Evaluating Predictive Models for Parking Availability
In the realm of parking management, accurate predictions of occupancy status can significantly impact user experience. This post outlines a recent evaluation designed to assess the effectiveness of a predictive model against a common baseline.
Understanding the Evaluation Design
Our evaluation was structured to accurately mirror real-world scenarios. We assessed predictions on 100 randomly selected parking stations over two time frames: 30 minutes and 60 minutes. To achieve this, we recorded their occupancy status 48 times daily for an entire week.
Benchmarking Against the Baseline
The model was compared to a baseline method known as the “Keep Current State” approach. This straightforward method assumes that the number of available parking spots in the future will remain the same as the current number.
Challenges of the Baseline Model
Despite its simplicity, this baseline is surprisingly difficult to outperform, particularly over shorter time horizons. For instance, data from the US East Coast indicated that no more than 10% of parking spots change their availability within a 30-minute timeframe. As a result, the basic prediction of “no change” proves to be accurate most of the time, thereby challenging any advanced predictive model to provide substantial added value.
Key Metrics for Accuracy
To gauge our model’s performance, we focused on two essential metrics: mean squared error (MSE) and mean absolute error (MAE). The ratio of MSE to MAE—at least 1—serves as a critical indicator of the model’s performance regarding whether a user will find at least one available parking spot (Yes/No).
Conclusion
Our rigorous evaluation highlights the intricate balance between simplicity and predictive accuracy in parking availability models. By understanding the strengths and weaknesses of both advanced methods and baseline models, we can better tailor solutions to meet user needs.
Related Keywords: parking availability, predictive modeling, occupancy prediction, parking management, data evaluation, mean squared error, user experience.
