# Assessing the Opportunity Offered by Electric Vehicles in Performing Service Trips to End Consumers
## 4 Results  
As previously stated, a test application was developed in a real-word area to assess the three _service patterns_ reported in Figure 1. In particular, as introduced earlier, the study area was the city of Rome, where a set \(C\) of 75 users has to be reached starting from a set of depots. In particular, in _service pattern 3_, the cargo e-bikes leave from five depots (micro-hubs) scattered throughout the study area. Each user is associated with a quantity of freight to be delivered, a time for operations (parking and delivery, [10]) and, eventually, a time window. Problems (1) and (2) are solved considering the three cases:  
* a service is performed by ICE LCVs,
* a service is performed by electric LCVs,
* a service is performed by combining the use of electric LCVs and cargo e-bikes. The solution procedure used is the adaptive large neighborhood search proposed by [52]. Using the API from Bing and VRP_Spreadsheet_Solver [52], the travel time on the real road network has been collected for several working days. After defining the end consumers and the types of freights and services, using the average travel times, the best customer sequence can be calculated. Table 3 reports a solution for the problem formulated by Equtaions (1)-(8) for the test case, while Figure 2 shows the routes found. In particular, in this test, the service is performed by four vehicles, travelling (in total) about 255 km in nine travel hours with a total service time (sum of the travel time and the operation time) of about 30 h.  
Taking into account _service pattern 3_, the problems to solve are expressed by Equtaions (9) and (10). In this test, the solution for problem (9) is composed of four trips (see Table 3 and Figure 3) for a total of 245.34 km travelled in 8.58 h, with a total service time of 15.53 h. This solution defines, in addition to the routes, the time windows that will serve as constraints for the service offered by the cargo e-bikes. In relation to the e-bike service (see Table 3 and Figure 4), eight tours are needed, for a total of 461.84 km travelled in 20.74 h with a total service time of 45.54 h.  
As stated, the methodology presented in the earlier section, Section 3, was specified for the evaluation of the impacts reported in Table 4. The effects of the implementation of each scenario have been evaluated in differential terms, i.e., as variations and differences of the variables representing them, between service patterns 2 or 3 and 1 (the status quo). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Service** & **Number of** & **Distance** & **Driving** & **Total** \\
**Pattern** & **Routes** & **Travelled [km]** & **Time [h]** & **Service Time [h]** \\ \hline
1, 2 & 4 & 255.64 & 9.07 & 33.87 \\ \hline
3 & 12 & 461.84 & 20.74 & 45.54 \\ \hline
3 LCVs & 4 & _245.34_ & _8.58_ & _15.53_ \\ \hline
3 Cargo e-bikes & \(8\) & _216.50_ & _12.16_ & _30.01_ \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The VRP solutions for _service patterns 1, 2_ and \(3\). Based on the analysis of the state of charge and energy consumption needed for the various delivery routes, several key observations emerge. The final states of charge for the routes span from 66.9% to 50.37% for service pattern 2 and from 64.29% to 53.98% for service pattern 3, indicating an appropriate sizing of the routes with respect to the autonomy of the vehicles used. The corresponding energy consumptions, ranging from 17.21 to 25.81 kWh for _service pattern 2_ and from 18.57 to 23.93 kWh for _service pattern 3_, with variations across the different routes, highlight the capacity of the vehicles in dealing with working hours without any need for charging. Despite these differences, the remaining energy reserve seems ample for accommodating additional delivery rounds in urban areas without encountering significant challenges [69]. Furthermore, the observed energy consumption levels for the missions undertaken appear to align reasonably well with anticipated requirements, suggesting an efficient utilization of resources.