A Statistical Spatial Analysis of the First-Mile-Lastmile Problem in Public Transportation
In using public transportation, a traveler typically needs to make additional trips to and from the public transit station. These trips are known as the first-mile-last-mile (FMLM) trips. Beyond a certain distance that a traveler is willing to cover to reach the station (the comfortable distance) the traveler considers alternative means of transportation. FMLM trips become an issue when a significant number of commuters are located beyond their comfortable-distances. For this reason, the accessibility to and from public transit stations needs to be addressed as part of the efforts geared toward increasing transit ridership. A variety of strategies have been studied and implemented to address this problem: public transit agencies have constructed walking paths and bicycle-friendly neighborhoods, and provided shuttles to certain areas around transit stations. More recently, public transit agencies have partnered with shared-mobility services (such as Uber and Lyft) and bike-sharing services, aiming to attract more ridership. In this thesis, strategies to address the first-mile-last-mile problem were studied using five steps. First, the areas within which ridership is generated were defined, and supply and demand data were collected and aggregated to match the defined areas of influence. The data was used to model the mode choice and ridership trends at transit station and their proximal areas. The models then were used to identify the stations with low-utilization rates. Finally, using the significant variables and the elasticities of the two developed statistical models, first-mile-last-mile solutions were prioritized for implementation at the identified low-utilization stations.
Labi, Purdue University.
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