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BIDIRECTIONAL LSTM AND KALMAN FILTER FOR PASSENGER FLOW PREDICTION ON BUS TRANSPORTATION SYSTEMS

Title:

BIDIRECTIONAL LSTM AND KALMAN FILTER FOR PASSENGER FLOW PREDICTION ON BUS TRANSPORTATION SYSTEMS

Wood, Hannah, Patterson, Zachary and Bouguila, Nizar (2022) BIDIRECTIONAL LSTM AND KALMAN FILTER FOR PASSENGER FLOW PREDICTION ON BUS TRANSPORTATION SYSTEMS. Masters thesis, Concordia University.

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Abstract

Forecasting travel demand is a complex problem facing public transit operators. Passenger flow prediction is useful not only for operators, used for long-term planning and scheduling, but also for transit users. The time is quickly approaching that short-term passenger flow prediction will be expected as a matter of course by transit users. To address this expectation, a
Bi-directional Long Short-Term Memory Neural Network model (BDLSTM NN) and a Bi-directional Long Short-Term Memory Neural Network Kalman Filter model (BDLSTM KF) predict short-term passenger flow and based on the dependencies between passenger count and spatial-temporal features. A comprehensive preprocessing framework is proposed leveraging historical data and extracting bidirectional features of passenger flow. The proposed model is based on [1] but adapted, applied, and analysed to produce optimal results for passenger flow forecasting on a bus route. Building on [2], a BDLSTM architecture is then combined with a Kalman filter. The Kalman filter reduces the training and testing complexity required for passenger flow forecasting. The BDLSTM-based Kalman filter produces predictions with less uncertainty than each method alone. Evaluating the BDLSTM-based Kalman filter with two months of real-world data, one year apart shows positive improvements for short-term forecasting in high complexity bus networks. It is possible to see that the BDLSTM outperforms traditional machine and deep learning techniques used in this context.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (Masters)
Authors:Wood, Hannah and Patterson, Zachary and Bouguila, Nizar
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Information Systems Security
Date:9 May 2022
Thesis Supervisor(s):Patterson, Zachary and Bouguila, Nizar
ID Code:990579
Deposited By: Hannah Wood
Deposited On:16 Jun 2022 15:20
Last Modified:16 Jun 2022 15:20
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