Authors:
Faisal Ahmed
and
Yaser E. Hawas
Affiliation:
United Arab Emirates (UAE) University, United Arab Emirates
Keyword(s):
Fuzzy Logic and Systems, Intelligent Transport System, Urban Incident Detection, Neuro-Fuzzy, Detector Count, Average Speed, Detection Rate, False Alarm Rate.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
Incident detection systems for the urban traffic network are still lacking efficient algorithms or models for better performance. This paper presents a new urban incident detection system based on the application of Fuzzy Logic modeling. Offline urban incident and corresponding non-incident scenarios are generated using a microscopic simulation model assuming varying traffic link flows, phase timing, cycle times, and link lengths. The traffic measures are extracted from three detectors on each link. Statistical significance analysis was utilized to identify the significant input variables to be used in developing the Neuro-fuzzy model. A set of data was generated and used for training of the proposed Neuro-fuzzy model, while another set was used for validation. The performance of the proposed model is assessed using the success and the false alarm rates of detecting an incident at a specific cycle time.