Paper Info
Title
Generating realistic urban traffic flows with evolutionary techniques.
Abstract
In this article we present a novel approach for calculating realistic traffic flows for traffic simulators, called Flow Generator Algorithm (FGA). We start with an original map from OpenStreetMap and traffic data collected at different measurement points, published by the city’s authorities, to produce a model consisting of the simulation map and a series of traffic flows (routes + vehicles) which match the real number of vehicles at those streets. Our approach does not need a full dataset to calculate the flows. In fact, just a few measurement points indicating the number of vehicles in the analyzed time interval were used. This and the use of evolutionary algorithms for such a complex task make our proposal different from the studies found in literature. Despite the fact we have chosen the SUMO traffic simulator for our experiments, this idea can be easily adapted to others. We have tested our proposal on two geographical areas of the city of Malaga, comprising different map sizes, number of sensors and vehicles, and have achieved measurement values from the simulation that are closer to the real values, showing an error lower than 10%. Our algorithm, as well as the realistic scenarios generated by using it, can be used as the basis for other research approaches, especially those focused on road traffic optimization.
Year
DOI
Venue
2018
10.1016/j.engappai.2018.07.009
Engineering Applications of Artificial Intelligence
Keywords
Field
DocType
Evolutionary algorithm,Traffic simulation,Smart mobility,SUMO,Road traffic optimization,O–D matrix
Evolutionary algorithm,Computer science,Flow (psychology),Traffic simulator,Road traffic,Real-time computing,Artificial intelligence,Real number,Machine learning
Journal
Volume
ISSN
Citations 
75
0952-1976
2
PageRank 
References 
Authors
0.37
17
2
Name
Order
Citations
PageRank
Daniel H. Stolfi1247.24
Enrique Alba23796242.34