Conntrans

Abstract

Transportation between satellite cities or inside the city center has always been a crucial factor in contributing to a better quality of life. This paper focuses on a multi-criteria distributed competitive route planning for parking slot cruising in regions where neither real-time nor historical availability of parking slots is accessible. An inference-than-planning framework is proposed for solving the parking slot searching using a zero-information distributed model with an availability inference for parking slots in areas with no sensor coverage. Meanwhile, a proposed Conntrans algorithm is suggested as a two-stage structure with three relaxing policies: adjacent cruising, on-orbital annealing, and orbital transitioning. The evaluation is conducted based on the simulation in a publicly accessible real-world parking data from SFPark in San Francisco; the area is divided into 3 separated regions with different urban characteristics. Overall results show that the proposed availability inference model can retrieve decent performance. Furthermore, Conntrans is able to outperform baselines and state-of-the-arts in overall score by at most 77% with a success rate at around 97% and maintains the quality of solutions under various circumstances.