Resilience as a Service for Transportation Networks: Definition and Basic Concepts

• Published in: Transportation research record Vol. 2678; no. 1; pp. 177 - 189
• Main Authors: Amghar, Rihab, Jaber, Sara, Moghaddam, S. M. Hassan Mahdavi, Bhouri, Neila, Ameli, Mostafa
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.01.2024; SAGE Journal
• Subjects: Civil Engineering; Engineering Sciences; Infrastructures de transport; planning and analysis; policy and organization; mobility as a service-MaaS; multimodal transportation; resilience as a service-RaaS; operations under disruption; public transportation; RaaS multimodal transportation operations under disruption policy and organization public transportation planning and analysis mobility as a service-MaaS; Transportation > Planning; RaaS; Resilience
• ISSN: 0361-1981; 2169-4052
• DOI: 10.1177/03611981231170180

Urban transportation systems' structure and functionality can be affected by unexpected disruptions for several reasons, such as natural hazards, intentional attacks, accidents, and so forth. The conventional definition of resilience is the capacity to withstand, assimilate, adjust, and expeditiously recuperate from various forms of perturbations such as shocks, disturbances, and deliberate attacks. Though multiple studies in the literature focus on resilience assessment and improving the resilience level of mobility services before disruption, few studies offer solutions for the operators of transportation systems during disruptions to alleviate their negative effects, such as reducing the recovery time. In this context, a new paradigm called ``resilience as a service'' (RaaS) has emerged in the field of operations management. The idea of RaaS is to integrate the available resources of different service providers to manage disruptions and maintain the system's resilience. This paper proposes a definition of RaaS dedicated to transportation systems. To provide a methodological example for the RaaS paradigm, we formulate a bi-level optimization problem to represent a solution example that RaaS providers can deliver. The upper-level model formulates the resource reallocation problem during disruption from the perspective of RaaS providers, while the lower-level model considers user perspectives. We provide a numerical example in a real test case of a French city to illustrate the benefits of implementing a RaaS solution. The results show that we can reduce the average travel delay of all users by 69%, including the delay results from the proposed RaaS strategy compared with the absence of RaaS.