## 1 Introduction
In Qatar, a study determined the crossing time for pedestrians who jaywalk as a function of several pedestrian and vehicle characteristics in addition to crossing behavioral attributes using multiple linear regression. The study showed age, gender, phone use, crossing with a group, and presence of a vehicle affected the crossing time [19]. In Greece, the gap acceptance for crossing at an uncontrolled section was studied, and a lognormal regression model was presented for the gaps accepted. The model contained several independent variables, including gender, vehicle size, distance, and illegal parking. The study indicated that the crossing decision was affected by the distance from the incoming vehicles and the waiting times of pedestrians [20]. In Egypt, the accepted gap size was studied at nine uncontrolled midblock sections, and a lognormal regression model was used to predict the minimum accepted gap. The model included different variables, including age, number of attempts, vehicle speed, road width, and rolling gap. The study suggested the gap size, the speed of the vehicles, rolling gap, and number of attempts affects that decision to cross the road [21]. In India, a regression model was developed to explain the gap acceptance behavior for pedestrians at a midblock section. The model included similar variables such as age, gender, pedestrian and vehicle speeds, crossing attempts, rolling gap, and crossing direction. The study revealed that the age, crossing speed, vehicle speed, and crossing direction affect the gap acceptance for pedestrians [22]. Another lognormal regression model was developed by the same authors to find the minimum gap. The model contained several variables such as yielding behavior of the driver, number of attempts, rolling gap, accepted lag or gap, and vehicle speed as independent variables. The study indicated that the rolling gap, yielding behavior of drivers, and number of attempts affect the crossing behavior for pedestrians [23]. In the United Kingdom, a fuzzy logic system was utilized to investigate the crossing behavior of pedestrians at a midblock crossing location. The study revealed that using fuzzy logic to explain the pedestrians' behavior is reliable and produced reasonable results when compared to the literature [24]. Moreover, some studies explored the probability of accepting a gap using logit or probit models according to different traffic and pedestrian characteristics [25; 26]. However, there is generally limited use of microscopic simulation models in many of the existing pedestrian behavior studies. Agent-based modeling is one of the advanced microsimulation methods in terms of ability and flexibility [27]. This method utilizes agents as pedestrians to replicate different behaviors. These agents are capable of gathering information about the surrounding environment and then make decisions. The main advantage of agent-based modeling is the ability to allow the agents to develop their own decisions, which provides more realistic outcomes. In summary, there is an increased interest in the development of simulation models to study pedestrian behavior in different schemes. However, there are limited simulation studies investigating the illegal crossing scenarios of pedestrians. For practical applications such as modeling pedestrian movements at unmarked midblock crossings, simulation techniques are well suited for this type of analysis and can provide realistic outcomes. The purpose of this study is to address this shortcoming by developing an agent-based microscopic model to investigate the gap acceptance behavior of crossing pedestrians considering various interaction behaviors among road users at unmarked midblock crossings. Different pedestrian attributes were investigated, including gender, age, type of clothing, carrying bags, using mobile phones, and crossing in a group.