Identification of parking spaces from multi-modal trajectory data

Mapping the parking spaces in cities is desirable for reducing cruising time and congestion in the city. But map information regarding parking spaces is often missing or incomplete, due to the variety of their nature: marked or unmarked, on-street or off-street, or public, domestic or commercial. Hence, we develop a new method for mapping parking spaces, and deliberately focus on a crowd-sourcing solution because of its global applicability. We will use smartphone trajectory data, as collected by person-bound navigation apps. A person-bound navigation app collects multi-modal trajectory data where the transitions from drive to walk or from walk to drive contain valuable information about parking spaces. Hence, mode detection is required with sufficient accuracy to be able to map parking spaces. We develop a novel mode detection focusing just on this problem and outperforming existing, generic mode detection algorithms. Further, we provide a methodology to identify the geographic locations of parking spaces from these collected trajectory data. The article presents the methodologies, their implementations, and a critical evaluation to achieve mapping of parking spaces.