Parking Guidance Models and Algorithms Considering the Earliest Arrival Time and the Latest Departure Time

Abstract

Parking is one of the major problems in many cities. Due to the influence of various factors, the number of available parking spaces and travel time is highly dynamic and random. To find the reliable parking lot and the reliable path for travelers, this article proposes two parking guidance models and algorithms considering the earliest arrival time and the latest departure time. First, an extended shifted lognormal distribution (a 3-parameter lognormal distribution) is introduced to describe travel time. Then, a parking guidance model considering the earliest arrival time and a solution algorithm based on travel time bounds are established to find the reliable parking lot, the earliest arrival time, and the corresponding reliable path. Next, a parking guidance model considering the latest departure time and a solution algorithm based on travel time bounds are developed to find the reliable parking lot, the latest departure time, and the corresponding reliable path. Finally, two case studies with a real-world road network are used to verify the effectiveness and superiority of the proposed models and algorithms.