A theoretical combustion model is developed to simulate the influence of ideal gas effects on various aeroacoustic parameters over a range of equivalence ratios. The motivation is to narrow the gap between laboratory and full-scale jet noise testing. The combustion model is used to model propane combustion in air and kerosene combustion in air. Gas properties from the combustion model are compared to real lab data acquired at the National Center for Physical Acoustics at the University of Mississippi as well as outputs from NASA’s Chemical Equilibrium Analysis code. Different jet properties are then studied over a range of equivalence ratios and pressure ratios for propane combustion in air, kerosene combustion in air and heated air. The findings reveal negligible differences between the three constituents where the density and sound speed ratios are concerned. Albeit, the area ratio required for perfectly expanded flow is shown to be more sensitive to gas properties, relative to changes in the temperature ratio.
The purpose of this study is to evaluate application of a type of supervised machine learning model called support vector machine (SVM) to freeway automatic incident detection. Many automatic incident detection algorithms are focused on identifying changes in traffic patterns but do not adequately investigate similarities in patterns observed under incident-free conditions. The most challenging part of real-time incident detection is recognition of traffic pattern changes when incidents happen during rush hour stop-and-go conditions. Incident detection can be described as a pattern classification problem and SVMs have pattern learning algorithms that have been successfully applied to incident detection. Previous evaluation studies have been based on either simulation data or the I-880 database. The possible issue with these is that non-incident traffic patterns may be biased by actual incident data. This study uses field traffic pattern data to overcome the problem of incident detection during peak hour. Data collected by the Dallas traffic control center including upstream and downstream speed and volume and typical upstream speed profiles. All parameters were used as base model input and different scenarios were defined, in terms of SVM kernel functions (the sigmoid and RBF) and different parameters combination. Cross-validation has been applied to increase classification accuracy. Based on this evaluation, the proposed SVM model provides reliable results.
,” Presented in 94th Annual Meeting of the Transportation Research Board. 2015.Abstract
This paper uses ordered-response model and linear regression model structures to evaluate the demographic and land-use factors that affect truck trip generation at a regional level. The data used for this paper were collected from the business establishments located in Williamson County, Texas through a mailout-mailback survey conducted in year 2014. The paper presents the empirical results and discusses the policy implicationsof these results for urban planning. Model results show that industry type, size and the location of business establishments affect their truck trip generation behavior. Business establishments with larger number of employees are more likely to attract more truck traffic, whereas, businesses owning their trucks are more likely to produce more truck traffic. Businesses located in areas with higher land-values tend to generate less truck traffic whereas businesses located in industrial land-use types are likely to generate more truck traffic.
This paper examines bicyclist injury severity in bicycle-motor vehicle crashes using the 2012 Texas Department of Transportation (TxDOT) Crash Records Information System (CRIS). Three different modeling frameworks are used: a binary logit, an ordered logit, and a multinomial logit model framework. All bike-motor crashes that involved a single motor vehicle and a single bicyclist are included. Three data sub-sets are examined to identify bike-motor crash risk factors and injury severity levels. These include all bike-motor vehicle crash data, only intersection related crashes and only non-intersection related crashes. Model results indicate that the common factors that affect all crashes include bicyclist and motor vehicle driver demographic characteristics, land use characteristics of the crash location, motor vehicle body type, and roadway speed limit. Motor vehicle driver age (age < 35 years), alcohol intoxication, and bicyclist age (age > 60 years) have larger effects on the bicyclist injury severity for intersection related crashes. Roadway speed (speed > 50 mph), road geometry (horizontal curve), and time of day have greater effects on bicyclist injury severity for non-intersection related crashes. Results of this study can help educate road users, improve traffic regulations, and also suggest roadway safety feature designs to enhance safety.
,” Presented at the 2014 Workshops on Big Data and Urban Informatics at the University of Illinois, Chicago. 2014. Publisher's VersionAbstract
Carsharing is an innovative transportation mobility solution which offers the benefits of a personal vehicle without the burden of ownership. Free-floating carsharing service is a relatively new concept and is gaining popularity because it offers additional flexibility allowing one-way auto rental and charging users usage by minute. Traditionally, carsharing services require returning the rented vehicle to the same location where rented with a minimum rental duration. Since free-floating service is a very new addition in the overall transportation system, the empirical research is still very limited. This study focuses on identifying the impact of land-use variables on free-floating carsharing vehicle rental choice and parking duration of Car2Go services in Austin, Texas on a typical weekday between 9:00 AM to 12:00 PM. Two different methodological approaches, namely a logistic regression model approach and a duration model technique, are used for this purpose. The results of this study indicate that land-use level demographic variables, the carsharing parking policy, and numbers of transit stops effect the usage of free-floating carsharing vehicles.