Econometric Frameworks for Energy Prediction

Global warming and associated role of energy consumption across various sectors is a well-researched topic in recent years. Understanding current urban energy consumption patterns will allow us to understand how future energy consumption patterns will evolve. With electrification of vehicles and potentially altering culture of work from home, the energy usage at regional level would see a significant change in the future. The current PhD dissertation contributes to energy consumption analysis of a region by analyzing residential energy consumption, commercial energy consumption and transportation energy use by households. The aggregation of these energy consumption within a region contributes to the total energy consumption of a region. As the share of electric vehicles increases, the proposed modeling frameworks provides the current consumption that serves as a baseline estimate. Specifically, for the energy consumption, we examine the choice of energy sources and the energy consumption by source. The share of electrical vehicles is currently increasing. As the share of electric vehicles increases within our transportation infrastructure, the spatio-temporal nature of current electricity demand is likely to alter with increased household electricity consumption for vehicle charging. To develop a future estimate of urban demand with electric vehicles, a model system of current consumption serves as a baseline estimate. The analysis of energy use in residential buildings and commercial buildings is conducted using Residential Energy Consumption Survey (RECS) and Commercial Building Energy Consumption (CBECS) datasets. The transportation energy use is analyzed using National Household Travel Survey (NHTS) and MPG of the vehicles taken from Vehicle Fuel Economy Estimates. Multiple Discrete Continuous Extreme Value (MDCEV) model and Joint Binary Logit - Fractional Split Model (Joint BLFSM) are used to analyze residential energy consumption. While Bi level MDCEV is used for commercial energy use and spatial weighted regression models are used to analyze transportation energy use

A comprehensive analysis of COVID-19 transmission and mortality rates at the county level in the United States considering socio-demographics, health indicators, mobility trends and health care infrastructure attributes.

BackgroundSeveral research efforts have evaluated the impact of various factors including a) socio-demographics, (b) health indicators, (c) mobility trends, and (d) health care infrastructure attributes on COVID-19 transmission and mortality rate. However, earlier research focused only on a subset of variable groups (predominantly one or two) that can contribute to the COVID-19 transmission/mortality rate. The current study effort is designed to remedy this by analyzing COVID-19 transmission/mortality rates considering a comprehensive set of factors in a unified framework.Methods and findingsWe study two per capita dependent variables: (1) daily COVID-19 transmission rates and (2) total COVID-19 mortality rates. The first variable is modeled using a linear mixed model while the later dimension is analyzed using a linear regression approach. The model results are augmented with a sensitivity analysis to predict the impact of mobility restrictions at a county level. Several county level factors including proportion of African-Americans, income inequality, health indicators associated with Asthma, Cancer, HIV and heart disease, percentage of stay at home individuals, testing infrastructure and Intensive Care Unit capacity impact transmission and/or mortality rates. From the policy analysis, we find that enforcing a stay at home order that can ensure a 50% stay at home rate can result in a potential reduction of about 33% in daily cases.ConclusionsThe model framework developed can be employed by government agencies to evaluate the influence of reduced mobility on transmission rates at a county level while accommodating for various county specific factors. Based on our policy analysis, the study findings support a county level stay at home order for regions currently experiencing a surge in transmission. The model framework can also be employed to identify vulnerable counties that need to be prioritized based on health indicators for current support and/or preferential vaccination plans (when available)

Evaluating the influence of information provision (when and how) on route choice preferences of road users in Greater Orlando:Application of a regret minimization approach

With the advancement in traffic management systems and improving accessibility to traffic information through various sources such as mobile apps, radio, variable message sign; road users tend to choose their route based on a complex interaction of various attributes including travel time, delay, travel cost and information provision mechanisms. While earlier research has examined route choice preferences in relation to travel time and travel cost (or toll), there is little guidance on the influence of information provision mechanisms. By accommodating for information provision attributes, the proposed research contributes to our understanding of the design of an active traffic management (ATM) system by quantitatively estimating the inherent trade-offs across the various attributes affecting route choice. Specifically, the research designs and elicits data using a web-based stated preference (SP) survey to understand road users’ preferencesin the Greater Orlando Region, USA. In the empirical analysis, the data compiled is utilized to develop random utility maximization and random regret minimization based panel mixed multinomial logit models. Route choice behavior is modeled using a comprehensive set of exogenous variables including trip characteristics, roadway characteristics and traffic information characteristics. The model results are utilized to conduct a comprehensive trade-off analysis across various attributes for the two model frameworks. In this research effort, we also customize the trade-off computation for regret minimization model for accommodating variable interactions. The trade-offs results provide useful insights on travel information provision (when and how).</p

The Potential Impacts of Urban and Transit Planning Scenarios for 2031 on Car Use and Active Transportation in a Metropolitan Area

Land use and transportation scenarios can help evaluate the potential impacts of urban compact or transit-oriented development (TOD). Future scenarios have been based on hypothetical developments or strategic planning but both have rarely been compared. We developed scenarios for an entire metropolitan area (Montreal, Canada) based on current strategic planning documents and contrasted their potential impacts on car use and active transportation with those of hypothetical scenarios. We collected and analyzed available urban planning documents and obtained key stakeholders&rsquo; appreciation of transportation projects on their likelihood of implementation. We allocated 2006&ndash;2031 population growth according to recent trends (Business As Usual, BAU) or alternative scenarios (current planning; all in TOD areas; all in central zone). A large-scale and representative Origin-Destination Household Travel Survey was used to measure travel behavior. To estimate distances travelled by mode, in 2031, we used a mode choice model and a simpler method based on the 2008 modal share across population strata. Compared to the BAU, the scenario that allocated all the new population in already dense areas and that also included numerous public transit projects (unlikely to be implemented in 2031), was associated with greatest impacts. Nonetheless such major changes had relatively minor impacts, inducing at most a 15% reduction in distances travel by car and a 28% increase in distances walked, compared to a BAU. Strategies that directly target the reduction of car use, not considered in the scenarios assessed, may be necessary to induce substantial changes in a metropolitan area