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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 716598, 14 pages
Research Article

Traffic Management as a Service: The Traffic Flow Pattern Classification Problem

1Department of Computer Engineering (DISCA), Universitat Politècnica de València, 46022 Valencia, Spain
2Institute for Pure and Applied Mathematics (IUMPA), Universitat Politècnica de València, 46022 Valencia, Spain

Received 30 July 2015; Revised 22 September 2015; Accepted 27 September 2015

Academic Editor: Shengbo Eben Li

Copyright © 2015 Carlos T. Calafate et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Intelligent Transportation System (ITS) technologies can be implemented to reduce both fuel consumption and the associated emission of greenhouse gases. However, such systems require intelligent and effective route planning solutions to reduce travel time and promote stable traveling speeds. To achieve such goal these systems should account for both estimated and real-time traffic congestion states, but obtaining reliable traffic congestion estimations for all the streets/avenues in a city for the different times of the day, for every day in a year, is a complex task. Modeling such a tremendous amount of data can be time-consuming and, additionally, centralized computation of optimal routes based on such time-dependencies has very high data processing requirements. In this paper we approach this problem through a heuristic to considerably reduce the modeling effort while maintaining the benefits of time-dependent traffic congestion modeling. In particular, we propose grouping streets by taking into account real traces describing the daily traffic pattern. The effectiveness of this heuristic is assessed for the city of Valencia, Spain, and the results obtained show that it is possible to reduce the required number of daily traffic flow patterns by a factor of 4210 while maintaining the essence of time-dependent modeling requirements.