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原文传递 Artificial Neural Network–Based Traffic State Estimation Using Erroneous Automated Sensor Data

题名：	Artificial Neural Network–Based Traffic State Estimation Using Erroneous Automated Sensor Data
其他题名：	Antoniou,C.,and Koutsopoulos,H.(2006)."Estimation of traffic dynamics models with machine-learning methods."Transportation Research Board of the National Academies,Washington,DC,103–111.
正文语种：	英文
作者：	Shrikant Fulari
关键词：	Intelligent transportation system;Artificial neural network;Erroneous data
摘要：	Capturing real-time traffic system characteristics is a primary step in any intelligent transportation system (ITS) application. The majority of the traffic sensors used to capture real-time data have been developed for homogeneous and lane-disciplined traffic conditions. Hence many of them may not perform accurately under heterogeneous and less-lane-disciplined traffic conditions, ultimately leading to reduced estimation accuracy of end applications. The present study addresses this issue by developing an artificial neural network (ANN)– based estimation scheme that can handle these errors and still generate reasonably accurate results. The estimation of location-based speed, stream-based density, and stream speed is carried out using erroneous data as inputs to an ANN trained with accurate data. The same is also performed under varying ranges of errors in inputs. The results show that the ANN can handle the errors in automated data and produce accurate traffic state estimates when trained with good-quality data, hence demonstrating its efficacy for real-time ITS implementation under such traffic conditions.
出版年：	2017
论文唯一标识：	P-72Y2017V143N08015
英文栏目名称：	TECHNICAL PAPERS
doi：	10.1061/JTEPBS.0000058
期刊名称：	Journal of Transportation Engineering
拼音刊名(出版物代码)：	P-72
卷：	143
期：	08
页码：	139-148