toward systematically tackling PrP in transportation settings by introducing a novel, quantitative mechanism to measure and monitor it. The authors develop an analytical framework that deploys Data Envelopment Analysis (DEA) to evaluate PrP in STSs. The proposed approach is applied to Traffic Control Centers (TCCs) at Infrabel, Belgium’s railway infrastructure company, where PrP is modeled as the trade-off between railway traffic density and operator workload. The results demonstrate that the model provides a nuanced understanding of the pressures faced by railway traffic controllers. In doing so, this study contributes to the growing need for robust, data-driven tools that integrate human and technical perspectives to support safe, efficient operations in STSs.]]></description>
      <pubDate>Tue, 07 Apr 2026 15:36:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680161</guid>
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    <item>
      <title>Optimal Control for Platooning Under Batch Dispatching Opportunities</title>
      <link>https://trid.trb.org/View/2561847</link>
      <description><![CDATA[Truck platooning is an innovative logistics approach to lower operational costs, particularly fuel consumption, while addressing contemporary transportation challenges. While recent studies on truck platooning have emphasized platoons’ energy savings, stability, and safety, there has been limited exploration of platoon formation and control. This paper uses optimal control theory to address the dispatching control of trucks with arriving platoons. In particular, trucks arrive at a highway station while platoons arrive alongside it. The station controls the truck holding and dispatching, where trucks are sent out with or without a platoon. Dispatching trucks with an arriving platoon reduces fuel consumption while waiting for a platoon to arrive increases the dwell time (i.e., transportation delay). We assume that an arriving platoon determines the number of trucks (i.e., the batch size) it can accept. Only a single truck can be dispatched if a platoon is absent. Hence, we formulate the dispatching control problem and derive the optimal policy for the discounted costs and the average cost governing the dispatch of trucks alongside platoons. We proved the optimality of threshold policies. Numerical results for the average cost case are presented. They are consistent with the optimal ones.]]></description>
      <pubDate>Mon, 23 Mar 2026 17:14:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561847</guid>
    </item>
    <item>
      <title>Data Imputation for Traffic State Estimation and Pre-diction Using Wi-Fi Sensors</title>
      <link>https://trid.trb.org/View/2113517</link>
      <description><![CDATA[Real-time monitoring of traffic conditions is essential to support control strategies and provide useful information to travelers. With the accelerated development in transportation management systems (TMSs), traffic data collection methods have progressed rapidly. Despite the development in the data collection systems, there is missing data due to occasional sensor damage, trans-mission error, or a low penetration rate of the probe vehicle, thereby affecting the reliability and effectiveness of the Intelligent Transportation Systems (ITS). There is a need for effective data imputation methods to ensure the integrity and quality of traffic data. Two clustering-based methods for such traffic imputations are proposed in this paper—one using k-means clustering and the other using speed bins. Mean Absolute Percentage Error (MAPE) is used as a performance efficiency index for both methods. The Speed bin method was found to be more effective with a maximum MAPE of 13.8%. The maximum MAPE observed for the k-means clustering method is 22.6%.]]></description>
      <pubDate>Tue, 24 Feb 2026 08:30:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113517</guid>
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    <item>
      <title>A Guidebook for Effective Use of Incident Data at Texas Transportation Management Centers</title>
      <link>https://trid.trb.org/View/2582234</link>
      <description><![CDATA[This guidebook provides methodologies and procedures for using incident data collected at Texas transportation management centers (TMCs) to perform two types of analysis – evaluation/planning analysis and predictive analysis. For the evaluation/planning analysis, this guidebook provides (1) guidelines for reporting incident characteristics, (2) methods for analyzing hot spots, (3) methodologies for estimating incident impacts, and (4) guidelines and procedures for calculating performance measures. For predictive analysis, this guidebook describes (1) methodologies for predicting incident duration using incident characteristics and (2) methodologies for predicting incident-induced congestion clearance time using combined historical and real-time traffic data. Examples of applications and results from the methodologies and procedures described are provided throughout this guidebook.]]></description>
      <pubDate>Mon, 17 Nov 2025 10:06:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582234</guid>
    </item>
    <item>
      <title>A Guidebook for Effective Use of Archived Operations Data at Texas Transportation Management Centers</title>
      <link>https://trid.trb.org/View/2582233</link>
      <description><![CDATA[This draft guidebook provides methodologies and procedures for using archived operations data collected at Texas Transportation Management Centers (TMCs). The guidebook provides an overview of existing ITS deployment and data management at Texas TMCs. The guidebook describes how historical data can be used to: (a) identify incident hot spots with incident data archives, (b) predict incident durations based on incident characteristics, (c) estimate incident impacts and predict incident-induced congestion clearance time using combined historical and real-time traffic data, and (d) calculate performance measures for performance reporting. This draft guidebook is a product of research results in Year 1 of project 0-5485. Case studies and examples using the methodologies and procedures provided in this guidebook will be completed and appended to the guidebook as part of the research effort in Year 2 of this project.]]></description>
      <pubDate>Sun, 09 Nov 2025 18:10:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582233</guid>
    </item>
    <item>
      <title>Status of TransLink® TMC Connections</title>
      <link>https://trid.trb.org/View/2567168</link>
      <description><![CDATA[As part of the activities associated with its research focus areas, TransLink® has developed laboratories within the Texas Transportation Institute as well as connections with external Transportation Management Centers (TMC). This letter report describes the current laboratory facilities and TMC connections.]]></description>
      <pubDate>Tue, 05 Aug 2025 11:40:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2567168</guid>
    </item>
    <item>
      <title>Incentive Systems for Fleets of New Mobility Services</title>
      <link>https://trid.trb.org/View/2512196</link>
      <description><![CDATA[Traffic congestion has become an inevitable challenge in large cities due to population increases and the expansion of urban areas. Various approaches are introduced to mitigate traffic issues, encompassing from expanding the road infrastructure to employing demand management. Congestion pricing and incentive schemes are extensively studied for traffic control in traditional networks where each driver/rider is a network “player”. In this setup, drivers’/riders’ “selfish” behavior hinders the network from reaching a socially optimal state. In future mobility services, on the other hand, a large portion of drivers/vehicles may be controlled by a small number of companies/organizations. In such a system, offering incentives to organizations can potentially be much more effective in reducing traffic congestion rather than offering incentives directly to drivers. This paper studies the problem of offering incentives to organizations to change the behavior of their individual drivers (or individuals relying on the organization’s services). The authors developed a model where incentives are offered to each organization based on their aggregated travel time loss across all drivers/riders in that organization. Such an incentive offering mechanism requires solving a large-scale optimization problem to minimize the system-level travel time. The authors propose an efficient algorithm for solving this optimization problem. Numerous experiments on Los Angeles County traffic data reveal the ability of the method to reduce system-level travel time by up to 7.15%. Moreover, the experiments show that incentivizing organizations can be up to 7 times more cost-effective than incentivizing individual drivers when aiming for maximum travel time reduction.]]></description>
      <pubDate>Fri, 13 Jun 2025 14:56:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2512196</guid>
    </item>
    <item>
      <title>Transportation Operations Center Operator Retention and Workload Mitigation Strategies</title>
      <link>https://trid.trb.org/View/2559170</link>
      <description><![CDATA[This study investigated strategies to mitigate the workload for and improve the retention of operators at the Virginia Department of Transportation’s (VDOT) Transportation Operations Centers (TOCs). The research involved a literature review, interviews with other state departments of transportation, observations of VDOT TOC operations, and interviews with TOC operators and managers. The study found that data fusion tools were not a significant need for operators, but challenges existed with data output systems. Operator salaries were found to be potentially uncompetitive compared with similar industries. Key factors affecting retention included compensation, career growth opportunities, work-life balance, and the lack of acknowledgment of contract employees’ roles by the larger department. Recommendations include formalizing a process for operators to report software issues, implementing findings from this study in future staffing contracts, and enhancing the prestige of performance awards. The study concludes that addressing these issues could improve operator morale, reduce turnover, and ultimately enhance the efficiency and effectiveness of TOC operations.]]></description>
      <pubDate>Sun, 01 Jun 2025 18:15:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2559170</guid>
    </item>
    <item>
      <title>Fargo-Moorhead Traffic Operations Center: Concept of Operations</title>
      <link>https://trid.trb.org/View/2539744</link>
      <description><![CDATA[A regional traffic operations center (TOC) is center for coordinating and supporting transportation system operations by bringing together various jurisdictions to focus on a common goal of optimizing the performance of the system and maximizing its safety and service to the traveling public. A TOC is a focal point for sharing and directing information related to traffic control, traffic management, traveler information, and traffic incident/emergency management. The Fargo-Moorhead (F-M) metropolitan area consists of several jurisdictions in two states, and therefore, transportation system operations involve several transportation, planning, and law enforcement agencies. In addition, the continued growth of the metropolitan area poses several challenges to effectively operate and manage the transportation system during recurring events (e.g., peak-hour traffic conditions), and non-recurring events (e.g., crashes, special events, etc.). Several of the regional transportation agencies would like to create a F-M TOC for actively monitoring and managing the transportation system. This document will discuss the operational aspects of a future Fargo-Moorhead TOC.]]></description>
      <pubDate>Sun, 27 Apr 2025 17:27:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2539744</guid>
    </item>
    <item>
      <title>Feasibility of a Traffic Operations Center for South Dakota</title>
      <link>https://trid.trb.org/View/2528390</link>
      <description><![CDATA[The South Dakota Department of Transportation (SDDOT) is pursuing a statewide Traffic Operations Center (TOC) to improve transportation on its roadways. The effort required to establish a TOC is significant; thus, the SDDOT commissioned a feasibility study and report. The project gathered stakeholder input, performed research on nationwide practices, and interviewed several nearby state DOTs on TOC functions, best practices, and lessons learned. Common TOC practices were described and categorized. Existing, planned, and future technologies used by SDDOT were documented in operational concepts, where examples were written to demonstrate what traffic operations would look like with a TOC compared to current practice. An alternatives analysis evaluated five models of a TOC ranging in complexity against a no-build option. The findings of the report recommend continuing efforts toward a TOC by starting with a simple TOC deployment and building toward a more complex 24/7/365 facility. Recommended next steps include conducting a facility assessment of the Sioux Falls Public Safety Campus, following the systems engineering processes to develop more planning documents, and pursuing grants to reduce the financial burden on the SDDOT.]]></description>
      <pubDate>Tue, 25 Mar 2025 09:30:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2528390</guid>
    </item>
    <item>
      <title>Blockchain-Based Proxy-Oriented Data Integrity Checking Mechanism in Cloud-Assisted Intelligent Transportation Systems</title>
      <link>https://trid.trb.org/View/2449230</link>
      <description><![CDATA[Cloud-assisted intelligent transportation systems depend on cloud computing to provide powerful computing capabilities and big data storage services. As precise intelligent traffic control and dispatch policies are heavily based on real-time traffic information (e.g., unmanned driving test information), any altered data may cause severe consequences. The integrity of outsourced critical traffic control data has been the most concerning security issue. To this end, a lightweight proxy-oriented data integrity checking mechanism has been devised, without incurring substantial certificates management. The mechanism enables a data manager in traffic information control center to delegate the proxy to produce the signatures of encrypted data and outsource them to the cloud server, dramatically alleviating the work intensity of the data manager. By integrating blockchain into the mechanism, it gives assistance to the data manager for validating malicious integrity checking behaviors. The comprehensive security analysis and performance evaluation demonstrate the feasibility of the mechanism in the deployment of cloud-assisted intelligent transportation systems.]]></description>
      <pubDate>Sun, 09 Mar 2025 17:15:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2449230</guid>
    </item>
    <item>
      <title>Traffic Demand Management in Guangzhou City for the 21st Century</title>
      <link>https://trid.trb.org/View/2263937</link>
      <description><![CDATA[This paper analyses the existing traffic problems in Guangzhou city and the main reasons of traffic congestion, the challenges to be faced by the city in terms of the urban transport in the 21st century, and presents the measures of traffic demand management (TDM) in Guangzhou in the 21st century with the consideration of the limitation of the road resources and based on the characteristics of the urban traffic demand.]]></description>
      <pubDate>Tue, 28 Jan 2025 14:52:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2263937</guid>
    </item>
    <item>
      <title>Challenges of Operators for Autonomous Shuttles</title>
      <link>https://trid.trb.org/View/2407604</link>
      <description><![CDATA[Autonomous shuttles can extend the flexibility of micro-mobility to small groups, families, and persons with mobility limitations and impairments. When integrating autonomous shuttles into public transportation systems, the challenge is to shift the tasks of the driving personnel to the operators in the traffic control center. From there, the autonomous shuttles are centrally monitored and controlled. At the same time, the service quality for the passengers should be fully maintained. This leads to the research question, “Which new tasks arise for operators in the traffic control center in the dispositive control of autonomous shuttles?” A task analysis of the previous tasks of the driving personnel was conducted to answer the research question. The analysis consists of a method mix of inductive and deductive methods to compensate for the disadvantages of both. The result is a systematic of tasks that have to be shifted to the operators in the traffic control center. Furthermore, the technical potentials to support these tasks by assistive systems are described. The results mean a human-centered design of autonomous shuttles for passengers and a basis of task design for operators in the traffic control center for public transport companies.]]></description>
      <pubDate>Tue, 31 Dec 2024 09:04:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407604</guid>
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