Characterizing emerging urban transportation modes : models and methods
The introduction of emerging transportation technologies, such as mobility-on-demand and shared modes, have caused disruptions in urban transportation systems. These services brought multiple challenges, including the lack of infrastructure, arbitrary pricing schemes, deficient operating rules and regulations, and safety concerns. Furthermore, the deployment of these technologies has increased the need and demand for improved management of the associated data. In particular, the volume of the collected information, the variability of data sources, the heterogeneous structure, and the inherent spatio-temporal nature highlight challenges for finding spatial and temporal relationships, dealing with computational complexity, and for the integration or fusion from various sources. This research work is based on the need for the implementation of models and methods for dealing with large-scale, diverse, and spatio-temporal datasets to adequately characterize emerging mobility technologies and their potential impacts on urban environments. Specifically, it assesses three main points: (1) the impact of emerging mobility modes on urban areas is still unknown, (2) it is not clear what is the effect of shared mobility services on public transit usage, (3) when available, the data may present several challenges. This dissertation designs and applies models and methods to evaluate emerging mobility services' impacts on different aspects of urban areas. The impacts in question are analyzed using four distinctive techniques based on advanced statistics and data analysis models and methods. These techniques are applied to several data sources describing ridesourcing (i.e., ride-hailing via transportation network companies or TNCs), microtransit (i.e., privately owned and operated shared transportation system that can have fixed or flexible routes and schedules), micromobility (e.g., bikesharing and dockless electric scooters or e-scooters), and public transit trips from Austin, Texas.
The results of the analyses show that the current fare system and pricing strategies can lead to disparities in TNC driver earnings. Temporal and spatial demand variations can exacerbate search frictions, which can cause an overall market failure. The results suggest that new pricing strategies are required and that there is a need for pricing regulations. A further examination of the ridesourcing effect on the airport ground access using Intelligent Transportation Systems (ITS) showed that the average airport-accessing speed decreases in the presence of TNCs. The use of ITS data is proposed to support airport decision-making processes. Finally, this study analyzed the integration of shared modes with the public transit system. Shared modes can complement the public transportation systems (like bus, train, and air) and solve first-mile-last-mile (FMLM) accessibility issues. However, this study's results suggest that this integration is not yet happening for TNCs and microtransit modes. An analysis of the use of Public-Private Partnerships (PPPs) to introduce share modes in areas with low public transit demand suggests the service was mainly used for intrazonal trips and not for FMLM. Further analysis of the relationship between e-scooters and public transit was able to identify areas with potential e-scooter and bus interaction. The results suggest that future collaborations and PPPs should focus on integrating these mobility services into the public transit system.