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Abstract $-$ Traffic accidents pose significant risks to public safety and economic stability, prompting ongoing efforts to understand their underlying causes. This research explores the analysis of traffic accident data collected by transportation agencies, focusing on factors contributing to accidents. By examining variables such as road conditions, driver behavior, and vehicle characteristics, this study aims to identify critical risk factors associated with various types of accidents. Statistical methods, including chi$-$square tests and logistic regression, are employed to uncover correlations and elucidate patterns within the data. Ultimately, the findings offer valuable insights for policymakers, urban planners, and transportation authorities in devising effective strategies to mitigate the occurrence and severity of traffic accidents.

I. INTRODUCTION

Traffic accidents represent a significant public health concern and a substantial economic burden worldwide. According to the World Health Organization, road traffic injuries are a leading cause of death globally, with millions of lives lost each year and countless more suffering from debilitating injuries. Understanding the complex interplay of factors contributing to traffic accidents is crucial for devising targeted interventions to reduce their frequency and severity.

Transportation agencies routinely collect vast amounts of data pertaining to traffic accidents, encompassing diverse variables such as road conditions, weather patterns, driver demographics, vehicle types, and accident severity. This rich dataset serves as a valuable resource for researchers and policymakers seeking to unravel the underlying causes of traffic accidents and develop evidence$-$based strategies for prevention and mitigation.

In this research, we focus on the analysis of traffic accident data with the goal of identifying key factors associated with different types of accidents. By leveraging statistical techniques such as chi$-$square tests and logistic regression, we aim to elucidate the relationships between various variables and the likelihood of accidents occurring. Specifically, we seek to answer critical questions such as:

What are the primary contributors to traffic accidents, and how do they vary across different contexts?

Which road conditions, such as weather, lighting, and surface quality, pose the greatest risk to road users?

How do driver behaviors, including speed, distraction, impairment, and compliance with traffic laws, influence accident outcomes?

Are certain vehicle characteristics, such as age, size, and safety features, correlated with the severity of accidents?

By systematically analyzing these factors, we aspire to provide actionable insights that can inform the development of targeted interventions and policy measures aimed at enhancing road safety and reducing the human and economic toll of traffic accidents. Through collaboration with transportation agencies, urban planners, law enforcement agencies, and other stakeholders, we endeavor to translate research findings into tangible initiatives that promote safer and more sustainable transportation systems.

II$.$ METHODOLOGY

This study employs the qualitative descriptive research methodology, which is common in descriptive qualitative research. It seeks to highlight the phenomena through a thorough examination of the issues that enable the researcher to comprehend traffic safety precautions and traffic accidents. The researcher used the data generated from the Metro Manila Accident Reporting and Analysis System $($MMARAS$)$ to collect data and perform document analysis to triangulate data sources.

Gathering data on traffic accidents from regional statistical sources is standardized at the national level, it facilitates easy replication and flexibility of the whole process. These records often include the location of the traffic accident, the kind of incident, how many individuals were involved, the outcomes, the features of the route, etc. To guarantee that there is not enough time available for the analysis, a minimum study term of three to five years is established as a guide for the data gathering. Finding the area with the highest frequency of traffic accidents is the goal of this step. The entire road is divided into segments of a fixed length, and for each segment, the frequency of road accidents is calculated using the formula below:

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Also, Multinomial logistic regression is used in this study to examine the accident database. Based on many independent variables, this methodology is used to estimate the likelihood of falling into a category in a dependent variable. The dependent variable in a multinomial logistic regression model might include more than two possibilities. The theoretical underpinnings of this statistical tech