Forecasting of Bicycle and Pedestrian Traffic Using Flexible and Efficient Hybrid Deep Learning Approach
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Science and Engineering (CEMSE) DivisionStatistics Program
KAUST Grant Number
OSR-2019-CRG7-3800Date
2022-04-28Permanent link to this record
http://hdl.handle.net/10754/676703
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Recently, increasing interest in managing pedestrian and bicycle flows has been demonstrated by cities and transportation professionals aiming to reach community goals related to health, safety, and the environment. Precise forecasting of pedestrian and bicycle traffic flow is crucial for identifying the potential use of bicycle and pedestrian infrastructure and improving bicyclists’ safety and comfort. Advances in sensory technology enable collecting massive traffic flow data, including road traffic, bicycle, and pedestrian traffic flow. This paper introduces a novel deep hybrid learning model with a fully guided-attention mechanism to improve bicycles and pedestrians’ traffic flow forecasting. Notably, the proposed approach extends the modeling capability of the Variational Autoencoder (VAE) by merging a long short-term memory (LSTM) model with the VAE’s decoder and using a self-attention mechanism at multi-stage of the VAE model (i.e., decoder and before data resampling). Specifically, LSTM improves the VAE decoder’s capacity in learning temporal dependencies, and the guided-attention units enable selecting relevant features based on the self-attention mechanism. This proposed deep hybrid learning model with a multi-stage guided-attention mechanism is called GAHD-VAE. Proposed methods were validated with traffic measurements from six publicly available pedestrian and bicycle traffic flow datasets. The proposed method provides promising forecasting results but requires no assumptions that the data are drawn from a given distribution. Results revealed that the GAHD-VAE methodology can efficiently enhance the traffic forecasting accuracy and achieved better performance than the deep learning methods VAE, LSTM, gated recurrent units (GRUs), bidirectional LSTM, bidirectional GRU, convolutional neural network (CNN), and convolutional LSTM (ConvLSTM), and four shallow methods, linear regression, lasso regression, ridge regression, and support vector regression.Citation
Harrou, F., Dairi, A., Zeroual, A., & Sun, Y. (2022). Forecasting of Bicycle and Pedestrian Traffic Using Flexible and Efficient Hybrid Deep Learning Approach. Applied Sciences, 12(9), 4482. https://doi.org/10.3390/app12094482Sponsors
Supported by funding from King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7-3800.Publisher
MDPI AGJournal
Applied SciencesAdditional Links
https://www.mdpi.com/2076-3417/12/9/4482ae974a485f413a2113503eed53cd6c53
10.3390/app12094482
Scopus Count
Except where otherwise noted, this item's license is described as Archived with thanks to Applied Sciences under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0/