I was interviewed for an article in the Christian Science Monitor about the impacts of the COVID pandemic on public transit. The reporter did a nice job of summarizing my thoughts on the role of public transit at this moment in history:
Self-driving cars offer some hope to reduce pollution in the near future. Yet progress has been slow, says Professor Miller, and autonomous vehicles aren’t likely to enter city streets within the decade. Even at their electrified best, he says, cars are still an inefficient form of transportation, and hence an imperfect solution to the climate crisis.
In his opinion, the moment demands a grand shift in thought. If viewed as a foundational piece of urban infrastructure, public transit could expand this decade and cement a larger role in the transportation ecosystem. Permitting residents a larger menu of options when traveling – say biking, walking, or riding buses or subways – would help conserve city space, lower spending, and protect the environment.
Abstract: Mobility is central to urbanity, and urbanity is central to our common future as the world’s population crowds into urban areas. This is creating a global urban mobility crisis due to the unsustainability of our 20th century transportation systems for an urban world. Fortunately, the science and planning of urban mobility is transforming away from infrastructure as the solution towards a sustainable mobility paradigm that manages rather than encourages travel, diminishes mobility and accessibility inequities, and reduces the harms of mobility to people and environments. In this essay, I discuss the contributions over the past decade of movement analytics to sustainable mobility science and planning. I also highlight two major challenges to sustainable mobility that should be addressed over the next decade.
Keywords: movement analytics, mobility science, animal movement ecology, sustainable mobility, urbanity
Abstract. The network‐time prism (NTP) is an extension of the space‐time prism that provides a realistic model of the potential pattern of moving objects in transportation networks. Measuring the similarity among NTPs can be useful for clustering, aggregating, and querying potential mobility patterns. Despite its practical importance, however, there has been little attention given to similarity measures for NTPs. In this research, we develop and evaluate a methodology for measuring the structural similarity between NTPs using the temporal signature approach. The approach extracts the one‐dimensional temporal signature of a selected property of NTPs and applies existing path similarity measures to the signatures. Graph‐theoretic indices play an essential role in summarizing the structural properties of NTPs at each moment. Two extensive simulation experiments demonstrate the feasibility of the approach and compare the performance of graph indices for measuring NTP similarity. An empirical application using bike‐share system data shows that the method is useful for detecting different usage patterns of two heterogenous user groups.