Before-After Methods: Policy and Performance

This study focuses on identifying and testing the potential use of before-after methods with "Big Data" to assess the links between policy and transportation system performance.  New data is increasingly becoming available with which the outcomes of policymaking could be explored.  But transportation studies do not always leverage such data to meaningfully inform program management and provide the capacity to pilot policymaking initiatives while assessing their implications along a broad variety of outcomes.  Moreover, with many new types of data which collect information on various elements of system performance, including safety, reliability, travel times or speeds, volumes, headways, and ridership, there is much potential to directly study trade-offs in policymaking.  What positive outcomes are we most meaningfully able to shape?  And how can we do this better?

Supported by City of Toronto