Professor, Concordia Institute for Information Systems Engineering
News & Press Releases
Advanced Quantitative Methods
Over the course of the semester, students will become familiar with the R programming environment at least for the conducting of statistical analysis, and hopefully also for data manipulation and preparation. They will become familiar with the kinds of statistical analysis that is typically used in the fields of geography, planning and environmental studies. They will also work towards understanding the type of statistical analysis that is likely to be done as part of their master’s thesis. Ideally, as part of the term project, the student will be able to work with the same data set they expect to use in the analysis for their thesis. Alternatively, another data set that resembles the data they expect to use for their thesis will be used.
It is open to graduate students. Download most recent course outline here.
Over the course of this class, students learn to design, implement and analyse the data, from a stated choice survey. The topic of the survey will have to do with an important environmental question. It is open to undergraduate and undergraduate students. Download the most recent undergraduate course outline here.
Download the latest course outline. This course introduces students to the transportation planning and modeling process aided by the use of a GIS-based transportation decision aid tool. A real-world case-study region and transportation system is used to illustrate the different elements of the planning and modeling process. The course aims to highlight both the strengths and weaknesses (particularly with regards to how it treats the interaction between the transportation system and land-use) of the traditional transportation planning approach.
*Co-authors I’ve supervised.
Sneha Paul, Zachary Patterson & Nizar Bouguila. (2023). DualMLP: A Two-stream Fusion Model for 3D Point Cloud Classification. The Visual Computer Journal. (Accepted for publication.)
Asiye Baghbani, Saeed Rahmani, Nizar Bouguila and Zachary Patterson. (2023). Predicting Passenger Flow Using Graph Neural Networks with Scheduled Sampling on Bus Networks. 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain. (Accepted for publication.)
Mohamed Chaaben, Asiye Baghbani, Nizar Bouguila and Zachary Patterson. (2023). Multi-STGAC: A Graph Attention Based Model for Short-term Bus Passenger Flow Forecasting. 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain. (Accepted for publication.)
Bobin Wang, E.O.D. Xun Ji, Hamed Naseri, Alex L. Loiselle, Ricardo A. Daziano, Zachary Patterson, and Matthew Feinberg. (2023). How to Effectively Communicate about Greenhouse Gas Emissions with Different Populations. Environmental Science & Policy, 147, September, pp. 29-43.
Saeed Rahmani,* Asiye Baghbani,* Nizar Bouguila and Zachary Patterson. Graph Neural Networks for Intelligent Transportation Systems: A Survey, IEEE Transactions ITS, 2022. Accepted for publication.
Ben Azoulay* and Zachary Patterson. Towards the Standardization of Reporting in Smartphone Travel Surveys: The Development and Application of the Smartphone Survey Reporting Guidelines (SSRGs). Transportation Research Procedia for the 12th International Conference on Transport Survey Methods.
Zachary Patterson, Aaron Bensmihen,* and Gavin Hermanson.* The Multimodal Accessibility Target (MAT). Transportation Research Record, accepted for publication. https://doi.org/10.1177/03611981231172503
Bochu Liu, Michael J. Widener, Lindsey G. Smith, Steven Farber, Dionne Gesink, Leia M. Minaker, Zachary Patterson, Kristian Larsen, and Jason Gilliland. Time-geographic project of household food provision: Conceptualization and a pilot case study. Annals of the American Association of Geographers.
Asiye Baghbani,* Nizar Bouguila, and Zachary Patterson. Short-term passenger flow prediction using a bus network graph convolutional LSTM neural network model. Transportation Research Record, 2022.
Godwin Badu-Marfo,* Bilal Farooq, and Zachary Patterson. Composite travel generative adversarial networks for tabular and sequential population synthesis. IEEE Transactions on Intelligent Transportation Systems, pages 1–10, 2022.
Long Pan,* E.O.D. Waygood, and Zachary Patterson. Would you wait? bus choice behavior analysis considering various incentives. Transportation Research Record, 2676(7), 2022.
Hamed Naseri,* E.O.D. Waygood, Bobin Wang,* Zachary Patterson, and Ricardo Daziano. A novel feature selection technique to better predict climate change stage of change. Sustainability, 14(40), 2022.
Pierre Laffont,* E.O.D. Waygood, and Zachary Patterson. How many electric vehicles are needed to reach co2 emissions goals? a case study from montreal, canada. Sustainability, 14(3), 2022.
Ravi Teja Vemuri,* Muhammad Azam,* Nizar Bouguila, and Zachary Patterson. A bayesian sampling framework for asymmetric generalized Gaussian mixture models learning. Neural Computing and Applications, 34:14123 –14134, 2022.
E.O.D. Waygood, Bobin Wang,* Ricardo Daziano, Zachary Patterson, and Markéta Braun
Kohlová. The climate change stage of change measure: Vehicle choice experiment. Journal of
Environmental Planning and Management, 65:1210–1239, 2022.
Ricardo Daziano Zachary Patterson Bobin Wang*, E.O.D. Waygood and Matthew Feinberg. Does hedonic framing improve people's willingness-to-pay for vehicle greenhouse gas emissions? Accepted in Transportation Research Part D, 2021.
E.O.D. Waygood, Bobin Wang,* Ricardo Daziano, Zachary Patterson, and Marketa Braun Kohlova. The climate change stage of change measure: Vehicle choice experiment. Accepted for publication in the Journal of Environmental Planning and Management, 2021.
Lindsey Smith, Michael Widener, Bochu Liu, Steven Farber, Leia Minaker, Zachary Patterson, Kristian Larsen and Jason Gilliland. Comparing household and individual measures of access through a food environment lens: what household food opportunities are missed when measuring access to food retail at the individual level? Accepted for publication in the Annals of the American Association of Geographers, 2021.
Ricardo Daziano, Owen Waygood, Zachary Patterson, Matthew Feinberg and Bobin Wang. Reframing Greenhouse Gas Emissions Information Presentation on the Environmental Protection Agency’s New-Vehicle Labels to Increase Willingness to Pay. Journal of Cleaner Production, 2020, DOI: https://doi.org/10.1016/j.jclepro.2020.123669.
Ali Yazdizadeh*, Zachary Patterson, and Bilal Farooq.Ensemble convolutional neural networks for mode inference in smartphone travel survey.IEEE Transactions on IntelligentTransportation Systems, 2019.
Zachary Patterson, Kyle Fitzsimmons*, Takeshi Mukai, and Stewart Jackson. Itinerum: Theopen smartphone travel survey platform. SoftwareX, 10, July-December 2019.
Godwin Badu-Marfo*, Bilal Farooq, and Zachary Patterson. Perturbation privacy for sensitivelocations in mobility data publication: A case study of montreal trajet surveys. Washington,DC, 2019a. Transportation Research Board. Accepted for publication in the TransportationResearch Record.
Godwin Badu-Marfo*, Bilal Farooq, and Zachary Patterson. A perspective on the challengesand opportunities for privacy-aware big transportation data. Journal of Big Data Analytics inTransportation, 2019b.
Ali Yazdizadeh*, Zachary Patterson, and Bilal Farooq. An automated approach from GPS tracesto complete trip information. International Journal of Transportation Science and Technology,2018.
Masoud Fallah Shorshani*, Marianne Hatzopoulou, Nancy Ross, Zachary Patterson, and ScottWeichenthal. Evaluating the impact of neighborhood characteristics on differences betweenresidential and mobility-based exposures to outdoor air pollution. Environmental science &technology, 2018.
Michael Widener, Leia Minaker, Taral Kamal Amadi, Jessica Reid, and David Hammond. Ac-tivity space-based measures of the food environment and their relationships to food purchasingbehaviours for young urban adults in Canada. Public Health Nutrition, 2018a.
Guilhem Poucin*, Bilal Farooq, and Zachary Patterson. Activity patterns mining in Wi-Fiaccess point logs. Computers, Environment and Urban Systems, 67:55–67, 2018.
Ricardo A Daziano, EOD Waygood, Zachary Patterson, and Mark ́eta Braun Kohlov ́a. Increa-sing the influence of CO2 emissions information on car purchase. Journal of Cleaner Production,164:861–871, 2017.
TRIP Lab Smartphone Travel Survey Platform Workshop
May 25 and 26, Concordia University
Download the Workshop Agenda
The TRIP Lab is holding a 2-day workshop on Smartphone Travel Surveys and how to use its new Smartphone Travel Survey Platform at Concordia University.
The TRIP Lab platform enables researchers to create their own free smartphone travel survey application.
The Platform is useful for anyone who wants to collect and analyze data on commuting and urban travel behaviour, including:
The Transportation Research for Integrated Planning (TRIP) Lab was founded in October 2011 as part of a Canadian Foundation for Innovation (CFI) grant associated with a Tier-II Canada Research Chair in Transportation and Land-use Linkages for Regional Sustainability. The TRIP lab is equipped with high-powered computers and servers for geographical and statistical analysis and is able to house up to 10 student researchers. It is also home to unique and expansive transportation and land-use data for Montreal as well as other major urban regions across the country. Analysis of this data is made possible by cutting-edge and sophisticated software for geographical analysis, transportation modeling and statistical analysis.
Research in the TRIP lab focuses on the modeling of transportation, the environment, land-use and their linkages. It also involves using new technologies to collect data on where people decide to live and how they move around the city. Recently, a smartphone travel survey application and platform (Itinerum™) has been developed to conduct origin-destination of members of the Concordia community.
Research on urban transportation at Concordia:
Master's or PhD students with capacity or experience in smartphone app (iOS and Android) development for transportation-related data collection.
MTLTrajet: an app with street smarts
© Concordia University