Smart Parking: Using a Crowd of Taxis to Sense On-Street Parking Space Availability

Monitoring the occupancy of on-street parking spaces on a city-wide scale is still an open issue. Past research demonstrated the viability of parking crowd-sensing by means of the standard on-board sensors of probe vehicles, foreseeing the use of high-mileage vehicles, like taxis. Nevertheless, the achievable spatio-temporal sensing coverage has never been deeply investigated. In this paper, we investigate the suitability of taxi fleets of different sizes to crowd-sense on-street parking availability. We considered 579 road segments in San Francisco (USA), covered both by sensors of the SFpark project and by the GPS traces of 536 taxis. For each of these segments, we computed the taxi transit frequencies, representing the achievable coverage by vehicles equipped with sensors detecting empty parking spots. By combining these frequencies with parking occupancy data coming from SFpark, we estimated the potential quality of crowd-sensed on-street parking information for different fleet sizes. Moreover, we investigated the impact of different misdetection amounts, and Kalman filters to handle them. The results show that a total of 300 taxis can crowd-sense on-street parking availability with an error of up to $± 1$ stall in 86% of the cases. Moreover, the quality of the sensors is as important as the fleet size (300 taxis with 10,% probability of misreadings provide availability information comparable to 486 taxis with 16,% probability), while the use of Kalman filters did not lead to statistically significant improvements. In conclusion, the traffic management authorities should consider parking crowd-sensing via probe vehicles as a promising alternative to the expensive deployment of the static parking sensors.