APOLO: A Mobility Pattern Analysis Approach to Improve Urban Mobility
Authors: 
Ademar T. Akabane, Rafael L. Gomes, Richard W. Pazzi, Edmundo R. M. Madeira, and Leandro A. VillasAuthors Info & Claims
Ademar T. Akabane, Rafael L. Gomes, Richard W. Pazzi, Edmundo R. M. Madeira, and Leandro A. VillasAuthors Info & ClaimsDecember 2017
Pages 1 - 6
Abstract
Urban mobility becomes one of the most challenging issues in large urban centers, since traffic congestion is a daily problem. In order to address this issue, a number of researchers, from both academia and industry, have studied several Traffic Management Systems (TMS) approaches to improve urban mobility. However, the existing approaches do not consider an essential factor: the population information. Within this context, this work proposes a new approach, called APOLO, that employs historical knowledge of mobility patterns of the drivers to obtain a global view of the road network. APOLO is different from others research approaches that need constant information exchange among the vehicles and the central server in order to obtain a global view of road traffic condition. These existing approaches can lead to network overload and have a high data processing cost in real-time. Results show that APOLO improves vehicles' mobility compared to well-known approaches, which indicates that APOLO could be a potential alternative for providing TMS services with valuable mobility knowledge.
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Published: 04 December 2017
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