Mobility hubs are physical locations that allow travelers to seamlessly switch between various modes of transportation such as public transit, ridehailing, and micromobility. These hubs, by enhancing connectivity and accessibility, contribute to an improved quality of travel and offer various socioeconomic benefits. Also, as integral components of transport networks, mobility hubs play a crucial role in integrating new mobility technologies.

• Research Objectives

  We have developed a methodology to assess the suitability of an area for establishing mobility hubs and identify potential locations. This involves considering five major factors, including

  • Accommodating multimodal travel needs and streamlining transfer processes,
  • Integrating with existing bikeped infrastructure,
  • Enhancing first-/last-mile connectivity,
  • Promoting access to destinations,
  • Promoting socioeconomic equity.

• Analytical Framework

  We have developed a multi-criteria approach for identifying multi-level mobility hubs. The approach considers bus stop clusters as the spatial units for potential hub locations (as it is widely recognized that mobility hubs are most effective when located at or near transit stops with high ridership activity). We calculate scores, assign weights, and calculate mobility indexes based on different scenarios. We then carry out the evaluation procedure at various scales, including neighborhood, district, or regional levels.

  

  The framework comprises several key steps:

  1. Cluster Transit Stops: Mobility hubs should be anchored by high-frequency transit. We use the DBSCAN algorithm to cluster stops and generate 1-mile buffer zones, and consider the buffer areas to be the spatial unit for siting mobility hubs. The below calcualtions are processed and scaled to these spatial units.

  2. Determine Criteria and Weights: The framework allows planners to adjust weights for different considerations (criteria) and construct the final mobility hub index. It can generate different results for different scenarios. Based on literature reviews and stakeholder discussions, five criteria for siting mobility hubs are identified: transit ridership and supply, first-/last-mile connectivity, accessibility, infrastructure, and social equity. Each criterion includes several indicators and sub-indicators. The final mobility hub index is constructed by summing the weighted scores of the five criteria, with varying weights based on scenarios or planning priorities.

  3. Compute Neighborhood-Level Hub Index: Using the results from Steps 1 and 2, we calculate the index value for the neighborhood-level hubs. We then identify the neighborhood-level hubs based on the index. Steps include selecting the site with the highest index value as the first hub, excluding potential hubs within 1.5 miles of selected hubs, and repeating these steps until the service area of the mobility hubs covers 75% of transit coverage areas or the total number of hubs reaches a specified limit (N).

  4. Identify a Network of Mobility Hubs: District- and regional-level hub indexes are then computed for the selected neighborhood hubs. In this step, we enlarge the catchment area of the spatial unit and assume catchment areas of 3 and 5 miles, respectively.

• Data sources

  1. Transit Data: Gainesville Regional Transit System (RTS) provides information on bus routes and stops through the General Transit Feed Specification (GTFS) dataset. RTS also provides bus ridership data, including passenger counts and on-board wheelchair and bicycle amounts.
  2. FM/LM Connectivity Data: Micromobility trip data, covering e-scooter and micro-transit trips, is collected from the City of Gainesville. Census block-level FM/LM gap scores are derived from the American Community Survey (ACS) and LEHD Origin-Destination Employment Statistics (LODES).
  3. Infrastructure Data: Intersection density data, indicating multi-modal and pedestrian-oriented facilities, is sourced from the Smart Location Database. Road infrastructure data is collected from OpenStreetMap (OSM), offering detailed information about road networks.
  4. Socioeconomic Data: Demographic variables related to population, race, age, income, and vehicle ownership are considered for socioeconomic analysis and are sourced from ACS.
  5. Accessibility Data: Smart Location Database provides data on destination accessibility via auto or transit. Walkability around bus stops is evaluated using walk scores obtained from the Walkscore API.

• Results

  We have generated results for six planning scenarios:

  • Enhancing transit
  • Enhancing first-/last-mile connectivity
  • Leveraging existing infrastructure
  • Promoting equity
  • Enhancing accessibility
  • Equal weights

  For each scenario, we identified a network of neighborhood-level, district-level, and regional-level mobility hubs in the City of Gainesville.

  

  Most neighborhood-level hubs are located in southwest and east Gainesville. District-level hubs should be built at Oak Mall, north Gainesville, and GNV airport, which has the highest FM/LM gap. Butler Plaza and downtown Gainesville are also potential sites for district-level hubs, which have higher transportation equity scores and transit supply. Shands Hospital is most suitable for siting the regional-level mobility hub, where ridership and accessibility were the highest.

  
  

• Conclusion

  In this project, we present a GIS-based analytical framework for identifying the most suitable locations for mobility hubs within the context of Gainesville, Florida. The proposed methodology is designed to evaluate and prioritize potential hub locations at different scales by assigning scores and weights to a variety of criteria. These criteria encompass essential factors such as transit supply availability, first-/last-mile connectivity, accessibility, road infrastructure, and socioeconomic equity. By integrating these criteria into a comprehensive evaluation process, this research aims to provide valuable insights and data-driven recommendations that will guide the strategic placement of mobility hubs in Gainesville, ultimately fostering a more efficient, equitable, and accessible urban transportation system.

To address the policy question of how e-scooters interact with existing public mobility options, we conducted a spatiotemporal analysis of e-scooters’ relationships with public transit and station-based bikeshare in Washington DC. Results suggested that the service areas of the three modes largely overlap, and most e-scooter trips could have been made by transit or bikeshare. In addition, we found that e-scooters enhance mobility services for some underserved neighborhoods. Before COVID-19, about 10% of all e-scooter trips were taken to connect with the Metrorail system

Key words: E-scooter, public transit, bikeshare

• Related publication:

Yan, X., Yang, W., Zhang, X., Xu, Y., Bejleri, I., Zhao, X. (2021). A spatiotemporal analysis of e-scooters’ relationships with transit and station-based bikesharing. [Download Preprint]. Transportation Research Part D: Transport and Environment. 12, 103088. https://doi.org/10.1016/j.trd.2021.103088

Some transit observers envision future public transit to be integrated systems with fixed-route services running along major corridors and ridesourcing servicing lower-density areas. This paper evaluates traveler preferences for a proposed integrated transit system versus the existing fixed-route system, with a particular focus on disadvantaged travelers. Results from low-income communties in Detroit and Ypsilanti suggest that a majority of survey respondents preferred a MOD transit system over a fixed-route one. However, some women may have safety concerns, and low technology self-efficacy can be a more serious barrier for many people to adopt MOD transit.

Key words: mobility on demand, public transit, transport equity

• Related publication:

Yan, X., Zhao, X., Han, Y., Van Hentenryck, P., Dillahunt, T. (2021). Mobility-on-demand versus fixed-route transit systems: An evaluation of traveler preferences in low-income communities. [Download Paper]. Transportation Research Part A: Policy and Practice, 148, 481-495. https://doi.org/10.1016/j.tra.2021.03.019

Electric scooters (or e-scooters) have quickly proliferated in cities worldwide, presenting a host of regulatory challenges. We analyze trip origins and destinations for shared e-scooter use at the street-segment level. Results show that street segments near tourist sites, hotels, and transit stops attract the most scooter-trip destinations. In constrast, the supply of available e-scooters is the dominant force shaping scooter-trip origins

Key words: E-scooter, street segment, public transit, travel demand

• Related publication:

Merlin, L., Yan, X., Zhao, X. (2021). A segment-level model of shared scooter origins and destinations. [Download Preprint]. Transportation Research Part D: Transport and Environment, 92, 102709. https://doi.org/10.1016/j.trd.2021.102709

In this paper, we developed a random forest model to examine which factors are key predictors of ridesplitting adoption rate (i.e., the proportion of ridesourcing trips with ridesharing authorization) and to explore their nonlinear relationships. The random forest model resulted in additional insights that were not obtainable from the commonly applied linear regression model.

Key words: random forest, machine learning, travel demand prediction

• Related publication:

Xu, Y., Yan, X., Liu, X., Zhao, X. (2021). Identifying key factors associated with ride-splitting rate and modeling their nonlinear relationships. [Download Preprint]. Transportation Research Part A: Policy and Practice, 144, 170-188. https://doi.org/10.10416/j.tra.2020.12.005

As ridesourcing continues to grow in popularity, being able to accurately predict the demand for it is essential for effective land-use and transportation planning and policymaking. In this research project, we show that using random forest, a machine learning model that can automatically capture nonlinear relationships and interactive effections, can significantly improve predictive accuracy compared to the traditional statistical model.

Key words: random forest, machine learning, travel demand prediction

• Related publication:

Yan, X., Liu, X., Zhao, X. (2020). Using machine learning for direct demand modeling of ridesourcing services in Chicago. [Download Preprint]. Journal of Transport Geography, 83, 102661.

In recently years, transit agencies have started to consider integrating ridesourcing services (i.e. on-demand, app-driven ridesharing services) with public transit. In this project, we evaluated traveler preferences for this type of integrated transit system. We find that when used to provide convenient last-mile connections, ridesourcing could provide a significant boost to transit ridership.

Key words: Ridesourcing, public transit, travel behavior, mode choice

• Related publication:

Yan, X., Levine, J., Zhao, X. (2019). Integrating ridesourcing services with public transit: An evaluation of traveler responses combining revealed and stated preference data. [Download Preprint]. Transportation Research Part C: Emerging Technologies, 105, 683-696. https://doi.org/10.1016/j.trc.2018.07.029