A Dynamic Information-Based Parking Guidance for Megacities considering Both Public and Private Parking

Jong-Ho Shin,Duck Young Kim,Namhun Kim,Hong-Bae Jun

doi:10.1155/2017/9452506

Abstract

The constantly increasing number of cars in the megacities is causing severe parking problems. To resolve this problem, many cities adopt parking guidance system as a part of intelligent transportation system (ITS). However, the current parking guidance system stays in its infant stage since the obtainable information is limited. To enhance parking management in the megacity and to provide better parking guidance to drivers, this study introduces an intelligent parking guidance system and proposes a new methodology to operate it. The introduced system considers both public parking and private parking so that it is designed to maximize the use of spatial resources of the city. The proposed methodology is based on the dynamic information related parking in the city and suggests the best parking space to each driver. To do this, two kinds of utility functions which assess parking spaces are developed. Using the proposed methodology, different types of parking management policies are tested through the simulation. According to the experimental test, it is shown that the centrally managed parking guidance can give better results than individually preferred parking guidance. The simulation test proves that both a driver’s benefits and parking management of a city from various points of view can be improved by using the proposed methodology.

Highlights

While automotive transportation is of vital importance in everyday urban life, the constantly increasing number of cars has, become a messy problem in big cities
The base preference chooses the nearest parking to the destination without any guidance, so no private parking is used in this case
We have proposed a novel intelligent parking guidance methodology for a megacity, which includes both public parking facilities and private parking

Summary

Introduction

While automotive transportation (hereafter called “car”) is of vital importance in everyday urban life, the constantly increasing number of cars has, become a messy problem in big cities. The current VMS provides drivers with only brief information, such as the number of vacant parking spots and the distance and the direction to parking facilities. Based on this information and driver’s experience (i.e., prior knowledge), drivers should decide the best parking facility by himself/herself. This decision is vague and erratic, since there is no clear way of rank-ordering the parking facilities to find the best one with respect to multiple decision criteria, such as the current number of available parking spots, parking cost, distance, or traffic conditions. These limitations must be taken into account when developing a new intelligent parking guidance system, so that it helps the drivers’ decision-making by providing dynamic routing guidance obtained by considering both the driver’s preference in selecting parking and real-time parking data

Methods

Results

Conclusion