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. • Bayesian network approach to demonstrate explainable artificial intelligence (XAI). • Network structure includes demographic, travel and temporal variables. • Bayesian networks is both behaviourally rich and intuitively interpretable. • Quantify vulnerability explanations for travel behaviour.

Full Text

Published Version
Open DOI Link

Get access to 115M+ research papers

Discover from 40M+ Open access, 2M+ Pre-prints, 9.5M Topics and 32K+ Journals.

Sign Up Now! It's FREE

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call