In an increasingly urbanized world, people remain connected by a complex nexus of roads, rails, paths, and sidewalks that form urban transportation systems and shape travel demand. On the supply side, transportation systems possess measurable topological and spatial network properties. On the demand side, travel demand also possesses properties, and recent research observes a Macroscopic Fundamental Diagram, which illustrates a certain pattern of capacity utilization. The central hypotheses of this proposal are: (1) a direct causal relationship exists between transport network structure, the accessibility the network provides, and the quantity and nature of travel demand (e.g. trip frequency, trip distance and time, activity space, and mode share. ) served by that network (after controlling for the network size); (2) this relationship is largely influenced by network structure, such as more complex networks result in less travel per capita; (3) this relationship is scalable (i.e. similar patterns occur in small and large cities), akin to many other urban properties.
This research will characterize cities according to their transportation systems, both from the supply and demand sides, which will provide a means to determine the presence of natural phenomena (e.g., between-cities comparisons, scaling) It will estimate the relationship between metrics of transportation network structure, particularly connectivity accessibility, and travel demand, using machine learning algorithms and testing a variety of hypotheses (e.g. does network complexity reduce travel demand?)
The opportunity ID for this research opportunity is 2228