AbstractParking has become a major problem in urban traffic management, stemming from insufficient understanding of vehicle parking demand. The rapidly growing intelligent transportation system enables traffic managers to detect vehicle movement continuously on a large scale and supports individual parking behavior analysis. Basic-overnight parking demand, as the subject of this study, is the important part of parking demand. This study proposed a method to calculate overnight parking behavior based on automatic vehicle identification (AVI) data and took Shanghai as a case study to analyze overnight parking demand characteristics. After delineation of traffic analysis zones (TAZs) and reconstruction of travel paths, a vehicle state estimation model based on spatiotemporal information and the parking location model based on density-based spatial clustering of applications with noise (DBSCAN) clustering was developed to calculate overnight parking behavior. This was the first time that individual overnight parking behavior at the metropolitan level was directly calculated, instead of estimated, to master the city’s total overnight parking demand. The accuracy of overnight parking position calculation reached 85.39% in the validation experiment. This study systematically analyzed both macro urban characteristics and micro individual characteristics of overnight parking demand. Most vehicles have the characteristics of commuters in parking for 14 h overnight and being more elastic on weekends, which supports refined parking management in the temporal dimension. The spatial distribution of overnight parking demand significantly correlates with residential districts but shows severe imbalance with parking supply. The micro analysis indicates that vehicles show an obvious polarization in the tendency of overnight parking in any area. Generally, vehicles have multiple overnight parking positions but only park in one or two positions on most nights. The quantitative results are helpful to further understand urban overnight parking demand and individual overnight parking behavior, supporting accurate formulation of parking policies.

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