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المؤلفGhanim, Mohammad S.
المؤلفShaaban, Khaled
المؤلفMiqdad, Motasem
تاريخ الإتاحة2020-04-30T12:59:33Z
تاريخ النشر2020
اسم المنشورProceedings of the International Conference on Civil Infrastructure and Construction
الاقتباسGhanim 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
الرقم المعياري الدولي للكتاب2958-3128
المعرّفP. O. Box: 2713 Doha-Qatar, Email: qupress@qu.edu.qa
معرّف المصادر الموحدwww.cic.qa
معرّف المصادر الموحدhttp://dx.doi.org/10.29117/cic.2020.0074
معرّف المصادر الموحدhttp://hdl.handle.net/10576/14760
الملخصPublic 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.
اللغةen
الناشرQatar Univesrity Press
الموضوعPublic transportation
Artificial neural networks
Travel time prediction
العنوانAn Artificial Intelligence Approach to Estimate Travel Time along Public Transportation Bus Lines
النوعConference Paper
الصفحات588-595
ESSN2958-3136
dc.accessType Open Access


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