# iRoute--An Adaptive Route Planning Solution for Commercial Vehicle Fleets
## 5 Discussion and Results  
For testing the developed algorithm, we used real anonymized data from a preselected group of customers. In total, the tests were run on 427 routes that were driven with 28 vehicles in 6 months, also having access to historical track data from the last 2 years. **(A)**: Comparing the results of our algorithm with other solutions  
First, we compared our algorithm with three existing standard solutions:  
1. The Vroom CVRPTW algorithm;
2. The HERE Maps API Routing algorithm with traffic data (Waypoints Sequence API 8.2.5);
3. The Google Maps API Routing algorithm with traffic data. Because our algorithm uses historical data, we could not use classic datasets such as [PERSON] [34] or Homberger and Gehring [35]. That is why we have used our anonymized data, for which we have historical executions. Testing approach:  
The purpose of the comparison is to have a high-level view of how our adaptive algorithm results compare when being used on different optimization algorithms. For this, we tested the first 20 routes from our historical set of routes by calling the API of the three solutions, then applying our adapted algorithm, and then comparing the results (Table 1). Testing results:  
D1 = Total distance of the planned route obtained by applying the standard algorithm  
D2 = Total distance of the iRoute adapted algorithm run on the results of the standard algorithm  
Diff = Improved difference between the execution and the plan (the formula is described in next section (B)). From the above tests, we can see that, in general, our algorithm obtains between 15% and 19% smaller differences between the real historical execution distance and the planned distance. We can also see that this difference is smaller for Here Maps and Google Maps, as they use the historical data on traffic. Still, there is an average improvement of at least 12% and this difference can be a game changer for some businesses in terms of efficiency. There are many planning solutions on the market. Most of them are commercial, like Google Maps or HERE Maps. We have chosen to use an open-source solution (VROOM)  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Route \#**} & \multirow{2}{*}{ \begin{tabular}{c} **No. of** \\ **Stops** \\ **Distance (Km)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Real** \\ **Historical** \\ **Execution** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **VROOM** \\ **Execution** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **Google Maps** \\ **H** **(Km)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **HEEE Maps** \\ **H** **(Km)** \\ \end{tabular}  } \\ \cline{5-10}  & & & & & & & & & & & & \\ \hline
1 & 16 & 150.41 & 109.49 & 117.48 & 19.53\% & 105.56 & 107.66 & 4.  
that uses OpenStreetMap road data so that our work can be reused by other research projects in the future at low cost and without any copyright issues. In the next section, we run our algorithm on optimized routes generated by the VROOM solution. **(B)** Calculate the improvements compared to the VROOM standard algorithm  
Next, we used only the VROOM solution and calculated the difference improvements in both distance and duration for all 427 routes by comparing the difference between execution and optimized routes versus execution and adapted routes. The results are presented below in Tables 2 and 3 and Figures 11-14, with an average decrease in the difference between executed routes and planned routes by 19.6% for distance and 14.2% for duration. Figure 11: Statistics for distances—sample (first 30 routes). Figure 12: Statistics for durations—sample (first 30 routes). Figure 13: Histogram of difference improvement (distance).