Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154492
Title: Learning heterogeneous traffic patterns for travel time prediction of bus journeys
Authors: He, Peilan
Jiang, Guiyuan
Lam, Siew-KeI
Sun, Yidan
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: He, P., Jiang, G., Lam, S. & Sun, Y. (2020). Learning heterogeneous traffic patterns for travel time prediction of bus journeys. Information Sciences, 512, 1394-1406. https://dx.doi.org/10.1016/j.ins.2019.10.073
Journal: Information Sciences
Abstract: In this paper, we address the problem of travel time prediction of bus journeys which consist of bus riding times (may involve multiple bus services) and also the waiting times at transfer points. We propose a novel method called Traffic Pattern centric Segment Coalescing Framework (TP-SCF) that relies on learned disparate patterns of traffic conditions across different bus line segments for bus journey travel time prediction. Specifically, the proposed method consists of a training and a prediction stage. In the training stage, the bus lines are partitioned into bus line segments and the common travel time patterns of segments from different bus lines are explored using Non-negative Matrix Factorization (NMF). Bus line segments with similar patterns are classified into the same cluster. The clusters are then coalesced in order to extract data records for model training and bus journey time prediction. A separate Long Short Term Memory (LSTM) based model is trained for each cluster to predict the bus travel time under various traffic conditions. During prediction, a given bus journey is partitioned into the riding time components and waiting time components. The riding time components are predicted using the corresponding LSTM models of the clusters while the waiting time components are estimated based on historical bus arrival time records. We evaluated our method on large scale real-world bus travel data involving 30 bus services, and the results show that the proposed method notably outperforms the state-of-the-art approaches for all the scenarios considered.
URI: https://hdl.handle.net/10356/154492
ISSN: 0020-0255
DOI: 10.1016/j.ins.2019.10.073
Rights: © 2019 Elsevier Inc. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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