Driver-Centric Urban Route Planning: Smart Search for Parking

Abstract

With urbanization, there is a growing need for mobility. Challenges for urban drivers include finding available parking spaces. Searching for a parking spot can be a frustrating experience, often time consuming and costly. Also, the increasing number of vehicles on the roads leads to an additional strain on traffic flow, while the search for parking spaces lowers the level of service. In inner cities, vehicles circulate in search of an available parking space, leading to an increase in travel time, fuel consumption, pollutant emissions, and a decrease in traffic safety. The search for a free parking space generates a significant increase in traffic in urban areas. To solve the parking search problem, it is necessary to develop certain strategies and measures that minimize circling in search of a parking space. The implementation of intelligent transportation systems stands out. By applying intelligent transport systems, drivers are provided with information about free parking spaces, which reduces the circulation of vehicles in search of free parking. Although initially ITS systems mainly provided services for closed parking lots and garages, with the further development of the system, the service was extended to street parking lots or open-type parking lots. These measures not only solve traffic challenges but also promote sustainability in urban areas. This article analyzes the effect of a cooperative approach of guiding vehicles to available parking spaces compared to a standard model of searching for an available parking space. Within the framework of the advanced model for searching for available space, four parking demand scenarios were defined and simulated. Based on the created traffic simulation, a comparative analysis was made between the classic and cooperative approach, while the primary differences are manifested in the load of the traffic flow A simulation model was developed using the road network from the urban center of Zagreb.

A Graph-Based Scheme Generation Method for Variable Traffic Organization in Parking Lots

“…Subsequently, various algorithms have been employed to devise routing strategies for parking guidance. Ćelić et al [26] advocated a cooperative vehicular guidance approach to directing drivers to available parking spots, simulating four distinct parking demand scenarios. During the reservation phase, the optimization objectives typically encompass minimizing drivers' total monetary costs, maximizing parking resource utilization, and enhancing profitability for platform operators [27,28].…”