ParkUs 2.0: Automated Cruise Detection for Parking Availability Inference

Pietro Carnelli, michael jones, Aftab Khan, Parag Kulkarni, Mahesh Sooriyabandara

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)


Recent studies show that a key contributor to congestion and increased CO2 emissions within cities are drivers searching (or cruising) to find a vacant on-street parking space. It has been shown that approximately (depending on the city) 20-30% of vehicles in congested urban areas were cruising to find a parking space with a parking search time varying in the order of several minutes. In the city of Bristol alone, we have shown, using our collected trip and publicly available census data that over 790 metric tons of CO2 is generated every year due to cruising. At a total cost of £368,000 (US$467,000) in terms of fuel wasted. The solution, described in this paper, aims to reduce parking search times using our automated real-time parking system called ParkUs 2.0. Our proposed method leverages sensor and location data collected from smartphones (carried by drivers), uses machine learning (classification) to detect cruising behaviour, automatically annotates parking availability on road segments based on the classified data and displays this information as a heatmap of parking availability information on the user’s smartphone. This is the first such attempt to automatically detect cruising to the best of our knowledge. Evaluation through controlled trials with volunteer participants highlights the potential of our novel approach as we are able to detect cruising with an accuracy of 81%.
Original languageEnglish
Title of host publicationParkUs 2.0: Automated Cruise Detection for Parking Availability Inference
Publication statusPublished - 18 Apr 2018


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