Using a Neural Network to Analyze the Impact of Passenger Activity on Bus Dwell Time and Travel Time

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.


Issue Date:
2005
Publication Type:
Journal Article
PURL Identifier:
http://purl.umn.edu/206774
Published in:
Journal of the Transportation Research Forum, Volume 44, Number 3
Page range:
131-141
Total Pages:
12




 Record created 2017-04-01, last modified 2017-08-28

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