An Artificial Intelligence Approach to Estimate Travel Time along Public Transportation Bus Lines
Abstract
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.
Collections
- Civil and Environmental Engineering [851 items ]
- Theme 2: Materials and Transportation Engineering [43 items ]