Applied sciences  
Exploring the Applications of Agent-Based Modeling in Transportation  
[PERSON]*(c) and [PERSON]  
Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies,  
010552 Bucharest, Romania; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]  
Agent-based modeling (ABM) has emerged as a distinct and innovative paradigm offering an alternative to conventional modeling techniques which often rely on equation-based representations to depict specific events or phenomena. The transformative nature of agent-based modelling lies in its focus on characterizing the subject of inquiry through the lens of individual agents and their intricate interactions [1]. Over the course of its evolution, agent-based modelling has witnessed a multitude of applications spanning various domains [2], transportation, nevertheless, being one of them. Transportation stands as a fundamental pillar of our contemporary society, serving as the essential conduit for facilitating the seamless transfer of commodities, individuals, and animals across different locales, encompassing modes such as air, sea, road, rail, cable, pipeline, and even space [3, 4]. The maturation of agent-based modelling techniques has facilitated a range of advancements within the realm of transportation, as evidenced by their practical implementations across diverse domains within the transportation sector. The advantages of using ABMs in the area of transportation are particularly useful for modeling various aspects due to the ABM ability to capture various individual behaviors in complex environments characterized by the occurrence of spatial interactions. Using ABM, one can set rules of behavior to the agents that operate at a micro level, but which, through simulation, can exhibit complex and emergent behavior at a macro level. For example, one can observe the formation of the traffic congestion patterns by simply studying the ABM, which cannot be achieved through the use of the traditional models. Furthermore, due to the inherent spatial nature of the transportation systems in general, ABMs provide an advantage over the classical modelling approaches as they consider the spatial context, allowing the interaction of agents with one another and with the environment. As a result of these interactions, emergent behavior can be easily observed by simply watching a simulation run. The underlying mechanisms driving transportation dynamics can be examined in detail through an ABM approach as the agents can change their behaviors over time through various adaptation mechanisms. These changes can be easily captured in an ABM. Various disruptions, such as delays in the schedule of various means of transport or the unavailability of one or more exit doors in a transport evacuation simulation, can be also captured in an ABM. Nevertheless, the \"what if\" scenario development option offered by ABM can be useful for testing various policies and scenarios before using them in practice. For example, in the airplane boarding problem, the test for the various boarding methods can be performed through the use of ABM for various quantities of luggage brought by the passengers inside the aircraft. The applications stemming from the utilization of ABMs within the realm of transportation have ignited a diverse array of domains, encompassing yet not confined to the ones discussed below. Within the domain of air transportation, the application of ABMs has predominantly centered around the following focal points of research: study of the airport terminal operations [5], estimating occupant density in airports [6], simulating the passenger flow in airport terminals [7], improving the airplane boarding process [8], improving passenger service time [9], assessing airport terminal resilience [10], analyzing the security and efficiency in airport terminals [11]. Among the delineated sub-areas within air transportation, the domain of airplane boarding has witnessed a pronounced surge in applications utilizing agent-based modeling [12].