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.^

Estimation of average annual daily traffic (AADT) using geographically weighted regression (GWR) method and geographic information system (GIS)

Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^

The relation of sports participation to academic performance of high school students

Over the past one hundred years, interscholastic athletic programs have evolved to a place of prominence in both public and private education across America. The National Federation of State High School Associations (NFHS) estimates that approximately 3.96 million males and 2.80 million females participated in organized high school athletic programs during the 2001–2002 school year at over 17 thousand public and private high schools. The popularity of interscholastic athletic programs has resulted in continuous investigations of the relationship between high school athletic programs and academic performance. ^ The present study extends earlier investigations by examining the relation of athletic participation to several indicators of academic performance for senior high school students. This research examined: (a) average daily attendance of varsity athletes and non-athletes; (b) final cumulative grade point average; and (c) test scores on the tenth grade Florida Comprehensive Assessment Test (FLAT) in both reading and in mathematics. ^ Data were collected on 2081 randomly selected male and female high school students identified as athletes or non-athletes at ten public senior high schools in the Miami-Dade County Public Schools district. The results of the overall analyses showed a positive and significant relationship between athletic participation and educational performance. On average, athletes were absent fewer days from school per year than non-athletes and athletes earned a significantly higher cumulative grade point average than their non-athlete peers. A significant statistical difference was also found in the tenth grade FCAT test scores in both reading and mathematics for athletes and non-athletes when eighth grade FCAT test scores in reading and mathematics were used as co-variates. Athletes earned significantly higher Grade 10 FCAT test scores in both reading and mathematics than non-athletes. ^ Although cause and effect cannot be inferred from this study, the findings do indicate the potentially beneficial value of athletic programs in public secondary education. The study concluded that Florida high school graduation requirements might seriously consider the role of interscholastic athletic programs as a valid and essential extra-curricular activity. ^

The Effect of Carbohydrate Amount, Quality and Type on Arterial Pulse Pressure in Cuban-Americans with and Without Type 2 Diabetes

Background: Arterial pulse pressure, the difference between systolic and diastolic blood pressure, has been used as an indicator (surrogate measure) of arterial stiffness. High arterial pulse pressure (> 40) has been associated with increased cardiovascular disease and mortality. Several clinical trials have reported that the proportion of calories from carbohydrate has an effect on blood pressure. The primary objective of this study was to assess arterial pulse pressure and its association with carbohydrate quantity and quality (glycemic load) with diabetes status for a Cuban American population. Methods: A single point analysis included 367 participants. There was complete data for 365 (190 with and 175 without type 2 diabetes). The study was conducted in the investigator’s laboratory located in Miami, Florida. Demographic, dietary, anthropometric and laboratory data were collected. Arterial pulse pressure was calculated by the formula systolic minus the diastolic blood pressure. Glycemic load, fructose, sucrose, percent of average daily calories from carbohydrate, fat and protein, grams of fiber and micronutrient intakes were calculated from a validated food frequency questionnaire. Results: The mean arterial pulse pressure was significantly higher in participants with (52.9 ± 12.4) than without (48.6 ± 13.4) type 2 diabetes. The odds of persons with diabetes having high arterial pulse pressure (>40) was 1.85 (95% CI =1.09, 3.13); p=0.023. For persons with type 2 diabetes higher glycemic load was associated with lower arterial pulse pressure. Conclusions: Arterial pulse pressure and diet are modifiable risk factors of cardiovascular disease. Arterial pulse pressure may be associated with carbohydrate intake differently considering diabetes status. Results may be due to individuals with diabetes following dietary recommendations. The findings of this study suggest clinicians take into consideration how medical condition, ethnicity and diet are associated with arterial pulse pressure before developing a medical nutrition therapy plan in collaboration with the client.

Understanding Grade Point Average Perspective in the Ninth Grade as Part of an Overall College Plan

This case study explores intervention strategies for social capital improvement of ninth grade students so that they can gain a grade point average perspective.