Presentation description

This research aims to explore the relationship between traffic volume changes and accident occurrences using data from the Automated Traffic Signal Performance Measures (ATSPM) and police accident records. The goal is to understand how traffic disruptions caused by accidents affect traffic patterns, focusing on non-recurrent events (e.g., accidents or road closures) and their impact on upstream and downstream traffic flow. The study involves analyzing traffic data and comparing it with accident dates, times, and locations. Despite initial efforts, no clear correlation was found between traffic volume fluctuations and accident occurrences. This could be due to various challenges, including data inconsistencies and external factors such as weather or special events. Future work will focus on adjusting variables like time of day, weather conditions, and road types to better capture the impact of accidents on traffic flow. Advanced machine learning techniques will be explored to refine predictions and provide more accurate insights into how accidents disrupt traffic and how to respond more effectively.