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. ^

The Food Service Industry: Beliefs Held by Academics

In his study - The Food Service Industry: Beliefs Held by Academics - by Jack Ninemeier, Associate Professor, School of Hotel, Restaurant and Institutional Management at Michigan State University, Associate Professor Ninemeier initially describes his study this way: “Those in the academic sector exert a great deal of influence on those they are training to enter the food service industry. One author surveyed educational institutions across the country to ascertain attitudes of teachers toward various segments of the industry.” Those essential segments of the industry serve as the underpinnings of this discussion and are four-fold. They are lodging, institutional, multi-unit, and single-unit properties. For each segment the analysis addressed factors relating to Marketing, management and operating concerns: Marketing, operations, fiscal management, innovation, future of the segment Employee-related concerns: quality of work life, training/education opportunities, career opportunities The study uses a survey of academicians as a guide; they point to segments of the food service industry students might be inclined to enter, or even ignore. The survey was done via a questionnaire sent from the campus of the School of Hotel, Restaurant and Institutional Management at Michigan State University to 1850 full-time faculty members in two and four-year hospitality programs in the United States. Through the survey, Ninemeier wishes to reasonably address specific problems now confronting the food service industry. Those problems include but are not limited to: reducing employee turnover, retaining staff, increasing productivity and revenue, and attracting new staff. “Teachers in these programs are, therefore, an important plank in industry's platform designed to recruit students with appropriate background knowledge and interest in their operations,” Ninemeier says. Your author actually illustrates the survey results, in table form. The importance to an employee, of tangibles and intangibles such as morale, ego/esteem, wages, and benefits are each explored through the survey. According to the study, an interesting dichotomy exists in the institutional property element. Although, beliefs the academics hold about the institutional element suggest that it offers low job stress, attractive working conditions, and non-demanding competitive pressures, the survey and Ninemeier also observe: “Academics do not believe that many of their graduates will enter the institutional segment.” “If academic beliefs are incorrect, an educational program to educate academics about management and employee opportunities in the segment may be in order,” Ninemeier waxes philosophically.