Learning heterogeneous traffic patterns for travel time prediction of bus journeys
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 rel...
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| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/154492 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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