Leveraging Location-Based Services Data to Optimize Generation of High-Demand and Equitable Bus Network Options

Abstract

The Massachusetts Bay Transportation Authority (MBTA) Bus Network Redesign project included a data-driven, customer-focused approach to creating the high-frequency core of the network. This approach used location-based services data to create an objective, repeatable process for developing this network based on the agency’s priorities. Centering equity and designed to limit human biases, the process algorithmically generated 100,000 possible high-frequency bus networks and scored them based on how much total demand and demand by low-income and minority populations was served by high-quality transit. The result was used to identify the high-frequency core of the bus network that approached the optimal for demand and equity while meeting resource constraints. The approach consisted of organizing the demand data, determining busable streets, generating potential bus corridors, combining those corridors into many sets, and scoring the sets based on how well they serve the region’s travel demand. The process emphasized equity by heavily weighting demand from low-income and minority populations in building and evaluating corridors and networks. Further, by using location-based services data, the approach was focused on where people were traveling, rather than traditional approaches that look to connect concentrations of trip generators and attractors without knowledge of where people are actually going. The result was a core, high-frequency bus network that has informed the decisions and design process of MBTA’s future bus system.