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    <title>Research in Progress (RIP)</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>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>System Diagnosis and Strategic Framing for Transportation Congestion Management in Miami Beach</title>
      <link>https://rip.trb.org/View/2780203</link>
      <description><![CDATA[The City of Miami Beach presents a unique and highly constrained transportation environment in which congestion is driven not only by infrastructure constraints, but also by the interaction of highly variable travel demand, limited access points, curbside activity, signal coordination conditions, commuter and recreational travel interactions, and traveler behavioral responses within a tightly coupled urban network. Unlike conventional urban congestion settings, localized operational disruptions within Miami Beach can rapidly propagate across the broader transportation system because of the limited number of access points serving the island and the substantial fluctuations in daily population associated with employment, tourism, events, and recreational travel activity. Accordingly, effective congestion mitigation strategies for Miami Beach require a system-level understanding of how congestion forms, how queues propagate across the network, how different travel purposes interact temporally and spatially, how curbside activity and operational friction reduce effective roadway capacity, and how traveler behavior contributes to overall network performance. Therefore, the purpose of this research project is to establish a collaborative effort with the City of Miami Beach to: (1) Develop a comprehensive understanding of network behavior under varying operational conditions; (2) Establish a framework for evaluating future congestion mitigation strategies; (3) Identify critical operational and behavioral contributors to system-wide congestion; (4) Refine the scope and priorities for subsequent strategy development and implementation phases. The effort will begin with a site visit and extensive discussions with City staff to identify transportation constraints and challenges, and to observe operating conditions. This will be followed by a data collection and analysis period, with a specific focus on system-level characterization. Specific network dynamics and failure mechanisms related to access point saturation, queue spillback, signal coordination, micromobility operations, curbside activity, and interactions among multiple travel purposes will be analyzed. Behavioral factors relating to mode choice, departure timing decisions, route selection, access-point selection, and responses to congestion will be analyzed to complete the picture at the network level. To aid in network characterization, specific evaluation metrics at key locations and time periods will be developed. Finally, these insights will be applied to propose strategic solutions designed to mitigate congestion at specific hotspots as well as throughout the network. Future opportunities to implement pilot programs will be determined in collaboration with the City, and their success will be evaluated using the metrics developed during this phase of work.]]></description>
      <pubDate>Fri, 18 Sep 2026 10:19:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780203</guid>
    </item>
    <item>
      <title>Potential Impact of Autonomous Vehicles on Reducing Congestion - Phase 2</title>
      <link>https://rip.trb.org/View/2733194</link>
      <description><![CDATA[Traffic congestion is a major problem in large metropolitan areas in the United States. In 2022, on average, a commuter lost about $1,259 in monetary terms annually due to congestion nationwide, which amounts to 8.7 billion lost hours in total.  The lack of coordination among individual users, who make routing decisions independently based on current traffic information without anticipating that others may follow similar decision-making patterns, contributes significantly to the high cost of congestion.    

The behavior of drivers optimizing their individual routes leads to a state known as the User Equilibrium, leading to travel times that can be significantly higher than travel times from the System Optimal, particularly in congested urban networks where the effects of individual decisions cascade throughout the system.  With the future emergence of autonomous vehicles, it is possible that organizations may now own more of the fleet of vehicles and control their routing, providing the organization more options for balancing route selections and thus making it possible to find routing solutions closer to the system optimal.  Driverless ride-hailing companies such as Waymo have already begun their service in five major cities across the United States and Tesla has started to test their Robotaxi service in Austin, Texas.

In Phase 1, the research team developed the research foundation for this problem. This work includes the literature review and the development of an online dispatch-and-relocation framework for a centrally controlled autonomous vehicle fleet. The Phase 1 framework matches requests to vehicles while accounting for pickup deadlines, near-term vehicle availability, and proactive repositioning toward forecasted demand. Phase 1 also establishes a comparison structure against a traditional human-driver ride-hailing system and an initial simulation capability that traces routes and estimates vehicle miles traveled, deadhead miles, passenger waiting time, revenue, and related performance measures.

Phase 2 will build directly on this foundation and is the primary focus of the next stage of the project. In Phase 2, the team will scale the optimization and simulation framework so it can solve problems at the size of major metropolitan areas. This includes extending the model to larger networks and richer demand patterns, improving computational tractability for larger instances, and strengthening the simulation module so it can evaluate passenger-vehicle matches and route decisions under more realistic operating conditions. To make the model scalable, the team will aggregate the service region into zones and solve the resulting problems repeatedly over short rolling horizons. The team will also need to calibrate the demand forecasting and routing inputs for large urban networks and test the algorithms on progressively larger instances to ensure that the solution quality and computation time remain practical. The purpose of Phase 2 is to determine how much centralized control of autonomous fleets can reduce system-wide travel, deadhead mileage, waiting times, and congestion when evaluated on realistic metropolitan-scale settings.

]]></description>
      <pubDate>Wed, 22 Jul 2026 17:37:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2733194</guid>
    </item>
    <item>
      <title>Digital twin for managing the curb and reducing congestion (Phase 2)</title>
      <link>https://rip.trb.org/View/2732948</link>
      <description><![CDATA[Curb space in dense urban cores is under intense pressure from freight deliveries, service vehicles, and passenger car parking activities. Without a data-driven view of curb regulations and demand, cities face double-parking, spillback congestion, and safety conflicts. This project addresses the gap by creating an open-data-based digital twin that links curb regulations, observed curb activity proxies, and network performance to support actionable curb management decisions.

The research team is developing a strategic curb digital twin for a portion of downtown Los Angeles (DTLA), built using publicly available data to ensure transparency and replicability. In Phase 1, the team integrated multiple open datasets (GIS networks from the LA GeoHub, land use data from DataLA, OpenStreetMap) and developed heterogeneous freight demand models distinguishing commercial and residential delivery behaviors. Phase 2 adds an analytical curb allocation optimization layer with targeted microsimulation validation. While the broader research agenda includes multimodal curb demand modeling (pursued in parallel work), Phase 2 addresses the policy question: given competing freight delivery and passenger parking demands, how should curb space be optimally allocated across blockfaces and time-of-day periods?]]></description>
      <pubDate>Wed, 22 Jul 2026 17:17:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732948</guid>
    </item>
    <item>
      <title>Routing Autonomous Trucks on Dedicated Lanes- Phase 1</title>
      <link>https://rip.trb.org/View/2676007</link>
      <description><![CDATA[Trucks are known to have a significant impact on congestion during traffic peak hours due to their size and slower dynamics. Human operated trucks for freight transport are faced with two constraints: those imposed by the service demand and those imposed by the human driver. For long haul operations, for example, truck drivers must meet the constraints of hours of service. For short haul they have to meet family and personal constraints which often do not allow them to operate during odd hours. With automation the human constraints are removed which opens the way to view truck routing and scheduling under different and more flexible constraints. The major problem faced by automated trucks operating with the rest of traffic, however, is safety as due to the different sizes involved the sensing problem is more challenging and potential accidents can be catastrophic.


Under this project the research team plans to analyze and evaluate the use of automated trucks that will operate on the surface network at times that the traffic demand is very low, so that lanes can be switched dynamically to dedicated automated truck lanes without affecting traffic. By doing so we can keep the automated trucks separated from manually driven vehicles which may be using the network, thereby addressing the issue of safety. This project will address the potential benefits of automated trucks on dedicated lanes operating at low volume traffic hours. In addition, it will extend the approach to automated truck platoons where automation will also lead to significant fuel savings (up to 20%) due to reduction in aerodynamic drag, bringing the potential to lower costs. Moving trucks from times of high congestion to times of no congestion will bring considerable benefits to trucking companies as well as to all other users of the road network, as fewer trucks will be operating during peak traffic hours. In addition, trucking companies that are short of truck drivers will be able to operate without disruptions and without human imposed constraints, saving on labor costs. The team plans to use as an example a network that includes Interstate 710 (I-710) and the Ports of Los Angeles/Long Beach, a route that generates considerable truck traffic. The team will identify the lanes that can be dynamically dedicated to automated trucks at certain hours and estimate the impact on congestion and fuel savings. The team will use real truck and traffic data to validate their traffic simulators which they will then use to run different scenarios.]]></description>
      <pubDate>Tue, 03 Mar 2026 16:31:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676007</guid>
    </item>
    <item>
      <title>Digital twin for managing the curb and reducing congestion - Phase 1</title>
      <link>https://rip.trb.org/View/2676005</link>
      <description><![CDATA[Curb space in dense urban cores is under intense pressure from e-commerce deliveries, service vehicles, ride-hailing, and passenger pick-up/drop-off. Without a data-driven view of curb regulations and heterogeneous delivery demand, cities face double-parking, spillback congestion, and safety conflicts. This project addresses the gap by creating an open-data-based digital twin that links curb regulations, observed curb activity proxies (differentiating between commercial and residential delivery behaviors), and network performance to support actionable curb management decisions.

This project is part of a larger project. The larger project is developing a proof-of-concept strategic curb digital twin to analyze curb demands and test curb management solutions. The digital twin will act as a virtual replica of a portion of downtown Los Angeles (DTLA), built using publicly available data to ensure the model is transparent, replicable, and directly useful to public agencies. This approach is centered on a transparent, agent-based simulation model. The research team will construct a high-fidelity virtual environment by integrating multiple open datasets and agency records, including geographic information system (GIS) road networks from the LA GeoHub, land use and business listings from DataLA, LADOT signal timing charts for realistic traffic control, and network details from OpenStreetMap. This allows the team to simulate crucial behaviors, such as a delivery driver’s search for parking calibrated using parking citation data, or a private car's decision process calibrated using open-source global positioning system (GPS) Exchange Format (GPX) data. This creates a reliable virtual testbed to evaluate various management strategies. The team can introduce and assess policies such as dynamic pricing for loading zones and passenger car parking, or time-of-day restrictions, and observe their combined effect on delivery efficiency and overall traffic congestion. The prototype will serve as proof-of-concept for this multi-agent simulation, establishing a foundational tool for holistic curb management.

This Phase 1 project focuses on freight deliveries and expands the larger project by introducing heterogeneity into freight delivery demands. Retail establishments may receive relatively large shipments and restaurants may receive daily shipments from multiple suppliers. Residents receive small package deliveries. Different types of deliveries imply differences in delivery vehicle dwell time, demand for a nearby parking space, and delivery route configurations. The team will use land use, employment, and demographic data to generate freight delivery demands. The team then classify these demands based on stop dwell times and commercial vs residential, because of the temporal differences in these demands. The different demands are operationalized as different agents within the model.]]></description>
      <pubDate>Tue, 03 Mar 2026 16:23:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676005</guid>
    </item>
    <item>
      <title>Data Integration to Support Digital Infrastructure and Efficient Mobility Insights</title>
      <link>https://rip.trb.org/View/2669548</link>
      <description><![CDATA[Data is at the core of understanding mobility trends, modeling and optimizing transportation systems and informing policy and decision-making. Understanding data is also key to advancing digital infrastructure for transportation, providing technological systems and frameworks to complement physical infrastructure. This project aims to support two other initiatives (the CEM Innovation Accelerator and the Advanced Transportation Optimization and Modeling project) by serving as the “data engine” to support both modeling as well as potential innovation projects. However, this project also stands alone as a data integration effort that will support the expansion and improvement of the previously-developed CEM Data Hub and serve to provide mobility data insights and digital infrastructure frameworks geared towards understanding transportation system efficiency.   

This project will develop a robust data integration framework, collect, assemble, and harmonize diverse transportation datasets to support the development of actionable mobility insights. By integrating data from traffic sensors, transit systems, GPS traces, and mobile applications, the project will create a comprehensive data ecosystem that reflects real-world travel behavior, congestion patterns, and modal interactions. This foundation will enable the development of analytical tools and decision-support systems that help agencies and planners optimize transportation networks for efficiency. The integrated data platform will support advanced analytics, visualization tools, and decision-support systems that can be used by planners, engineers, and policymakers to evaluate mobility strategies.   

Key components of the project include:  

Data Ecosystem Development – Establishing a scalable and secure data architecture that supports integration of diverse transportation datasets   

Collaboration – Engaging with ongoing projects across the CEM consortium to ensure collaborative use of available data   

Analytical Tools and Insights – Developing dashboards, predictive models, and scenario planning tools to support efficient and healthy mobility decisions.  

Ultimately, this effort will position data as a central asset in advancing efficient mobility across urban and rural contexts.  ]]></description>
      <pubDate>Thu, 12 Feb 2026 15:35:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669548</guid>
    </item>
    <item>
      <title>Understanding Risks and Opportunities for Ramp Metering Control in a Mixed-autonomy Future</title>
      <link>https://rip.trb.org/View/2651988</link>
      <description><![CDATA[Vehicle automation may change traffic flow dynamics. This will also impact the control of traffic flow via infrastructure-based systems such as ramp metering control. In this work the research team investigated the impact that different levels of automation and connectivity will have on ramp metering control, and proposed modifications to existing ramp metering algorithms to improve their performance under different automation scenarios. The team finds that low-level automation such as adaptive cruise control may decrease mainline throughput by up to 58% on average and increase travel time by 61%. However, full connectivity and automation may decrease travel time by up to 40%. Based on these potential impacts, modifications to the ramp metering algorithm settings were developed for each of the seven automation scenarios. These modifications are shown to improve operations in each scenario.]]></description>
      <pubDate>Thu, 08 Jan 2026 15:26:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2651988</guid>
    </item>
    <item>
      <title>Improving Traveler Experience Via Alternatives to Roadway/Railway Grade Crossings  </title>
      <link>https://rip.trb.org/View/2646961</link>
      <description><![CDATA[There are more than 240,000 at-grade crossings between railroads and roadways in the U.S. and as the number of freight trains increases, the times of interface and blocked crossings also increases. USDOT reports numerous driver complaints about delays and frequent disruptions, and in some cases, there are delays to emergency vehicles due to excessive numbers of blocked trains. Work is underway to continue documentation and to consider strategies and address the frequent and repeated delays caused by long trains. The most requested remedy is grade separation. Grade separations are extremely expensive, and planning and construction lead times are long, so there is a need to identify other more short-term strategies that will offer travelers and emergency responders options to waiting on the long trains.  

The focus of this research will be Fort Bend County and Harris County, Texas, which include major freight corridors from Port Houston, the 3rd largest container port in the country. Between the two counties, there are at least 11,000 at grade crossings. Specifically, this work will assemble delay time data showing frequency and duration for the identified railroad crossings. The team will conduct literature review and on-line and in-person conversations to determine options and strategies underway by entities (e.g., railroad operators), municipalities, and others to address better traveler information and options to reduce and avoid delay time. Potential options include cameras noting delays and following with notifications to emergency services proximate to locations with frequent delays. The study team will examine whether this information distribution could be expanded to additional users. An additional option to be examined is message signs alerting travelers to blocked crossings in time to adjust their travel route. The expected research outcome is to provide an option to grade separations that will reduce delay time for travelers caused by blocked train crossings. ]]></description>
      <pubDate>Tue, 06 Jan 2026 17:10:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646961</guid>
    </item>
    <item>
      <title>Toward Ubiquitous Trajectory‐Based Traffic Network Diagnosis Systems
</title>
      <link>https://rip.trb.org/View/2625315</link>
      <description><![CDATA[This project aims to develop a trajectory-based traffic network diagnosis system to address urban congestion by leveraging vehicle trajectory data and open-source tools. The system operates at both planning and operational levels, offering scalable, real-time diagnosis and mitigation of congestion issues. It integrates advanced equilibrium models and mesoscopic simulations, prioritizing computational efficiency and actionable results. By democratizing access to traffic diagnostics and enabling rapid deployment, the project envisions empowering cities worldwide to manage congestion sustainably, enhance urban mobility, and improve quality of life.

]]></description>
      <pubDate>Thu, 13 Nov 2025 16:01:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625315</guid>
    </item>
    <item>
      <title>Bridging the Gap Between Theory and Implementation for CAV-Based Mixed Traffic
Smoothing

</title>
      <link>https://rip.trb.org/View/2625306</link>
      <description><![CDATA[In the last decade, dozens of algorithms have been published that rely on using individual automated vehicles (AVs) to smooth traffic flow, reducing congestion and emissions. However, none of these algorithms have been implemented on production vehicles, begging the question, “Why?” One possibility is that much of the AV-based traffic flow smoothing theory is based on simplistic traffic models that do not hold up to reality, while another possibility is that vehicle-level delays make these strategies impractical to implement.

This project focuses on developing and validating advanced traffic-smoothing controllers for connected and automated vehicles (CAVs) to address critical safety and mobility challenges in mixed traffic environments. The research will address uncertainties in vehicle dynamics, actuation delays, and human compliance, leveraging high-resolution traffic data, cutting-edge simulation tools, and real-world testing on a state-of-the-art CAV platform at a professional test track in Indiana.]]></description>
      <pubDate>Thu, 13 Nov 2025 14:56:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625306</guid>
    </item>
    <item>
      <title>MPO Board Member Training on Congestion Reduction</title>
      <link>https://rip.trb.org/View/2589112</link>
      <description><![CDATA[Metropolitan Planning Organization (MPO) Governing Board members are responsible for establishing a long-range transportation vision for their metropolitan area and for making a variety of other significant transportation planning and programming decisions. These decisions have noteworthy and long-lasting impacts on the character of their communities, including the impact on system-wide and corridor level congestion. Yet, the vast majority of MPO Governing Board members come to their position without any background in transportation system development and even less on the nature of congestion. This training program, created under the guidance of the Association of Metropolitan Planning Organizations (AMPO), the National Association of Regional Councils (NARC), and the Florida MPO Advisory Council (MPOAC), will educate MPO Governing Board members on their role in managing and reducing congestion in their metropolitan area. The modular podcast-style video training program (which will be available for free download on several websites, including the AMPO, NARC, and MPOAC websites) will cover a number of topics including the definition of transportation congestion, the root causes of congestion, the advantages and disadvantages of congestion, and the techniques for managing and reducing congestion where appropriate. It will also include a description of the basic process and product requirements for MPOs and the role played by MPO Governing Board members. Upon completion of the training, MPO Governing Board members will better understand their role in defining the future of congestion on their regional transportation system, resulting in a more robust planning and programming approach to congestion reduction nationwide.]]></description>
      <pubDate>Sat, 16 Aug 2025 23:45:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2589112</guid>
    </item>
    <item>
      <title>Network Level Proactive Traffic Operations Indicator (NPTOI) Using Artificial Intelligence (AI) and Connected Vehicle Data Integration</title>
      <link>https://rip.trb.org/View/2529964</link>
      <description><![CDATA[This project will focus on developing an extensive and implementable artificial intelligence (AI)-driven Network Level Proactive Traffic Operations Indicator (NPTOI) system aimed at mitigating urban traffic congestion and delays through effective and proactive prediction and prevention of traffic disruptions. The system will be built on real-time sensor-based and connected vehicle data, including various crowdsourced data from platforms such as the newly released Streetlight connected vehicles data, Lytx, LYNX, etc. and infrastructure-based data from Automated Traffic Signal Performance Measures (ATSPM), and as needed Microwave Vehicle Detection Systems (MVDS) and data from other available sensors, e.g., Close Circuit TVs (CCTVs), that could be used for validation or augment crowdsourced data. The NPTOI system can be integrated into existing traffic management infrastructure through testing and validation. NPTOI can also be used in a dashboard system that evaluates change over time and alerts operators to changes in the field that may affect traffic operations. Additionally, the University of Central Florida (UCF) team will analyze and report on the most effective Connected Vehicles data types for enhancing Florida Department of Transportation (FDOT) operations. Machine Learning (ML) methods such as Graph Neural Networks (GNN) and other techniques will be deployed to make such predictions since several ML algorithms are able to capture spatial and temporal features. Various data sources would be explored, and a combination of the sources will be experimented to obtain the best output predictions. The expected outcome of the research would enable FDOT to transition to AI-driven analysis reports that can screen network level traffic to give mobility indicators. The expectation is that such metric can enable operators to make decisions that can alleviate congestion.]]></description>
      <pubDate>Fri, 28 Mar 2025 08:00:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2529964</guid>
    </item>
    <item>
      <title>Development and Implementation of Coordinated Adaptive Ramp Metering (CARM) in Southern California Interstates for Congestion Mitigation</title>
      <link>https://rip.trb.org/View/2506242</link>
      <description><![CDATA[The goal of this proposed Coordinated Adaptive Ramp Metering (CARM) implementation project is to mitigate congestion and improve traffic flow along the interstate I-15 corridor in the Southern California region. This advanced traffic management system aims to optimize freeway operations by dynamically adjusting ramp metering rates based on real-time traffic conditions across the entire I-15 corridor. By implementing CARM, the project seeks to increase average speeds, reduce travel times, enhance safety, and improve overall traffic efficiency. The existing ramp metering approach at key on-ramps such as Magnolia Ave, Ontario Ave, El Cerrito Road, and Cajalco Road lacks automatic responsiveness to crashes and lane-blocking events, coordination across multiple ramps, and optimal queue distribution to increase corridor throughput.

To address these issues, a Coordinated Adaptive Ramp Metering (CARM) system is proposed. CARM is an advanced traffic management strategy that dynamically adjusts ramp metering rates based on real-time traffic conditions across an entire freeway corridor. This system aims to optimize freeway operations by (a) Coordinating multiple ramp meters to work together, balancing the flow of vehicles entering the freeway (b) Using centralized adaptive algorithms to analyze data from the entire corridor and adjust metering behavior accordingly (c) Maximizing freeway throughput while minimizing queues on the ramps d) Responding in real-time to traffic incidents and congestion.

The project will implement the following objectives- (1) Develop and implement a CARM system on I-15 southbound between SR-91 and Cajalco Road to reduce PM peak period congestion by automatically adjusting metering rates in response to real-time traffic conditions, including crashes and lane-blocking events. (2) Optimize ramp metering algorithms to distribute queues across multiple on-ramps (Magnolia Ave, Ontario Ave, El Cerrito Road, and Cajalco Road) to increase overall corridor throughput during peak periods.


USDOT Priorities:

The USDOT Strategic Plan for FY 2022-2026 prioritizes strategies that improve system operations to increase travel time reliability, manage travel demand, and improve connectivity. This project directly aligns with those strategies by promoting the adoption of transportation management and operations (TSMO) practices. Also, this project improves travel time reliability on congested corridors. This project is aligned with Objective 1 of MCEEST’s goals - Create a safer, more reliable, and more resilient transportation system that improves equity through increased access to jobs, housing, services, and other opportunities for historically underserved communities. Interstate 15 in the San Bernardino and Riverside counties in California passes through historically disadvantaged communities. The congestion on I-15 is a major reason why these communities have difficulties accessing jobs and services.]]></description>
      <pubDate>Fri, 07 Feb 2025 16:07:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2506242</guid>
    </item>
    <item>
      <title>Physics-Informed Learning and Control of Connected and Autonomous Vehicles for Congestion Reduction</title>
      <link>https://rip.trb.org/View/2458989</link>
      <description><![CDATA[Building upon previous work in lane changing using physics-informed machine learning for autonomous vehicles, the goal of this project is to develop physics-informed machine learning and data-driven control-based tools for the combined longitudinal and lateral planning and control of connected and autonomous vehicles (CAVs). This research initiative holds the promise to have the following advantages: 1) Utilizing physics-informed machine learning as a tool can significantly enhance computational efficiency, which is beneficial for real-time control in complex scenarios; 2) Combining neural networks with physical models can greatly reduce over-reliance on data; 3) During the training phase of neural networks, any differentiable objective function and various constraints can be considered, allowing it to solve constrained multi-objective model predictive control problems without affecting computational speed. In addition, this project will design a lane-change decision-making module based on deep reinforcement learning and validate the congestion-reducing scheme using NGSIM data and SUMO simulations.]]></description>
      <pubDate>Thu, 21 Nov 2024 17:29:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2458989</guid>
    </item>
    <item>
      <title>Transportation Demand Management (TDM) Calculators</title>
      <link>https://rip.trb.org/View/2417306</link>
      <description><![CDATA[This research consists of reviewing relevant key performance Indicators (KPI) and past research, primarily through a literature review, and examining how they could be applied by the Colorado Department of Transportation (CDOT). Also of interest is an analysis of TDM Calculators used by other agencies, to assist CDOT in developing their own. Benefits are to understand and better use state-managed funds and demonstrate the effectiveness of transportation demand management (TDM) to manage congestion and reduce greenhouse gas (GHG) emissions. The goal is to provide a consistent, accurate standard of assessment for TDM projects.
]]></description>
      <pubDate>Wed, 14 Aug 2024 12:16:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2417306</guid>
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