Paper Info
Title
Modelling Traffic Dynamics In Motorway Networks
Abstract
Due to the rapid growth of traffic density, the necessity to increase the operational efficiency and capabilities of intelligent transportation systems (ITSs) has led to the development of various traffic modelling theories. The Lighthill-Whithman and Richards (LWR) model [?, ?], uses fluid based partial differential equations to capture traffic dynamics along continuous stretches of road. In contrast to the LWR model, the artificial neural network model [?, ?] utilizes historical observations of traffic flow-rates to forecast flow-rate locally. This paper aims to introduce a new hybrid macroscopic model which combines the complementary features of the LWR and artificial neural network models, to effectively simulate traffic flow in road networks. The model developed in this paper demonstrates the ability to, within a certain degree of accuracy, forecast traffic flow in a road network that includes junctions and continuous stretches of road. Furthermore, the proposed model offers an appropriate trade-off between accuracy and computational complexity, therefore it is suitable for real time applications.
Year
DOI
Venue
2012
10.1109/ITSC.2012.6338708
2012 15TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)
Keywords
Field
DocType
neural networks,intelligent transportation systems,artificial neural networks,partial differential equations,traffic flow,neural nets,mathematical model,computational modeling,predictive models
Traffic generation model,Traffic flow,Simulation,Microscopic traffic flow model,Traffic simulation,Engineering,Traffic congestion reconstruction with Kerner's three-phase theory,Intelligent transportation system,Artificial neural network,Computational complexity theory
Conference
ISSN
Citations 
PageRank 
2153-0009
0
0.34
References 
Authors
3
3
Name
Order
Citations
PageRank
aidan fitzgerald100.34
Salissou Moutari2388.35
Adele H. Marshall3178.77