Abstract
By analyzing the drivers’ main consideration about how to choose a parking space, this
paper is committed to path optimization of parking lot by grey entropy relation grade
multi-attribute decision making. The decision attribute matrix is identified by driving distance
which is deduced through Dijkstra algorithm, walking distance deduced through Euclidean
distance and parking space environment value deduced through the expectation of triangular
fuzzy number. Finally, an example would be analyzed to verify the feasibility of the method.
Keywords:parking lot optimal routing;grey entropy relation grade;Multiple Attribute
Decision Making (MADM)
1. Introduction
The establishment of the internal parking guidance system is probable along with the
development of techniques such as communication, dynamic traffic signs, parking space
detection. In recent years, parking guidance system has been gradually introduced by
many car parking management company, and newly built large parking lots of this system
have been set up. But in the process of system providing parking guidance to driver, it’s
easy to have a dissatisfied about the system’s assigning parking space, then the parking
guidance system is not effect. The root cause is that the existing parking guidance system
is not considering the perspective of the driver and just directly give a random parking
space or a shortest path method based parking space [1]. Therefore, how to find drivers
satisfying parking space and make drivers trust parking guidance system will be resolved
in turn to achieve the parking lots operational efficency.
This paper calculates the driving distance, walking distance and the environment value
of parking lots as the decision attribute value by analyzing the main influence factors of
parking choices, then makes the optimal routing take advantage of grey entropy relation
grade MADM