Optimized multistage fuzzy-based model for incident detection and management on urban streets

This study proposes formulation of a system for incident detection and management of traffic signals constituting urban traffic networks. A system prototype has been developed and tested in a simulation environment under several incident scenarios. Following incident detection, the proposed system deploys a multistage fuzzy-logic model (FLM) to manage traffic signals. Details of FLM calibration have been presented and discussed. The proposed system has been calibrated under various traffic conditions and incident scenarios. A parametric sensitivity analysis was performed to optimize the proposed FLM, and further analysis has been performed to demonstrate robustness when tested under conditions different from those it has been optimized for, thereby leading to development of response surface methodology (RSM) models to determine the most robust parameters of FLM. RSM has been calibrated using the Box–Behnken design (BBD). Three different non-linear regression models have been used to identify those robust parameters concerning incident detection and traffic management that are likely to minimize the overall network travel time.