Abstract:
To optimize on-street parking resources and create effective parking policies, parking occupancy data is essential. Advanced and expensive occupancy monitoring technologies often provide such data. Integrating bay-level occupancies and parking data from other systems is difficult. Accurate occupancy–payment data are needed for analyzing payment behavior, estimating and forecasting occupancy, and assessing enforcement policies. This study proposes a metaheuristic optimization algorithm to integrate simple camera snapshots of bay-level parking occupancy with conventional parking payment management system transactions. Using parking payment data only, the integrated data developed, calibrated, and validated a parking occupancy estimation method. The algorithm and modelling technique’s design, implementation, and validation are described. Logistic regression analysis tuned data integration algorithm parameters. Parking occupancy was modeled using deep learning, gradient boosting, and random forests. The algorithm integrated bay occupancies and payment transactions with 76% accuracy. When tested with a random sample of integrated data, the best occupancy estimation model had a R 2 above 94% and an RMSE of 1.2 (occupied bays).
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