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Explainable Bayesian Networks for Transport Policy

Explainable Bayesian Networks for Transport Policy

Abstract

To deal with increasing amounts of data, decision and policymakers frequently turn to advances in machine learning and artificial intelligence to capitalise on the potential reward. But there is also a reluctance to trust black-box models, especially when such models are used to support decisions and policies that affect people directly, like those associated with transport and people's mobility. Recent developments focus on explainable artificial intelligence to bolster models' trustworthiness. In this paper, we demonstrate the use of an explainable-by-design model, Bayesian Networks, on travel behaviour. The model incorporates various demographic and socioeconomic variables to describe full-day activity chains: activity and mode choice, as well as the activity and trip durations. More importantly, this paper shows how the model can be used to provide the most relevant explanation for people's observed travel behaviour. The overall goal is to show that model explanations can be quantified and, therefore, assist policymakers to truly make evidence-based decisions. This goal is achieved through two case studies to explain people's vulnerability as it pertains to their total trip duration.

Presentation Video

About the Presenter

Alta de Waal, Ph.D.
BMW Software Factory South Africa

Alta is a senior data scientist at the BMW Software Factory in South Africa. She has more than 20 years of experience in the design, development, and implementation of different components in the AI value chain. Her current research focus is natural language processing (NLP) and explainable methods in AI for the purpose of actionable insights, fairness, and accountability in these systems.

Presentation Slides

Previous Presentations

  • Bayesian Networks for Knowledge Discovery and Curriculum Optimisation in Academic Programmes (Laval Virtual World, 2020)
  • Activity-Based Travel Demand Generation Using Bayesian Networks (Laval Virtual World, 2020)
  • Spatially Discrete Probability Maps for Anti-Poaching Efforts (Paris, 2017)

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