Data-Driven Urban Performance Measures

Abstract

Performance measures typically are used by transportation agencies to measure progress toward organizational goals. As cities have reoriented their transportation priorities toward people instead of cars and have put more emphasis on multimodal transportation options, relatively few studies have identified measures that capture the urban context and are sensitive to the multimodal nature of urban transportation systems. Moreover, some studies have focused only on the measures without fully considering the available resources needed to capture these measures and the limitations in data. This lack often hampers implementation of performance measures and prevents agencies from creating a sustainable and reliable performance measurement program. The research presented in this paper defined a data-driven framework for monitoring mobility performance for urban transportation systems through a case study in the District of Columbia, conducted as part of an overall study to understand better the state of the District's transportation system. The study identified multimodal measures that were repeatable and that were supported by readily available, attainable, and reliable data sources. Measures that were common and could be compared across modes were considered in the selection process. The initial list was then refined and prioritized according to the data inventory by considering the availability and quality of data sources. Twelve multimodal measures that can be supported by valid, usable, and reliable data sets are recommended in the final metric list for evaluating multimodal mobility in the District.