Using a Neural Network to Analyze the Impact of Passenger Activity on Bus Dwell Time and Travel Time
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This paper applies neural network modeling approach to analyze the impact of passenger activities
on bus dwell time and station-to-station travel time. Data used to develop the model was collected by
onboard AVL/APC devices. Sensitivity analyses based on a trained neural network were performed
to evaluate the relative significance of each passenger activity variable to variation of dwell time
and/or station-to-station travel time. Transit providers can use these methods to identify the causes of
schedule deviation and to develop improvement measures that are most effective to transit service.