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Journal of Advanced Transportation
Volume 2017 (2017), Article ID 1943846, 12 pages
Research Article

Design and Evaluation of a Mathematical Optimization Model for Traffic Signal Plan Transition Based on Social Cost Function

1Department of Industrial Engineering, Universidad del Norte, Atlantico, Colombia
2Department of Civil and Environmental Engineering, Universidad del Norte, Atlantico, Colombia
3Department of Transport, Universidad de Cantabria, Cantabria, Spain

Correspondence should be addressed to Rita PeƱabaena-Niebles

Received 20 January 2017; Revised 24 August 2017; Accepted 7 September 2017; Published 19 October 2017

Academic Editor: Jose E. Naranjo

Copyright © 2017 Rita Peñabaena-Niebles 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.


Signal plan transition is the process of changing from one timing plan to another. It begins when the first intersection starts adjusting signal timing plans and ends when the last intersection completes adjusting signal timing plans. The transition between signal timing plans is required because traffic patterns change during the day. Therefore, it is necessary to modify signal timing parameters offset, phase split, and cycle length for different expectations of traffic volume. This paper presents an alternative and new mathematical model to enhance the performance of traffic signals coordination at intersections during the transition phase. This model is oriented to describe the transition regarding coordination parameters in all intersections of an arterial road for minimizing the social cost during the transition phase expressed in function of costs due to delays, fuel consumption, and air emissions. An ant colony algorithm was designed, coded, and simulated to find the optimal transition parameters using available data. The model was evaluated based on its ability to minimize social costs during the transition period. Results showed that the proposed method performs better than traditional ones.