Optimal parking allocation for heterogeneous vehicle types

Abstract

Parking problems have been inflicting significant negative impacts on the economy. Drivers, in the United States, pay a staggering amount of almost $96 billion annually for parking-related issues, with cruising for parking being the most expensive component at around $73 billion. Additionally, businesses suffer major losses due to limited parking availability, with 39% of U.S. drivers avoiding shopping destinations and 29% avoiding sports and leisure activities because of parking limitations. These issues are primarily caused by a lack of information about parking occupancy and limited parking supply. This lack of information results in drivers cruising for parking, which accounts for 30 % of traffic in some cities. To address these problems, this research proposes an optimization model that allocates arriving individual vehicles with heterogeneous characteristics to specific on-street or off-street parking spaces. The model seeks to reach a system optimal solution by reducing private costs, congestion, emissions, and mitigating cruising. The model utilizes inputs such as vehicle and driver attributes, parking duration, and value of time (VOT) along with information about the network and current parking occupancy to optimize the allocation decisions. This model can be incorporated into a system maintained by cities to assign parking within smart cities and to optimally divide curbside parking by vehicle type and time of day. The results show that vehicles with higher VOTs should be closer to their destination compared with relatively lower VOT vehicles. The results of the model also show that parking systems do not break once demand exceeds supply, rather the critical state of the system is controlled by multiple factors, e.g., demand, vehicle types, parking duration. Hence, the model can provide useful insights by studying scenarios of parking systems breakdown which can be targeted through policy interventions.