  Travel time estimation is a crucial task for not only personal travel
scheduling but also city planning. Previous methods focus on modeling toward
road segments or sub-paths, then summing up for a final prediction, which have
been recently replaced by deep neural models with end-to-end training. Usually,
these methods are based on explicit feature representations, including
spatio-temporal features, traffic states, etc. Here, we argue that the local
traffic condition is closely tied up with the land-use and built environment,
i.e., metro stations, arterial roads, intersections, commercial area,
residential area, and etc, yet the relation is time-varying and too complicated
to model explicitly and efficiently. Thus, this paper proposes an end-to-end
multi-task deep neural model, named Deep Image to Time (DeepI2T), to learn the
travel time mainly from the built environment images, a.k.a. the morphological
layout images, and showoff the new state-of-the-art performance on real-world
datasets in two cities. Moreover, our model is designed to tackle both
path-aware and path-blind scenarios in the testing phase. This work opens up
new opportunities of using the publicly available morphological layout images
as considerable information in multiple geography-related smart city
applications.
