Traffic State Prediction using Convolutional Neural Network

被引：0
|
作者：
Toncharoen, Ratchanon [1 ]
Piantanakulchai, Mongkut [1 ]
机构：
[1] Thammasat Univ, Sirindhorn Int Inst Technol, Pathum Thani, Thailand
来源：
2018 15TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER SCIENCE AND SOFTWARE ENGINEERING (JCSSE) | 2018年
关键词：
Convolutional Neural Network; Traffic State Prediction; Classification; Intelligent Transportation System; NEAREST NEIGHBOR MODEL; FLOW PREDICTION; VOLUME;
D O I：
暂无
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Traffic state prediction methods have been considered by many researchers since accurate traffic prediction is an important part of the successful implementation of the Intelligent Transportation System (ITS). This study develops the traffic prediction model based on real traffic data in congested hours of expressways in Bangkok, Thailand. Unlike most studies, this model utilizes data from 40 nodes along the expressway instead of a single sensor. A Convolutional Neural Network (CNN) model was applied and compared to other widely used models. The result shows that the accuracy of CNN model is higher than other models.
引用
收藏
页码：250 / 255
页数：6
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