Browsing by Subject "prediction model"
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Item Epidemiological aspects of Claviceps africana, causal agent of Sorghum ergot(Texas A&M University, 2005-02-17) Noe, Montes Garcia; Noe, Montes GarciaSorghum ergot, caused by Claviceps africana Frederickson, Mantle & de Milliano, is a disease that affects non-fertilized ovaries in sorghum male-sterile plants and infects hybrids if there is pollen sterility at flowering time. Sphacelia containing macroconidia could play a role in the survival of the pathogen. This study developed risk assessment models and evaluated environmental conditions affecting viability of macroconidia and transition from sphacelial to sclerotial tissues. Effect of weather on ergot severity was evaluated under natural conditions (in monthly planting dates) in nine sorghum genotypes at College Station, Weslaco, Rio Bravo, and Celaya. Panicles were inoculated daily beginning at flower initiation with a suspension of 1.6 x 106 C. africana conidia ml-1. Weather triad values were used to identify weather parameters correlated with the disease. Ergot severity was statistically greater in A-lines than hybrids because of the possible interference of pollen on some dates. Celaya had the greatest amount of ergot in hybrids. A-line ATx2752 had the lowest average ergot severity throughout years, locations and planting dates, as did the hybrid NC+8R18. Maximum and minimum temperature had a negative correlation with ergot at Rio Bravo, College Station and Weslaco, while at Celaya it was positive. The highest correlation was 7 to 9 days before initiation of flowering, suggesting that cooler temperatures during this period could cause male sterility. A-lines showed the same relationships between ergot and maximum and minimum temperatures after initiation of flowering. Minimum relative humidity had a positive correlation with ergot after initiation of flowering in both sorghum plant types. Sphacelia stored under cool temperatures (-3oC to 7oC) maintained conidial viability, and newly-formed sphacelia located on the sphacelia surface had the highest conidial viability. However, they show a greater viability reduction through time compared with conidia from older sphacelia, showing that conidial maturity can play a role in the survival of the conidia. Sphacelia on plants grown at 10oC, 20oC and 30oC with low relative humidity did not had any sclerotial development up to 4 weeks after formation of sphacelia. However, higher temperatures promoted an increase in the sphacelia dry weight during that time.Item The prediction of bus arrival time using Automatic Vehicle Location Systems data(Texas A&M University, 2005-02-17) Jeong, Ran HeeAdvanced Traveler Information System (ATIS) is one component of Intelligent Transportation Systems (ITS), and a major component of ATIS is travel time information. The provision of timely and accurate transit travel time information is important because it attracts additional ridership and increases the satisfaction of transit users. The cost of electronics and components for ITS has been decreased, and ITS deployment is growing nationwide. Automatic Vehicle Location (AVL) Systems, which is a part of ITS, have been adopted by many transit agencies. These allow them to track their transit vehicles in real-time. The need for the model or technique to predict transit travel time using AVL data is increasing. While some research on this topic has been conducted, it has been shown that more research on this topic is required. The objectives of this research were 1) to develop and apply a model to predict bus arrival time using AVL data, 2) to identify the prediction interval of bus arrival time and the probabilty of a bus being on time. In this research, the travel time prediction model explicitly included dwell times, schedule adherence by time period, and traffic congestion which were critical to predict accurate bus arrival times. The test bed was a bus route running in the downtown of Houston, Texas. A historical based model, regression models, and artificial neural network (ANN) models were developed to predict bus arrival time. It was found that the artificial neural network models performed considerably better than either historical data based models or multi linear regression models. It was hypothesized that the ANN was able to identify the complex non-linear relationship between travel time and the independent variables and this led to superior results. Because variability in travel time (both waiting and on-board) is extremely important for transit choices, it would also be useful to extend the model to provide not only estimates of travel time but also prediction intervals. With the ANN models, the prediction intervals of bus arrival time were calculated. Because the ANN models are non parametric models, conventional techniques for prediction intervals can not be used. Consequently, a newly developed computer-intensive method, the bootstrap technique was used to obtain prediction intervals of bus arrival time. On-time performance of a bus is very important to transit operators to provide quality service to transit passengers. To measure the on-time performance, the probability of a bus being on time is required. In addition to the prediction interval of bus arrival time, the probability that a given bus is on time was calculated. The probability density function of schedule adherence seemed to be the gamma distribution or the normal distribution. To determine which distribution is the best fit for the schedule adherence, a chi-squared goodness-of-fit test was used. In brief, the normal distribution estimates well the schedule adherence. With the normal distribution, the probability of a bus being on time, being ahead schedule, and being behind schedule can be estimated.