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Zachary Patterson - IEEE Xplore Author Profile

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Author details

Zachary Patterson

Publications

13

Citations

130

Publications by Year

20172024

Co-Authors:

Muhammad AzamGodwin Badu-MarfoAsiye BaghbaniKairavi BajajNizar Bouguila

Show All Co-Authors (19)

Zachary Patterson

Affiliation

Concordia Institute for Information Systems Engineering

Concordia University

Montreal, Canada

Publication Topics

Convolutional Neural Network,
Graph Convolution,
Neural Network,
Graph Neural Networks,
Graph Convolutional Network,
Recurrent Neural Network,
Root Mean Square Error,
Spatial Dependence,
Autoregressive Integrated Moving Average,
Bus Stop,
Deep Learning,
Hidden State

Biography

Zachary Patterson (Member, IEEE) received the Ph.D. degree from McGill University. He was a Post-Doctoral Researcher with the Transport and Mobility Laboratory (TRANSP-OR), Ecole Polytechnique Federale de Lausanne, Switzerland. He was an Assistant and Associate Professor with the Department of Geography, Planning Environment, Concordia University, in 2010. In 2021, he joined the Concordia Institute for Information Systems Engineering (CIISE). Professionally, he worked as a Transportation Modeler with the Montreal Regional Public Transportation Planner, l’Agence Metropolitan de Transport. His research focuses on using emerging technologies in transport-related data collection, computational geospatial data processing, data analysis and infer...

Publications

13

Citations

130

Publications by Year

20172024

Co-Authors:

Muhammad Azam
Godwin Badu-Marfo
Asiye Baghbani
Kairavi Bajaj
Nizar Bouguila

Show All Co-Authors (19)

Author's Published Works

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Showing 1-13 of 13 results

Conferences (10)

Journals (3)

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Single Year
Zachary Patterson(13)
Nizar Bouguila(8)
Asiye Baghbani(3)
Saeed Rahmani(2)
Ali Yazdizadeh(2)
Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada(5)
Department of Transport and Planning, Delft University of Technology, Delft, The Netherlands(2)
Transportation Research for Integrated Planning Lab, Concordia University, Montréal, Québec, Canada(1)
Concordia Institute of Information System Engineering, Concordia University, Montréal, Québec, Canada(1)
Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC, Canada(1)
IEEE Transactions on Intelligent Transportation Systems(3)
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)(2)
2023 International Conference on Machine Learning and Applications (ICMLA)(2)
2017 International Smart Cities Conference (ISC2)(1)
2022 IEEE International Conference on Industrial Technology (ICIT)(1)
IEEE(13)
Bilbao, Spain(2)
Jacksonville, FL, USA(2)
Laguna Hills, CA, USA(2)
Montreal, QC, Canada(1)
Shanghai, China(1)
Convolutional Neural Network(7)
Neural Network(7)
Graph Convolution(6)
Graph Neural Networks(5)
Graph Convolutional Network(4)

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A Privacy-Preserving Edge Computing Solution for Real-Time Passenger Counting at Bus Stops using Overhead Fisheye Camera

Pardis Ghaziamin;Kairavi Bajaj;Nizar Bouguila;Zachary Patterson

2024 IEEE 18th International Conference on Semantic Computing (ICSC)

Year: 2024 | Conference Paper |

Cited by: Papers (2)

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Successful transit planning in smart cities requires automated and efficient passenger counts at the bus stop while also respecting the privacy of passengers-a paramount consideration in the age of responsible AI. In this paper, we describe the implementation and development of a real-time passenger counting system at the bus stop on Nvidia Edge devices with limited battery powered by solar panels...Show More

A Privacy-Preserving Edge Computing Solution for Real-Time Passenger Counting at Bus Stops using Overhead Fisheye Camera

Pardis Ghaziamin;Kairavi Bajaj;Nizar Bouguila;Zachary Patterson

2024 IEEE 18th International Conference on Semantic Computing (ICSC)

Year: 2024 | Conference Paper |

TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting

Naghmeh Shafiee Roudbari;Charalambos Poullis;Zachary Patterson;Ursula Eicker

2023 International Conference on Machine Learning and Applications (ICMLA)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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The hydrometric prediction of water quantity is useful for a variety of applications, including water management, flood forecasting, and flood control. However, the task is difficult due to the dynamic nature and limited data of water systems. Highly interconnected water systems can significantly affect hydrometric forecasting. Consequently, it is crucial to develop models that represent the relat...Show More

TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting

Naghmeh Shafiee Roudbari;Charalambos Poullis;Zachary Patterson;Ursula Eicker

2023 International Conference on Machine Learning and Applications (ICMLA)

Year: 2023 | Conference Paper |

Semantic Segmentation Using Transfer Learning on Fisheye Images

Sneha Paul;Zachary Patterson;Nizar Bouguila

2023 International Conference on Machine Learning and Applications (ICMLA)

Year: 2023 | Conference Paper |

Cited by: Papers (2)

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While semantic segmentation has been extensively studied in the realm of regular perspective images, its application to fisheye images remains relatively unexplored. Existing literature on fisheye semantic segmentation mostly revolves around multi-task or multi-modal models, which are computationally intensive. This motivated us to assess the performance of current segmentation methods, specifical...Show More

Semantic Segmentation Using Transfer Learning on Fisheye Images

Sneha Paul;Zachary Patterson;Nizar Bouguila

2023 International Conference on Machine Learning and Applications (ICMLA)

Year: 2023 | Conference Paper |

Multi-STGAC: A Graph Attention Based Model for Short-term Bus Passenger Flow Forecasting

Mohamed Chaaben;Asiye Baghbani;Nizar Bouguila;Zachary Patterson

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2023 | Conference Paper |

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Forecasting short-term passenger flow is of a great importance to transit operation planning. However, it is subject to variation and uncertainty, due to the wide range of influencing factors. In this study, we propose Multi-Component Spatial Temporal Graph Attention Convolution Neural Network (Multi-STGAC), a graph deep learning model to predict short-term bus passenger flow. It incorporates a Gr...Show More

Multi-STGAC: A Graph Attention Based Model for Short-term Bus Passenger Flow Forecasting

Mohamed Chaaben;Asiye Baghbani;Nizar Bouguila;Zachary Patterson

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2023 | Conference Paper |

Predicting Passenger Flow Using Graph Neural Networks with Scheduled Sampling on Bus Networks

Asiye Baghbani;Saeed Rahmani;Nizar Bouguila;Zachary Patterson

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2023 | Conference Paper |

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Predicting short-term passenger flows in bus networks is crucial to improving the overall performance of such systems and increasing their attractiveness. This study develops a graph neural network-based framework for multi-step passenger flow prediction specifically designed for bus networks to capture their unique characteristics. We propose the Multi-step Multi-component Graph Convolutional Lon...Show More

Predicting Passenger Flow Using Graph Neural Networks with Scheduled Sampling on Bus Networks

Asiye Baghbani;Saeed Rahmani;Nizar Bouguila;Zachary Patterson

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2023 | Conference Paper |

A Variational Graph Convolution Network with Normalizing Flows for Passenger Flow Prediction

Siavash Farazmand;Raghav Narula;Zachary Patterson;Nizar Bouguila

2023 Fifth International Conference on Transdisciplinary AI (TransAI)

Year: 2023 | Conference Paper |

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Passenger flow prediction plays a crucial role in intelligent transportation systems, aiming to facilitate affordable and eco-friendly public transportation. In this context, we introduce a comprehensive framework known as the Variational Graph Convolution Network with Normalizing Flows for Passenger Flow Prediction (VGConvNF). By combining variational graph convolution with the acquisition of a p...Show More

A Variational Graph Convolution Network with Normalizing Flows for Passenger Flow Prediction

Siavash Farazmand;Raghav Narula;Zachary Patterson;Nizar Bouguila

2023 Fifth International Conference on Transdisciplinary AI (TransAI)

Year: 2023 | Conference Paper |

CrossMoCo: Multi-Modal Momentum Contrastive Learning for Point Cloud

Sneha Paul;Zachary Patterson;Nizar Bouguila

2023 20th Conference on Robots and Vision (CRV)

Year: 2023 | Conference Paper |

Cited by: Papers (5)

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The point cloud is a 3D geometric data that lacks a specific structure and is permutation-invariant. The applications of point clouds have gained significant attention recently in the field of vision tasks. However, most existing works on point clouds utilize supervised learning on large labelled data, which are costly and laborious to collect. To this end, unsupervised learning, for example, self...Show More

CrossMoCo: Multi-Modal Momentum Contrastive Learning for Point Cloud

Sneha Paul;Zachary Patterson;Nizar Bouguila

2023 20th Conference on Robots and Vision (CRV)

Year: 2023 | Conference Paper |

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Saeed Rahmani;Asiye Baghbani;Nizar Bouguila;Zachary Patterson

IEEE Transactions on Intelligent Transportation Systems

Year: 2023 | Volume: 24, Issue: 8 | Journal Article |

Cited by: Papers (46)

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Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no comprehensive review of recent advancements and future ...Show More

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Saeed Rahmani;Asiye Baghbani;Nizar Bouguila;Zachary Patterson

IEEE Transactions on Intelligent Transportation Systems

Year: 2023 | Volume: 24, Issue: 8 | Journal Article |

Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction

Naghmeh Shafiee Roudbari;Zachary Patterson;Ursula Eicker;Charalambos Poullis

2022 IEEE Symposium Series on Computational Intelligence (SSCI)

Year: 2022 | Conference Paper |

Cited by: Papers (2)

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In recent years, graph neural networks (GNNs) combined with variants of recurrent neural networks (RNNs) have reached state-of-the-art performance in spatiotemporal forecasting tasks. This is particularly the case for traffic forecasting, where GNN models use the graph structure of road networks to account for spatial correlation between links and nodes. Recent solutions are either based on comple...Show More

Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction

Naghmeh Shafiee Roudbari;Zachary Patterson;Ursula Eicker;Charalambos Poullis

2022 IEEE Symposium Series on Computational Intelligence (SSCI)

Year: 2022 | Conference Paper |

Bayesian Model and Feature Selection in Asymmetric Generalized Gaussian Mixtures

Ravi Teja Vemuri;Muhammad Azam;Nizar Bouguila;Zachary Patterson

2022 IEEE International Conference on Industrial Technology (ICIT)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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This paper addresses an advanced unsupervised Bayesian framework for learning a finite mixture of asymmetric generalized Gaussian distributions (AGGD) using Markov Chain Monte Carlo (MCMC), incorporating Metropolis-Hastings within Gibbs sampling for estimating mixture parameters and reversible jump MCMC (RJMCMC) to dynamically select the number of model components. To enhance learning, we develope...Show More

Bayesian Model and Feature Selection in Asymmetric Generalized Gaussian Mixtures

Ravi Teja Vemuri;Muhammad Azam;Nizar Bouguila;Zachary Patterson

2022 IEEE International Conference on Industrial Technology (ICIT)

Year: 2022 | Conference Paper |

Composite Travel Generative Adversarial Networks for Tabular and Sequential Population Synthesis

Godwin Badu-Marfo;Bilal Farooq;Zachary Patterson

IEEE Transactions on Intelligent Transportation Systems

Year: 2022 | Volume: 23, Issue: 10 | Journal Article |

Cited by: Papers (13)

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Agent-based microsimulation has become the standard to analyze intelligent transportation systems, using disaggregate travel demand data for entire populations, data that are not typically readily available. Population synthesis approaches are thus needed. We present Composite Travel Generative Adversarial Network (CTGAN), a novel deep generative model to estimate the underlying joint distribution...Show More

Composite Travel Generative Adversarial Networks for Tabular and Sequential Population Synthesis

Godwin Badu-Marfo;Bilal Farooq;Zachary Patterson

IEEE Transactions on Intelligent Transportation Systems

Year: 2022 | Volume: 23, Issue: 10 | Journal Article |

Ensemble Convolutional Neural Networks for Mode Inference in Smartphone Travel Survey

Ali Yazdizadeh;Zachary Patterson;Bilal Farooq

IEEE Transactions on Intelligent Transportation Systems

Year: 2020 | Volume: 21, Issue: 6 | Journal Article |

Cited by: Papers (52)

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We develop ensemble convolutional neural networks (CNNs) to classify the transportation mode of trip data collected as part of a large-scale smartphone travel survey in Montreal, Canada. Our proposed ensemble library is composed of a series of CNN models with different hyper-parameter values and CNN architectures. In our final model, we combine the output of CNN models using “average voting,” “maj...Show More

Ensemble Convolutional Neural Networks for Mode Inference in Smartphone Travel Survey

Ali Yazdizadeh;Zachary Patterson;Bilal Farooq

IEEE Transactions on Intelligent Transportation Systems

Year: 2020 | Volume: 21, Issue: 6 | Journal Article |

Semi-Supervised travel mode detection from smartphone data

Mohsen Rezaie;Zachary Patterson;Jia Yuan Yu;Ali Yazdizadeh

2017 International Smart Cities Conference (ISC2)

Year: 2017 | Conference Paper |

Cited by: Papers (6)

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With the advent of the incorporation of GPS receivers and then GPS-enabled smartphones in transportation data collection, many studies have looked at how to infer meaningful information from this data. Research in this field has concentrated on the use of heuristics and supervised machine learning methods to detect: trip ends, trip itineraries, travel mode and trip purpose. All the methods used un...Show More

Semi-Supervised travel mode detection from smartphone data

Mohsen Rezaie;Zachary Patterson;Jia Yuan Yu;Ali Yazdizadeh

2017 International Smart Cities Conference (ISC2)

Year: 2017 | Conference Paper |

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