<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Optimal Variable Speed Limit Control for the Mixed Traffic Flows in a Connected and Autonomous Vehicle Environment</title>
      <link>https://trid.trb.org/View/1571382</link>
      <description><![CDATA[Traffic demand has grown rapidly over the past decades around the world, which leads to severe traffic congestion problems. Congestion has numerous negative effects, such as wasting time of drivers and passengers, increasing delays, decreasing travel time reliability, wasting fuel, and increasing air pollution and greenhouse gas (GHG) emission. In addition, when congestion occurs, the variation in speeds and headways between vehicles might lead to longer queues, longer travel time on the highways, higher accident possibilities and more frustrated drivers. In conclusion, traffic congestion is detrimental to the operational efficiency as well as travelers’ safety. In order to relieve highway congestion, the state departments of transportation (DOTs) have been seeking new ways to satisfy the increasing demand and make full use of the infrastructure resources. Thus, some ad hoc traffic management strategies have been developed and deployed by the state DOTs so that the existing roadway resources can be fully optimized. Among different types of traffic management strategies, active traffic management (ATM) is a scheme that can be used for relieving congestion and improving traffic flow on the highways. Among the ATM strategies, variable speed limit (VSL) control has been implemented around the world (e.g., Germany, the United Kingdom, and the United States). VSL control systems are deployed to relieve freeway congestion, improve safety, and/or reduce the emission of greenhouse gases and fuel consumption under different situations. Moreover, with the development of emerging technologies, various novel methods on the basis of the intelligent transportation systems have been developed in recent years. Connected autonomous vehicle (CAV) belongs to such technology. The CAVs integrate vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) and infrastructure-to-vehicle (I2V) communication into control systems. The existing research efforts proved that enhanced performances could be achieved using CAV technologies. The research intends to systematically develop a VSL control framework in a CAV environment, in which the V2V, V2I, I2V, and platooning technologies are integrated with the VSL control. In addition, mixed traffic flows (including trucks and cars) are taken into account in the developed VSL control models. The policies (such as left-lane truck restriction policy) that are used to reduce the impacts of trucks on cars and CAV technologies (e.g., vehicle platooning) integrated with VSL control are explored. Multi-objective optimization models are formulated. In terms of the discrete speed limit values in the real world, discrete optimization techniques, such as genetic algorithm (GA) and tabu search (TS), are employed to solve the optimization control models. Different scenarios are designed to compare the control results. Sensitivity analyses are presented, and comprehensive characteristics underlying the VSL control are discussed in detail. Summary and conclusions are made, and further research directions are also given.]]></description>
      <pubDate>Sun, 09 Dec 2018 21:51:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/1571382</guid>
    </item>
    <item>
      <title>Analyzing Travelers’ Response to Different Active Traffic Management (ATM) Technologies</title>
      <link>https://trid.trb.org/View/1563606</link>
      <description><![CDATA[Maryland Department of Transportation State Highway Administration (MDOT SHA) is investigating innovative solutions to improve mobility with the help of the Active Traffic Management (ATM) technologies. Developing appropriate modeling and simulation tools with the capability of analyzing traffic pattern, travel demand, and traveling/driving behavior responses along the most critical corridors in Maryland are the prerequisite to implementing any ATM strategies. During the past ten years, MDOT SHA has successfully developed several effective modeling tools for traffic operations, dynamic traffic simulation, planning analysis, and travel demand forecasting, in collaboration with the University of Maryland. The Coordinated Highway Action Response Team (CHART) at MDOT SHA has integrated dynamic traffic monitoring, traveler information, weather information, and agency updates into their real-time operations and incident/emergency responses. In this project, the Maryland Transportation Institute (MTI) research team has developed an integrated travel behavior and dynamic traffic assignment modeling tool and adapted the tool for real-time and dynamic analysis of active traffic management (ATM) strategies. The model is fully calibrated and validated using disaggregated data collected in the base year of 2015, including hourly traffic counts, corridor-level travel times aggregated in 15-minute intervals, individual vehicle trajectories, and energy consumption at the trip-level. A series of performance measures has been developed and employed to assess the effectiveness of each ATM strategy and evaluate the combined effect. Travel behavioral responses are also modeled and measured, including the departure time responses and the driving behavior.]]></description>
      <pubDate>Wed, 31 Oct 2018 17:22:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/1563606</guid>
    </item>
    <item>
      <title>A Planning Tool for Active Traffic Management Combining Microsimulation and Dynamic Traffic 
 Assignment (FHWA 0-6859-1)</title>
      <link>https://trid.trb.org/View/1565640</link>
      <description><![CDATA[Active traffic management (ATM) strategies have been considered as a tool for congestion mitigation in the last few decades. They rely on real-time traffic observations to regulate the flow of traffic. This research focuses on developing tools for evaluating the effectiveness of ATM strategies for freeway corridors. The research efforts can be categorized into two parts. The first part performs a detailed microsimulation analysis for four ATM strategies commenting on their effectiveness under cases of recurring and non-recurring congestion and develops a hybrid microsimulation-DTA (dynamic traffic assignment) model to capture the combined microscopic and network-level impacts of an ATM strategy. The second part develops spreadsheet tools which are useful to predict effectiveness of an ATM strategy under different levels of data availability. Ramp metering, variable speed limits, and hard shoulder running are found effective on the Williamson County test network, whereas dynamic ramp control and freeway arterial coordinated operations do not lead to any significant improvement. The authors also find that ATM strategies can improve the performance over a corridor while simultaneously reducing the performance of frontage roads due to spillover effects. The authors' findings also indicate that a hybrid microsimulation-DTA model is useful for an accurate analysis. However, based on the network characteristics, changes in route choice patterns may/may not be significant. The regression models used in the spreadsheet tool in the second part provide a good fit to the simulation results and thus can be used as an initial tool for testing effectiveness of ATM strategies during planning stage.]]></description>
      <pubDate>Wed, 31 Oct 2018 16:27:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/1565640</guid>
    </item>
    <item>
      <title>Evaluation of the Impact of the I-66 Active Traffic Management System: Phase II</title>
      <link>https://trid.trb.org/View/1564447</link>
      <description><![CDATA[In early 2013, construction began on a Virginia Department of Transportation (VDOT) project to install an Active Traffic Management (ATM) system on I-66 from US 29 in Centreville to the Capital Beltway (I-495). Construction was completed in September 2015. This project was intended to improve safety and operations on I-66 without physically expanding the roadway through better management of the existing facility. The main components of the installed system included advisory variable speed limits (AVSL), lane use control signals (LUCS), and hard shoulder running (HSR). In 2016, the Virginia Transportation Research Council completed a Phase I evaluation of the system, covering its first 5 months of operation. A before-after study to quantify the effectiveness of the system was performed using “after” data from October 2015–February 2016 (21 weeks) for the operational analysis and data from October 2015–December 2015 (13 weeks) for the safety analysis. Since the operational and safety analyses were performed using limited amounts of data, the results were preliminary. The analysis showed several benefits attributable to dynamic HSR, but only 1.5 months of data were available with the AVSL active. In Phase II, the project was expanded to evaluate the long-term effects of the I-66 ATM system. For this phase, data from October 2015–November 2017 were used for the operational analysis and data from October 2015–December 2016 were used for the safety analysis. The operational measures of effectiveness were the same as for Phase I and included the ATM utilization rate, average travel time, and travel time reliability. In order to evaluate the safety impacts, the empirical Bayes method was used with safety performance functions developed for Virginia. Segment-level analysis was performed to determine the segments that had benefited the most from the implementation of the ATM system. From this segment-level analysis, it was determined that HSR was the ATM component that created most of the improvements on I-66. The operational analysis showed that travel time improved significantly during off-peak hours after the ATM system was activated but that travel time during peak periods in the peak direction of travel generally did not improve. Further analysis revealed that most of these improvements occurred on the sections with HSR. The safety evaluation showed 6%, 10%, and 11% reductions in total (all severity), multiple-vehicle (all severity), and rear-end (all severity) crashes, respectively. Segment-level analysis again showed that the most safety benefits were found for locations with HSR (crash reductions of 25% to 40%), and no statistically significant reductions were found for sections with only AVSL and LUCS. The results of the analysis showed that HSR could produce statistically significant operational and safety benefits but that the effects of other ATM components were more limited. The study recommends that VDOT’s Operations Division and regions use the results from I-66 to inform decisions about future ATM and HSR use in Virginia.]]></description>
      <pubDate>Tue, 30 Oct 2018 17:31:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/1564447</guid>
    </item>
    <item>
      <title>Developing an algorithm to assess the rear-end collision risk under fog conditions using real-time data</title>
      <link>https://trid.trb.org/View/1514463</link>
      <description><![CDATA[This study aims to propose a new algorithm to evaluate the rear-end collision risk under fog conditions considering reduced visibility. The proposed algorithm compares the safe stopping distance of the leading and following vehicles. According to the relationship between clearance distance between the two consecutive vehicles and visibility distance, the car-following maneuver is divided into different situations and the algorithms to calculate the safe stopping distances are suggested correspondingly. The visibility distance is collected by a new visibility detection system with adaptive learning modules and the clearance distance is obtained from a vehicle-based detector. By comparing the safe stopping distances of the following and leading vehicles, the potential rear-end collision can be identified. Subsequently, statistical tests are conducted to analyze rear-end collision risk and compare the different impact of reduced visibility on the collision risk for different vehicle types and lanes. Furthermore, random parameters logistic and negative binomial models are estimated by using individual vehicle data and aggregated traffic flow data, respectively, in order to explore the relationship between the potential rear-end crash and the reduced visibility together with other traffic parameters. The results suggest that the proposed algorithm works well in evaluating rear-end collision risk under fog conditions. It is found that reduced visibility has significant impact on the rear-end collision risk and the impact vary by the different vehicle types and by lane. Further, it is concluded that the driving maneuver of the leading and following vehicles can affect the rear-end collision risk. It is expected that the proposed algorithm can be implemented in a Traffic Management context to improve road safety under fog conditions. Specifically, it is suggested to implement the proposed algorithms in real-time and integrate it with Intelligent Transportation Systems (ITS) technologies such as Variable Speed Limit (VSL) and Dynamic Message Signs (DMS) to enhance traffic safety when the visibility declines. This car following algorithm could also be extended to adapt for the advent of Connected Vehicles in Fog conditions.]]></description>
      <pubDate>Tue, 12 Jun 2018 16:38:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/1514463</guid>
    </item>
    <item>
      <title>Active Traffic Management (ATM) Implementation and Operations Guide</title>
      <link>https://trid.trb.org/View/1498252</link>
      <description><![CDATA[Today, most agencies have levels of operational capability, detection, and information dissemination mechanisms that would have been unimaginable two decades ago. As a result, agencies are able to leverage these resources and capabilities through the application of a wide variety of approaches to improve mobility and safety. However, agencies continue to face the challenges of changing travel patterns, growing demand, evolving traveler behaviors, limited resources, and increasing traveler expectations. Active transportation and demand management (ATDM) is an agency’s capability to improve trip reliability, safety, and throughput of the surface transportation system by deploying operational strategies that dynamically manage and control travel and traffic demand and available capacity, based on prevailing and anticipated conditions. The objective of this Guide is to provide regional and local agencies the guidance on how to strategically and effectively implement and operate ATM strategies. The Guide describes the stepwise approach to accomplishing this implementation through the application of the system engineering process; comprehensive planning; and organizational considerations, capabilities, and design considerations. It utilizes a combination of relevant existing resources and documents along with best practices and lessons learned gleaned from early adopters to offer practical guidance. It also emphasizes the value of ATM and what these strategies can offer to operating agencies as part of their broader Transportation Systems Management and Operations (TSMO) program. The intended audience(s) of the Guide includes agencies interested in implementing ATM in their region, as well as agencies that have implemented ATM and are interested in guidance on operating their ATM systems and strategies more effectively]]></description>
      <pubDate>Wed, 24 Jan 2018 09:11:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/1498252</guid>
    </item>
    <item>
      <title>Sensitivity analysis of the mainline travel lane pavement service life when utilizing part-time shoulder use with full depth paved shoulders</title>
      <link>https://trid.trb.org/View/1497446</link>
      <description><![CDATA[Part-time Shoulder Use (PTSU) is an Active Traffic Management (ATM) strategy that utilizes the shoulder as an additional travel lane during high congestion periods to temporarily increase roadway capacity. This strategy has become increasingly popular on freeways around major metropolitan areas in the United States (U.S.) and Europe. This research focused on freeways near major metropolitan areas where the roadways are structurally sound, but widening roadways has become increasingly expensive to implement. These areas typically utilize full-depth paved shoulders, defined as shoulders built to the same pavement structural profile as the mainline travel lanes and structurally sufficient to carry heavy vehicle loading. As a result of PTSU, the load repetitions on the mainline travel lanes can be reduced for multiple hours during each day by shifting vehicles to the shoulder and this load reduction may indirectly increase the mainline pavement’s service life. This research explored the potential benefits of PTSU based on a variety of conditions, including climate conditions, pavement types, and traffic loading levels. The results suggest that across all of these conditions an extension of 10–20% in service life can be expected for both flexible and rigid pavements in the mainline travel lanes.]]></description>
      <pubDate>Mon, 15 Jan 2018 10:59:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/1497446</guid>
    </item>
    <item>
      <title>Definition of a merging assistant strategy using intelligent vehicles</title>
      <link>https://trid.trb.org/View/1478059</link>
      <description><![CDATA[In the area of active traffic management, new technologies provide opportunities to improve the use of current infrastructure. Vehicles equipped with in-car communication systems are capable of exchanging messages with the infrastructure and other vehicles. This new capability offers many opportunities for traffic management. This paper presents a novel merging assistant strategy that exploits the communication capabilities of intelligent vehicles. The proposed control requires the cooperation of equipped vehicles on the main carriageway in order to create merging gaps for on-ramp vehicles released by a traffic light. The aim is to reduce disruptions to the traffic flow created by the merging vehicles. This paper focuses on the analytical formulation of the control algorithm, and the traffic flow theories used to define the strategy. The dynamics of the gap formation derived from theoretical considerations are validated using a microscopic simulation. The validation indicates that the control strategy mostly developed from macroscopic theory well approximates microscopic traffic behaviour. The results present encouraging capabilities of the system. The size and frequency of the gaps created on the main carriageway, and the space and time required for their creation are compatible with a real deployment of the system. Finally, the authors summarise the results of a previous study showing that the proposed merging strategy reduces the occurrence of congestion and the number of late-merging vehicles. This innovative control strategy shows the potential of using intelligent vehicles for facilitating the merging manoeuvre through use of emerging communications technologies.]]></description>
      <pubDate>Tue, 29 Aug 2017 10:07:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/1478059</guid>
    </item>
    <item>
      <title>Development of a Queue Warning System Utilizing ATM Infrastructure System Development and Field-Testing</title>
      <link>https://trid.trb.org/View/1473617</link>
      <description><![CDATA[MnDOT has already deployed an extensive infrastructure for Active Traffic Management (ATM) on I-35W and I-94 with plans to expand on other segments of the Twin Cities freeway network. The ATM system includes intelligent lane control signals (ILCS) spaced every half mile over every lane to warn motorists of incidents or hazards on the roadway ahead. This project developed two separate systems that can identify lane-specific shockwave or queuing conditions on the freeway and use existing ILCS to warn motorists upstream for rear-end collision prevention. The two systems were field tested at two locations in the ATM equipped network that have a high frequency of rear-end collisions. These locations experience significantly different traffic-flow conditions, allowing for the development and testing of two different approaches to the same problem. The I-94 westbound segment in downtown Minneapolis is known for its high crash rate due to rapidly evolving shockwaves while the I-35W southbound segment north of the TH-62 interchange experiences longstanding queues extending into the freeway mainline. The Minnesota Traffic Observatory developed the I-94 Queue Warning system while the University of Michigan, under contract, developed the I-35W system. Prior to the I-94 installation, based on data collected in 2013, there were 11.9 crashes per VMT and 111.8 near crashes per VMT. In the first three months of the system’s deployment, event frequency reduced to 9.34 crashes per million vehicle miles of travel (MVMT) and 51.8 near crashes per MVMT, a 22% decrease in crashes and a 54% decrease in near crashes. The I-35W system did not undergo a similarly thorough evaluation, but for most of the lane segments involved, it showed that queue warning messages help reduce the speed variance near the queue locations and the speed difference between upstream and downstream locations. This also implicated a satisfactory level of compliance rate from travelers.]]></description>
      <pubDate>Tue, 18 Jul 2017 17:13:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/1473617</guid>
    </item>
    <item>
      <title>Effectiveness of Predictive Weather-Related Active Transportation and Demand Management Strategies for Network Management</title>
      <link>https://trid.trb.org/View/1439570</link>
      <description><![CDATA[This paper presents the development, implementation, and evaluation of predictive active transportation and demand management (ATDM) and weather-responsive traffic management (WRTM) strategies to support operations for weather-affected traffic conditions with traffic estimation and prediction system models. First, the problem is defined as a dynamic process of traffic system evolution under the impact of operational conditions and management strategies (interventions). A list of research questions to be addressed is provided. Second, a systematic framework for implementing and evaluating predictive weather-related ATDM strategies is illustrated. The framework consists of an offline model that simulates and evaluates the traffic operations and an online model that predicts traffic conditions and transits information to the offline model to generate or adjust traffic management strategies. Next, the detailed description and the logic design of ATDM and WRTM strategies to be evaluated are proposed. To determine effectiveness, the selection of strategy combination and sensitivity of operational features are assessed with a series of experiments implemented with a locally calibrated network in the Chicago, Illinois, area. The analysis results confirm the models’ ability to replicate observed traffic patterns and to evaluate the system performance across operational conditions. The results confirm the effectiveness of the predictive strategies tested in managing and improving traffic performance under adverse weather conditions. The results also verify that, with the appropriate operational settings and synergistic combination of strategies, weather-related ATDM strategies can generate maximal effectiveness to improve traffic performance.]]></description>
      <pubDate>Wed, 15 Mar 2017 09:09:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/1439570</guid>
    </item>
    <item>
      <title>A V2I (Vehicle-to-Infrastructure) based Dynamic Merge Assistance Method based on Instantaneous Virtual Trajectories: A Microscopic Implementation of Gap Metering</title>
      <link>https://trid.trb.org/View/1439525</link>
      <description><![CDATA[Lane-changing activities near freeway merging, diverging, and weaving sections are one major contributing factor to recurrent bottleneck congestion. Active Traffic Management (ATM) technologies such as ramp metering, variable speed limit, queue warning, and dynamic merge control technologies have been proposed to mitigate bottleneck congestion through macroscopic control. The paper proposes a microscopic control solution, the Connected Vehicle (CV) Vehicle-To-Infrastructure (V2I)-based Dynamic Merge Assistance (DMA) method. The method analyzes vehicle trajectory data collected at CV roadside units (RSU) through Dedicated Short Range Communication (DSRC) or cellular communication and implements the “vehicle- gap” pairing, gap synchronization, maintaining, and approaching among mainline and onramp vehicles. The vehicle-gap pairing is determined by dynamically predicting the intersecting points between the “instantaneous virtual trajectories” (IVTs) of mainline and onramp vehicles. The IVTs are calculated according to the prevailing speed profiles on both auxiliary and through lane. Furthermore, Adaptive Cruise Control (ACC) models are proposed to control both the mainline gap maintaining between the putative leading and following vehicle and the ramp gap approaching of a merging vehicle. The proposed method is evaluated with a simulated network built and calibrated with field data collected on Interstate 35 at Austin, TX during morning peak hours. The proposed method is implemented by using VISSIM external driver model Application Programming Interface (API). The simulation results indicate the reduction on the average travel time at merge sections during congestion and increased time-to-collision (TTC) during merging compared with existing microscopic merge control models.]]></description>
      <pubDate>Wed, 15 Mar 2017 09:09:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/1439525</guid>
    </item>
    <item>
      <title>Comparison and Evaluation of Potential Technology Enhancements to School Bus Stop Ahead Signs</title>
      <link>https://trid.trb.org/View/1438233</link>
      <description><![CDATA[School Bus Stop Ahead Signs are commonly used by state departments of transportation (DOTs) to warn motorists of locations where school buses typically stop to load and unload children, predominantly at locations where there are horizontal or vertical sight distance issues. There are many technologies available and some others that are becoming more readily available that can provide an active warning of a school bus stop only during times when a school bus stop is being used. The paper provides a summary of current practice and potential new implementations based on thirteen potential technologies used for sensing, as well as four types of warning devices. An evaluation of potential safety impacts, costs, and benefits was conducted to look at promising technologies. In addition, several considerations are described to provide practitioners some guidance when considering the use of an active warning system. Based on current costs and benefits, a Bluetooth-based system using flashing beacons to warn drivers of an upcoming school bus stop is the most cost effective option that has a high probability of success, but with rapid changes and availability of other technologies, there are other options that should be considered, particularly if the system were incorporated into a connected vehicle infrastructure.]]></description>
      <pubDate>Wed, 08 Mar 2017 09:07:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/1438233</guid>
    </item>
    <item>
      <title>Evaluation of Operational Effects of I-66 Active Traffic Management System</title>
      <link>https://trid.trb.org/View/1437821</link>
      <description><![CDATA[In September 2015, the Virginia Department of Transportation instituted an active traffic management system on I-66 in Northern Virginia. I-66 is a major commuter route into Washington, D.C., that experiences significant recurring and nonrecurring congestion. The active traffic management system sought to manage existing capacity dynamically and more effectively with hard shoulder running, advisory variable speed limits, lane use control signs, and queue warning systems. An initial before-and-after analysis of the system’s operational effectiveness was performed with probe-based travel time data from the provider, INRIX, and used records from the active traffic management’s traffic operations center. On weekdays, statistically significant improvements were often observed during off-peak periods, but conditions did not improve during peak periods. Weekends showed the greatest improvements, with travel times and travel time reliability measures improving by 10% to 14%. Segment-level analysis revealed that most of the benefits were attained because of the use of hard shoulder running outside of the peak periods, which created additional capacity on I-66. Benefits due to advisory variable speed limits were inconclusive because of limited data.]]></description>
      <pubDate>Mon, 27 Feb 2017 09:27:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/1437821</guid>
    </item>
    <item>
      <title>Transportation Systems Management and Operations and Performance Based Practical Design CASE STUDY 5: Demonstrating Performance-Based Practical Design through Analysis of Active Traffic Management</title>
      <link>https://trid.trb.org/View/1445975</link>
      <description><![CDATA[As states and local agencies become increasingly challenged with addressing their system performance, mobility, and safety needs in the current era of financial limitations, Federal Highway Administration (FHWA) is providing guidance, delivering technical assistance, and sharing resources related to performance-based practical design (PBPD). The FHWA Office of Operations is supporting the overall Agency PBPD effort by highlighting the role transportation systems management and operations (TSMO) alternatives and analysis tools can play in supporting PBPD. To illustrate the range of TSMO strategies and tools and how they can be applied by transportation planners and designers in a PBPD context, five case studies were developed. This Case Study 5 illustrates how a PBPD approach be used to analyze and make tradeoffs when examining potential Active Traffic Management (ATM) strategies along freeways, as was done in developing ATM recommendations, a Concept of Operations and an ATM Implementation Plan for the New Jersey Department of Transportation (NJDOT).]]></description>
      <pubDate>Tue, 31 Jan 2017 10:51:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/1445975</guid>
    </item>
    <item>
      <title>Move over one</title>
      <link>https://trid.trb.org/View/1426370</link>
      <description><![CDATA[The effectiveness of Intelligent Lane Control Signs (ILCS) in Minneapolis is discussed in this article. ILCS is the centerpiece of Minnesota's Smart Lanes - the state's version of Active Traffic Management (ATM) strategies, which have been deployed in cities throughout the world in order to deal with highway congestion and safety. ICLS has been implemented on I-35W and I-94, the two busiest freeways in Minneapolis and St Paul. At the time of its installation, no other U.S. city had anything similar, and it consists of one full-matrix electronic sign over each lane of traffic, spaced approximately every half-mile. Lane-specific real-time information can be provided by the ILCS in order to help motorists make informed decisions. Details of the planning and implementation of this system for incident management are presented here.]]></description>
      <pubDate>Fri, 21 Oct 2016 16:32:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/1426370</guid>
    </item>
  </channel>
</rss>