Space-time prisms

Movement analytics for sustainable mobility

New paper: Miller, H.J. (2020) “Movement analytics for sustainable mobility.” Journal of Spatial Information Science, 20, 115-123.

Invited essay for 10th anniversary issue

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

Robust accessibility: Measuring accessibility based on travelers’ heterogeneous responses to travel time uncertainty

New paper: Lee, J. and Miller, H.J. (2020) “Robust accessibility: Measuring accessibility based on travelers’ heterogeneous strategies for managing travel time uncertainty,” Journal of Transport Geography, 86, 102747.

Highlights

  • We develop an analytical framework for measuring accessibility considering travelers’ heterogeneous safety margin plans and routing strategies under travel time uncertainty.
  • We explore how accessibility changes under various safety margin plans and routing strategies.
  • We define and measure robust accessibility: geographic areas that are accessible regardless of the safety margin planning and routing strategy.
  • Robust accessibility provides a conservative and reasonable view of accessibility under travel time uncertainty.
  • We apply our framework to measure the accessibility impacts of new public transit service under travel time uncertainty.

Abstract

Uncertainties in travel times due to traffic congestion and delay are risks for drivers and public transit users. To avoid undesired consequences such as losing jobs or missing medical appointments, people can manage the risks of missing on-time arrivals to destinations using different strategies, including leaving earlier to create a safety margin and choosing routes that have more reliable rather than fastest travel times. This research develops a general analytical framework for measuring accessibility considering automobile or public transit travelers’ heterogeneous strategies for dealing with travel time uncertainty. To represent different safety margin plans, we use effective travel time (expected time + safety margin), given specified on-time arrival probabilities. Heterogeneity in routing strategy is addressed using different Pareto-optimal routes with two main criteria: faster travel time vs. higher reliability. Based on various safety margin and routing strategy combinations, we examine how accessibility changes under varying safety margin plans and routing strategies. Also, we define and measure robust accessibility: geographic regions that are accessible regardless of the safety margin planning and routing strategy. Robust accessibility can provide a conservative and reasonable view of accessibility under travel time uncertainty. To demonstrate the applicability of the methods, we carry out an empirical study on measuring the impacts of new transit service on healthcare accessibility in a deprived neighborhood in Columbus, Ohio, USA.

New publication: Jaegal, Y. and Miller, H.J. (2020) “Measuring the structural similarity of network time prisms using temporal signatures with graph indices,” Transactions in GIS, 24, 3-26.

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.