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Activity Based Travel Demand Generation Using Bayesian Networks

🇿🇦 Activity-Based Travel Demand Generation Using Bayesian Networks

Presented at the 8th Annual BayesiaLab Conference on October 27, 2020.

Abstract

While activity-based travel demand generation has improved over the last few decades, the behavioural richness and intuitive interpretation remain challenging. We argue that it is essential to understand why people travel the way they do and not only be able to predict the overall activity patterns accurately. If one cannot understand the ‘why?” then a model's ability to evaluate the impact of future interventions is severely diminished. Bayesian networks (BNs) provide the ability to investigate causality and is showing value in recent literature to generate synthetic populations. This research is novel in extending the application of BNs to daily activity tours. Results show that BNs can synthesise both activity and trip chain structures accurately. It outperforms a frequentist approach and can cater for infrequently observed activity patterns, and patterns unobserved in small sample data. It can also account for temporal variables like activity duration.

Presentation Video

About the Presenter

Alta de Waal, Ph.D.
Centre for Artificial Intelligence Research
Department of Statistics, Faculty of Natural and Agricultural Sciences
University of Pretoria, South Africa

Alta currently holds a senior lecturer position in the Department of Statistics, University of Pretoria, South Africa. She has 20 years of experience in the design, development, and implementation of different components in the AI value chain. She develops Bayesian network models in application areas such as student throughput models, wildlife security, environmental risk management, and transportation. Alta also studies natural language processing (NLP) with a special interest in probabilistic distributional semantic methods.

Previous Presentations

Spatially Discrete Probability Maps for Anti-Poaching Efforts (Paris, 2017)


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