## 1 Introduction  
The global economy is undergoing continuous adaptation to a rapidly changing environment in recent years. Since the beginning of the 21 st century, the transportation sector has evolved from being less prominent to becoming one of the most crucial drivers of economic development. Presently, the primary focus is on cost reduction and enhancing mobility, especially in response to unexpected events. Simultaneously, investments in transportation have shifted toward innovative modes such as bicycles, electric cars, electric trucks, geospatial technology, and car sharing [1]. Agent-based modeling (ABM) has emerged as a powerful method for observing the emergence of systems and the interactions among the event's key actors [2]. Over time, ABM has been successfully applied to various research fields as it offers the possibility to model the subject under investigation in terms of agents and their interactions [3]. Noteworthy among these realms of applicability, though not exhaustive, are economics [4], dividend policy [5], sustainable resources management [6], organizational studies [7], manufacturing [8], marketing [9], electricity markets [10], social research [11], human infectious disease epidemiology [12], mobility behavior [13], land use [14], constructions [15], education [16], archeology [17], environmental management [18], evacuation [19, 20], and social networks [21]. In terms of modeling, various issues have been posed in the scientific literature regarding the description, design, computational features, simulation, validation, or evaluation of agent-based models (ABMs) [22; 23; 24; 25]. Numerous scholars have endeavored to ascertain optimal approaches for constructing and elucidating ABMs [26; 27]. Concurrently, certain researchers have put forth a range of protocols aimed at enhancing the comprehensibility and accessibility of ABM documentation [28]. Among these, the Overview, Design concepts, and Details (ODD) protocol has gained widespread acceptance for its capacity to facilitate model depiction and replication, while maintaining a judicious balance of technical detail [28]. Comprising seven distinct components organized into three principal categories, the ODD protocol has found utility across diverse research domains, adapting over time through iterative refinement to enhance clarity, reproducibility, and structural veracity [28]. In contemporary contexts, an extension of the ODD protocol, known as ODD + D, has been developed to specifically address the incorporation of human decision-making processes within ABM descriptions [29]. Moreover, select works within the field have focused their efforts on formulating protocols for a sensitivity analysis of ABMs [30]. Having these protocols has also contributed to the increasement in the research that uses ABM in various fields in recent years. The utility of ABMs in the field of transportation lies in their capability to effectively model various aspects [31]. ABMs excel in capturing individual behaviors within complex environments marked by spatial interactions. With ABMs, it is possible to define behavioral rules for micro-level agents that, through simulation, can demonstrate intricate and emergent patterns at the macro level. For instance, the study of ABMs enables the observation of traffic congestion patterns' formation, an outcome unattainable through traditional modeling approaches [31]. As the utilization of ABMs in the field of transportation science has witnessed significant growth over time, this paper endeavors to provide an extensive overview of the applications employing this approach. It does so through a bibliometric analysis aimed at identifying prominent authors, institutions, publications, and journals closely associated with this subject [32; 33; 34; 35]. The analysis encompasses the extraction and examination of significant keywords, trends, and research directions. Additionally, the study sheds light on the geographic regions where the ABM approach to transportation holds substantial influence.