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Real-time Travel Time Estimation with Sparse Reliable Surveillance Information

Published: 18 March 2020 Publication History

Abstract

Origin-destination (OD) travel time estimation is of paramount importance for applications such as intelligent transportation. In this work, we propose a new solution for OD travel time estimation, with road surveillance camera data. The surveillance information supports accurate and reliable observations at camera-equipped intersections, but is associated with missing and incomplete surveillance records at the camera-free intersections. To overcome this, we propose a modified version of multi-layer graph convolutional networks. The camera surveillance data is used to extract the traffic flow of each intersection, the extracted information serves as the input of the multi-layer GCN based model, based on which the real-time traffic status can be predicted. To enhance the estimation accuracy, we address the effects of various features for the travel time estimation with encoder-decoder networks and embedding techniques. We further improve the generalization of our model by using multi-task learning. Extensive experiments on real datasets are done to verify the effectiveness of our proposals.

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Cited By

View all
  • (2023)Data-Driven Methods for Travel Time Estimation: A Survey2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC57777.2023.10422502(1292-1299)Online publication date: 24-Sep-2023
  • (2022)Fine-Grained Trajectory-Based Travel Time Estimation for Multi-City Scenarios Based on Deep Meta-LearningIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.314538223:9(15716-15728)Online publication date: Sep-2022
  • (2022)Travel Time Prediction Using Hybridized Deep Feature Space and Machine Learning Based Heterogeneous EnsembleIEEE Access10.1109/ACCESS.2022.320638410(98127-98139)Online publication date: 2022
  • Show More Cited By

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 4, Issue 1
March 2020
1006 pages
EISSN:2474-9567
DOI:10.1145/3388993
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 March 2020
Published in IMWUT Volume 4, Issue 1

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Author Tags

  1. embedding
  2. encoder-decoder networks
  3. multi-layer graph convolutional networks
  4. travel time

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  • Research-article
  • Research
  • Refereed

Funding Sources

  • CAS Pioneer Hundred Talents Program
  • NSFC
  • Anhui Science Foundation for Distinguished Young Scholars
  • Jiangsu Natural Science Foundation

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Cited By

View all
  • (2023)Data-Driven Methods for Travel Time Estimation: A Survey2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC57777.2023.10422502(1292-1299)Online publication date: 24-Sep-2023
  • (2022)Fine-Grained Trajectory-Based Travel Time Estimation for Multi-City Scenarios Based on Deep Meta-LearningIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.314538223:9(15716-15728)Online publication date: Sep-2022
  • (2022)Travel Time Prediction Using Hybridized Deep Feature Space and Machine Learning Based Heterogeneous EnsembleIEEE Access10.1109/ACCESS.2022.320638410(98127-98139)Online publication date: 2022
  • (2021)Traffic Light Optimization Based on Modified Webster FunctionJournal of Advanced Transportation10.1155/2021/33282022021(1-10)Online publication date: 3-Aug-2021
  • (2020)SnacapProceedings of the 2020 International Symposium on Wearable Computers10.1145/3410531.3414305(96-100)Online publication date: 4-Sep-2020

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