A Real-Time Autonomous Highway Accident Detection Model Based on Big Data Processing and Computational Intelligence

TL;DR

This paper presents a real-time autonomous accident detection system utilizing big data processing and computational intelligence, aiming to improve traffic safety and reduce congestion by early accident prediction.

Contribution

It introduces a novel real-time accident detection model using big data and machine learning techniques applied to Istanbul's traffic data from 2015.

Findings

The system can predict accident likelihood with reasonable accuracy.

False alarms are more common than actual accident detections, indicating room for improvement.

The approach demonstrates potential for early accident detection in urban traffic management.

Abstract

Due to increasing urban population and growing number of motor vehicles, traffic congestion is becoming a major problem of the 21st century. One of the main reasons behind traffic congestion is accidents which can not only result in casualties and losses for the participants, but also in wasted and lost time for the others that are stuck behind the wheels. Early detection of an accident can save lives, provides quicker road openings, hence decreases wasted time and resources, and increases efficiency. In this study, we propose a preliminary real-time autonomous accident-detection system based on computational intelligence techniques. Istanbul City traffic-flow data for the year 2015 from various sensor locations are populated using big data processing methodologies. The extracted features are then fed into a nearest neighbor model, a regression tree, and a feed-forward neural network model. For the output, the possibility of an occurrence of an accident is predicted. The results indicate that even though the number of false alarms dominates the real accident cases, the system can still provide useful information that can be used for status verification and early reaction to possible accidents.