  Estimating the travel time for a given path is a fundamental problem in many
urban transportation systems. However, prior works fail to well capture moving
behaviors embedded in paths and thus do not estimate the travel time
accurately. To fill in this gap, in this work, we propose a novel neural
network framework, namely {\em Deep Image-based Spatio-Temporal network
(DeepIST)}, for travel time estimation of a given path. The novelty of DeepIST
lies in the following aspects: 1) we propose to plot a path as a sequence of
"generalized images" which include sub-paths along with additional information,
such as traffic conditions, road network and traffic signals, in order to
harness the power of convolutional neural network model (CNN) on image
processing; 2) we design a novel two-dimensional CNN, namely {\em PathCNN}, to
extract spatial patterns for lines in images by regularization and adopting
multiple pooling methods; and 3) we apply a one-dimensional CNN to capture
temporal patterns among the spatial patterns along the paths for the
estimation. Empirical results show that DeepIST soundly outperforms the
state-of-the-art travel time estimation models by 24.37\% to 25.64\% of mean
absolute error (MAE) in multiple large-scale real-world datasets.
