## 4 Conclusions  
An ABM was developed to model different pedestrian attributes at an unmarked midblock crossing. The study confirmed that the ABM is capable of generating pedestrian movement profiles \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & & **Observations** & **D** & \(p\)**-Value** \\ \hline Gender & Male & 591 & 0.477 & 0.015 \\  & Female & 11 & & \\ Age & Middle-age & 589 & 0.325 & 0.166 \\  & Old-age & 12 & & \\ Type of clothing & Normal & 565 & 0.147 & 0.442 \\  & Traditional & 37 & & \\ Carrying bags & No & 492 & 0.128 & 0.105 \\  & Yes & 110 & & \\ Using mobile phones & No & 590 & 0.187 & 0.805 \\  & Yes & 12 & & \\ Crossing in a group & No & 484 & 0.273 & \textless{}0.001 \\  & Yes & 118 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of Kolmogorov–Smirnov test statistics. that match reality to an extent. The model developed provided an accurate and robust duplication of the actual pedestrian motion. Different pedestrian attributes were investigated, including gender, age, type of clothing, carrying bags, using mobile phones, and crossing in a group. The results showed that pedestrians add a positive risk factor to the speed of approaching vehicles before evaluating a gap, then proceed with the crossing decision. This factor was smaller in the case of female pedestrians, which may imply that they are more prone to taking risks during crossing when compared with men. Another explanation can be that they have a better ability to discern vehicle speed and thus assess the critical gap. The factor was also smaller for pedestrians crossing in a group, which can be an indication that pedestrians have a higher sense of safety when crossing as a group. The analysis also suggested that there is no difference in perception between old and middle-age pedestrians, pedestrians carrying bags or not, and pedestrians using a mobile phone while crossing or not. It should be noted that the risk factors identified in this study are applicable only to the conditions studied. However, the methodology can be replicated to identify the risk factors for any other conditions. Identifying these factors for different pedestrian attributes can play a major role in several applications, such as testing and validating autonomous driving systems and advanced driver assistance systems. It can also help researchers, public agencies, and decision-makers better understand the behavior of pedestrians and compare scenarios related to pedestrian treatments in urban areas. Taking the planning and design of uncontrolled intersections as an example, the risk factor can be used to study and compare different alternatives to select the most perceived safe type of uncontrolled midblock crossing by the pedestrians. The outcome can also be useful in developing more reliable simulation models. Furthermore, the study provides guidance for educational programs and driver training. The results indicate that some pedestrians take more risks than others. It is, therefore, necessary to develop specific training programs aimed at pedestrians facing higher risks to increase their awareness and promote safer road crossing practices. Despite its numerous merits, some study limitations should be noted. The amount of data collected for various subsets of the demographics was low. However, the low percentage of children, locals, and female pedestrians in the sample is common in the region due to unique demographics, social characteristics, and infrastructure conditions [35; 36]. In addition, an assumption was made that the pedestrians move at constant speeds, which does not affect the average time required to cross. This assumption was based on the low percentage of pedestrians changing speeds in this dataset. However, future work should attempt to consider more realistic pedestrian movements by capturing the time dynamics of occupancy and spatial density for pedestrians, which may vary in other conditions [37; 38]. Furthermore, in this study, the pedestrian attributes were extracted from the videos based on visual appearance.