Cupertino Active Transportation Plan Update

David acted as the analytics lead for Cupertino Active Transportation Plan's Needs Assessment by developing a gap analysis approach that combined network analysis with and evaluation of short trips that could be made by biking or walking respectively. Alongside developing the approach for the analysis, he built a Python based geoprocessing tool, that produced travel times estimates along a network between origin-destination pairs matrices. This was used in the plan to evaluate for every sub-three-mile bicycle trip and sub-one-mile walking trip and merged them with BLTS/PLTS comfort scores, NCHRP impedance multipliers, and survey-identified barrier zones. The model calculated straight-line, network, and stress-adjusted travel times for each pair, converting the differences into gap scores that reveal where high-potential trips are suppressed by indirect or high-stress segments. By coupling these gap scores with Active Trip Potential demand, David worked with planners and analysts on the project to provide ranked OD tables, narrative insights, and interactive map layers that now steer Cupertino’s corridor-prioritization and investment decisions toward the greatest mode-shift returns.