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.
Agent-based Microsimulation of Urban Transportation and Land Use 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. This model 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:
Agent-based Integrated Modelling for Microsimulating Residential Energy Usage Before, During and After COVID-19:
For more information about this microstimulation model, please click here.
Travel Demand Forecasting
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 traveldemand management such as flexible working hours, and emissions reduction and investing in sustainable transportation modes.
Cycling Demand Modelling for Cities in Canada and New Zealand:
For more information about cycling demand modelling, please click here.
Modelling the Changes in Out-of-home and In-home Activities during the COVID-19 Pandemic:
For more information about modelling changes of activities during the COVID-19 pandemic, please click here.
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.
Modelling Destination Choice Behaviour of Dockless Bikeshare Users:
Modelling the Demand for Shared E-Scooter Services in Kelowna:
For more information about modelling destination choice behaviour of bikeshare users, please click here.
For more information about modelling for demand of shared e-scooter services, please click here.
Travel Behaviour Analysis
This research focuses on improving travel behaviour analysis methods. Particularly, this research disentangles transportation and land use interactions by analyzing behaviour related decisions including residential location choice, vehicle ownership, and commute mode choice, and their changes and inter-dependencies over the life-course of the individuals/households.
Modelling Residential Mobility Decisions using a Life History-oriented Perspective:
For more information about travel behaviour Information, please click here.
For more information about preferences towards autonomous vehicles, please click here.
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
For more information about Modelling Vehicle Collision Injury Severity, please click here.
Modelling Injury Severity for Unconventional Vehicle Occupants (UVO) of a Developing Country: Dhaka, Bangladesh
For more road safety analysis information, please click here.
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.