{"type":"ScholarlyArticle","title":"Travel Time Estimation without Road Networks: An Urban Morphological Layout Representation Approach","authors":[{"name":"Wuwei Lan","slug":"wuwei-lan","given":"Wuwei","family":"Lan","affiliation":"Department of Computer Science and Engineering , Ohio State University , Columbus , OH 43210"},{"name":"Yanyan Xu","slug":"yanyan-xu","given":"Yanyan","family":"Xu","affiliation":"Department of City and Regional Planning , University of California , Berkeley , CA 94720"},{"name":"Bin Zhao","slug":"bin-zhao","given":"Bin","family":"Zhao","affiliation":"Wisense AI , Jinan , China"}],"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.","doi":"10.24963/ijcai.2019/245","url":"https://ijcai.org/proceedings/2019/245","pdf":"https://ijcai.org/proceedings/2019/0245.pdf","markdown":"https://ijcai.org/proceedings/2019/245.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/245","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Knowledge Representation and Reasoning","track":"Main track","number":245,"pages":{"first":1772,"last":1778},"keywords":["Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning","Machine Learning: Deep Learning","Machine Learning: Time-series;Data Streams","Multidisciplinary Topics and Applications: Transportation"],"videos":[],"references":[{"position":1,"raw":"[Albert et al., 2017] Adrian Albert, Jasleen Kaur, and Marta C Gonzalez. 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