A data-driven methodology for equitable value-capture financing of public transit operations and maintenance

摘要

Despite the importance of rail infrastructure to the effective and efficient functioning of dense urban areas and their commercial business districts, funding for operations and maintenance of transit systems is a common challenge for cities. Operational funds are derived from a range of sources, including fare and toll revenues, taxes, and fees. In cities with aging infrastructure, traditional funding mechanisms are falling short of actual need, even as many cities experience record ridership levels. Therefore, new funding streams are necessary to safely, efficiently, and equitably operate and maintain an aging rail infrastructure in the face of growing demand. This paper presents a socio-spatial model of rail transit ridership demand to develop a computational method for value capture funding mechanisms that link existing commercial properties and transit infrastructure operations. Using a diverse range of large-scale data for New York City (NYC) and the surrounding region, our methodology provides a data-driven approach to address fundamental issues of horizontal and vertical equity in value-capture fees, including (1) the magnitude of the special assessment, (2) the property types to include, and (3) the boundaries of the special assessment district. We find that a marginal special assessment of $0.50 to $1.00 per square foot on commercial properties, proportionate to the lost wages and output associated with system delays, within 1/4-mile of a subway station in NYC's core commercial district could yield between $332 and $664 million annually to support the Metropolitan Transit Authority's operating budget. This is equivalent to the revenue generated by an average, system-wide per ride fare increase of $0.22, and significantly less than the estimated implicit transit subsidy for these buildings of $4.58 per square foot per year.