Dynamic capacity drop propagation in incident-affected networks: Traffic state modeling with SIS-CTM

Abstract

Mitigating the impact of traffic incidents on incident-affected networks is a critical requirement for traffic incident management. To this end, previous studies have focused on the capacity drop (CD) phenomenon at lane-drop bottlenecks. However, investigations of CD and propagation of CD in a network-level traffic flow modeling context have been limited, especially considering their dynamic interactions and temporal characteristics. This study presents a network-level traffic flow model for an incident-affected network to describe the time-varying characteristics of traffic flow on links. The cell transmission model (CTM) is adopted as the fundamental dynamic model. However, previous research on CTM and its stochastic characteristics has focused on unraveling the inherent stochasticity of the fundamental diagram. In this study, a new approach to investigate the stochastic nature of CD and congestion is used; the susceptible-infectious-susceptible (SIS) model is introduced and the SIS-CTM is proposed. This provides a probabilistic description of the link state. The proposed model can differentiate states with potential congestion and describe the unstable transitions as links switch between congested and uncongested conditions. It also can describe the influence of CD, congestion spread, and the propagation of CD with congestion spread. Numerical simulations in the Nguyen–Dupuis and Ziliaskopoulos networks case studies demonstrate the superiority of SIS-CTM. SIS-CTM can reflect the stochasticity of congestion and CD in time and space, while CTM cannot. Further, CTM underestimates the impact of congestion and CD, which is demonstrated as SIS-CTM spends 9.26% more time dissipating traffic demand in the network.