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|Title:||Bus travel speed prediction using attention network of heterogeneous correlation features||Authors:||Yidan, Sun
|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/
|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|
Updated on Jun 22, 2022
Updated on Jun 22, 2022
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