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

IF 2.7 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Jiho Cho, Hongsuk Yi, Heejin Jung, Khac-Hoai Nam Bui
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引用次数: 2

Abstract

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.
一种用于大规模路网交通密度分类的图像生成方法
摘要近年来,随着深度学习模型的快速发展,利用图像数据集进行交通分析越来越受到关注。具体而言,网络流量可以表示为图像,作为深度学习模型的输入,以提供各种应用(例如时空流量预测)。在这项研究中,我们提出了一种新的图像生成方法,用于大规模路网中的交通密度分类。特别是,某些区域的交通量和速度可以通过使用监控系统(例如环路检测器)进行测量。然而,测量密度是困难的,这取决于从网络的角度来看的空间相关性。因此,针对这一问题,提出了一种基于车辆到达和离开时间信息的有效图像生成方法。关于实验,使用卷积神经网络对11个连续交叉口的路侧设备数据进行交通密度分类,以评估所提出方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.50
自引率
0.00%
发文量
18
审稿时长
27 weeks
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