Academic Journal
Short term traffic flow prediction in heterogeneous condition using artificial neural network
| Title: | Short term traffic flow prediction in heterogeneous condition using artificial neural network |
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| Authors: | Kranti Kumar, Manoranjan Parida, Vinod Kumar Katiyar |
| Source: | Transport, Vol 30, Iss 4 (2015) |
| Publisher Information: | Vilnius Gediminas Technical University, 2015. |
| Publication Year: | 2015 |
| Collection: | LCC:Transportation engineering |
| Subject Terms: | artificial neural network, traffic flow, intelligent transportation system, modelling, speed, Transportation engineering, TA1001-1280 |
| More Details: | Traffic congestion is one of the main problems related to transportation in developed as well as developing countries. Traffic control systems are based on the idea to avoid traffic instabilities and to homogenize traffic flow in such a way that risk of accidents is minimized and traffic flow is maximized. There is a need to predict traffic flow data for advanced traffic management and traffic information systems, which aim to influence traveller behaviour, reducing traffic congestion and improving mobility. This study applies Artificial Neural Network for short term prediction of traffic volume using past traffic data. Besides traffic volume, speed and density, the model incorporates both time and the day of the week as input variables. Model has been validated using actual rural highway traffic flow data collected through field studies. Artificial Neural Network has produced good results in this study even though speeds of each category of vehicles were considered separately as input variables. First published online: 16 Oct 2013 |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 16484142 1648-4142 1648-3480 |
| Relation: | https://journals.vgtu.lt/index.php/Transport/article/view/1708; https://doaj.org/toc/1648-4142; https://doaj.org/toc/1648-3480 |
| DOI: | 10.3846/16484142.2013.818057 |
| Access URL: | https://doaj.org/article/e116ad861fb240b99f97b1bc7689a9e6 |
| Accession Number: | edsdoj.116ad861fb240b99f97b1bc7689a9e6 |
| Database: | Directory of Open Access Journals |
| ISSN: | 16484142 16483480 |
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| DOI: | 10.3846/16484142.2013.818057 |
| Published in: | Transport |
| Language: | English |