RAPID FLOW TECHNOLOGIES — Department of Transportation SBIR Phase I: 142FH2
RAPID FLOW TECHNOLOGIES — SBIR Phase I award from Department of Transportation.
Phase I SBIR feasibility signal
- Phase I awards fund proof-of-concept work. For capture teams, they mark early interest from Department of Transportation in a technical approach.
- Watch for Phase II follow-ons from the same firm/topic family — that conversion path is where budgets and transition pressure rise.
- Obligated amount $149,824. Cross-check similar awards in the same agency and technology tags for going-rate context.
- Topic code 142FH2 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $149,824
- Agency
- Department of Transportation
- Program / Phase
- SBIR · Phase I
- Topic
- 142FH2
- Solicitation
- DTRT5714RSBIR2
- NAICS
- —
- Place of performance
- PA
- Period
- 2015-02-20 → 2015-09-20
Description
Drivers searching extensively for parking, a behavior known as cruising, leads to excess congestion and pollution. Many smart parking interventions have attempted to address this issue in recent years. However, the problem of effectively detecting and measuring cruising remains largely unsolved. We propose to develop a low cost approach to automatic detection of cruising behavior using dense networks of Bluetooth AVI sensors combined with parking occupancy prediction. Bluetooth AVI sensors are widely used for travel time measurement on freeways and arterials, as most new vehicles contain Bluetooth devices that can be passively detected and tracked across multiple stationary sensors. We will extend these techniques to urban environments for vehicle route reconstruction and travel time measurement. Cruising routes are sufficiently distinct from normal travel routes, so we will develop a classifier to reliably classify vehicles as traveling or cruising. To strengthen the classifier, parking occupancy information and payment events from parking kiosks will be correlated with routes. Improved prediction of parking occupancy from transactional data is also proposed..