  Predicting a trip's travel time is essential for route planning and
navigation applications. The majority of research is based on international
data that does not apply to Pakistan's road conditions. We designed a complete
pipeline for mining trajectories from sensors data. On this data, we employed
state-of-the-art approaches, including a shallow artificial neural network, a
deep multi-layered perceptron, and a long-short-term memory, to explore the
issue of travel time prediction on frequent routes. The experimental results
demonstrate an average prediction error ranging from 30 seconds to 1.2 minutes
on trips lasting 10 minutes to 60 minutes on six most frequent routes in
regions of Islamabad, Pakistan.
