Machine Learning Based Multi-Modal Transportation Network Planner

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 bike network and 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 relevant data sources for the planning problem, such as existing bike share ridership data, transit data from General Transit Feed Specification (GTFS), demographic data from the Census, and built environment factors such as amenities and land use data. The K-means clustering algorithm is used to model the region’s features across the selected variables and identify potential areas to expand into. Network analysis is performed to observe the network effects of expansion and determine candidate locations where future bike share stations may be built.