# Using Machine Learning to Predict Pedestrian Compliance at Crosswalks in Jordan
## 2 Literature Review  
When examining previous research globally, many tools, software, statistical methods, and ML algorithms are available to analyze traffic-related data sets in urban areas [8; 9; 10; 11; 12; 13]. All these technological procedures have facilitated the process of collecting and analyzing data and obtaining results and conclusions that help in understanding the variables and factors that impact the safety of pedestrians and the way they think and act when crossing urban intersections. For example, when talking about the most critical computer programs for traffic modeling purposes, we find that software such as SUMO Simulation of Urban Mobility ([[https://eclipse.dev/sumo/](https://eclipse.dev/sumo/)]([https://eclipse.dev/sumo/](https://eclipse.dev/sumo/))) [14] and VISSIM ([[https://www.ptvgroup.com/en/products/ptv-vissim](https://www.ptvgroup.com/en/products/ptv-vissim)]([https://www.ptvgroup.com/en/products/ptv-vissim](https://www.ptvgroup.com/en/products/ptv-vissim))) [15] are used to simulate the urban environment and its elements, including pedestrians, infrastructure, vehicles, and traffic control devices such as traffic lights. Also, the process of recording videos of these urban elements and the methods for identifying their characteristics, such as the number of pedestrians and vehicles, their speeds, and their categories, can now be implemented using systems such as Video Management Systems (VMS). In addition to the above, analyzing and visualizing spatial elements has become available through geographic information systems (GIS) such as ArcGIS ([[https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview](https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview)]([https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview](https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview))) [16]. Statistical software packages like R ([[https://www.r-project.org/](https://www.r-project.org/)]([https://www.r-project.org/](https://www.r-project.org/))) [17] and SPSS ([[https://www.ibm.com/products/spss-statistics](https://www.ibm.com/products/spss-statistics)]([https://www.ibm.com/products/spss-statistics](https://www.ibm.com/products/spss-statistics))) [18] are used as statistical tools to analyze quantitative data and find trends and patterns through statistical methods like hypothesis testing, analysis of variance (ANOVA), and linear regression. Additionally, spatial analysis tools, which are integrated within GIS or available as packages in R, are used to examine spatial and spatiotemporal data. These resources aid in a more thorough comprehension of the interactions between pedestrians and traffic signals in urban areas. Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) were used as ML tools and algorithms to analyze photos and videos to track pedestrian movement and classify behaviors at traffic crossings. In addition, pedestrian activity patterns were analyzed and categorized using traditional ML methods like Principal Component Analysis (PCA), Random Forests, and Support Vector Machines (SVM). Additionally, research has been performed on using Reinforcement Learning Modeling to create interactive models that can adapt to changes in the infrastructure or traffic environment while understanding how pedestrian behavior changes in response. These tools and techniques provide the basis for a complex and multidimensional study of pedestrian behavior in urban areas. They allow precise analysis of interactions between pedestrians and traffic lights and the development of effective strategies to improve traffic safety. Many previous studies in various geographical locations demonstrated the ability of ML models to analyze pedestrian safety conditions at urban signalized intersections. For example, [PERSON] et al. (2018) [19] used deep learning tools to accurately classify pedestrians at crossings through field images taken from OpenStreetMap and Google Street View in multiple regions of Brazil.