Improved Annual Average Daily Traffic (AADT) estimation for local roads using parcel-level travel demand modeling

Annual Average Daily Traffic (AADT) is a critical input to many transportation analyses. By definition, AADT is the average 24-hour volume at a highway location over a full year. Traditionally, AADT is estimated using a mix of permanent and temporary traffic counts. Because field collection of traffic counts is expensive, it is usually done for only the major roads, thus leaving most of the local roads without any AADT information. However, AADTs are needed for local roads for many applications. For example, AADTs are used by state Departments of Transportation (DOTs) to calculate the crash rates of all local roads in order to identify the top five percent of hazardous locations for annual reporting to the U.S. DOT. ^ This dissertation develops a new method for estimating AADTs for local roads using travel demand modeling. A major component of the new method involves a parcel-level trip generation model that estimates the trips generated by each parcel. The model uses the tax parcel data together with the trip generation rates and equations provided by the ITE Trip Generation Report. The generated trips are then distributed to existing traffic count sites using a parcel-level trip distribution gravity model. The all-or-nothing assignment method is then used to assign the trips onto the roadway network to estimate the final AADTs. The entire process was implemented in the Cube demand modeling system with extensive spatial data processing using ArcGIS. ^ To evaluate the performance of the new method, data from several study areas in Broward County in Florida were used. The estimated AADTs were compared with those from two existing methods using actual traffic counts as the ground truths. The results show that the new method performs better than both existing methods. One limitation with the new method is that it relies on Cube which limits the number of zones to 32,000. Accordingly, a study area exceeding this limit must be partitioned into smaller areas. Because AADT estimates for roads near the boundary areas were found to be less accurate, further research could examine the best way to partition a study area to minimize the impact.^

An investigation of location effect on trip production using artificial neural networks

As traffic congestion exuberates and new roadway construction is severely constrained because of limited availability of land, high cost of land acquisition, and communities' opposition to the building of major roads, new solutions have to be sought to either make roadway use more efficient or reduce travel demand. There is a general agreement that travel demand is affected by land use patterns. However, traditional aggregate four-step models, which are the prevailing modeling approach presently, assume that traffic condition will not affect people's decision on whether to make a trip or not when trip generation is estimated. Existing survey data indicate, however, that differences exist in trip rates for different geographic areas. The reasons for such differences have not been carefully studied, and the success of quantifying the influence of land use on travel demand beyond employment, households, and their characteristics has been limited to be useful to the traditional four-step models. There may be a number of reasons, such as that the representation of influence of land use on travel demand is aggregated and is not explicit and that land use variables such as density and mix and accessibility as measured by travel time and congestion have not been adequately considered. This research employs the artificial neural network technique to investigate the potential effects of land use and accessibility on trip productions. Sixty two variables that may potentially influence trip production are studied. These variables include demographic, socioeconomic, land use and accessibility variables. Different architectures of ANN models are tested. Sensitivity analysis of the models shows that land use does have an effect on trip production, so does traffic condition. The ANN models are compared with linear regression models and cross-classification models using the same data. The results show that ANN models are better than the linear regression models and cross-classification models in terms of RMSE. Future work may focus on finding a representation of traffic condition with existing network data and population data which might be available when the variables are needed to in prediction.

Application of intervention analysis to evaluate the impacts of special events on freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

Application of Intervention Analysis to Evaluate the Impacts of Special Events on Freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

A methodology to estimate time varying user responses to travel time and travel time reliability in a road pricing environment

Road pricing has emerged as an effective means of managing road traffic demand while simultaneously raising additional revenues to transportation agencies. Research on the factors that govern travel decisions has shown that user preferences may be a function of the demographic characteristics of the individuals and the perceived trip attributes. However, it is not clear what are the actual trip attributes considered in the travel decision- making process, how these attributes are perceived by travelers, and how the set of trip attributes change as a function of the time of the day or from day to day. In this study, operational Intelligent Transportation Systems (ITS) archives are mined and the aggregated preferences for a priced system are extracted at a fine time aggregation level for an extended number of days. The resulting information is related to corresponding time-varying trip attributes such as travel time, travel time reliability, charged toll, and other parameters. The time-varying user preferences and trip attributes are linked together by means of a binary choice model (Logit) with a linear utility function on trip attributes. The trip attributes weights in the utility function are then dynamically estimated for each time of day by means of an adaptive, limited-memory discrete Kalman filter (ALMF). The relationship between traveler choices and travel time is assessed using different rules to capture the logic that best represents the traveler perception and the effect of the real-time information on the observed preferences. The impact of travel time reliability on traveler choices is investigated considering its multiple definitions. It can be concluded based on the results that using the ALMF algorithm allows a robust estimation of time-varying weights in the utility function at fine time aggregation levels. The high correlations among the trip attributes severely constrain the simultaneous estimation of their weights in the utility function. Despite the data limitations, it is found that, the ALMF algorithm can provide stable estimates of the choice parameters for some periods of the day. Finally, it is found that the daily variation of the user sensitivities for different periods of the day resembles a well-defined normal distribution.

A Look Back To Look Forward: New Patterns In The Supply/Demand Equation In The Lodging Industry

In his dialogue entitled - A Look Back to Look Forward: New Patterns In The Supply/Demand Equation In The Lodging Industry - by Albert J. Gomes, Senior Principal, Pannell Kerr Forster, Washington, D.C. What the author intends for you to know is the following: “Factors which influence the lodging industry in the United States are changing that industry as far as where hotels are being located, what clientele is being served, and what services are being provided at different facilities. The author charts these changes and makes predictions for the future.” Gomes initially alludes to the evolution of transportation – the human, animal, mechanical progression - and how those changes, in the last 100 years or so, have had a significant impact on the hotel industry. “A look back to look forward treats the past as prologue. American hoteliers are in for some startling changes in their business,” Gomes says. “The man who said that the three most important determinants for the success of a hotel were “location, location, location” did a lot of good only in the short run.” Gomes wants to make you aware of the existence of what he calls, “locational obsolescence.” “Locational obsolescence is a fact of life, and at least in the United States bears a direct correlation to evolutionary changes in transportation technology,” he says. “…the primary business of the hospitality industry is to serve travelers or people who are being transported,” Gomes expands the point. Tied to the transportation element, the author also points out an interesting distinction between hotels and motels. In addressing, “…what clientele is being served, and what services are being provided at different facilities,” Gomes suggests that the transportation factor influences these constituents as well. Also coupled with this discussion are oil prices and shifts in transportation habits, with reference to airline travel being an ever increasing method of travel; capturing much of the inter-city travel market. Gomes refers to airline deregulation as an impetus. The point being, it’s a fluid market rather than a static one, and [successful] hospitality properties need to be cognizant of market dynamics and be able to adjust to the variables in their marketplace. Gomes provides many facts and figures to bolster his assertions. Interestingly and perceptively, at the time of this writing, Gomes alludes to America’s deteriorating road and bridge network. As of right now, in 2009, this is a major issue. Gomes rounds out this study by comparing European hospitality trends to those in the U.S.

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.

Demographic, Behavioral and Perceptual Comparisons of U.S Visitor Experience with Group Package Tours and Free Independent Travel to China

U.S. visitor demand for the China travel experience is anticipated to rise significantly through 2105, causing the Chinese government to employ 100 million service providers over the next six years and raising concern about service delivery and perceptions of the on-site China experience. In an effort to better understand these issues concerning U.S. visitors, this study investigated two specific types of U.S. travelers to China: Group Package Tour (GPT) visitors and Free Independent Travel (FIT) visitors. Results indicated that GPT visitors were more likely to be older and have higher household income than FIT visitors. Four trip-related characteristics of GPT and FIT visitors were found to be significantly different, with GPT visitors showing higher levels of satisfaction with the overall China on-site travel experience.

Understanding Immigrants' Travel Behavior in Florida: Neighborhood Effects and Behavioral Assimilation

The goal of this study was to develop Multinomial Logit models for the mode choice behavior of immigrants, with key focuses on neighborhood effects and behavioral assimilation. The first aspect shows the relationship between social network ties and immigrants’ chosen mode of transportation, while the second aspect explores the gradual changes toward alternative mode usage with regard to immigrants’ migrating period in the United States (US). Mode choice models were developed for work, shopping, social, recreational, and other trip purposes to evaluate the impacts of various land use patterns, neighborhood typology, socioeconomic-demographic and immigrant related attributes on individuals’ travel behavior. Estimated coefficients of mode choice determinants were compared between each alternative mode (i.e., high-occupancy vehicle, public transit, and non-motorized transport) with single-occupant vehicles. The model results revealed the significant influence of neighborhood and land use variables on the usage of alternative modes among immigrants. Incorporating these indicators into the demand forecasting process will provide a better understanding of the diverse travel patterns for the unique composition of population groups in Florida.