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Finding the nearest parking location in road networks is one of the most commonly faced challenges in everyday life of green transportation. A main challenge faced by the state-of-the-art existing parking allocation methods is to optimally offer the nearest parking location for a group of <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> users at the cost of minimal overall traveling time to ensure the traffic and environmental sustainability. In this article, we model it as a Multiple Nearest Parking Location Allocation (MNPLA) problem, and devise a spatial index tree, called SCP-tree, to accelerate the nearest parking location allocation within the users&#x2019; time constraints. During the search process in SCP-tree, we build a pruning strategy relevant to the Geographical Preference Estimation, travel time and parking capacity to determine which branch to visit so that the search accuracy can be improved. Considering the users&#x2019; behaviors are often impacted by the geographical location and some personalized attribute information, we set the user priority based on them to help the parking officer determine the allocation sequence. We evaluate our allocation scheme using large real-world dataset with on-street parking sensor data, and extensive experimental results reveal (i) a minimum improvement of 15.9&#x0025;, 1.4&#x0025;, 96.9&#x0025;, 160&#x0025; in parking allocation time, average traveling time, I/O cost and service utility compared to the progressive methods, and (ii) a minimum improvement of 8.9&#x0025;, 11.1&#x0025;, 78.2&#x0025;, 714&#x0025; in parking allocation time, average traveling time, I/O cost and service utility compared to the baseline methods.

SCP-Tree: Finding Multiple Nearest Parking Spots With Minimal Group Travel Cost