Research Projects

UiTR, formerly known as CeTLUR, is a transportation and land use research facility at the University of British Columbia’s Okanagan (UBCO) campus. The lab. is led by Dr. Mahmudur Fatmi, a transportation professor at UBCO. Research in UiTR revolves around the broad research domain of integrated transportation and land use modelling, with a particular focus on transportation and land use interactions, travel demand forecasting, travel behaviour analysis, autonomous vehicle adoption, shared mobility usage, urban system microsimulation, econometric modelling, machine learning models, vehicular emissions and energy modelling, road safety analysis, and survey design and methods. A brief description of research conducted at UiTR can be found below.

Int. Dev. of Transport Data, Model & Community Outreach Tool for Urban & Rural Region

Community members and researchers join forces to tackle the complex challenges of transportation planning in both urban and rural regions. Recognizing the diverse needs and contexts of these areas, our collaborative approach seeks to co-design solutions that are inclusive, responsive, and sustainable. Together, we will develop comprehensive transport data, modeling tools, and community outreach strategies.

BC Activity Time Use Survey

The British Columbia Activity Time Use Survey (BC ATUS) is a collaborative effort between the University of British Columbia and the federal government (Environment and Climate Change Canada).

The goal of the survey is to gather data about the daily activities of all household members, both at home and away. This information will be used to analyze travel and in-home/online activity patterns, and their relationships, which will be critical to accurately forecast travel demand and develop appropriate transportation plans and policies, and invest in infrastructures. This is a three-year longitudinal survey; meaning, data will also be collected from the same participants the second and third year around the same time. The purpose is to understand the changes in travel behaviour over time, and how to prepare to meet the evolving needs of the residents. The data will also contribute to creating an innovative modelling system for accurately predicting travel pattern, and reduce congestion and transportation-related greenhouse gas emissions.

Climate change Research project

This research project aims to investigate the intricate relationship between climate change, travel demand, and infrastructure requirements, with a focus on developing robust modeling frameworks for sustainable transportation planning. With global temperatures rising and extreme weather events becoming more frequent, understanding and mitigating the impacts of climate change on transportation systems are imperative for ensuring societal resilience and environmental sustainability.

Exploration of the possible Implementation of Autonomous Vehicle on the Okanagan Rail Trail.

This research project explores the feasibility and implications of integrating autonomous vehicles (AVs) into the transportation ecosystem of the Okanagan Rail Trail, a popular multi-use pathway in the Okanagan Valley. As autonomous technologies continue to advance rapidly, there is growing interest in leveraging AVs to enhance accessibility, safety, and environmental sustainability in diverse mobility settings. This study employs a mixed-methods approach, combining quantitative analysis, stakeholder engagement, and simulation modeling to evaluate the potential benefits, challenges, and trade-offs associated with AV deployment on the trail.

Agent-based Microsimulation of Regional Transportation and Energy Systems

UiTR primarily focuses to develop a new generation agent-based integrated transportation and land use modelling system that integrates population demographics, location choice, vehicle ownership, and daily activities within a unified modelling framework to predict the changes in land use pattern, transportation network and the environment over time and space for an entire urban region. The model, known as STELARIS, has the capacity to test the impacts of unprecedented events such as COVID-19 and newer technologies such as autonomous and electric vehicle usage. The fundamental contribution of this research is to disentangle the interactions among transportation-related decisions and changes occurring at different stages of an individual’s life; for example, how our decisions of where to live interact with our decisions of how many vehicles to own and which travel mode to choose. Therefore, this tool simulates agents’ activities over time to predict the evolution and interactions among transportation decisions such as mode choice, land use configuration such as residential location choice, life-cycle events such as birth of a child, and their impacts on the urban environment such as vehicular emissions and residential energy consumption. This research develops advanced econometrics, machine learning and microsimulation modelling techniques to address the two-way feedback between transportation and land use decisions, which in-turn improves the predicting accuracy and consequently assists in effective transportation and land use policy making and infrastructure investment decision-making. A relevant example research output can be found below:

Population Synthesis Accommodating Heterogeneity: A Bayesian Network and Generalized Raking Technique:

Fig. Population Synthesis Framework

Fig. Difference between Census and synthetic population at the dissemination area level: (a) absolute error percentage (gender) and (b) absolute error percentage (dwelling type).

Fig. Joint Distribution of both household- and individual-level attributes: (a) public use microdata file and (b) synthetic pool.


Modelling Residential Mobility Decisions using a Life History-oriented Perspective:

Fig: Goodness of Fit Measures for Hazard Model.

Fig: Model Results of the Residential Mobility Mode.


Agent-based Integrated Modelling for Microsimulating Residential Energy Usage Before, During and After COVID-19:

Fig: A Modelling Framework for Agent-based Energy Microsimulation Model.

Fig: Total Energy Consumption Versus In-home (IH) and Out-of-Home (OH) Duration.

Fig: Building Energy and Consumption of Dwelling in Okanagan Region.

Transportation and Climate Action Research

This inter-disciplinary research adopts an integrated approach to collect newer data and develop a novel modelling system to better assess travel, location choice and vehicle ownership behaviour, and emissions, followed by developing and testing emerging policies to reduce vehicular emissions. This study will evaluate the evolution and the longer-term effects of COVID-19 on travel behaviour including housing, vehicle ownership, in-home and out-of-home activities, mode choice, and how that impact the carbon footprint. The scope includes Metro Vancouver and Central Okanagan regions from BC. A multi-disciplinary team of researchers from the two UBC campuses are involved: Dr. Mahmudur Fatmi, Dr. Khalad Hasan, Dr. Andrea MacNeil, Dr. Rehan Sadiq, Dr. Kasun Hewage, Dr. Naomi Zimmerman, Dr. Jon Corbertt. The project also involves local, regional, provincial, and federal governments and agencies such as City of Kelowna, City of West Kelowna, City of Vancouver, District of North Vancouver, Metro Vancouver, Transport Canada, BC Ministry of Energy, Mines and Low Carbon Innovation, among others. The project has been funded by the Environment and Climate Change Canada (ECCC) under their Climate Action and Awareness Fund (CAAF).

Here are few news article on this project: UBCO SoE, ECCC

In addition, Dr. Fatmi is also involved with another CAAF project led by Dr. Ahsan Habib at the Dalhousie University. This five year project aims to develop a framework for data collection, tool development, and mathematical modeling to determine how much GHG is emitted daily within the transportation sector. The scope of the project involves many cities in Canada and the research team includes multi-disciplinary researchers from Dalhousie University, University of British Columbia, McMaster University and the University of Toronto. Click here for a news article on this project.

Fig. Conceptual Framework of the Project Methodology

Activity Base Modelling

This research focuses on developing state-of-the-art travel demand forecasting models particularly, contributing to the development of an agent-based travel activity simulator. Activity-based modelling approach has been adopted to understand and predict individuals’ activities including in-home activities such as work-from-home and travel activities such as mode choice, vehicle choice, travel partner choice, and route choice decisions, among others. Alternative modelling methods are developed to better capture the interactions among individual’s decision-making processes and further translate such behaviour within a microsimulation environment for improved forecasting. We have also invested significant efforts to model the demand for sustainable alternative transportation options such as biking, as well as investigate the effects of unprecedented socio-economic shocks such as COVID-19 on travel demand. This research assists in developing strategies for travel demand management such as flexible working hours, and emissions reduction and investing in sustainable transportation modes.

Activity Engagement, Duration, and Destination Location:

Fig. Descriptive Statistics for the Okanagan Travel Survey (OTS)

Fig. Modeling Framework

How Will In-Person and Online Grocery Shopping and Meal Consumption Activities Evolve After COVID-19:

Fig. Distribution of in-person and online ordering of groceries and foods of the respondents who shopped at least a few times a month.

Fig. Percentage of respondents conducting in-store and online ordering in the week prior to taking the survey. 

Modelling the Changes in Out-of-home and In-home Activities during the COVID-19 Pandemic:

Fig: Change in out-of-home Activities during COVID-19.

Fig: Change in Frequency of In-home Activities during COVID-19.

Smart and Shared Mobility

This research focuses on developing advanced modelling methods to understand the adoption and usage of newer mobility technologies such as autonomous vehicles, and shared mobility options such as dockless bike share and e-scooter share services. This research leverages the existence of big data such as GPS records to develop advanced methods such as machine learning algorithms to improve the usability of big data. Consequently, innovative econometric models are developed to analyze the user behaviour of share mobility services including usage demand, destination choice, and infrastructure choice. In addition, this research focuses on developing models to understand individuals’ preferences towards the usage of autonomous vehicles as a shared mobility option as well as private ownership.

Cycling Demand Modelling for Cities in Canada and New Zealand:

Fig: Distribution of Cycling Demand with Precipitation.

Fig: Distribution of Monthly Cycling Demand.

Fig: Distribution of Daily Cycling Demand for Different Seasons.

Modelling Destination Choice Behaviour of Dockless Bikeshare Users:

Fig: Predicted Probability of Choosing Destination by Dockless Bikeshare Users.

Modelling the Demand for Shared E-Scooter Services in Kelowna:

Fig: Percentage Distribution of Shared E-scooter Demand in Kelowna.
Fig: Shared E-scooter Predicted Demand in Kelowna.

Modelling Individuals’ Longer-term Preferences Towards Autonomous Vehicles:

Fig: Conceptual framework for modelling individuals’ longer-term preferences towards autonomous vehicles.

Road Safety Analysis

This research assists road safety engineers and planners to identify effective countermeasures and awareness programs to reduce collision frequency and crash injury severity. For example, one of the research projects in this area focuses on investigating the interactions between distracted driving and injury severity. Advanced econometric models are developed for analyzing crash injury severity of vehicle occupants and pedestrians. Research in this area also focuses on investigating road safety challenges for developing countries.

Modelling Vehicle Collision Injury Severity Involving Distracted Driving: Assessing the Effects of Land Use and Built Environment:

Fig: Distribution of Vehicle Occupant Injury Severity Levels.

Fig: Distribution of Age and Injury Severity Levels of Vehicle Occupants.

Modelling Injury Severity for Unconventional Vehicle Occupants (UVO) of a Developing Country: Dhaka, Bangladesh:

Fig: Injury Severity of UVOs based on Vehicle Type.

Fig: Injury Severity of UVOs based on Age.

Fig: Injury Severity of UVOs based on Collision Type.

Survey Design and Methods

UiTR, contributes to designing and administering surveys to collect specialized housing and transportation-related data. For example, one of the completed research projects was to design and implement a retrospective Travel Technology and Mobility Survey (TTMS) that collected information from the Okanagan residents regarding their housing career, technology use, preference for future technology, employment record, vehicle ownership history, and attitudinal preferences, among others. Another project focused to collect information regarding the impacts of COVID-19 on travel behaviour.

Surveys Conducted by the UiTR :

Fig. Shared Mobility Survey (SMS) (2022)

Fig. Impacts of COVID-19 On Daily Activities Survey (ICDAS) (2022)

Fig. Pre and Post COVID-19 Travel Survey (2021)