## 3 Dataset Analysis
### Analysis of Literature
#### 3.4.2 Top Three Most Cited Papers--Review  
Below, a concise overview of the papers featured in the top three most cited papers is presented, accompanied by summaries of their respective contributions. [PERSON] et al. [61] conducted an analysis of human mobility patterns using a dataset comprising 100,000 individuals' tracked mobile phones over a 6-month period. The primary objective of the study was to gain a comprehensive understanding of human mobility patterns, irrespective of the modes of transportation employed by the studied population. Drawing from the analyzed data, the authors noted that, despite the diverse travel histories of the individuals in the study, discernible yet replicable travel patterns emerged. The authors elucidated the significance of diffusion models, which estimate human trajectories based on specified constraints and priorities, yielding robust insights into mobility patterns. Unexpectedly, employing Levi flights and random walk models resulted in human trajectories appearing to be random and challenging to predict. However, the analysis using diffusion models revealed spatial and temporal regularities. Various simulations were conducted across diverse locations and timelines. The investigation incorporated the analysis of mobile phone towers, recorded tracks, and service area limitations, providing insights into human trajectories primarily characterized by short distances, with infrequent occurrences of longer distances (exceeding hundreds of kilometers). The frequency of returning to a location was found to be contingent on travel distances and the temporal availability \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Paper (First Author, Year, Journal, Reference)** & **Number of Authors** & **Region** & **Total Citations (TCs)** & **Total Citations per Year (TCY)** & **Normalized TC (NTC)** \\ \hline \multirow{2}{*}{1} & [PERSON], 2008, Nature, [61] & 3 & USA & 3785 & 235.56 & 7.99 \\  & [PERSON], 2014, Transportation Research & & & & & \\  & Part C: Emerging Technologies, [85] & 2 & USA & 683 & 68.30 & 16.43 \\ \multirow{2}{*}{3} & [PERSON], 2013, Ecosystem Services, [86] & 4 & USA, Spain & 349 & 31.72 & 9.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Top three most globally cited documents. of each individual. In conclusion, the study highlights the consistent and reproducible nature of human behavior, even under the impact of epidemics or emergencies. These findings bear significant relevance in the domains of traffic forecasting, the determination of variables for agent-based models, emergency response planning, a human mobility analysis, and urban planning considerations [61]. Further considering the citations for the paper authored by [PERSON] et al. [61], it can be observed that 23.79% of the citing papers have been published in journals indexed in the computers science information systems category of WoS (counting for 900 papers). The remainder of the citing references have been indexed in either computer science (computer science theory methods, computer science artificial intelligence, computer science interdisciplinary applications, computer science hardware architecture, computer science software engineering) or transportation-specific categories (transportation science technology, transportation, or urban and environmental sciences), counting for 53.89% of the citing papers. As for the evolution of the citations, it has been observed that the trend has been ascending in the 2008-2015 period. Between 2015 and 2021, the trend has been constant, a number of citations per year between 327 and 351 being recorded. In 2022, the number of citations was slightly smaller, reaching 275 citations. [PERSON] and [PERSON] [85] introduced an agent-based model designed for the optimization of shared autonomous vehicle (SAV) operations. The central concept underlying this model involves the generation of trips within a grid-based urban environment, accounting for predefined origins, destinations, and departure times.