Open AccessSHORT TERM TRAFFIC FLOW PREDICTION IN HETEROGENEOUS CONDITION USING ARTIFICIAL NEURAL NETWORK
Author(s) -
Kranti Kumar,
Manoranjan Parida,
V. K. Katiyar
Publication year - 2013
Publication title -
transport
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 31
eISSN - 1648-4142
pISSN - 1648-3480
DOI - 10.3846/16484142.2013.818057
Subject(s) - traffic flow (computer networking) , artificial neural network , traffic generation model , traffic congestion reconstruction with kerner's three phase theory , floating car data , traffic congestion , computer science , advanced traffic management system , transport engineering , traffic volume , term (time) , network traffic simulation , network traffic control , volume (thermodynamics) , intelligent transportation system , engineering , real time computing , computer network , artificial intelligence , physics , quantum mechanics , network packet
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.