Traffic Density Estimation using a Convolutional Neural Network

TL;DR

This paper presents a CNN-based system for estimating traffic density from camera images to improve traffic flow and reduce congestion in Singapore.

Contribution

It introduces an end-to-end traffic density estimation and control system utilizing CNNs and demonstrates its feasibility with real-world traffic camera data.

Findings

CNN effectively estimates traffic density from images

System can be integrated into traffic control for congestion reduction

Feasibility shown using LTA traffic camera dataset

Abstract

The goal of this project is to introduce and present a machine learning application that aims to improve the quality of life of people in Singapore. In particular, we investigate the use of machine learning solutions to tackle the problem of traffic congestion in Singapore. In layman's terms, we seek to make Singapore (or any other city) a smoother place. To accomplish this aim, we present an end-to-end system comprising of 1. A traffic density estimation algorithm at traffic lights/junctions and 2. a suitable traffic signal control algorithms that make use of the density information for better traffic control. Traffic density estimation can be obtained from traffic junction images using various machine learning techniques (combined with CV tools). After research into various advanced machine learning methods, we decided on convolutional neural networks (CNNs). We conducted experiments on our algorithms, using the publicly available traffic camera dataset published by the Land Transport Authority (LTA) to demonstrate the feasibility of this approach. With these traffic density estimates, different traffic algorithms can be applied to minimize congestion at traffic junctions in general.