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

2025-08-18

AI Summary

To address the scarcity of parking resources and the challenge of dynamic supply-demand matching in university campuses, this paper proposes a hardware-free, lightweight parking availability prediction framework. Methodologically, it integrates heterogeneous spatiotemporal data—including street networks, mobility trajectories, and meteorological records—models vehicle parking behavior via spatial join operations, and evaluates four predictive models: linear regression, support vector regression (SVR), random forest, and long short-term memory (LSTM) networks, optimized via grid search and assessed using RMSE, MAE, and R². Key contributions include: (i) the first sensor-free approach leveraging geospatial operations to uncover implicit parking patterns; and (ii) the incorporation of multi-scale spatiotemporal features to significantly enhance prediction robustness. Experimental results show that the random forest model achieves the best overall performance (RMSE = 0.142, R² = 0.582), while LSTM demonstrates greater potential for improvement in long-horizon forecasting scenarios.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Predicting parking availability using geospatial data and machine learning
• Addressing campus parking challenges during peak class hours
• Developing sensor-free framework through spatial data integration

Innovation

• Integrating geospatial and meteorological data via spatial join
• Evaluating multiple ML models including LSTM and Random Forest
• Using location services instead of physical parking sensors