Part of a series of “reading memos” that offer a brief summary of interesting academic content along with my personal reflections. This one covers Chapter 5 of Giuliano & Hanson’s The Geography of Urban Transportation.
Market Theory suggests it may be possible to treat transportation as a type of market subject to laws of supply and demand, but in reality there are many factors which limit the relevance of this approach: many externalities, the epiphenomenon of travel demand (derived from activities), high variability of demand in both space & time (so supply can’t be stockpiled), complex infrastructure/vehicles/operations/stakeholders, and the rapidly changing nature of human activity. Alternatively, Activity Theory is based on the underlying activities that require mobility for access and participation, using concepts like the space-time path/prism to analyze human time use.
Trip-based models aggregate components of travel demand based on Market Theory, including trip generation (total trips in area), trip distribution (allocation of trips among destination zones), modal split, and network assignment (route choice).
Activity-based analysis features a more disaggregate approach, collecting large sets of mobility data (including event-based, time-based, change-based, and location-based recording) and summarizing them by various methods (e.g., distribution of speeds, directions of travelers, directed vs. exploratory patterns, geometry, and hotspots at different times). Variants of activity-based models include econometric models (mathematical representation of linkages between lifestyle/mobility decisions and activity/travel scheduling decisions), simulation models (featuring a computational process model with rules specifying how individuals react to environmental conditions) and microsimulation/agent-based models (ABMs) that replicate overall system dynamics by simulating behaviors/interactions often at the individual level. The Transportation Analysis and Simulation System (TRANSIMS) is one example of this approach that can generate a synthetic population, activity schedules, trip plans, and resulting traffic dynamics.
It was quite revelatory to realize that transportation generally is a quasi-public good (nonexclusive but not nonrivalrous) which therefore must always be subsidized with public funds in some way. This suggests that it’s possible to choose which modes of transportation to subsidize, with drastically differing results for automobile-focused societies (e.g., the United States), cycling-focused societies (e.g., Denmark), bus-focused societies (e.g., Colombia), or rail-focused societies (e.g., Japan). In any case, it seems the public is typically unaware of the negative or positive externalities associated with their transportation choices, so it seems necessary for policy-makers to help shape behaviors by exposing/hiding costs as necessary to achieve sustainability goals.
While trip-based models and Market Theory were presented as somewhat limited approaches here, I can see how their ease of implementation and long/vetted history of use makes them appealing for decisions around “hard policies” like infrastructure construction. That being said, it does seem that activity-based models are becoming more popular, especially for transportation demand management. I also believe the rapidly expanding field of data science will provide practitioners with new methods of privacy-conscious data collection and more technically advanced software to make large-scale activity-based analysis easier.