Zipstall – On-line and Off-line Parking Availability Prediction

Searching for parking has many terrible impacts, such as wasted time, fuel, and emissions, overpaying for parking etc. To ease the pain of parking, the goal of this project is to develop a method of collecting information from multiple sources (crowd-sourced information from parkers, active paid session information from managers/parking enforcement, and availability information from enforcement patrols) and utilize various machine learning methods including K-Nearest Neighbors, Neural Network, Decision Tree and Time Series models to predict real-time on-street and off-street parking availability at the users’ estimated arrival time. Afterward, Zipstall can provide the users with personalized parking recommendation once the availability prediction of parking areas is accurate. The partner will benefit from participating in the program as advancements in the methodology of tracking and predicting real-time parking availability, which will enable the delivery of a vastly superior customer experience to the partner’s clients.

Faculty Supervisor:

Linglong Kong

Student:

Haihan Xie

Partner:

Zipstall

Discipline:

Statistics / Actuarial sciences

Sector:

Professional, scientific and technical services

University:

University of Alberta