Predicting Traffic Congestion at Urban Intersections Using Data-Driven Modeling

TL;DR

This paper presents a data-driven predictive model for urban intersection traffic congestion, leveraging extensive datasets and feature engineering to aid city planning and traffic management.

Contribution

It introduces a novel predictive approach using diverse features and advanced data handling techniques for congestion forecasting at intersections.

Findings

Model effectively predicts congestion at intersections.

Incorporating weather and location features improves accuracy.

Potential to assist urban traffic management strategies.

Abstract

Traffic congestion at intersections is a significant issue in urban areas, leading to increased commute times, safety hazards, and operational inefficiencies. This study aims to develop a predictive model for congestion at intersections in major U.S. cities, utilizing a dataset of trip-logging metrics from commercial vehicles across 4,800 intersections. The dataset encompasses 27 features, including intersection coordinates, street names, time of day, and traffic metrics (Kashyap et al., 2019). Additional features, such as rainfall/snowfall percentage, distance from downtown and outskirts, and road types, were incorporated to enhance the model's predictive power. The methodology involves data exploration, feature transformation, and handling missing values through low-rank models and label encoding. The proposed model has the potential to assist city planners and governments in anticipating traffic hot spots, optimizing operations, and identifying infrastructure challenges.