This paper is published in Volume-3, Issue-5, 2017
Area
Traffic Management.
Author
Canan Tastimur
Co-authors
Mehmet Karakose, Erhan Akin
Org/Univ
Firat University, Elazig, Turkey, Turkey
Pub. Date
03 October, 2017
Paper ID
V3I5-1175
Publisher
Keywords
Activity Recognition, Fuzzy Logic Controller, Intelligent Transport, Particle Swarm Optimization, Traffic Management, Traffic Optimization.

Citationsacebook

IEEE
Canan Tastimur, Mehmet Karakose, Erhan Akin. An Information Extraction Approach with Vehicle and Pedestrian Activity Monitoring For Traffic Management in Smart City, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Canan Tastimur, Mehmet Karakose, Erhan Akin (2017). An Information Extraction Approach with Vehicle and Pedestrian Activity Monitoring For Traffic Management in Smart City. International Journal of Advance Research, Ideas and Innovations in Technology, 3(5) www.IJARIIT.com.

MLA
Canan Tastimur, Mehmet Karakose, Erhan Akin. "An Information Extraction Approach with Vehicle and Pedestrian Activity Monitoring For Traffic Management in Smart City." International Journal of Advance Research, Ideas and Innovations in Technology 3.5 (2017). www.IJARIIT.com.

Abstract

Traffic congestion is one of main concerns of big cities for traffic management. Among different dimensions that make a city smart, one of the very important is transportation. This paper offers an efficient information extraction approach from vehicle and pedestrian activity monitoring for traffic management in smart city. The proposed approach is related to vehicle and pedestrian activity recognition and traffic optimization in order to manage traffic in smart. The traffic management system includes implementation of Particle Swarm Optimization algorithm and Fuzzy Logic Controller in the solution of the traffic signal timing optimization. Moreover, activity recognition is performed to detect traffic lights that are in wrong place, missing and unnecessary. It has been interested not only in activity recognition of vehicles but also in activity recognition of pedestrians in this work. The implementation of this method does not entail complex equipment and feasibility of this proposed method is quite high.
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