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Urban resilience is a multifaceted concept including the recovery of the physical infrastructure and various urban activities that depend on that physical infrastructure. It is relatively straightforward to quantify infrastructure resilience by tracking the recovered facilities in time and marking the time that the infrastructure is fully functioning again. However, the physical infrastructure recovery does not necessarily indicate that the urban activities bounce back to the predisaster conditions. The restoration of urban activities depends on the areas that a particular infrastructure serves (e.g., residential, commercial) and the connections with other critical facilities (e.g., health, education). It is important to investigate the infrastructure recovery and “resilience divide” with respect to the enabled services and affected populations in order to achieve all-inclusive resilience. For this purpose, we examined the resilience of different physical elements such as power feeders (i.e., underground or overhead lines), critical facilities (e.g., fire and rescue services, hospitals) and different socio-demographic segments of the population (i.e., different age groups, ethnicities, and income levels) which constitute an urban environment. The analyses were conducted using the power outages experienced after Hurricane Hermine in Tallahassee, as a case study. The findings show that overall resilience performance can be distinct and/or not homogeneous for the resilience of different physical elements, urban services, and population groups.

Utilization of public transportation depends on reliability and safety of the transit system, which relate to how the delays occur and propagate on the transit network. Similar to roadway transportation, the delays at rail transit systems can also be categorized as non-recurrent and recurrent. This study focuses on the latter in order to reveal delay patterns and knock-on effects occurring on the transit network. For this purpose, we utilize crowdsourced data for the analysis of the recurrent delays occurring at Long Island Rail Road (LIRR) commuter rail system in the State of New York. We conduct an exploratory analysis of the real time transit information app onTime data and calculate delay estimates for different origin-destination (OD) rail service lines and individual stations. The data consists of real-time estimated delays to the stops, based on the difference between the scheduled arrival time and the actual position of the app user. The results indicate that there are distinct differences between delay patterns based on stations proximity to bottleneck stations and the priorities of individual OD lines on the overall transit network. Moreover, findings imply that transportation authority might be adjusting right of way of routes systematically at bottleneck locations to balance and compensate with the accumulated delays.

Resilience is mostly considered as a single-dimension attribute of a system. Most of the recent works on resilience treat it as a single-dimension attribute of a system or study the different dimensions of the resilience separately without considering its multi-domain nature. In this paper, we propose an advanced causal inference approach combined with machine learning to characterize the spatio-temporal and multi-domain vulnerability of an urban infrastructure system against extreme weather events. With the proposed causality approach, we perform vulnerability assessment for electricity outages and roadway closures through considering the meteorological, topographic, and demographic attributes of urban areas in the aftermath of the extreme weather events. This proposed holistic approach to multi-network vulnerability assessment paves the ground for characterizing the resilience in a multi-network scheme, which is coined as the concept of “co-resilience.” The proposed causal framework for multi-network vulnerability assessment is validated using the actual data for the impacts of the Hurricane Hermine 2016 and the January Storm 2017 on the Tallahassee, FL, USA. The results achieved from the proposed causality approach indicate a high causal relationship among electricity outages, roadway closures, topographic aspects, and meteorological variables in an urban area. Findings show that the proposed multi-network approach for vulnerability assessment improves the performance of the estimation and prediction of the disaster outcomes and the evaluation of the overall system resilience.

Florida's emergency relief operations were significantly affected by recent hurricanes such as Hermine and Irma that caused massive roadway and power system distributions. During these recent devastating hurricanes, the problems associated with providing accessibility and safety became even more challenging, especially for those vulnerable communities and disadvantaged segments of the society, such as aging populations were considered - that is, those who need and benefit from the emergency services the most. This complexity is magnified in states like Florida, considering the diverse physical, cognitive, economic and demographic variation of its population. As such, with a major focus on real-life data on roadway closures and power outages for the Hurricane Hermine, combined resilience (co-resilience) of emergency response facilities in the City of Tallahassee, the capital of Florida, was extensively studied based on the (a) temporal reconstruction of the reported power outages and roadway closures, and (b) development of co-resilience metrics to identify and visually map the most affected power system feeders and transportation network locations. Results show those regions with reduced emergency response facility accessibility, and those power lines and roadways under a disruption risk after Hermine hit Tallahassee.

Load forecasting plays a very crucial role in many aspects of electric power systems including the economic and social benefits. Previously, there have been many studies involving load forecasting using time series approach, including weather-load relationships. In one such approach to predict load, this paper investigates through different structures that aim to relate various daily parameters. These parameters include temperature, humidity and solar radiation that comprises the weather data. Along with natural phenomenon as weather, physical aspects such as traffic flow are also considered. Based on the relationship, a prediction algorithm is applied to check if prediction error decreases when such external factors are considered. Electricity consumption data is collected from the City of Tallahassee utilities. Traffic count is provided by the Florida Department of Transportation. Moreover, the weather data is obtained from Tallahassee regional Airport weather station. This paper aims to study and establish a cause and effect relationship between the mentioned variables using different causality models and to forecast load based on the external variables. Based on the relationship, a prediction algorithm is applied to check if prediction error decreases when such external factors are considered.