Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
