Show simple item record

AuthorGhanim, Mohammad S.
AuthorShaaban, Khaled
AuthorMiqdad, Motasem
Available date2020-04-30T12:59:33Z
Publication Date2020
Publication NameProceedings of the International Conference on Civil Infrastructure and Construction
CitationGhanim M. S., Shaaban K., Miqdad M., An Artificial Intelligence Approach to Estimate Travel Time along Public Transportation Bus Lines, International Conference on Civil Infrastructure and Construction (CIC 2020), Doha, Qatar, 2-5 February 2020, DOI: https://doi.org/10.29117/cic.2020.0074
ISSN2958-3128
IdentifierP. O. Box: 2713 Doha-Qatar, Email: qupress@qu.edu.qa
URIwww.cic.qa
URIhttp://dx.doi.org/10.29117/cic.2020.0074
URIhttp://hdl.handle.net/10576/14760
AbstractPublic transportation sectors have played significant roles in accommodating passengers and commodities efficiently and effectively. The modes of public transportation often follow pre-defined operation schedules and routes. Therefore, planning these schedules and routes requires extensive efforts in analyzing the built environment and collecting demand data. Once a transit route is operational as an example, collecting and maintaining real-life information becomes an important task to evaluate service quality using different Key Performance Indicators (KPIs). One of these KPIs is transit travel time along the route. This paper aims to develop a transit travel time prediction model using an artificial intelligence approach. In this study, 12 public bus routes serving the Greater City of Doha were selected. While the ultimate goal is to predict transit travel time from the start to the end of the journeys collected over a period of one-year, routespecific inputs were used as inputs for this prediction. To develop a generalized model, the input variables for the transit route included the number and type of intersections, number of each type of turning movements and the built environment. An Artificial Neural Networks (ANN) model is used to process 78,004 valid datasets. The results indicate that the ANN model is capable of providing reliable and accurate transit travel time estimates, with a coefficient of determination (R2) of 0.95. Transportation planners and public transportation operators can use the developed model as a tool to estimate the transit travel time.
Languageen
PublisherQatar Univesrity Press
SubjectPublic transportation
Artificial neural networks
Travel time prediction
TitleAn Artificial Intelligence Approach to Estimate Travel Time along Public Transportation Bus Lines
TypeConference Paper
Pagination588-595
ESSN2958-3136
dc.accessType Open Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record