| Abstract | The state of Qatar has experienced rapid population growth over the last few years.
This growth of population has caused authorities to promote the use of public
transportation, by introducing new public transport systems such as transit buses
and metro lines. The existing bus system was introduced in 2004 to the local
community in Qatar. Despite the importance of this system, there are limited studies
that are done to analyze and identify its characteristics. There is not much analysis
of the stop-to-stop travel time or schedule reliability. The objective of this research
is to develop a prediction model for transit route travel time. The model can predict
the travel time of buses using several independent variables that are different for
each transit route. The prediction model can be used as a useful tool to the decision
makers and public transport officials, which can be used for planning, system
reliability and quality control, and real-time advanced travelers’ information
systems.
The data was collected for 12 routes over a period of one year (2015-2016) within
The Greater City of Doha using Automatic Vehicle Location (AVL) system. Transit
travel time data was obtained from Mowasalat records, the sole operator of public
transport buses in Qatar. The collected data include travel time data, route
information, geometric configurations, land use, and traffic data. After systematic
checking of errors in the collected data and elimination of irreverent records, more
than 78,004 trips were analyzed using Artificial Neural Networks (ANN) data
mining technique. Prediction model, with R2 of 0.95 was developed. The results
indicate that the developed model is accurate and reliable in predicting the travel time. The model can be generalized as well to be applied to newly planned routes,
or updating the schedules of existing routes. |
