WINTER TRAFFIC MODELLING, ANALYTICS, AND TRANSPORTATION RESILIENCE

Big and Thick Traffic Data Analytics & Modelling

- Highway traffic pattern analysis, Hierarchical grouping of highway types
- Quantitative analysis of the relationships between weather and classified traffic volume
- Manipulating over 300 million lines of raw Weigh-In-Motion (WIM) data collected in the Alberta highway network for over 10 years
- Manipulating big and thick traffic data using Visual Basic (VB), Visual Basic Application (VBA), and R and Python statistical computing language
- Application of the vehicle classification algorithm, Probabilistic Neural Network (PNN) on raw WIM traffic data

- Winter traffic data imputation using the machine learning k-nearest neighbour (K-NN) method
- Application of machine learning, stochastic gradient descent and mini-batch gradient descent method to model traffic data during seasons

- Travel demand analysis & modelling using discrete choice models
- Study on mode choice behaviour for various transportation user groups
- Performance comparison of optimization algorithms adopted for discrete choice model estimation
- Application of the non-linear programming technique to advance the performance of the choice model optimization algorithm

- Developing a road deterioration model using a road inventory database
- Developing a Life Cycle Cost Analysis (LCCA) tool to optimize budget split over the planning horizon for the municipal road network

- Development of highway network analysis methods to assess the risks associated with the transportation of small modular reactors
- Spatial analysis along a transportation network
- Transportation infrastructure modelling

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