# Using Machine Learning to Predict Pedestrian Compliance at Crosswalks in Jordan
###### Abstract  
This study employs machine learning (ML) techniques to predict pedestrian compliance at crosswalks in urban settings in Jordan, aiming to enhance pedestrian safety and traffic management. Utilizing data from 2437 pedestrians at signalized intersections in Amman, Irbid, and Zarqa, four models based on different ML algorithms were developed: an artificial neural network (ANN), a support vector machine (SVM), a decision tree (ID3), and a random forest (RF). The results have shown that local infrastructure and traffic conditions influence pedestrian behavior. The RF model, with its excellent accuracy and precision, has proven to be an excellent choice for accurately predicting pedestrian behavior. This research provides valuable insights into the demographic and spatial aspects that influence pedestrian compliance with laws and regulations in the local environment. Additionally, this work highlights the ability of ML algorithms to improve urban traffic dynamics. Policymakers and urban planners, particularly with the rise of theories and trends toward the humanization of urban roads, should firmly establish this understanding among themselves to create environments that make pedestrians safer. This strategy could be a measurable solution for international urban situations if future research focuses on integrating these prediction models with real-time traffic management systems to improve pedestrian safety dynamically.  
pedestrian; machine learning; artificial neural network; support vector machine; decision tree; random forest +
Footnote †: journal:  
traffic safety for all road users. Using ML tools to predict pedestrian behavior can provide valuable insights and contribute to designing more effective preventive measures. By analyzing detailed data derived from the intersections under study and marked in Amman, Irbid, and Zarqa, this research aims to fill the gap in the current literature and make new contributions to improving the understanding of traffic dynamics. The issue of understanding the dynamics and variables that frame human interaction with the surrounding environment around them has a growing need that has led to a remarkable development in pedestrian behavior research in urban areas [6]. Accordingly, closely studying pedestrian behavior at traffic lights in urban areas with high pedestrian density sheds light on multiple aspects of the interaction between pedestrians and urban infrastructure and explores the extent to which pedestrians comply with traffic laws and regulations designed to protect them. Specifically, many of these studies focus on observing and analyzing pedestrian behavior at traffic signals, as these points are among the most sensitive and dangerous areas for pedestrians in the urban environment [7]. Interest in the extent to which pedestrians adhere to the traffic signals assigned to them comes from the fact that it directly reflects traffic safety and security levels in cities.  
The remainder of this paper is organized as follows. The Section 2, \"Literature Review\", surveys previous studies and key findings relevant to the use of ML in predicting pedestrian behaviors, focusing on crosswalk compliance within various contexts. In the Section 3, \"Methodology\", the ML techniques and data collection methods used in this study are described. In the Section 4, \"Results and Discussion\", the results of the predictive models are presented and discussed. Finally, in Section 5, \"Conclusions\", the findings are summarized, their implications for pedestrian safety and urban planning in Jordan are discussed, and directions for future research are suggested.