## 2 Literature Review
The vehicle routing problem has been analyzed through several studies which added a set of elements that provide a more qualified comprehension of the daily activity [14; 15; 16]. [PERSON] and [PERSON] (2016) presented a new problem to study the least cost green vehicle of routing planning, where the vehicles transport the goods from the providers to the clients concerning the constraints of CO2 [14]. [PERSON] et al. (2020) developed a routing problem that included a set of activities of an electric vehicle (EV) and charging stations, the authors solved the routing problem by the Ant system algorithm to guide the EVs using routes to their destinations where chagining stations are closed [17]. A novel contribution has been done by [PERSON] and [PERSON] (2020), in which the demand was included as a parameter of the method that used an epistemic representation [18]. [PERSON] and [PERSON] (2020) developed an approach to model the behavior of electric vehicles concerning transport modes, charging stations, locations of the activities, and the departure times of the travelers [16]. However, MATSim which uses the co-evolutionary algorithm, was used to understand in more solid form the routing problems. In this research, MATSim, which is an agent-based modeling, activity-based microsimulation model using a co-evolutionary algorithm to simulate the daily activity plans of travelers, was used [19]. MATSim can simulate extensive projects of up to \(10^{7}\) agents in competitive time, compared with other software [20]. The MATSim tool is built in Java, which is a more flexible programing language. The MATSim loop is used to optimize the utility of travelers based on the Charypar and Nagel utility function, which includes mobility simulation, scoring, and re-planning steps. The selection of a plan, after the re-planning step, is carried out by Logit model probability [19]. The re-planning step is where a mutation and selection of a plan are executed (genetic algorithm) to generate a good solution, rather than the optimum one. It is worth mentioning that the scoring depends on several factors, such as the time and cost, as well as Vickrey bottleneck model parameters [21; 22]. Vickrey bottleneck model parameters include the following utilities for traveling, waiting, and being late: \(-\)12/h, \(-\)6/h, and \(-\)18/h for \(\beta_{\text{trav}}\), \(\beta_{\text{wait}}\) and \(\beta_{\text{late-ar}}\), respectively [22]. Scholars have published several articles on autonomous vehicles and their envisaged impact on the mobility of travelers. A study in Germany by [PERSON] and [PERSON] (2016) showed that one AV could replace around 10 conventional cars, in the case of an unshared ride, and around 6 conventional cars in the case of a shared ride [23]. Travelers are forced to wait a certain time for an AV before starting their travel, and this waiting time is considered to be acceptable if it is not beyond 10 min, as stated in the literature [24]. In this regard, a Zurich study showed that people could wait for an AV for 10 to 15 min during peak hours and 5 min during other periods [24]. In reality, a group of people asked for a highly accessible service, which can only be provided to them by their cars, but in the case of AVs, accessibility is increased based on the assumption that this technology is not bounded to a certain area, and the travel time is decreased because the number of cars on the road network can be replaced by a fleet of AVs, which are smaller than private cars. People can track the location of AVs at their home, rather than wait at a public transport stop, and this is more efficient for them with respect to variables such as weather conditions [23]. Furthermore, the waiting time for public transport could be less than the AV waiting time, but travelers have to walk to reach the nearest public transport station.