Project Summary
Stop-and-go waves on I-680/SR-24 waste fuel, heighten crash risk, and disproportionately burden disadvantaged travelers. Building on small-scale field testing that cut stoppage time by ~70% and hard-braking by 85%, Contra Costa Transportation Authority now seeks to deliver smartphone speed advisories to over 1,000 commuters. This UC Berkeley project supplies the data integrity, governance, and community engagement required for the success of that Phase IV launch. Seven integrated tasks will: (1) mine 90 days of PeMS records to flag faulty loop detectors, (2) verify and repair cabinets in the field, (3) calibrate remaining detectors with roof-mounted LiDAR ground truth, (4) time-align and lane-disaggregate INRIX probe speeds, (5) publish an IRB-approved Concept of Operations with live detector-health monitoring, (6) recruit over 30% of volunteers from CalEnviroScreen top-quartile tracts through Community-Based Organizations and equip buses, and (7) release an open-source toolkit and Policy & Scaling Brief. Outcomes include California’s first dual-sensor freeway benchmark, a reusable equity-centered consent framework, and cost metrics that position LiDAR-assisted calibration as statewide best practice, thus compressing start-up timelines and de-risking large-scale connected-vehicle flow-smoothing deployments.
