## 3 Dataset Analysis
### Analysis of Literature
#### 3.4.2 Top Three Most Cited Papers--Review
Additionally, the model incorporates elements to replicate realistic travel patterns. To validate the effectiveness of the model, a case study is presented, focusing on a mid-sized city. According to the authors, preliminary findings suggest that each SAV has the potential to replace approximately eleven conventional vehicles while introducing a modest increase of approximately 10% in travel distance compared to non-shared autonomous vehicle journeys of a similar nature [85]. This, in turn, results in overall positive emission impacts when accounting for fleet-efficiency improvements and emission profiles both during production and in-use phases [85]. Furthermore, one of the authors of the study ([PERSON]) has collaborated with two other researchers ([PERSON] and [PERSON]) on another work regarding the implications of vehicle and charging infrastructure, which holds fourth place based on the number of citations, gathering 220 citations, with a TCY of 22.00 and an NTC of 5.13. In this paper, [PERSON] et al. [1] commence their study by building upon a finding that underscores the sensitivity of fleet size to factors such as battery recharge time and vehicle range. The authors proceed to explore the implications of decisions related to both vehicles and charging infrastructure. The researchers specifically analyze travel patterns within the context of Austin, Texas, and employ an agent-based modeling approach to simulate the operations of a shared autonomous vehicle fleet. The results of their investigation reveal a significant dependence of fleet size on the adequacy of charging infrastructure and vehicle range [1]. Notably, from a financial perspective, the analysis suggests that despite necessitating the largest fleet and the most charging stations, the base 80-mile-range shared autonomous electric vehicle (SAEV) fleet equipped with Level II charging stations emerges as the most cost-effective option on a per-mile basis among all the electric vehicle (EV) scenarios considered [1]. In terms of citations, it has been observed that 582 of the 683 citing papers (representing 85.21%) have been published in areas related to transportation science technology and transportation in the WoS platform, highlighting once more the contribution of the paper authored by [PERSON] and [PERSON] [85] to the transportation field. [PERSON] et al. [86] place particular emphasis on the significance of spatial connectivity between ecosystems and their beneficiaries within their research. In light of this focus, the authors introduced a class of agent-based models called Service Path Attribution Networks (SPANs) tailored to address a spectrum of ecosystem services, which correlates the source, sink, and locations. SPANs use different classes of agents: sink agents, who reduce the available quantity by carrier agents; carrier agents, which move between locations following specific routes; and user agents, which use the products from carrier agents, and if they are satisfied, can access more products from the carrier, or it has the possibility to buy from competitors. Furthermore, the SPAN Algorithm takes into account the individual traveler's historical experiences and preferences, alongside considerations of distance, travel infrastructure, and available transportation modes [86]. The iterative process is repeated multiple times until the optimum is determined through simulations, utilizing various ecosystem services and activities such as carbon storage, subsistence fisheries, riverine food regulation, and recreation. Once the optimum is identified, the SPAN model aspires to integrate into the Artificial Intelligence for Ecosystem Service (ARIES) modeling platform. ARIES supports diverse ecosystems across different regions, each characterized by unique particularities.