July 31, 2023
Optimizing Urban Mobility: Graff Assesses Full Cost of Multimodal Travel
A routine trip to the grocery store. It’s something most of us take for granted, but for people who need to take public transportation or use other means of indirect travel, it can have many costs beyond the fare. PhD candidate Lindsay Graff recently completed research on quantifying the impact of these journeys. While past research focused on single modes of transportation, Graff’s stands out because it looks at multimodal trips.
“Previous studies evaluated accessibility in a transportation network assuming that travelers used a single mode like public transit or personal vehicle and were subject to a single cost of travel time,” she says.
But as cities expand and people travel further for goods and services, single-mode transportation isn’t always optimal.
“Cities are rapidly integrating new mobility options such as bike-share and e-scooter to provide commuters with greater flexibility,” Graff adds. “These options allow travelers to reach more essential service destinations by constructing more convenient trips.”
“Cities are rapidly integrating new mobility options such as bike-share and e-scooter to provide commuters with greater flexibility.”
When Graff began her research, a method hadn’t yet been developed to quantify accessibility in a large-scale network that allows for multimodal trips—where people use any reasonable combination of a personal vehicle, transportation network companies (Uber/Lyft), carshare (Zipcar), transit, personal bike, bike-share, scooter, and walking. She also took into account additional travel costs such as fees, travel time, reliability, and risk.
To begin the process, Graff created a multimodal network modeling framework that accounted for five costs of travel across all travel modes: day-to-day mean travel time, monetary expense, reliability represented by day-to-day travel time variability, safety risks, and discomfort. A combination of these five perceived costs is referred to as the “generalized travel cost.”
She also evaluated accessibility by the time of day to account for the fact that public transit is schedule-based and affected by traffic conditions.
“The multimodal network model allows us to find the shortest multimodal path between any origin-destination pair. In a traditional mapping app, ‘shortest’ might mean the lowest travel time, but in this case, ‘shortest’ means the lowest generalized travel cost. We can consequently compare shortest paths across origin-destinations pairs and identify areas for network improvements,” she adds.
The network model was tested in three cases on Pittsburgh’s transportation network. Graff believes that the modeling framework will be helpful to policymakers and planners as new transportation systems are assessed.
“The model can provide insights into spatio-temporal mobility disparities across population groups and evaluate the potential impact of different network/service investments.”