Toward A Better Understanding of the Evolution of Hotel Development: A Discussion of Product-specific Lodging Demand

In his essay - Toward a Better Understanding of the Evolution of Hotel Development: A Discussion of Product-Specific Lodging Demand - by John A. Carnella, Consultant, Laventhol & Horwath, cpas, New York, Carnella initially describes his piece by stating: “The diversified hotel product in the united states lodging market has Resulted in latent room-night demand, or supply-driven demand resulting from the introduction of a lodging product which caters to a specific set of hotel patrons. The subject has become significant as the lodging market has moved toward segmentation with regard to guest room offerings. The author proposes that latent demand is a tangible, measurable phenomenon best understood in light of the history of the guest room product from its infancy to its present state.” The article opens with an ephemeral depiction of hotel development in the United States, both pre’ and post World War II. To put it succinctly, the author wants you to know that the advent of the inter-state highway system changed the complexion of the hotel industry in the U.S. “Two essential ingredients were necessary for the next phase of hotel development in this country. First was the establishment of the magnificently intricate infrastructure which facilitated motor vehicle transportation in and around the then 48 states of the nation,” says Carnella. “The second event…was the introduction of affordable highway travel. Carnella goes on to say that the next – big thing – in hotel evolution was the introduction of affordable air travel. “With the airways filled with potential lodging guests, developers moved next to erect a new genre of hotel, the airport hotel,” Carnella advances his picture. Growth progressed with the arrival of the suburban hotel concept, which wasn’t fueled by developments in transportation, but by changes in people’s living habits, i.e. suburban affiliations as opposed to urban and city population aggregates. The author explores the distinctions between full-service and limited service lodging operations. “The market of interest with consideration to the extended-stay facility is one dominated by corporate office parks,” Carnella proceeds. These evolutional states speak to latent demand, and even further to segmentation of the market. “Latent demand… is a product-generated phenomenon in which the number of potential hotel guests increases as the direct result of the introduction of a new lodging facility,” Carnella brings his unique insight to the table with regard to the specialization process. The demand is already there; just waiting to be tapped. In closing, “…there must be a consideration of the unique attributes of a lodging facility relative to its ability to attract guests to a subject market, just as there must be an examination of the property's ability to draw guests from within the subject market,” Carnella proposes.

Spatial-temporal mining of archived automatic vehicle location (AVL) data for transit service planning applications

An Automatic Vehicle Location (AVL) system is a computer-based vehicle tracking system that is capable of determining a vehicle's location in real time. As a major technology of the Advanced Public Transportation System (APTS), AVL systems have been widely deployed by transit agencies for purposes such as real-time operation monitoring, computer-aided dispatching, and arrival time prediction. AVL systems make a large amount of transit performance data available that are valuable for transit performance management and planning purposes. However, the difficulties of extracting useful information from the huge spatial-temporal database have hindered off-line applications of the AVL data. ^ In this study, a data mining process, including data integration, cluster analysis, and multiple regression, is proposed. The AVL-generated data are first integrated into a Geographic Information System (GIS) platform. The model-based cluster method is employed to investigate the spatial and temporal patterns of transit travel speeds, which may be easily translated into travel time. The transit speed variations along the route segments are identified. Transit service periods such as morning peak, mid-day, afternoon peak, and evening periods are determined based on analyses of transit travel speed variations for different times of day. The seasonal patterns of transit performance are investigated by using the analysis of variance (ANOVA). Travel speed models based on the clustered time-of-day intervals are developed using important factors identified as having significant effects on speed for different time-of-day periods. ^ It has been found that transit performance varied from different seasons and different time-of-day periods. The geographic location of a transit route segment also plays a role in the variation of the transit performance. The results of this research indicate that advanced data mining techniques have good potential in providing automated techniques of assisting transit agencies in service planning, scheduling, and operations control. ^

Private higher education policy in Ohio, 1980--2006: A case study demonstrating the usefulness of Zumeta's public policy posture model

This study describes the case of private higher education in Ohio between 1980 and 2006 using Zumeta's (1996) model of state policy and private higher education. More specifically, this study used case study methodology and multiple sources to demonstrate the usefulness of Zumeta's model and illustrate its limitations. Ohio served as the subject state and data for 67 private, 4-year, degree-granting, Higher Learning Commission-accredited institutions were collected. Data sources for this study included the National Center for Education Statistics Integrated Postsecondary Data System as well as database information and documents from various state agencies in Ohio, including the Ohio Board of Regents. ^ The findings of this study indicated that the general state context for higher education in Ohio during the study time period was shaped by deteriorating economic factors, stagnating population growth coupled with a rapidly aging society, fluctuating state income and increasing expenditures in areas such as corrections, transportation and social services. However, private higher education experienced consistent enrollment growth, an increase in the number of institutions, widening involvement in state-wide planning for higher education, and greater fiscal support from the state in a variety of forms such as the Ohio Choice Grant. This study also demonstrated that private higher education in Ohio benefited because of its inclusion in state-wide planning and the state's decision to grant state aid directly to students. ^ Taken together, this study supported Zumeta's (1996) classification of Ohio as having a hybrid market-competitive/central-planning policy posture toward private higher education. Furthermore, this study demonstrated that Zumeta's model is a useful tool for both policy makers and researchers for understanding a state's relationship to its private higher education sector. However, this study also demonstrated that Zumeta's model is less useful when applied over an extended time period. Additionally, this study identifies a further limitation of Zumeta's model resulting from his failure to define "state mandate" and the "level of state mandates" that allows for inconsistent analysis of this component. ^

Effect of Pavement-Vehicle Interaction on Highway Fuel Consumption and Emission

Vehicle fuel consumption and emission are two important effectiveness measurements of sustainable transportation development. Pavement plays an essential role in goals of fuel economy improvement and greenhouse gas (GHG) emission reduction. The main objective of this dissertation study is to experimentally investigate the effect of pavement-vehicle interaction (PVI) on vehicle fuel consumption under highway driving conditions. The goal is to provide a better understanding on the role of pavement in the green transportation initiates. Four study phases are carried out. The first phase involves a preliminary field investigation to detect the fuel consumption differences between paired flexible-rigid pavement sections with repeat measurements. The second phase continues the field investigation by a more detailed and comprehensive experimental design and independently investigates the effect of pavement type on vehicle fuel consumption. The third study phase calibrates the HDM-IV fuel consumption model with data collected in the second field phase. The purpose is to understand how pavement deflection affects vehicle fuel consumption from a mechanistic approach. The last phase applies the calibrated HDM-IV model to Florida’s interstate network and estimates the total annual fuel consumption and CO2 emissions on different scenarios. The potential annual fuel savings and emission reductions are derived based on the estimation results. Statistical results from the two field studies both show fuel savings on rigid pavement compared to flexible pavement with the test conditions specified. The savings derived from the first phase are 2.50% for the passenger car at 112km/h, and 4.04% for 18-wheel tractor-trailer at 93km/h. The savings resulted from the second phase are 2.25% and 2.22% for passenger car at 93km/h and 112km/h, and 3.57% and 3.15% for the 6-wheel medium-duty truck at 89km/h and 105km/h. All savings are statistically significant at 95% Confidence Level (C.L.). From the calibrated HDM-IV model, one unit of pavement deflection (1mm) on flexible pavement can cause an excess fuel consumption by 0.234-0.311 L/100km for the passenger car and by 1.123-1.277 L/100km for the truck. The effect is more evident at lower highway speed than at higher highway speed. From the network level estimation, approximately 40 million gallons of fuel (combined gasoline and diesel) and 0.39 million tons of CO2 emission can be saved/reduced annually if all Florida’s interstate flexible pavement are converted to rigid pavement with the same roughness levels. Moreover, each 1-mile of flexible-rigid conversion can result in a reduction of 29 thousand gallons of fuel and 258 tons of CO2 emission yearly.