Travel Time Estimation without Road Networks: An Urban Morphological Layout Representation Approach

TL;DR

This paper introduces DeepI2T, a novel deep neural model that estimates travel time using urban morphological layout images, outperforming existing methods and applicable to various city planning scenarios.

Contribution

The paper presents a new end-to-end multi-task neural model that leverages built environment images for travel time estimation, bypassing explicit feature modeling.

Findings

Achieved state-of-the-art performance on real-world datasets from two cities.

Effectively handles both path-aware and path-blind scenarios.

Demonstrated the utility of morphological layout images in travel time prediction.

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 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.