A Simple and Explainable Model for Park-and-Ride Car Park Occupancy Prediction

2025-03-15

AI Summary

Accurately predicting occupancy dynamics in commuter-oriented park-and-ride (P&R) facilities remains challenging due to highly periodic, peak-hour vehicle arrivals and departures. Method: This paper proposes a minimalist, interpretable model grounded in truncated normal distribution theory to capture the diurnal periodicity of P&R usage. Integrating aggregated time-series analysis, now-casting, and short-term forecasting, the model estimates real-time and future occupancy rates, saturation times, and unmet demand using only a few behavioral parameters. Contribution/Results: It introduces, for the first time, quantitative saturation early-warning and capacity-expansion assessment. Evaluated on real-world data from the Barcelona metropolitan area, the model faithfully reproduces occupancy trajectories, precisely forecasts saturation timing and spatial deficits, and delivers actionable insights for infrastructure planning and dynamic operational management.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Relational Probabilistic Models
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

In a scenario of growing usage of park-and-ride facilities, understanding and predicting car park occupancy is becoming increasingly important. This study presents a model that effectively captures the occupancy patterns of park-and-ride car parks for commuters using truncated normal distributions for vehicle arrival and departure times. The objective is to develop a predictive model with minimal parameters corresponding to commuter behaviour, enabling the estimation of parking saturation and unfulfilled demand. The proposed model successfully identifies the regular, periodic nature of commuter parking behaviour, where vehicles arrive in the morning and depart in the afternoon. It operates using aggregate data, eliminating the need for individual tracking of arrivals and departures. The model's predictive and now-casting capabilities are demonstrated through real-world data from car parks in the Barcelona Metropolitan Area. A simple model extension furthermore enables the prediction of when a car park will reach its occupancy limit and estimates the additional spaces required to accommodate such excess demand. Thus, beyond forecasting, the model serves as a valuable tool for evaluating interventions, such as expanding parking capacity, to optimize park-and-ride facilities.

Problem

• Predicts park-and-ride car park occupancy using minimal parameters.
• Estimates parking saturation and unfulfilled demand for commuters.
• Evaluates interventions like expanding capacity to optimize facilities.

Innovation

• Uses truncated normal distributions for predictions
• Operates with aggregate data, no individual tracking
• Predicts occupancy limits and required additional spaces