content updates and changes

Author: Melvin Wong

content/publications/Djavadian_etal_2019.md

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+---
+# put bibliography in this format
+# bibliograhpy : "<authors>, <year>.
+#    **<title>**.
+#    <journal location + etc>.
+bibliography: "Djavadian, S., Farooq, B., Vasquez, R. and Yip, G., 2019.
+  **Virtual Immersive Reality based Analysis of Behavioral Responses in Connected and Autonomous Vehicle Environment**.
+  arXiv preprint arXiv:1901.07151." # surround Title with **<title>**
+date: 2019-01-22
+preview: paper_djavadianetal_2019.jpg
+arxiv: "https://arxiv.org/abs/1901.07151"
+link: ""
+abstract: "Recently, we developed a dynamic distributed end-to-end vehicle routing system (E2ECAV) using a network of intelligent intersections and level 5 CAVs (Djavadian & Farooq, 2018). The case study of the downtown Toronto Network showed that E2ECAV has the ability to maximize throughput and reduce travel time up to 40%. However, the efficiency of these new technologies relies on the acceptance of users in adapting to them and their willingness to give control fully or partially to CAVs. In this study a stated preference laboratory experiment is designed employing Virtual Reality Immersive Environment (VIRE) driving simulator to evaluate the behavioral response of drivers to E2ECAV. The aim is to investigate under what conditions drivers are more willing to adapt. The results show that factors such as locus of control, congestion level and ability to multi-task have significant impact."
+---

content/publications/FarooqCherchiSobhani_2018.md

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 ---
+# put bibliography in this format
+# bibliograhpy : "<authors>, <year>.
+#    **<title>**.
+#    <journal location + etc>.
 bibliography: "Farooq, B., Cherchi, E. and Sobhani, A., 2018.
   **Virtual immersive reality for stated preference travel behavior experiments: a case study of autonomous vehicles on urban roads**.
   Transportation research record, 2672(50), pp.35-45." # surround Title with **<title>**

content/publications/KalatianFarooq_2019.md

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 ---
+# put bibliography in this format
+# bibliograhpy : "<authors>, <year>.
+#    **<title>**.
+#    <journal location + etc>.
 bibliography: "Kalatian, A., Farooq, B., 2019.
   **A semi-supervised deep residual network for mode detection in Wi-Fi signals**.
   arXiv preprint arXiv:1902.06284." # surround Title with **<title>**
-date: 2019-02-17
-preview: paper_kalatianfarooq_2019.jpg
-arxiv: "https://arxiv.org/abs/1902.06284"
-link: ""
+date: 2019-02-17 # date of publication / posting on arXiv
+preview: paper_kalatianfarooq_2019.jpg # link to paper/journal thumbnail. leave blank "" if not applicable
+arxiv: "https://arxiv.org/abs/1902.06284" # link to arXiv. leave blank "" if not applicable
+link: "" # link to journal publication. leave blank "" if not applicable
 abstract: "Due to their ubiquitous and pervasive nature, Wi-Fi networks have the potential to collect large-scale, low-cost, and disaggregate data on multimodal transportation. In this study, we develop a semi-supervised deep residual network (ResNet) framework to utilize Wi-Fi communications obtained from smartphones for the purpose of transportation mode detection. This framework is evaluated on data collected by Wi-Fi sensors located in a congested urban area in downtown Toronto. To tackle the intrinsic difficulties and costs associated with labelled data collection, we utilize ample amount of easily collected low-cost unlabelled data by implementing the semi-supervised part of the framework. By incorporating a ResNet architecture as the core of the framework, we take advantage of the high-level features not considered in the traditional machine learning frameworks. The proposed framework shows a promising performance on the collected data, with a prediction accuracy of 81.8% for walking, 82.5% for biking and 86.0% for the driving mode."
 ---

content/publications/WongFarooqBliodeau_2018.md

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 ---
+# put bibliography in this format
+# bibliograhpy : "<authors>, <year>.
+#    **<title>**.
+#    <journal location + etc>.
 bibliography: "Wong M., Farooq B., Bilodeau, G.-A., 2018.
   **A combined entropy and utility based generative model for large scale multiple discrete-continuous travel behaviour data**.
   Journal of Choice Modelling 42, pp. 152-168." # surround Title with **<title>**
-date: 2018-12-01
-preview: journal_choicemodelling.jpg
-arxiv: "https://arxiv.org/abs/1901.06415"
-link: "https://www.sciencedirect.com/science/article/pii/S1755534517300970"
+date: 2018-12-01 # date of publication / posting on arXiv
+preview: journal_choicemodelling.jpg # link to paper/journal thumbnail. leave blank "" if not applicable
+arxiv: "https://arxiv.org/abs/1901.06415" # link to arXiv. leave blank "" if not applicable
+link: "https://www.sciencedirect.com/science/article/pii/S1755534517300970" # link to journal publication. leave blank "" if not applicable
 abstract: "Generative models, either by simple clustering algorithms or deep neural network architecture, have been developed as a probabilistic estimation method for dimension reduction or to model the underlying properties of data structures. Although their apparent use has largely been limited to image recognition and classification, generative machine learning algorithms can be a powerful tool for travel behaviour research. In this paper, we examine the generative machine learning approach for analyzing multiple discrete-continuous (MDC) travel behaviour data to understand the underlying heterogeneity and correlation, increasing the representational power of such travel behaviour models. We show that generative models are conceptually similar to choice selection behaviour process through information entropy and variational Bayesian inference. Specifically, we consider a restricted Boltzmann machine (RBM) based algorithm with multiple discrete-continuous layer, formulated as a variational Bayesian inference optimization problem. We systematically describe the proposed machine learning algorithm and develop a process of analyzing travel behaviour data from a generative learning perspective. We show parameter stability from model analysis and simulation tests on an open dataset with multiple discrete-continuous dimensions and a size of 293,330 observations. For interpretability, we derive analytical methods for conditional probabilities as well as elasticities. Our results indicate that latent variables in generative models can accurately represent joint distribution consistently w.r.t multiple discrete-continuous variables. Lastly, we show that our model can generate statistically similar data distributions for travel forecasting and prediction."
 ---

content/publications/WongFarooqITSC_2018.md

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 ---
+# put bibliography in this format
+# bibliograhpy : "<authors>, <year>.
+#    **<title>**.
+#    <journal location + etc>.
 bibliography: "Wong, M. and Farooq, B., 2018.
   **Modelling Latent Travel Behaviour Characteristics with Generative Machine Learning**.
   In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 749-754." # surround Title with **<title>**

content/publications/WongFarooq_2019.md

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+---
+# put bibliography in this format
+# bibliograhpy : "<authors>, <year>.
+#    **<title>**.
+#    <journal location + etc>.
+bibliography: "Wong, M., Farooq, B., 2019.
+  **A combined entropy and utility based generative model for large scale multiple discrete-continuous travel behaviour data**.
+  arXiv preprint arXiv:1901.06415." # surround Title with **<title>**
+date: 2019-01-18
+preview: paper_wongfarooq_2019.jpg
+arxiv: "https://arxiv.org/abs/1901.06415"
+link: ""
+abstract: "Generative models, either by simple clustering algorithms or deep neural network architecture, have been developed as a probabilistic estimation method for dimension reduction or to model the underlying properties of data structures. Although their apparent use has largely been limited to image recognition and classification, generative machine learning algorithms can be a powerful tool for travel behaviour research. In this paper, we examine the generative machine learning approach for analyzing multiple discrete-continuous (MDC) travel behaviour data to understand the underlying heterogeneity and correlation, increasing the representational power of such travel behaviour models. We show that generative models are conceptually similar to choice selection behaviour process through information entropy and variational Bayesian inference. Specifically, we consider a restricted Boltzmann machine (RBM) based algorithm with multiple discrete-continuous layer, formulated as a variational Bayesian inference optimization problem. We systematically describe the proposed machine learning algorithm and develop a process of analyzing travel behaviour data from a generative learning perspective. We show parameter stability from model analysis and simulation tests on an open dataset with multiple discrete-continuous dimensions and a size of 293,330 observations. For interpretability, we derive analytical methods for conditional probabilities as well as elasticities. Our results indicate that latent variables in generative models can accurately represent joint distribution consistently w.r.t multiple discrete-continuous variables. Lastly, we show that our model can generate statistically similar data distributions for travel forecasting and prediction."
+---