On-Street Parking Occupancy Inference Based on Payment Transactions

Behrang Assemi,Douglas Baker,Alexander Paz

doi:10.1109/tits.2021.3095277

Behrang Assemi, Douglas Baker + Show 1 more

Open Access

https://doi.org/10.1109/tits.2021.3095277

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Abstract

A record of spatial and temporal parking occupancy is critical to optimize on-street parking resources and to develop effective parking policies. Such data are often obtained through advanced and costly occupancy monitoring technologies. Moreover, it is usually challenging to integrate bay-level occupancies and parking data from other systems. Accurate occupancy–payment data are required for a wide range of analytical and practical purposes, including but not limited to investigating payment behavior, estimating and forecasting occupancy, and evaluating the efficiency and effectiveness of enforcement policies. This study proposes a metaheuristic optimization algorithm to integrate snapshots of bay-level parking occupancy, captured using simple cameras, with transactions from a conventional parking payment management system. The resulting integrated data were used to develop, calibrate and validate a parking occupancy estimation method utilizing parking payment data only. Details of the design, implementation, and validation of the proposed algorithm and modelling technique are provided. Logistic regression analysis was used to tune parameters of the data integration algorithm. Deep learning, gradient boosting and random forests were used to develop a model of parking occupancy. Evaluation of the algorithm indicated an accuracy of 76% of correct data integration; that is, individual bay occupancies integrated with the correct corresponding payment transactions. The best occupancy estimation model also showed a very high accuracy, with an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> above 94% and a root mean square error (RMSE) of 1.2 (occupied bays), when tested with a random sample from the integrated data.

Highlights

R ELIABLE occupancy data are needed for effective monitoring and management of on-street parking
This study investigates on-street parking in three separate streets in the Brisbane central business district (CBD), which are assumed to be representative of different spatio-temporal characteristics of parking behavior
This section presents algorithm validation results using number plates. It elaborates on two street parking performance indicators, relative parking occupancy and hourly turnover, estimated at an aggregate level using payments and occupancies separately

Summary

Introduction

R ELIABLE occupancy data are needed for effective monitoring and management of on-street parking. Predictions of parking behavior and occupancy estimation are crucial to support effective management of on-street parking space. Without observational and bay monitoring data, studies have relied on modelling behavior [5], [10], [11] to estimate or predict parking occupancies. Such an approach mitigates the need for installation and maintenance of permanent occupancy monitoring technologies. Modelling scales have varied considerably in the literature, from individual parking bays [12] to multiple data sources in networks, but all infer causal relationships for parking behavior [13], [14]. Experiments were conducted on each data source to estimate parking occupancies

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