Mn/DOT Maintenance Decision Support System/Automated Vehicle Location Project Directions: Read the description of the Mn/DOT MDSS/AVL project below. In your group, determine what expertise would be needed on the implementation team in order to make sure the project implementation was successful.
Background: During winter months, officials at the DOTs and city governments around Minnesota keep tabs on forecasted weather data for possible snowfall. When snowfall is predicted, trucks are sent out to the roads to pre-wet them with liquid salt to prevent bonding between the pavement and the ice. After the snowfall, the snowplows clear the roads according to the priorities. All the management is manual, from choosing routes to assigning vehicles and personnel to determining the materials (e.g., salt, de-icing chemicals, etc.) and material amounts for each route. Most existing methods of snowplowing management, such as resource allocation, inventory management, and routing, are performed manually by the person or persons in charge of snow removal operations. These methods also tend to result in inefficient operations and less-than-optimal results and increase the costs involved in snow removal. •
A lengthier reaction time and a small margin of error during heavy storm conditions can prove deadly.
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Human-based methods are always prone to errors.
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The existing methods of snow removal do not substantially utilize any analytical tools before planning snowplowing operations to reduce costs and maximize overall effectiveness (e.g., shortest or quickest path from the snowplow station to the snowplow route; taking into consideration the cost of vehicle operation, availability of drivers or vehicles, change in weather conditions, etc.
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The existing methods do not use automated inventory management and control.
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Existing snow removal methods do not provide visual feedback about routing and resource allocation.
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The current methods do not use integrated weather information that could alert the snow removal crews and provide scenario-based decision support.
Innovation Description: This project implements significant advances in technology (both hardware and software) for Mn/DOT Maintenance using data about weather, road conditions, and equipment operations in both winter (snow and ice control) and summer maintenance (e.g., patching, striping, & spraying (PALS)). Minnesota along with 12 other states and FHWA, pooled funds to contract with Meridian Environmental Technology, Inc. to develop a Maintenance Decision Support System. This system uses forecasts, existing road conditions, principles of chemistry, physics of road surfaces, and deicing chemicals to recommend maintenance treatments and timings that will most efficiently maintain clear roads and regain service levels. Transportation departments’ deployment decisions would depend on the costs and benefits associated with the adoption of MDSS. The Western Transportation Institute at Montana State University used historical data as input into MDSS to create maintenance treatment scenarios. These scenarios were then compared to the actual material, equipment, and personnel used to determine the return on investment for the MDSS. MDSS and Automated Vehicle Location (AVL) are not directly dependent on one another; however, the two can be combined to provide significant benefits to both. Use of AVL speeds operator acceptance of MDSS technology and provides a more robust dataset for use in the benefit/cost study. Additionally, use during MDSS implementation affords AVL the chance to be studied in an operational setting. Anticipated Outcomes: Data are still being analyzed. Experiences gained through the development and deployment of the Federal Highway Administration’s MDSS Functional Prototype and the subsequent MDSS Pooled Fund Study product suggest these benefits are possible: •
Improved driver safety
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Reduced environmental impact
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Reduce material and equipment use
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More efficient scheduling of maintenance workers
Project Scope: A pilot project was conducted to determine the effectiveness and financial viability of implementing MDSS statewide. The pilot project used hardware and software to collect the plow position (up or down), pavement temperature, spreader (on or off), chemical being applied, and rate and vehicle location data.