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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>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>MDSS Implementation Costs in Wisconsin</title>
      <link>https://trid.trb.org/View/2635951</link>
      <description><![CDATA[Maintenance Decision Support System (MDSS) technology is a tool initially developed using Federal Highway Administration (FHWA) funding. It is designed to integrate state-of-the-art weather forecasting and pavement modeling with an agency’s rules of practice for winter operations to generate recommended maintenance actions. The goal is to enhance winter maintenance decision making in order to provide more effective responses to winter weather events. This report attempts to quantify the costs associated with the deployment of the Pooled Fund Study MDSS in Wisconsin so that other state departments of transportation (DOTs) have an idea of the various levels of deployment possible and the costs associated with each.]]></description>
      <pubDate>Mon, 23 Feb 2026 16:30:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635951</guid>
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      <title>The Winter Maintenance Decision Support System (MDSS): Demonstration Results and Future Plans</title>
      <link>https://trid.trb.org/View/2036071</link>
      <description><![CDATA[Managing a winter maintenance program today is an increasingly complex endeavor. Just making sure that a plow blade is at the ready when the first flake falls is only a small part of the task. With tight budgets and the high expectation of the public for keeping roads clear of snow and ice, today’s maintenance manager has to be able to handle multiple tasks or risk getting behind the onslaught of winter weather. All of the regulations about chemical applications, environmental impacts and multiple, often contradictory weather forecasts can lead to information overload. The Federal Highway Administration (FHWA) recognized this potential problem in the late 1990’s. Generally speaking, there were plenty of weather forecasts, along with a few companies that issued road-specific forecasts, but there was a lack of linkage between the information available and the decisions made by winter maintenance managers. It was this weak link that became the genesis for the winter Maintenance Decision Support System (MDSS). The MDSS has since matured into a functional prototype. During the winter of 2002-2003, the prototype was deployed at several maintenance garages in central Iowa for a field demonstration. This paper will document the implementation of the demonstration, a summary of lessons learned, verification statistics, and technology transfer activities. It will also describe plans for a longer, more comprehensive demonstration during the winter of 2003-2004.]]></description>
      <pubDate>Wed, 19 Oct 2022 16:36:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2036071</guid>
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    <item>
      <title>The Federal Highway Administration's Maintenance Decision Support System Project: Summary Results and Recommendations</title>
      <link>https://trid.trb.org/View/2036072</link>
      <description><![CDATA[The U.S. Federal Highway Administration (FHWA) Office of Transportation Operations Road Weather Management (RWM) program began a project in fiscal year 1999 to develop a prototype winter road Maintenance Decision Support System (MDSS). The MDSS capabilities are based on feedback received by the FHWA in 2001 from maintenance managers at a number of State Departments of Transportation (DOTs) as part of an initiative to capture surface transportation weather decision support requirements.  The MDSS project goal is to seed the implementation of advanced decision support services provided by the private sector for state DOTs. This has been achieved by developing core software capabilities that serve as a basis for these tailored products. After the 2001 user needs assessment was completed, the MDSS program was extended with the objective of developing and demonstrating a functional prototype MDSS. A field demonstration of the prototype MDSS occurred in Iowa between February and April 2003, and a second field demonstration was conducted during the winter of 2004.  The performance of the prototype MDSS was much improved during the second demonstration. The weather and road condition predictions were more accurate and the treatment recommendations generated by the system were reasonable given the predicted conditions. Iowa garage supervisors actively considered the treatment guidance and on occasion they successfully used the recommended treatments without modification.  This paper describes the status of the MDSS project, results and lessons learned from the field demonstrations, and future development efforts.]]></description>
      <pubDate>Wed, 19 Oct 2022 16:36:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2036072</guid>
    </item>
    <item>
      <title>Maintenance Decision Support System: Pilot Study and Cost-Benefit Analysis (Phase 2.5)</title>
      <link>https://trid.trb.org/View/1488764</link>
      <description><![CDATA[This project focused on several tasks: development of in-vehicle hardware that permits implementation of a Maintenance Decision Support System (MDSS), development of software to collect and process road and weather data, a cost-benefit study, and pilot-scale implementation. Two Automatic Vehicle Location (AVL) systems were developed at the University of Nevada, Reno (UNR) for the Nevada Department of Transportation (NDOT): one for rural Nevada and one for urban environments where cellular phone data are available. For rural areas a system was developed that relies on the statewide Enhanced Digital Access Communications Systems (EDACS) radio system for sending/receiving data. The system developed for urban areas currently uses a Droid cell phone to transmit data. No driver interaction is needed in either system, which avoids distracted driving concerns. Both systems communicate with a central server located at UNR. Using both snow plows and light duty trucks, a total of 37 vehicle installations were completed (not including two installations at UNR). A survey of other DOTs' experiences with MDSS and interviews with 3 MDSS service providers were also completed. Most DOTs have one of two goals for their MDSS: 1) to increase the level of service while using the same resources or 2) maintaining the same level of service using fewer resources. The authors' conclusion is that if NDOT were to aim for maintaining the same level of service it can expect to see substantial savings if they choose to implement MDSS. With an annual salt/sand budget of about $2.6 million, NDOT can expect to save between $520k to $1 million annually. The authors' research has also identified several potential impediments to implementing an MDSS in Nevada. The most pressing issue is related to data communication in rural Nevada. It is envisioned that two types of AVL systems will be needed, one that makes use of the Statewide radio network and another that uses mobile (cellular) data. An AVL system that seamlessly switches between modes would be desirable. Both systems will need to safely provide snowplow drivers with updated treatment plans without distracting the operator. The pilot-scale systems developed can serve as templates for eventual vendor Requests for Proposals (RFPs). The second issue that could hinder implementation of an MDSS is Nevada’s Material Management System (MMS). Because it is currently difficult for NDOT personnel to accurately report where along the route material is being used, it would be difficult to determine where MDSS benefits/losses are being realized. Thirdly, building trust by NDOT personnel in MDSS recommendations may be difficult. Nevada’s many microclimates in the northern region of the state are attributed to making it difficult for making accurate weather predictions. If the MDSS has inaccurate weather forecasts, then the treatment recommendations it provides will not engender any trust in the supervisors consulting the system. Finally, the MDSS community, which includes many state DOTs and private vendors, have to deal with intellectual property (patent) issues. The authors' analysis indicates that NDOT/UNR is not infringing on any of the claims in any of the seven patents reviewed. However, the group of patents issued to Concaten and Iwapi remains a concern for all DOTs using an MDSS and all providers of MDSS solutions. Nevada cannot proceed to implement an MDSS without developing a plan to address the intellectual property issues.]]></description>
      <pubDate>Mon, 27 Nov 2017 16:35:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/1488764</guid>
    </item>
    <item>
      <title>Maintenance Decision Support System: Pilot Study and Cost-Benefit Analysis (Phase 2)</title>
      <link>https://trid.trb.org/View/1488363</link>
      <description><![CDATA[This project focused on several tasks: development of in-vehicle hardware that permits implementation of a maintenance decision support system (MDSS), development of software to collect and process road and weather data, a cost-benefit study, and pilot-scale implementation. Two Automatic Vehicle Location (AVL) systems were developed at the University of Nevada, Reno (UNR) for the Nevada Department of Transportation (NDOT): one for rural Nevada and one for urban environments where cellular phone data are available. For rural areas a system was developed that relies on the statewide Enhanced Digital Access Communications Systems (EDACS) radio system for sending/receiving data. The system developed for urban areas currently uses a Droid cell phone to transmit data. No driver interaction is needed in either system, which avoids distracted driving concerns. Both systems communicate with a central server located at UNR. Using both snow plows and light duty trucks, a total of 37 vehicle installations were completed (not including two installations at UNR). A survey of other DOTs' experiences with MDSS and interviews with 3 MDSS service providers were also completed. Most DOTs have one of two goals for their MDSS: 1) to increase the level of service while using the same resources or 2) maintaining the same level of service using fewer resources. The authors' conclusion is that if NDOT were to aim for maintaining the same level of service it can expect to see substantial savings if they choose to implement MDSS. With an annual salt/sand budget of about $2.6 million, NDOT can expect to save between $520k to $1 million annually. The authors' research has also identified several potential impediments to implementing an MDSS in Nevada. The most pressing issue is related to data communication in rural Nevada. It is envisioned that two types of AVL systems will be needed, one that makes use of the Statewide radio network and another that uses mobile (cellular) data. An AVL system that seamlessly switches between modes would be desirable. Both systems will need to safely provide snowplow drivers with updated treatment plans without distracting the operator. The pilot-scale systems developed can serve as templates for eventual vendor RFPs. The second issue that could hinder implementation of an MDSS is Nevada’s Material Management System (MMS). Because it is currently difficult for NDOT personnel to accurately report where along the route material is being used, it would be difficult to determine where MDSS benefits/losses are being realized. Thirdly, building trust by NDOT personnel in MDSS recommendations may be difficult. Nevada’s many microclimates in the northern region of the state are attributed to making it difficult for making accurate weather predictions. If the MDSS has inaccurate weather forecasts, then the treatment recommendations it provides will not engender any trust in the supervisors consulting the system. Finally, the MDSS community, which includes many state DOTs and private vendors, have to deal with intellectual property (patent) issues. The authors' analysis indicates that NDOT/UNR is not infringing on any of the claims in any of the seven patents reviewed. However, the group of patents issued to Concaten and Iwapi remains a concern for all DOTs using an MDSS and all providers of MDSS solutions. Nevada cannot proceed to implement an MDSS without developing a plan to address the intellectual property issues.]]></description>
      <pubDate>Mon, 20 Nov 2017 16:45:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/1488363</guid>
    </item>
    <item>
      <title>Plug-and-Play Initiative: Phase II</title>
      <link>https://trid.trb.org/View/1459700</link>
      <description><![CDATA[Clear Roads has undertaken an initiative to establish a Plug-and-Play protocol that fosters interoperability between various winter maintenance equipment and a point location (central office). This report documents the data types and communication methods to allow communicate between the plow equipment and a point location. By implementing concepts discussed in this report, winter maintenance agencies will strive to allow interoperation between various equipment vendors through the use of open protocol standards like the National Transportation Communications for Intelligent Transportation System Protocol (NTCIP) and Maintenance Decision Support System (MDSS). Additionally, they will gain the ability to interoperate between agencies by transmitting data in an open and consistent format. The project included a literature review, an agency survey, freight industry survey, and the development of a list of data types to be considered for inclusion in the Plug-and-Play standard. Currently, no single protocol addresses all the needs identified and it was deemed to be not feasible to incorporate such needs into any given protocol. Instead, it is recommended to implement a “family” of open standards that include appropriate data types.]]></description>
      <pubDate>Fri, 28 Apr 2017 10:42:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/1459700</guid>
    </item>
    <item>
      <title>A Benefit-Cost Analysis Toolkit for Road Weather Management Technologies</title>
      <link>https://trid.trb.org/View/1306371</link>
      <description><![CDATA[Highway agencies face increasing demands and customer expectations regarding mobility and safety during inclement weather, while confronting unprecedented budget and staffing constraints and a growing awareness of environmental challenges related to the use of chemicals and abrasives. To address these different issues, benefit-cost analysis is typically employed before making investment decisions. In the winter maintenance context, where the costs and benefits of road weather management tools can vary greatly, this presents a significant challenge to managers. This work presents an overview of a new benefit-cost analysis tool developed for winter maintenance practitioners to evaluate different equipment, operations and materials, including road weather management tools, in a streamlined, web-based environment. The toolkit was used to conduct two case studies of the use of Road Weather Information Systems (RWIS) and Maintenance Decision Support Systems (MDSS) for road weather management. The first case study examined the current use of RWIS statewide in Iowa and found that it produced an agency-specific benefit-cost ratio of 3.8 and a total ratio of 45.4. A second case study examined the potential use of MDSS in one subdistrict in Indiana found that a benefit-cost ratio for the agency would be 1.5, while the total ratio would be 3.0.]]></description>
      <pubDate>Wed, 28 May 2014 15:22:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/1306371</guid>
    </item>
    <item>
      <title>Road Weather Information System (RWIS) Statewide Implementation Plan</title>
      <link>https://trid.trb.org/View/1264504</link>
      <description><![CDATA[The road weather information system (RWIS) network is a collection of environmental sensor stations (ESS), which gives state departments of transportation (DOTs) unprecedented access to detailed, accurate, timely, and roadway-relevant weather information to effectively and efficiently promote safety, mobility and productivity in the face of weather-related challenges. ESSs currently installed across the United States are providing valuable road weather data to the DOTs, which have been integrated into winter maintenance decision support systems (MDSS) to assist maintenance managers about road treatments, such as salting, plowing, or a combination of approaches. The New York State Department of Transportation (NYSDOT) has a very limited network of RWIS stations in various states of disrepair. A comprehensive plan is needed to upgrade (if appropriate) and expand the existing network to better serve current operational needs and support a future MDSS. The objective of this project is to develop a detailed plan for deploying a statewide RWIS to support both current NYSDOT operations and future use MDSS applications. To develop the RWIS implementation plan, various information and data sources shall be visited, including the current condition of NYSDOT's RWIS network, potential RWIS station sites, data needed for supporting statewide MDSS applications, and NYS meteorological zones. A GIS-based model shall be developed to optimize the RWIS network, considering contiguous segments having similar maintenance requirements by associating them with features that can affect the degree of required road maintenance (e.g. topography, meteorology, traffic, etc.). Optimally, the RWIS network shall allow winter road maintenance agencies to make decisions based on accurate and timely weather information, which ultimately leads to a higher level of service and reduced weather-related congestion delay and accidents, reduced cost, redundancy and environmental/ecological impacts, more efficient use of manpower, contractor services, fleet, and asset management, and increased accountability resulting in more prudent and efficient spending. It is expected that the efficient and effective RWIS network used in conjunction with a future NYSDOT MDSS model will achieve significant operational savings while maintaining acceptable levels of service, particularly in the winter months.]]></description>
      <pubDate>Tue, 08 Oct 2013 01:01:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/1264504</guid>
    </item>
    <item>
      <title>Cost benefits in Deploying a Fully Operational Maintenance Decision Support System (MDSS)</title>
      <link>https://trid.trb.org/View/1253641</link>
      <description><![CDATA[During the 2008-2009 winter season, the Indiana Department of Transportation (INDOT) conducted a state-wide deployment of a Maintenance Decision Support System (MDSS) into their winter operations strategy. Similar to other agencies, INDOT was looking for the opportunity to bring a research project into operations and provide tangible cost savings for their winter maintenance budgets. MDSS was poised to fill this need; so, with the support from upper management, the state embarked on a full deployment of the MDSS technology as a cost saving package and a cornerstone of their winter maintenance philosophy. Large amounts of road and weather data are collected for the MDSS to perform optimally and the resulting answers from MDSS could be used in an agency’s Traffic Management Center (TMC) as well. The information in MDSS would provide known areas where weather is impacting the roads, current status of an agency’s snow fighting vehicles, and forecasted locations of where hazardous road conditions may be encountered by the traveling public. This information could and should be used to inform the travelling public of: locations of snow fighting equipment (including a specific lane and direction), locations where weather (especially winter weather) may be impacting traffic, and actual road conditions (icy, wet, dry, etc.). This paper will address the lessons learned during the Indiana MDSS Implementation, and how those lessons can be applied to other system implementations within an agency. The paper also addresses the possible uses of data within MDSS for the ITS arena, including suggestions on how to integrate the information available from MDSS into TMC operations.]]></description>
      <pubDate>Thu, 27 Jun 2013 09:17:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/1253641</guid>
    </item>
    <item>
      <title>Decision Support Technologies: Beyond Winter Operations</title>
      <link>https://trid.trb.org/View/1138906</link>
      <description><![CDATA[Since the late 1990s there has been a push to develop and deploy a system that can integrate maintenance and weather data for winter maintenance operation decision makers to conduct safe and effective maintenance strategies on the roadway. In 2002, a group of states joined together [Pooled Fund Study Maintenance Decision Support System (MDSS)] to develop a MDSS that could augment current winter operation techniques with weather information and provide a one-stop shop for decision makers regarding snow and ice. As states deployed MDSS technology into their maintenance operations, their benefits included actual dollars saved, improved internal communications, and a more consistent level of service on the roadway. A new question arose as these benefits were realized: Are there more benefits to be realized with this data, and could it be integrated with other weather-based type operations that occur throughout the year? As the understanding of MDSS technology has grown so has the realization of MDSS’ utility beyond winter maintenance operations. One of the new uses of MDSS data is expanding the route-specific recommendations to non-wintertime operations performed by agencies. These operations include but are not limited to pavement repair, pavement preservation, lane and intersection marking, herbicide spraying, or any other highway operation that is dependent on the weather and road conditions. Each operation has specific weather thresholds that will restrict or not restrict that specific operation to occur. Accurate weather forecast information is critical because many non-wintertime operations projects can be completely ruined or ineffective if precipitation occurs or if temperatures or moisture levels fluctuate quicker than expected. The combination of improved weather forecasts with acceptable operating weather thresholds for non-winter maintenance activities provide agencies a larger cost benefit when deploying a fully operational MDSS within their agency. This paper and presentation will investigate and illustrate possible uses of MDSS data and current methods to communicate that data. Examples will be presented assessing the use of MDSS for non-wintertime operations decisions.]]></description>
      <pubDate>Fri, 25 May 2012 10:32:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/1138906</guid>
    </item>
    <item>
      <title>Present and Future of Practical Road Weather Decision Support Research</title>
      <link>https://trid.trb.org/View/1138905</link>
      <description><![CDATA[Maintaining control of snow and ice buildup on roadway surfaces during winter storms is challenging for road maintenance entities. Some of the critical challenges include making effective and efficient decisions for treatment types and timing of treatments, and knowing the location of greatest impact to the roadway based on precipitation rates and types and other weather conditions. These decisions are critical because of the implications to roadway safety, as well as economic impacts to the agency and the environmental impacts of treatments. In order to mitigate the challenges associated with winter road maintenance, the FHWA under the United States Department of Transportation (DOT) initiated the development of the Maintenance Decision Support System (MDSS) in 1999. MDSS provides a single platform that blends existing road and weather data sources with numerical weather and road condition models in order to provide information on the diagnostic and prognostic state of the atmosphere and roadway (with emphasis on the 1- to 72-h lead time period) as well as a decision support tool for roadway maintenance treatment options. MDSS was engineered with a robust modular design. The flexibility of this design allows for smaller sections of the system to be modified without having to redesign the entire system. Porting MDSS to a decision support system for non-wintertime weather impacts (visibility, flooding, non-winter maintenance, etc.) to the roads is being considered as the next step for this system. Also, customizing modules for other non-DOT user groups (e.g., emergency medical services, school planning, and fleet trucking companies), which are also highly impacted by adverse road weather, is a future direction for this research. This paper gives an overview of the most recent advancements to the federal prototype MDSS system as well as a discussion on the future direction of road weather decision support for not only the road maintenance community but also different user groups that may benefit from better and more useful road weather decision support.]]></description>
      <pubDate>Fri, 25 May 2012 10:32:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/1138905</guid>
    </item>
    <item>
      <title>Operational Blowing Snow Modeling: Benefits for the End User</title>
      <link>https://trid.trb.org/View/1138921</link>
      <description><![CDATA[Wintertime precipitation presents a maintenance decision maker with challenges on how to address the varying road conditions that inevitably occur. Many locations that respond to snow and ice also undergo excessive high wind conditions resulting in blowing snow. Along with freezing rain, blowing snow conditions can strike fear into any maintenance decision maker on how best to address blowing snow effects and whether chemical treatments are required. The need to know when blowing snow will be affecting the roadway, including timing and severity of the event, is critical for the maintenance decision maker. The process of modeling blowing snow within the roadway presents many challenges. These challenges include the ability to effectively determine the snow pack characteristics (e.g., snow pack surface tension, snow pack temperature, snow depth, snow age, and more), the analysis of weather conditions along the roadway, and surrounding roadway vegetation contributing to snow fetch distance. The use of snow pack model data allows for an assessment of snow pack conditions, depth, and temperature. This information is then integrated to provide locations where snow may be affected by the wind and to an extent given in snow mass flux. Once an analysis of snow pack conditions has been determined and route-specific weather information is integrated into the model, a forecast of blowing snow conditions can be produced on a route-by-route basis. This information is valuable to winter maintenance decision makers in the field. However, a mechanism for conveying this information is critical for reviewing and assessing the future risk. The integration of blowing snow information into a maintenance decision support system (MDSS) provides the needed integrated interface for users to access the information. The MDSS can provide users a route-by-route visualization of blowing snow risk along with the severity of the alert that will be affecting the roadway for an extended period into the future. The blowing snow model also provides MDSS information about blowing snow risks, which allows the system to provide precautionary recommendations to maintainers. This information can be invaluable when weather conditions, other than wind, seem to be mundane and nonthreatening, but blowing snow may still present serious maintenance issues. In this paper and presentation the modeling aspects of the blowing snow model will be presented and discussed. The integration of observations and model data will demonstrate how this integration is brought to the route level. A demonstration and case study approach will be shown on how the blowing snow model provides MDSS the information needed to alert maintenance managers of possible issues that may occur due to blowing snow. Also, information will be presented on the effectiveness of the model to assist MDSS in the suppression of deicing chemical when blowing snow is a concern on the roadway.]]></description>
      <pubDate>Fri, 25 May 2012 10:32:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/1138921</guid>
    </item>
    <item>
      <title>Assessment and Evaluation of Maintenance Decision Support System Recommendations</title>
      <link>https://trid.trb.org/View/1139463</link>
      <description><![CDATA[Since the late 1990s there has been an effort to develop and deploy a system integrating maintenance and weather data for winter maintenance operation decision makers to conduct safe and effective maintenance strategies on the roadway. In 2002, a group of state departments of transportation (DOT) created the Pooled-Fund Study (PFS) maintenance decision support system (MDSS) to develop a system that could augment current winter operation techniques and provide decision makers with a one-stop shop for road weather information. The primary objective was the integration of road, weather, maintenance actions, and traffic information to generate the most cost-effective maintenance recommendations for snow and ice control. The PFS MDSS solution was developed as a cooperative research program and has evolved into an operational decision support tool for the seventeen states that have participated in the program. As part of the ongoing evaluation of the program the DOT participants in this study have asked “Are DOT personnel who make the final maintenance treatment decisions using the recommendations; and, if not, why not?” An evaluation tool was developed to capture whether maintenance personnel accepted or rejected the MDSS recommendations during routine operations and, if they rejected the MDSS recommendations, why the MDSS recommendation was declined. This paper discusses how the evaluation tool was developed and the results from the evaluation. It addresses the process developed to assess how, when, and what recommendations would be evaluated and who would participate in the evaluation. Case studies were done on the road weather conditions concurrent with and prior to the evaluated recommendations to provide greater insight into the DOT selection regarding whether to use or not use the recommendation. The paper discusses the findings of these case studies and the complexities involved in assessing whether to honor an MDSS recommendation or use alternative maintenance actions.]]></description>
      <pubDate>Fri, 25 May 2012 10:32:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/1139463</guid>
    </item>
    <item>
      <title>Best Practices in Winter Maintenance</title>
      <link>https://trid.trb.org/View/1107500</link>
      <description><![CDATA[The purpose of the Winter Maintenance Scanning Tour was to seek out and observe the progress that state and local highway agencies are making in advancing today’s technology in the area of winter roadway maintenance. While this was the first tour in the United States, it was tailored after three previous scanning tours that had been conducted in European and Asian countries in 1994, 1998, and 2002. Much of what had been learned from these earlier international scans had become a new benchmark to several U.S. counterparts, inspiring them to pursue similar advances. The Winter Maintenance Scan was conducted from March 25 to April 7, 2009. The scanning team visited Minnesota, Colorado, Utah, Indiana, and Virginia. The focus areas included: 1. Maintenance Decision Support Systems (MDSSs);  2. Automatic Vehicle Location (AVL) Systems;  3. Equipment Technologies;  4. Training and Development;  5. Management Issues; and  6. Integration of Weather, Traffic, and Maintenance Operations.  Best practices were examined, and recommendations were made based on the findings of the scanning tour.]]></description>
      <pubDate>Mon, 25 Jul 2011 07:11:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/1107500</guid>
    </item>
    <item>
      <title>Maintenance Decision Support Systems: A Proven, Cost-effective Tool for State and Local DOTs</title>
      <link>https://trid.trb.org/View/930616</link>
      <description><![CDATA[When state or local departments of transportation (DOT) prepare for a snow or ice storm, they do more than operate snow plows. Timely decisions must be made on when to call in crews, whether overtime will be required, what kinds and how much material to load on trucks, whether and when to pretreat the roads, and how to optimize equipment assignments. During a storm event, supervisors also make ongoing tactical decisions based on a storm’s progress and the effectiveness of maintenance activities, all of which has immediate and vital consequences for the agency, the traveling public, and the environment. A proven, cost-effective tool called the Maintenance Decision Support System (MDSS) is increasingly being used by state and local maintenance departments for reliable and accurate decision-making under stressful weather and adverse roadway conditions.]]></description>
      <pubDate>Thu, 16 Sep 2010 12:42:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/930616</guid>
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