An image generation approach for traffic density classification at large-scale road network

Abstract

Recently, with the rapid development of deep learning models, traffic analysis using image datasets recently has attracted more attention. Specifically, the network traffic can be represented to images as the input for deep learning models to provide various applications (e.g. Spatio-Temporal traffic forecasting). In this study, we propose a new image generation approach for traffic density classification in terms of large-scale road network. Particularly, traffic volume and speed are at certain areas able to be measured by using surveillance systems (e.g. loop detectors). However, measuring the density is difficult which depends on the spatial correlation from the perspective of the network. Consequently, an effective image generation approach, based on information arrival and departure time of vehicles, is proposed to deal with this problem. Regarding the experiment, traffic density classification using a convolutional neural network is executed on roadside equipment data of 11 continuous intersections for evaluating the effectiveness of the proposed approach.

A Graph-Based Framework for Traffic Forecasting and Congestion Detection Using Online Images From Multiple Cameras

“…As a result, YOLO achieved the best accurancy. Cho et al [18] proposed a method to classify the density of road networks using an image generation approach. The nodes in the traffic network are converted to polygons whose shapes represent traffic conditions in different directions.…”