# Using Machine Learning to Predict Pedestrian Compliance at Crosswalks in Jordan
## 2 Literature Review
The research results demonstrated an accuracy of approximately 94.12% using a convolutional neural network (CNN) with the VGG architecture and further improved to 96.3% by refining their database. This study confirmed the great benefit of automated data collection in enhancing training and maintenance resilience in diverse urban environments. In addition, [PERSON] and [PERSON] (2018) [20] investigated pedestrian behavior while crossing signalized intersections in India. Using the analysis of videos that recorded pedestrians crossing and their behavior at intersections, the researchers developed a model capable of predicting pedestrian behavior, which showed that 46% of pedestrians do not adhere to crossing rules in order to not be delayed due to waiting and to save time. The results of this research are essential for urban planners and traffic engineers who aim to raise the level of safety and increase pedestrian compliance with traffic rules when crossing the road near urban intersections. In the United States, [PERSON] and [PERSON] (2019; 2020) [21; 22] conducted studies using Bayesian Networks Analysis to investigate the relationship between pedestrian and car behaviors at signalized crosswalks in Nevada. Their research provided insight into the crucial aspects that impact pedestrians' compliance with crossing signals and cars' willingness to yield, delivering a comprehensive understanding that can inform pedestrian infrastructure design to enhance compliance rates. In addition, [PERSON] et al. (2020) [23] investigated the forecasting of pedestrian crossing during red lights in Florida using a long-short-term memory (LSTM) neural network. Their model exhibited a notable accuracy rate of 91.6% in forecasting pedestrian motives, highlighting the potential of sophisticated computational models to actively improve pedestrian safety. Implementing this method into communication networks between automobiles and infrastructure has the potential to assist in avoiding crashes by notifying drivers of possible unforeseen pedestrian crossings. These studies collectively show that machine-learning approaches can be used to understand and enhance pedestrian compliance and safety at crosswalks. These approaches offer a significant framework for our research in Jordan, as they may be modified to tackle specific issues in managing pedestrian traffic. In 2021, [PERSON] et al. [24] investigated the relationship between vehicle and pedestrian conflicts in unregulated crossing areas in South Korea using an analytical methodology. This study focused on understanding the ability of pedestrian behavioral analysis, both qualitatively and causally, to predict the factors that may lead to conflict between pedestrians and vehicles, which paves the way for improving traffic safety in urban areas. Also, [PERSON] et al. (2021) [25] and subsequent studies published by [PERSON] et al. (2022) [26] and [PERSON] et al. (2022) [27] aimed to raise the level of understanding of pedestrian environment and safety through ML and artificial intelligence (AI) methodologies. These studies have enhanced the ability to identify vehicles in smart transit areas and increased the predictive ability to determine the probability of crashes between vehicles and pedestrians in urban environments through virtual reality (VR) contexts. The significance of these studies lies in their increasing reliance on AI technology for the development of road and intersection safety models and their recognition of the field's crucial role in enhancing traffic safety in urban areas. [PERSON] and [PERSON] (2023) [28] and [PERSON] et al. (2023) [29] made a distinctive addition to their research as they evaluated the level of service for pedestrians and how to identify and predict patterns of behavior in pedestrian crashes.