No abstract

Residential location choice is a key determinant of activity-travel behavior and yet, little is known about the underlying reasons why people choose to move, or not move, residences. Such understanding is critical to being able to model residential location choices over time, and design built environments that people find appealing. This paper attempts to fill this gap by developing a joint model of the choice to move residence and the primary reason for moving (or not moving). The model is estimated on the Florida subsample of the 2009 National Household Travel Survey. Model results shed considerable light on the socio-economic and demographic variables that impact household decision whether to move residence and the primary reason underlying that decision. Kortum, Paleti, Bhat and Pendyala 1 INTRODUCTIONResidential location choice is a topic of much interest because decisions about where to work, shop, go to school, or pursue recreational activities are all inextricably tied to people's residential location (1). Although there is considerable literature devoted to modeling and describing residential location choice behavior, an understanding of the underlying factors that contribute to a household decision to relocate residence (or not) continues to be challenging and in need of further enquiry. To set the context, we first briefly identify the factors that have been identified as determinants of residential relocation in the next section, followed by an overview of the methods used for residential relocation modeling in the subsequent section. Within each of these two sections, we position and highlight the salient aspects of this study.

Home and personal travel decisions have important consequences for greenhouse gas (GHG) emissions, yet there has been little data on and investigation into the connections between such decisions and decision makers' opinions on energy policy options. This study examines such data for the Austin metropolitan area and attempts to infer directions for fruitful energy policy.Nearly all respondents recognized global warming as a problem (95%), and most agreed that lifestyle changes are needed to combat climate change (85%). Many also believe that climate change can be combated by application of stricter policies in the areas of vehicle technology (68%), fuel economy (86%), and building design (85%). Results of the study illuminate the importance of home-zone attributes on vehicle ownership, vehicle miles, and emissions. Most (56%) households agree that energy regulations should be pursued to curb global climate change, and most prefer caps on consumption over taxation. Data and empirical results suggest that substantial U.S. energy and greenhouse gas savings are likely to come from vehicle fuel-economy regulation, rebates on relatively fuel-efficient vehicle purchases, home heating and cooling practices, caps on maximum household energy use, and long-term behavioral shifts.

No abstract

TxDOT has a fleet value of approximately $500,000,000 with an annual turnover of about $50,000,000. Substantial cost savings with fleet management has been documented in the management science literature. For example, a 1983 Interfaces article discussed how Phillips Petroleum saved $90,000 annually by implementing an improved system for a fleet of 5300 vehicles. Scaling up to the TxDOT fleet, the corresponding savings would be around $350,000 in 2008 dollars. Similar savings were reported in a 2008 presentation by Mercury Associates. TxDOT Research Project 7-4941 (1997), Equipment Replacement Criteria Based on LCCBA, created a SAS decision analysis tool to be used by the department in its equipment replacement process. While the 7-4941 analysis tool met project scope within the data limitations existing at the time of its delivery, an improved vehicle cost data base will now allow a more normative decision support tool for fleet replacement optimization. In this sense, optimization means minimizing the life-cycle sum of maintenance cost and replacement cost (new equipment price minus resale value). The Department needs a system which recommends whether to retain or replace a unit of equipment, given that class of equipment's age, mileage, resale value, and the cost of replacement equipment. TxDOT categorizes, accounts for, and replaces equipment based on classes of equipment; the new automated fleet optimization system must use these class codes. The objective of this project is to (1) determine the best optimization methodology; (2) evaluate commercial fleet management systems; (3) develop the model if this is cost-effective relative to purchasing a commercial model; and (4) validate the new model as needed using data available on TxDOT's current fleet. To accomplish this project, the research team will formulate the equipment replacement optimization problem as a Mixed-Integer Linear Programming (MILP) model, and propose both Deterministic Dynamic Programming (DDP) and Stochastic Dynamic Programming (SDP) approaches to solving the Equipment Replacement Optimization (ERO) problem. Certainly, this system will be user-friendly and designed so that it can be easily used by non-technical district personnel (to evaluate individual district units against a class) and by technical division personnel (Fleet Manager) to develop optimal aggregate classcode replacement cycles.17.

No abstract

No abstract