1. Snow and Ice Removal Route Optimization
Ben Blandford, PhD
Erin Lammers, EIT
Eric Green, PhD, PE

2. Background KYTC Snow and Ice Removal Program: Project goal:
Responsible for more than 63,000 lane miles of roads across 120 counties
Typically expends $40-$80 million/year
Utilizes both state truck routes and contract routes
Project goal:
Investigate the extent to which GIS analytical tools could be used to optimize the system
System optimization would:
Improve mobility
Make roads safer
Save time and money

3. Research Methods
ESRI ArcMap tools: Network Analyst/Vehicle Routing Problem
Network-based spatial analysis tools for solving complex routing problems
Goal is to best service “orders” and minimize the overall operating cost for a fleet of vehicles
“Orders” adapted as deliveries of salt to the road
Iterative process: altering the constraints a little can lead to very different results
Routing issues
Desire for most efficient routes?
Or desire for most realistic/efficient routes?
Input from local transportation officials is critical to producing meaningful results

4. Factors Included in Modeling Process
Route priority
Interstates and parkways treated every hour
Other A routes treated every 2 hours
B routes treated every 4 hours
C routes treated every 8 to 16 hours
Salt capacity of trucks
Location of salt storage facilities
Plow widths and lane widths
Multiple lanes on routes
Truck travel speed
Simplifying as much as possible
Input from drivers treating the routes

5. Route Trees
Addresses the problem of needing to structure truck routes simply
Structures each route around a central high priority road
Each time that road is treated, truck also treats a subset of the adjacent roads
Drivers become familiar with their areas and routes
Also makes it easier for drivers to address inevitable complexities of “non-typical” winter storm
This allows drivers to work within a designated area, becoming familiar with the routes in that area and even taking ownership of those routes. This is a concept that we came up with. This allows us to be both realistic and efficient. Minimizes dead-head time.

6. Model Results
For two counties in District 6 (Grant and Pendleton), the model demonstrated the potential to eliminate the need for six contract trucks, which would result in a cost savings of $25,000 per truck per year.
Similar results were found for two counties in District 7 (Clark and Montgomery), where route optimization demonstrated the potential to eliminate the need for three contract trucks, with a similar cost savings of $25,000 per truck per year.
Within just these four counties then, there is a potential cost savings of $225,000 per year.

7. Implementation
Currently developing multi-county optimizations for all remaining districts in Kentucky
Preliminary results for District 7 show the potential to eliminate up to 23 contract truck routes (out of 93 total)
Preliminary results for District 6 show the potential to eliminate up to 18 contract truck routes (out of 67 total)
Preliminary results for District 3 show the need to add several more trucks