  In building intelligent transportation systems such as taxi or rideshare
services, accurate prediction of travel time and distance is crucial for
customer experience and resource management. Using the NYC taxi dataset, which
contains taxi trips data collected from GPS-enabled taxis [23], this paper
investigates the use of deep neural networks to jointly predict taxi trip time
and distance. We propose a model, called ST-NN (Spatio-Temporal Neural
Network), which first predicts the travel distance between an origin and a
destination GPS coordinate, then combines this prediction with the time of day
to predict the travel time. The beauty of ST-NN is that it uses only the raw
trips data without requiring further feature engineering and provides a joint
estimate of travel time and distance. We compare the performance of ST-NN to
that of state-of-the-art travel time estimation methods, and we observe that
the proposed approach generalizes better than state-of-the-art methods. We show
that ST-NN approach significantly reduces the mean absolute error for both
predicted travel time and distance, about 17% for travel time prediction. We
also observe that the proposed approach is more robust to outliers present in
the dataset by testing the performance of ST-NN on the datasets with and
without outliers.
