Optimal allocation of parking spaces for heterogeneous vehicle types

Abstract

Parking problems pose a huge burden to the economy, in the United States (US), drivers pay annually $95.7 billion for parking related issues. The bulk of this cost, $72.7 billion is for cruising for parking, the rest of the cost is due to parking fines and overpaying. There are also major losses incurred by businesses, 39% of US drivers surveyed in 2016, reported avoiding shopping destinations where they know parking is limited, while 29% reported avoiding sports and leisure activities for the same reason. The main reasons behind these issues are limited availability of parking supply and lack of information about parking occupancies. This lack of information is the main reason forcing drivers to cruise for parking. Cruising has been estimated to be 30% of the traffic within cities, and it varies a lot between cities. In New York City, for example, it takes on average 15 minutes per trip to find a vacant on-street parking space, and 13 minutes per trip for off-street parking spaces.This research tackles these parking problems by building two optimization models that allocate individual vehicles (passenger cars, buses, delivery trucks, and service vehicles) arriving in a neighborhood to a specific on-street or off-street parking space, with the objective of reducing congestion, emissions and eliminate cruising. These two models include: (1) A static model in the case of small parking turnover, where the interarrival times are negligible; (2) A time-expanded model which considers variation in arrival times and the reusability of parking spaces. The proposed models take as inputs the vehicle and driver’s attributes from destination, parking duration, time of arrival and value of times (VOTs) along with information about the network and current parking occupancies. This information is used within an integer linear optimization problem to output specific destinations for individual arriving vehicles. Such models can serve as a core of a system maintained by cities to assign parking within smart cities. They can also be used by cities to optimally divide curbside parking by vehicle type and time of day. The results of the applied models show the superiority of the dynamic model, and it shows that vehicles with higher VOTs are better off parking near building entrances. The model also provides insights about scenarios of system breakdown that can be targeted by policy interventions.