Ali Nejat

Developing a Test Data Set for Electric Vehicle Applications in Smart Grid Research

We analyze a detailed set of driving traces for 536 GPS-equipped taxi vehicles and combine them with the features of four different plug-in hybrid electric vehicle (PHEV) brands that currently dominate the North American market in order to develop a test data set for PHEV-related research in the field of smart grid. Our developed data set is made available to public in [1]. It consists of various information, including but not limited to per-PHEV traces of state-of-charges (SoCs), per-PHEV traces of charging loads at different carefully identified charging stations, per-PHEV information on SoC and charging deadline when the PHEV is parked at a charging station, and some information about the potential of PHEVs for vehicle-to-grid applications.

Development, specification and validation of Hurricane Resiliency Index

Combination of well-chosen indicators into a composite Hurricane Resiliency Index is proposed to assess and monitor hurricane resiliency level of coastal communities across geographical boundaries and the changing process over time. The index is constructed nonparametrically by assigning fixed standardization factors as weights to each of the indicators. The validation addresses the question of whether the index is representative of the resiliency dimensions of interest. Results from cross-correlation calculation and binary interaction regression model show that Hurricane Resiliency Index has the capability to broadly measure the dynamics of regional economic activities, and a higher value tends to have a greater mitigating effect over the hurricane impacts.

MODELING DYNAMICS OF POST-DISASTER RECOVERY

Natural disasters result in loss of lives, damage to existing facilities, and interruption of businesses. The losses are not instantaneous, but rather continue to occur until the community is restored to a functional socio-economic entity. Hence, it is essential that policy makers recognize this dynamic aspect of the incurred losses and make realistic plans to enhance recovery. However, this cannot take place without understanding how homeowners react to recovery signals. These signals can come in different ways: from policy makers showing their strong commitment to restore the community by providing financial support and/or restoration of lifeline infrastructure; or from the neighbors showing their willingness to reconstruct. The goal of this research is to develop a model that can account for neighbors’ dynamic interactions by incorporating their signals in a spatial domain. A multi-agent framework is used to capture emergent behavior such as formation of clusters. The results from the model confirm the important role of spatial externality in agents’ decision-making and the process of recovery. The results further highlight the significant impact of discount factor and the accuracy of the signals on the percentage of reconstruction. Finally, cluster formation was shown to be an emergent phenomenon during the recovery process.

Expenditure on Snow and Ice Control for Roadway Maintenance

AbstractThis paper presents the relationships between various winter weather conditions and the total expenditure on purchasing and applying snow and ice control materials and operation costs (labo...

Predictors for the Number of Warning Information Sources During Tornadoes

People may receive tornado warnings from multiple information sources, but little is known about factors that affect the number of warning information sources (WISs). This study examined predictors for the number of WISs with a telephone survey on randomly sampled residents in Tuscaloosa, Alabama, and Joplin, Missouri, approximately 1 year after both cities were struck by violent tornadoes (EF4 and EF5) in 2011. The survey included 1006 finished interviews and the working sample included 903 respondents. Poisson regression and Zero-Inflated Poisson regression showed that older age and having an emergency plan predicted more WISs in both cities. Education, marital status, and gender affected the possibilities of receiving warnings and the number of WISs either in Joplin or in Tuscaloosa. The findings suggest that social disparity affects the access to warnings not only with respect to the likelihood of receiving any warnings but also with respect to the number of WISs. In addition, historical and social contexts are important for examining predictors for the number of WISs. We recommend that the number of WISs should be regarded as an important measure to evaluate access to warnings in addition to the likelihood of receiving warnings. (Disaster Med Public Health Preparedness. 2017;11:168-172).