A Cost-Effective Framework for Predicting Parking Availability Using Geospatial Data and Machine Learning

TL;DR

This paper presents a cost-effective, sensor-free framework that predicts parking availability on university campuses by integrating geospatial, mobility, and weather data using machine learning models, notably Random Forest and LSTM.

Contribution

The study introduces a novel data integration approach and evaluates multiple machine learning models for parking prediction without installing sensors.

Findings

Random Forest achieved lowest RMSE of 0.142

LSTM may outperform others with more data

Framework effectively predicts parking using only location-based data

Abstract

As urban populations continue to grow, cities face numerous challenges in managing parking and determining occupancy. This issue is particularly pronounced in university campuses, where students need to find vacant parking spots quickly and conveniently during class timings. The limited availability of parking spaces on campuses underscores the necessity of implementing efficient systems to allocate vacant parking spots effectively. We propose a smart framework that integrates multiple data sources, including street maps, mobility, and meteorological data, through a spatial join operation to capture parking behavior and vehicle movement patterns over the span of 3 consecutive days with an hourly duration between 7AM till 3PM. The system will not require any sensing tools to be installed in the street or in the parking area to provide its services since all the data needed will be collected using location services. The framework will use the expected parking entrance and time to specify a suitable parking area. Several forecasting models, namely, Linear Regression, Support Vector Regression (SVR), Random Forest Regression (RFR), and Long Short-Term Memory (LSTM), are evaluated. Hyperparameter tuning was employed using grid search, and model performance is assessed using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Coefficient of Determination (R2). Random Forest Regression achieved the lowest RMSE of 0.142 and highest R2 of 0.582. However, given the time-series nature of the task, an LSTM model may perform better with additional data and longer timesteps.