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. ^

Hydroperiod and seasonal effects on fish decomposition in an oligotrophic Everglades marsh

In the Everglades, the majority of fish detrital inputs occur during the dry scason, when waterlevel drawdown reduces aquatic habitat. While these mortality events are highly seasonal, the remineralization and recycling of fish detrital nutrients may represent an important stimulus to the ecosystem in the following wet season. The goal of this study was to quantify the rate of detrital fish decomposition during three periods of the year to determine seasonal variations in decomposition patterns in this ecosystem. A multiple regression analysis showed that hydroperiod and water depth both played a role in determining fish decomposition rates within this ecosystem. Decomposition rates ranged from a low of 13% day−1 in December 2000 to a high of 50% day−1 in April 2001, the height of the dry season. Phosphorus analysis showed that Gambusia holbrooki, the dominant small fish species in the Everglades, contains 7.169±1.46 mg P g−1 wet fish weight. Based on the observed decomposition rates and the average biomass added, the estimafed daily flux of phosphorus from the experimental detrital loading ranged from a low of 27.04 mg P day−1 to a high of 108.14 mg P day−1 during the decomposition period. We estimated that these inputs could represent an input of 43 μg P m−2 day−1 to the total temporal Everglades phosphorus budget. Although much of this phosphorus is likely incorporated into the macroinvertebrate pool, detrital inputs peak during the dry season when nutrients are most likely to be incorporated into the soil and occur when decomposition of vegetative material is moisture-limited. These inputs may therefore play an important role in stimulating vegetative production during the early wet season.