Deep Architecture for Citywide Travel Time Estimation Incorporating Contextual Information

Kun Tang, Shuyan Chen, Aemal J. Khattak, Yingjiu Pan

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

To meet the growing demand of accurate and reliable travel time information in intelligent transportation systems, this a develops a deep architecture incorporating contextual information to estimate travel time in urban road network from a citywide perspective. First, several categories of features that affect travel time significantly are analyzed and extracted. On this basis, a deep architecture, which utilizes sparse denoising auto-encoders as building blocks, is proposed to learn the feature representations for travel time estimation. To train the deep architecture successfully, a greedy layer-wise semi-unsupervised learning algorithm is devised. The proposed approach inherently incorporates both the geographical features and contextual features, and accounts for the spatial correlation of adjacent road segments. It is a deep architecture with powerful modeling capabilities for the complex nonlinear phenomena in transportation. The information contained in the huge amount of unlabeled data are fully extracted and utilized. The feature representations for estimation are adaptively learned layer by layer from the input with an unsupervised fashion. The proposed model is applied to the real case study of the road network in Beijing, China, based on the large-scale GPS trajectories collected from a sample of taxicabs. Empirical results on extensive experiments demonstrate that the novel deep architecture provides a promising and robust approach for citywide travel time estimation, and outperforms the competing methods.

Fingerprint

Travel Time
Travel time
Road Network
Taxicabs
Intelligent Transportation Systems
Nonlinear Phenomena
Unsupervised learning
Unsupervised Learning
Spatial Correlation
Denoising
Encoder
Building Blocks
Learning algorithms
Global positioning system
Learning Algorithm
China
Adjacent
Trajectories
Architecture
Trajectory

Keywords

  • Contextual features
  • data-driven
  • deep learning
  • sparse denoising auto-encoder
  • urban road network

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Automotive Engineering
  • Aerospace Engineering
  • Computer Science Applications
  • Applied Mathematics

Cite this

@article{59a7967cc6574608a40494463a6bf230,
title = "Deep Architecture for Citywide Travel Time Estimation Incorporating Contextual Information",
abstract = "To meet the growing demand of accurate and reliable travel time information in intelligent transportation systems, this a develops a deep architecture incorporating contextual information to estimate travel time in urban road network from a citywide perspective. First, several categories of features that affect travel time significantly are analyzed and extracted. On this basis, a deep architecture, which utilizes sparse denoising auto-encoders as building blocks, is proposed to learn the feature representations for travel time estimation. To train the deep architecture successfully, a greedy layer-wise semi-unsupervised learning algorithm is devised. The proposed approach inherently incorporates both the geographical features and contextual features, and accounts for the spatial correlation of adjacent road segments. It is a deep architecture with powerful modeling capabilities for the complex nonlinear phenomena in transportation. The information contained in the huge amount of unlabeled data are fully extracted and utilized. The feature representations for estimation are adaptively learned layer by layer from the input with an unsupervised fashion. The proposed model is applied to the real case study of the road network in Beijing, China, based on the large-scale GPS trajectories collected from a sample of taxicabs. Empirical results on extensive experiments demonstrate that the novel deep architecture provides a promising and robust approach for citywide travel time estimation, and outperforms the competing methods.",
keywords = "Contextual features, data-driven, deep learning, sparse denoising auto-encoder, urban road network",
author = "Kun Tang and Shuyan Chen and Khattak, {Aemal J.} and Yingjiu Pan",
year = "2019",
month = "1",
day = "1",
doi = "10.1080/15472450.2019.1617141",
language = "English (US)",
journal = "Journal of Intelligent Transportation Systems",
issn = "1547-2450",
publisher = "Taylor and Francis Ltd.",

}

TY - JOUR

T1 - Deep Architecture for Citywide Travel Time Estimation Incorporating Contextual Information

AU - Tang, Kun

AU - Chen, Shuyan

AU - Khattak, Aemal J.

AU - Pan, Yingjiu

PY - 2019/1/1

Y1 - 2019/1/1

N2 - To meet the growing demand of accurate and reliable travel time information in intelligent transportation systems, this a develops a deep architecture incorporating contextual information to estimate travel time in urban road network from a citywide perspective. First, several categories of features that affect travel time significantly are analyzed and extracted. On this basis, a deep architecture, which utilizes sparse denoising auto-encoders as building blocks, is proposed to learn the feature representations for travel time estimation. To train the deep architecture successfully, a greedy layer-wise semi-unsupervised learning algorithm is devised. The proposed approach inherently incorporates both the geographical features and contextual features, and accounts for the spatial correlation of adjacent road segments. It is a deep architecture with powerful modeling capabilities for the complex nonlinear phenomena in transportation. The information contained in the huge amount of unlabeled data are fully extracted and utilized. The feature representations for estimation are adaptively learned layer by layer from the input with an unsupervised fashion. The proposed model is applied to the real case study of the road network in Beijing, China, based on the large-scale GPS trajectories collected from a sample of taxicabs. Empirical results on extensive experiments demonstrate that the novel deep architecture provides a promising and robust approach for citywide travel time estimation, and outperforms the competing methods.

AB - To meet the growing demand of accurate and reliable travel time information in intelligent transportation systems, this a develops a deep architecture incorporating contextual information to estimate travel time in urban road network from a citywide perspective. First, several categories of features that affect travel time significantly are analyzed and extracted. On this basis, a deep architecture, which utilizes sparse denoising auto-encoders as building blocks, is proposed to learn the feature representations for travel time estimation. To train the deep architecture successfully, a greedy layer-wise semi-unsupervised learning algorithm is devised. The proposed approach inherently incorporates both the geographical features and contextual features, and accounts for the spatial correlation of adjacent road segments. It is a deep architecture with powerful modeling capabilities for the complex nonlinear phenomena in transportation. The information contained in the huge amount of unlabeled data are fully extracted and utilized. The feature representations for estimation are adaptively learned layer by layer from the input with an unsupervised fashion. The proposed model is applied to the real case study of the road network in Beijing, China, based on the large-scale GPS trajectories collected from a sample of taxicabs. Empirical results on extensive experiments demonstrate that the novel deep architecture provides a promising and robust approach for citywide travel time estimation, and outperforms the competing methods.

KW - Contextual features

KW - data-driven

KW - deep learning

KW - sparse denoising auto-encoder

KW - urban road network

UR - http://www.scopus.com/inward/record.url?scp=85066912635&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85066912635&partnerID=8YFLogxK

U2 - 10.1080/15472450.2019.1617141

DO - 10.1080/15472450.2019.1617141

M3 - Article

AN - SCOPUS:85066912635

JO - Journal of Intelligent Transportation Systems

JF - Journal of Intelligent Transportation Systems

SN - 1547-2450

ER -