Adaptive Route Planning Algorithm based on Historical Executions for Commercial Vehicle Fleets

This paper describes an algorithm that is supposed to reduce the differences between the theoretical planned routes of commercial vehicles and the practical execution of routes based on historical validated data. The resulted routes will not be as optimized as the they can be, but they should reflect more accurately the reality, keeping in mind local specific conditions, driver habits or other business factors that can not be used in a route planning algorithm. The first prototype of the algorithm use input parameters received from the historical execution of real-life vehicle routes. The reason for developing such an adaptive algorithm is to get smaller differences between planned and executed routes, a very important business indicator for commercial vehicle fleets. This paper is based on the work on Eureka project PN-III-P3-3.S-EUK-2019-0210.