Travel Time Estimation without Road Networks: An Urban Morphological
Layout Representation Approach
Abstract
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 fi-
nal 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 landuse 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 effi-
ciently. 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