Automatic passenger counter (APC) systems have been implemented in various public transit systems to obtain bus occupancy along with other information such as location, travel time, etc. Such information has great potential as input data for a variety of applications including performance evaluation, operations management, and service planning. In this study, a dynamic model for predicting bus-arrival times is developed using data collected by a real-world APC system. The model consists of two major elements: the first one is an artificial neural network model for predicting bus travel time between time points for a trip occurring at given time-of-day, day-ofweek, and weather condition; the second one is a Kalman filter-based dynamic algorithm to adjust the arrival-time prediction using up-to-the-minute bus location information. Test runs show that this model is quite powerful in modeling variations in bus-arrival times along the service route.
Short-term travel-time prediction is very important to the real-time traveler information and route guidance system. Various methodologies have been developed for dynamic travel-time prediction. However, most existing studies assume that path travel time is the simple addition of travel times on the consisting links. Through simulation, it is shown that, under recurrent traffic conditions, direct measuring of path-based (or movement-based) travel time rather than link-based travel time could generate a more accurate prediction. Factors that would have an effect on the prediction accuracy are analyzed.
Observations of river inundation areas, water levels, and flow variability from orbital sensors have the potential to directly measure the runoff component of the Earth's hydrologic cycle [Birkett et al., 2002; Brakenridge et al., 1998; Sippel et al., 1994, 1998; Townsend, 2001]. A remote‐sensing‐based measurement strategy for rivers and streams is emerging: Surface water data can be collected, their accuracy evaluated, and the results disseminated without regard to political boundaries. The results can be used to address a wide variety of applications.
In this article, the needs for such measurements, a river reach‐based methodology for their collection, and some sample results are presented. Because the international observational capability is increasing, some future opportunities for improving this strategy are also described.
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