Unsupervised learning (UL) has a long and successful history in untangling the complexity
of cities. As the counterpart of supervised learning, it discovers patterns from intrinsic data …

Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

Urban Geography studies forms, social fabrics, and economic structures of cities from a
geographic perspective. Catalysed by the increasingly abundant spatial big data, Urban …

To formulate policies for low-carbon transport development, previous studies largely focus
on estimating the substitution effect of ride-hailing travel on public transport, but how transit …

This book has emerged through years of discussions, comments and questions we have
received about building agentbased models and integrating geographical information …

Modern public transit systems are often run with automated fare collection (AFC) systems in
combination with smart cards. These systems passively collect massive amounts of detailed …

Data collected through smart travel cards in public transport networks have become a
valuable source of information for transport geography studies. During the last two decades …

Transit-oriented development (TOD) is being promoted as a promising development strategy
to integrate transit and land use around public transportation stations. The node-place …

Smart card data derived from automatic fare collection (AFC) systems of public transit enable
us to study resident movement from a macro perspective. The rhythms of traffic generated by …

To examine the variability of travel behaviour over time, transportation researchers need to
collect longitudinal data. The first studies around day-to-day variability of travel behaviour …