Public 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.

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Published on 01/01/2020

Volume 2020, 2020
DOI: 10.29117/cic.2020.0074
Licence: CC BY-NC-SA license

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