Assessment and optimization of parking reservation strategy for Park-and-Ride system emissions reduction

Abstract

The increasing of carbon emissions due to traffic congestion in urban centers has been plaguing city managers. Park-and-Ride system are considered as important solutions to reduce urban carbon emissions. However, research on parking reservation on park-and-ride systems is still insufficient. Therefore, this paper explores the impact of parking reservation mechanisms on park-and-ride systems. A multi-modal agent-based network model is constructed to simulate the park-and-ride behavior, different parking reservation strategies are assessed and optimized which aim to minimize carbon emissions. Taking the Suzhou Guanqian Street Commercial District, China as the numerical example, we analyzed the impact of parking space allocation on carbon emissions. When parking spaces are reserved in proportion to demand, emissions decrease until a 70% reservation ratio. Beyond this threshold, emissions increase. Employing Genetic Algorithm (G.A.) to optimize the allocation of reserved parking spaces in each parking lot can further reduce carbon emissions.