Integrated Incident Decision-Support using Traffic Simulation and Data-Driven Models

Abstract

This paper introduces the framework of an innovative incident management platform with the main objective of providing decision-support and situation awareness for transport management purposes on a real-time basis. The logic of the platform is to detect and then classify incidents into two types: recurrent and non-recurrent, based on their frequency and characteristics. Under this logic, recurrent incidents trigger the data-driven machine learning module which can predict and analyze the incident impact, in order to facilitate informed decisions for transport management operators. Non-recurrent incidents activate the simulation module, which then evaluates quantitatively the performance of candidate response plans in parallel. The simulation output is used for choosing the most appropriate response plan for incident management. The current platform uses a data processing module to integrate complementary data sets, for the purpose of improving modeling outputs. Two real-world case studies are presented: 1) for recurrent incident management using a data-driven model, and 2) for non-recurrent incident management using traffic simulation with parallel scenario evaluation. The case studies demonstrate the viability of the proposed incident management framework, which provides an integrated approach for real-time incident decision-support on large-scale networks.