A Generalized Network Level Optimized Disruption Strategy Selection Model for Urban Zone Transport Systems

Abstract

A fast recovery from disruptions is of vital importance for the reliability and sustainability of urban zone transit systems. It’s a complex task to coordinate multiple transit departments to mitigate disruption. There are many ways to response but it’s not always obvious how to combine them in an optimized manner. This study presents a new attempt to tackle this problem in a comprehensive and hierarchical way. At phase (i), a network-scale strategy selection optimization model is formulated as a joint routing and resource allocation (nJRRA) problem. This model produces solutions for efficient allocation of network resources to facilitate inter-department coordination. By constraining the problem further into an $\epsilon $ -constrained nJRRA problem, classic solution algorithms can be applied to solve the quadratically constrained quadratic program (QCQP). On top of this “basic model”, we propose adding a decision to delay the resource allocation decisions up to a maximum initiation time when the incident duration is stochastic. To test the models, a quasi-dynamic evaluation program with a given incident duration distribution is constructed using discretized time steps and discrete distributions. Five different demand patterns and four different disruption duration distributions (20 combinations) are tested on a small transit network. The results show that the two models outperform benchmark strategies such as using only line level adjustment or only bus bridging. They also highlight conditions when delaying the decision is preferred.

Keywords

• Disruption mitigation
• public transportation
• urban zone transport
• user path recommendation