{"title":"Mobility resilience and recovery dynamics: Parsimonious framework beyond V-shapes","authors":"Fang Tang , Xiangyong Luo , Xuesong (Simon) Zhou","doi":"10.1016/j.trc.2025.105122","DOIUrl":null,"url":null,"abstract":"<div><div>System resilience is increasingly crucial in responding to disruptions from human activities, climate change, earthquakes, pandemics, etc. Existing literature commonly employs V-shaped or variants, such as U-shaped or trapezoid-shaped models, to describe system performance trends, but capturing the non-linear dynamics, identifying steady-state points, and analyzing impacts by interventions have been challenging, especially across multiple resolutions. While the challenges of extending the model to capture multiple waves are acknowledged, this study focuses exclusively on single-wave scenarios. To gain a deeper understanding of system resilience, this paper proposes an Ordinary Differential Equation (ODE): the Double Quadratic Queue (DQQ) model, which is adapted from the fluid-based Polynomial Arrival Queue (PAQ) model proposed by Newell (1982). The DQQ Model offers a parsimonious framework for understanding and estimating the dynamic evolution of disruption and recovery processes. The queue-theoretical ODE is adapted to multiple resolutions from national to route level in complex systems and is capable of identifying steady-state points and quantifying system resilience metrics. To validate the applicability, this paper employs transit mobility data at national, state, and county resolutions from Google COVID-19 Community Mobility Reports and two-way ridership data for RapidRide routes in King County, Seattle. The results reveal that the DQQ model demonstrates a notable improvement over the traditional models, particularly in identifying and approximating stabilization status at the end of the recovery process. Also, this paper conducts regression analysis to examine the correlation between resilience metrics. In addition, to examine the impact of the interventions on the system’s recovery capability, statistical analysis is conducted to analyze the impact of the new opening of the H Line to other lines.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"175 ","pages":"Article 105122"},"PeriodicalIF":7.6000,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part C-Emerging Technologies","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0968090X25001263","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TRANSPORTATION SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
System resilience is increasingly crucial in responding to disruptions from human activities, climate change, earthquakes, pandemics, etc. Existing literature commonly employs V-shaped or variants, such as U-shaped or trapezoid-shaped models, to describe system performance trends, but capturing the non-linear dynamics, identifying steady-state points, and analyzing impacts by interventions have been challenging, especially across multiple resolutions. While the challenges of extending the model to capture multiple waves are acknowledged, this study focuses exclusively on single-wave scenarios. To gain a deeper understanding of system resilience, this paper proposes an Ordinary Differential Equation (ODE): the Double Quadratic Queue (DQQ) model, which is adapted from the fluid-based Polynomial Arrival Queue (PAQ) model proposed by Newell (1982). The DQQ Model offers a parsimonious framework for understanding and estimating the dynamic evolution of disruption and recovery processes. The queue-theoretical ODE is adapted to multiple resolutions from national to route level in complex systems and is capable of identifying steady-state points and quantifying system resilience metrics. To validate the applicability, this paper employs transit mobility data at national, state, and county resolutions from Google COVID-19 Community Mobility Reports and two-way ridership data for RapidRide routes in King County, Seattle. The results reveal that the DQQ model demonstrates a notable improvement over the traditional models, particularly in identifying and approximating stabilization status at the end of the recovery process. Also, this paper conducts regression analysis to examine the correlation between resilience metrics. In addition, to examine the impact of the interventions on the system’s recovery capability, statistical analysis is conducted to analyze the impact of the new opening of the H Line to other lines.
期刊介绍:
Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.