Skip to main content
ICTD 2024 Main banner
Icon Legend
This session is not in your schedule.
This session is in your schedule. Click again to remove it.
ViewAttendees 3

IPOD Abstract for presentation (Poster or Podium) with a Paper in the Conference Proceedings

Active Transportation (Bike/Ped)

1664760 - Machine Learning based Multi-modal Transportation Network Planner

Monday, June 17, 2024
2:20 PM - 2:45 PM ET
Room: 223
Abstract: Multi-modal transportation relies on efficient network connectivity among active transportation services, such as biking and transit stops. In many cases, road users must walk or bike varying distances to reach a transit stop and then continue their journey to their final destinations. As shared mobility services, like installations of shared bike docking stations, increase in urban settings, planning additional docking stations that efficiently connect with existing transit stops becomes a significant challenge. This paper proposes a machine learning (ML)-based multi-modal transportation network planner to address the optimization challenge of expanding shared bike docking stations (Cincinnati Red Bike) that connect with the existing transit network (Cincinnati Go Metro) and amenities within neighborhoods of the City of Cincinnati. We identify and collect relevant data sources for the planning problem, such as existing bike share ridership data, transit data from GTFS (General Transit Feed Specification), demographic data from the Census, and built environment factors such as amenities and land use data. We also attempt to leverage ML models, such as the Random Forest Regressor, to perform demand prediction and identify key features that support bike share ridership and effective network expansion, such as proximity to high-frequency transit stops, first and last mile connectivity, and key destinations. Data analysis could play a significant role in assisting urban planners and communities in selecting optimal locations for bike docking stations that are both data-driven and community-preferred. For example, Red Bike plans expansion specifically into Walnut Hills, Avondale, and Evanston purely from a community development perspective. With intensive community engagement, the team is collecting data through surveys, where community members place pins on a map indicating where they would like to ride a bike. Survey results serve as data points to guide expansion into those community spaces. The team also considers the demographics of the bike stations' service areas. However, there is no hard and fast science that guarantees success or failure, as it depends on the community's adoption of bike riding. Another challenge to overcome is data scarcity, as data-driven methods require a large amount of data for generalization and performance. Combining data-driven insights for newly proposed expansion spots with other metrics, like community preference, would significantly optimize the effectiveness of the expansion process of bike share systems. The approach used and results obtained may also be helpful for further mobility planning, such as choosing optimal locations for smart mobility hubs, park & ride, and other similar mobility services. 

Learning Objectives:

Attendees can expect to learn the following from this session:
  • Upon completion, participants will be able to identify and collect data from relevant sources such as bike-share, transit (GTFS), land use, built environment factors, demographics, amenities, and stakeholder inputs.
  • Upon completion, participants will be able to fuse different data sources such as land use, transportation modes, ridership and origin-destination analysis to gain insights into transportation network connectivity.
  • Upon completion, participants will be able to explore innovative visualization methods such as kepler.gl to visualize multimodal transportation network connectivity and trip data that will benefit expansion and planning.