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Welcome to CTRF’s 60th Annual Conference! Enjoy Ottawa
Monday May 26, 2025 11:10am - 11:30am EDT
In travel behavior research, joint modeling of multiple interdependent decisions has primarily relied on theory-based econometric models, with limited exploration of data-driven multi-task learning (MTL) methods. Existing MTL studies have mostly focused on discrete-discrete outputs, overlooking mixed-type decisions such as travel mode (discrete) and departure time (continuous"”though some studies have treated it as discrete). This study develops a joint artificial neural network (ANN) to simultaneously model travel mode and trip departure time. We evaluate two MLT-ANN architectures: hard-parameter sharing MTL (HP-MTL) and cross-stitch MTL (CS-MTL). Additionally, we compare them against single-output neural networks (SO-NNx) and econometric models. The results indicate that the more complex CS-MTL performed similarly to SO-NNx in most measures but was outperformed by HP-MTL, likely due to negative learning from increased complexity given task interdependencies. In contrast, HP-MTL showed significant improvements: for trip departure time prediction, it enhanced R² and mean squared error (MSE) by 21.4% and 8.3% over SO-NNx, and by 4.7 times and 27% over the hazard model. For travel mode choice, HP-MTL achieved slight accuracy gains, notably improving transit mode predictions by ~10%. This research contributes to transportation modeling literature by demonstrating the effectiveness of ML, particularly HP-MTL, in modeling joint discrete-continuous decisions.
Speakers
MF

Mahmudur Fatmi

Assistant Professor, University of British Columbia - Okanagan
Dr. Mahmudur Fatmi is a transportation professor at UBCO. He has started as an assistant professor at UBCO since July 2018. Dr. Fatmi contributes by developing advanced econometric modelling methods and agent-based microsimulation modelling techniques to assist in making effective... Read More →
Monday May 26, 2025 11:10am - 11:30am EDT
Desmarais 1130 55 Laurier Ave E, Ottawa ON K1N 6N5

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