24 citations · 35 across the 8 of their papers we have counts for
6 papers · 1 filter
Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction
Naghmeh Shafiee Roudbari, Zachary Patterson, Ursula Eicker +1
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 tas…
Multi-task Recurrent Neural Networks to Simultaneously Infer Mode and Purpose in GPS Trajectories
Ali Yazdizadeh, Arash Kalatian, Zachary Patterson +1
Multi-task learning is assumed as a powerful inference method, specifically, where there is a considerable correlation between multiple tasks, predicting them in an unique framewor…
A Differentially Private Multi-Output Deep Generative Networks Approach For Activity Diary Synthesis
Godwin Badu-Marfo, Bilal Farooq, Zachary Patterson
In this work, we develop a privacy-by-design generative model for synthesizing the activity diary of the travel population using state-of-art deep learning approaches. This propose…
Composite Travel Generative Adversarial Networks for Tabular and Sequential Population Synthesis
Godwin Badu-Marfo, Bilal Farooq, Zachary Paterson
Agent-based transportation modelling has become the standard to simulate travel behaviour, mobility choices and activity preferences using disaggregate travel demand data for entir…
Ensemble Convolutional Neural Networks for Mode Inference in Smartphone Travel Survey
Ali Yazdizadeh, Zachary Patterson, Bilal Farooq
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, C…
Semi-supervised GANs to Infer Travel Modes in GPS Trajectories
Ali Yazdizadeh, Zachary Patterson, Bilal Farooq
Semi-supervised Generative Adversarial Networks (GANs) are developed in the context of travel mode inference with uni-dimensional smartphone trajectory data. We use data from a lar…