International Journal of Applied Information Systems |
Foundation of Computer Science (FCS), NY, USA |
Volume 12 - Number 16 |
Year of Publication: 2018 |
Authors: Oyenike M. Olanrewaju, A. A. Obiniyi, S. B. Junaidu |
10.5120/ijais2018451776 |
Oyenike M. Olanrewaju, A. A. Obiniyi, S. B. Junaidu . Architectural Framework for Intelligent Vehicle-Pedestrian Traffic Control. International Journal of Applied Information Systems. 12, 16 ( October 2018), 5-15. DOI=10.5120/ijais2018451776
There exist different modes of transportation in which many flow entities have to share the transportation infrastructures. Of particular concern is the safety implication of sharing road infrastructures that bring pedestrians and vehicles into close contact. This make the issue of dynamic traffic control paramount in other to ensure safety of lives. Intelligent transportation research mostly focuses on sensitive system that manages signal timing for vehicular signals without incorporating adequate pedestrian facilities. This research work designed an architectural framework incorporating pedestrian facility into vehicular traffic control with intelligent reasoner for optimized management of both pedestrian crossing and vehicle driver’s demand. The methodology involves review of related literatures, architectural design of the framework and intelligent fuzzy logic model. MATLAB was used to implement the reasoner that harmonized both vehicular traffic’s variables and pedestrian traffic’s variables to dynamically generate the signal timing. A four-way intersection road network in Kano, Nigeria was modeled using VISSIM traffic simulator as test bed. The interfacing of MATLAB and VISSIM was done with VISSIM COM for the communication flow. Traffic network scenario experiments were performed as signalized fixed time traffic control and as Fuzzy Intelligent Traffic Control (FITC) using the developed framework. From the evaluation of the system, the FITC achieved average improvement of 53.19% over fixed time traffic control, FITC Pedestrian delay improved by 13.13% over fixed time.