Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147444
Title: Bus travel speed prediction using attention network of heterogeneous correlation features
Authors: Yidan, Sun
Jiang, Guiyuan
Lam, Siew-Kei
Chen, Shicheng
He, Peilan
Keywords: Engineering::Computer science and engineering::Computer applications::Social and behavioral sciences
Issue Date: 2019
Source: Yidan, S., Jiang, G., Lam, S., Chen, S. & He, P. (2019). Bus travel speed prediction using attention network of heterogeneous correlation features. Proceedings of the 2019 SIAM International Conference on Data Mining (SDM), 73-81. https://dx.doi.org/10.1137/1.9781611975673.9
Abstract: Accurate bus travel speed prediction can lead to improved urban mobility by enabling passengers to reliably plan their trips in advance and traffic administrators to manage the bus operations more effectively. However, the increasing complexity of public transportation networks pose a significant challenge to existing prediction methods as the bus operations are affected by numerous factors such as varying traffic conditions, tight bus operation schedules, wideranging travel demands, frequent accelerations/decelerations at bus stops, delays at intersections, etc. This paper aims to achieve accurate bus speed prediction by identifying important intrinsic and extrinsic features that impact the bus speed, and their significance in specific situations. We propose to jointly incorporate multiple feature components that provide discriminating information to train the prediction model by exploring the spatial correlation, temporal correlation, as well as contextual information (e.g. road characteristics and weather conditions). In particular, we introduce an attribute-driven attention network model to integrate the feature components, which considers the heterogeneous influence of different feature components on bus speed and dynamically assigns weights to the learned latent features based on specific traffic situations. Extensive experiments using real bus travel data involving 42 bus services show that our proposed method outperforms six well-known methods.
URI: http://www.siam.org/
https://hdl.handle.net/10356/147444
DOI: 10.1137/1.9781611975673.9
Rights: © 2019 Society for Industrial and Applied Mathematics (SIAM). All rights reserved. This paper was published in Proceedings of the 2019 SIAM International Conference on Data Mining (SDM) and is made available with permission of Society for Industrial and Applied Mathematics (SIAM).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

Files in This Item:
File Description SizeFormat 
SDM1900.pdfBus Travel Speed Prediction using Attention Network of Heterogeneous Correlation Features574.44 kBAdobe PDFView/Open

Page view(s)

61
Updated on Jun 22, 2022

Download(s) 50

17
Updated on Jun 22, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.