  As the number of vehicles continues to grow, parking spaces are at a premium
in city streets. Additionally, due to the lack of knowledge about street
parking spaces, heuristic circling the blocks not only costs drivers' time and
fuel, but also increases city congestion. In the wake of recent trend to build
convenient, green and energy-efficient smart cities, we rethink common
techniques adopted by high-profile smart parking systems, and present a
user-engaged (crowdsourcing) and sonar-based prototype to identify urban
on-street parking spaces. The prototype includes an ultrasonic sensor, a GPS
receiver and associated Arduino micro-controllers. It is mounted on the
passenger side of a car to measure the distance from the vehicle to the nearest
roadside obstacle. Multiple road tests are conducted around Wheatley, Oxford to
gather results and emulate the crowdsourcing approach. By extracting parked
vehicles' features from the collected trace, a supervised learning algorithm is
developed to estimate roadside parking occupancy and spot illegal parking
vehicles. A quantity estimation model is derived to calculate the required
number of sensing units to cover urban streets. The estimation is
quantitatively compared to a fixed sensing solution. The results show that the
crowdsourcing way would need substantially fewer sensors compared to the fixed
sensing system.

Human-Computer Interaction;Machine Learning