# iRoute--An Adaptive Route Planning Solution for Commercial Vehicle Fleets
## 5 Discussion and Results
The improvement percent was calculated by the following formula:  
\[\text{Distance Improvement}=\frac{(\text{Dist}_{Excc}-\text{Dist}_{P,STD}\ )-( \text{Dist}_{Exec}-\text{Dist}_{P,AD})}{\text{Dist}_{Excc}-\text{Dist}_{P,STD}}= \frac{\text{Dist}_{P,AD}-\text{Dist}_{P,STD}}{\text{Dist}_{Excc}-\text{Dist}_{P,STD}}\]  
\[\text{Duration Improvement}=\frac{(\text{Time}_{Excc}-\text{Time}_{P,STD}\ )-( \text{Time}_{Excc}-\text{Time}_{P,AD})}{\text{Time}_{Excc}-\text{Time}_{P,STD}}= \frac{\text{Time}_{P,AD}-\text{Time}_{P,STD}}{\text{Time}_{Excc}-\text{Time}_{P,STD}}\]  
where:  
\[\text{Dist}_{Excc}=\text{Distance of Real Executed Route}\]  
\[\text{Dist}_{P,STD}=\text{Distance of Planned Optimized Route}\ (\text{Standard})\]  
\[\text{Dist}_{P,AD}=\text{Distance of Planned Adapted Route}\ (i\text{Route})\]  
\[\text{Time}_{Excc}=\text{Duration of Real Executed Route}\]  
\[\text{Time}_{P,STD}=\text{Duration of Planned Optimized Route}\ (\text{Standard})\]  
Figure 12:0  
Based on these charts, we can see that the improvements are scaling between zero--for not enough historical data or when the path used was the same as the historical one--and 35%. The longer the route, the more probable it is that an improvement can be obtained, as the combination of roads to be used increases. From the histograms, we can see that 75 routes (17.5% of 427) were not improved or had improvements of less than 5%. But, for 273 routes (63.9%) the improvements were more than 10%. That means that if a customer had used our algorithm for planning his routes, he would have better scheduled his routes and known from the start of the day when his vehicles would be available. In the long term, this knowledge is useful for a more agile strategy when taking new orders, employing more drivers, or buying more vehicles. A more realistic plan can also increase customer satisfaction by obtaining more accurate visit schedules. The motivation of the drivers could also increase to respect the plan, as they can see that the route segments are similar to what they know can be achieved on the road. Below, we have illustrated an example of a route adaptation according to the historical tracks. The information is represented in different colors: the theoretical optimized route is green, while the historical tracks are blue and the final adapted route is red (Figures 15-17). Figure 12:1 Histogram of difference improvement (duration). Figure 12:2 Historical routes (the most used track is drawn with a more opaque blue color). Figure 12:3 Initial optimized route. The example above illustrates a case where the historical tracks indicate that the drivers use the national road (DN12C) instead of the regional road (DJ126), even if the distance is greater (14 km vs. 12 km). The reason is that the regional road is very bad (Figure 18), with road construction signs and lots of potholes, and the smaller distance takes 5 min more to be driven by the truck. Our solution delivers routes that are more accurate, reliable, and efficient than other traditional solutions. While we may not find the fastest routes, our routes are more likely to be followed by drivers and result in a smaller difference between planned and executed routes. This benefits our customers by offering their end customers a reliable estimated time  
Figure 12:4 Initial optimized route (green) versus adapted optimized route (red). Figure 12:5 Potholes on a regional road (Source: Google Maps—Street View). of arrival, increasing drivers' adoption of the solution, and improving customer satisfaction. Nonetheless, driving on known roads reduces the risk of driving events by avoiding road hazards that a driver frequently driving in that region might know. **(C)**: Validate the solution with customers  
To validate the need for our solution approach in the logistics business, we conducted a quantitative survey with 68 business owners in this industry. The questions are presented in Appendix B.