Identification of parking spaces from multi‐modal trajectory data

Abstract

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.

Map-Matching Error Identification in the Absence of Ground Truth

“…where K j is the Kalman gain matrix in the jth state, H the measurement matrix for the observation process, Z j the observations at the jth state, and M e the co-variance matrix of the measurement error, that is, the Gaussian noise in the measurement N(0, σ 2 ), where σ is the standard deviation of the measurement error [14]. We will get the estimated position ĉj = ( xj , ŷj ) from Xj .…”

“…Validation and performance assessment of the automatic error identification requires ground truth data for the map-matched segments. Since ground truth of map-matched segment data is not available with large-trajectory datasets [14,32], we apply visual in-spection of the map-matched segments, applying criteria of reasonable sequencing described above.…”

“…Existing map-matching algorithms consider numerous approaches to handle mapmatching errors [10,11]. However, they are still prone to errors due to many reasons, such as the inevitable occurrence of systematic GNSS errors, including blocked signals and multipath effects (measurement error) [4,12,13], low sample rate GNSS trajectories (sampling error) [10], limitations in the efficiency of online map-matching algorithms, incomplete map data [14], matching errors at junctions [11], and matching to wrong mobility networks [8,[10][11][12].…”