On-Street and Off-Street Parking Availability Prediction Using Multivariate Spatiotemporal Models

Tooraj Rajabioun · Pétros Ioannou

191 citations0/0 files storedIEEE Transactions on Intelligent Transportation Systems

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Smart Parking Systems Research Transportation Planning and Optimization Traffic Prediction and Management Techniques

Abstract

Parking guidance and information (PGI) systems are becoming important parts of intelligent transportation systems due to the fact that cars and infrastructure are becoming more and more connected. One major challenge in developing efficient PGI systems is the uncertain nature of parking availability in parking facilities (both on-street and off-street). A reliable PGI system should have the capability of predicting the availability of parking at the arrival time with reliable accuracy. In this paper, we study the nature of the parking availability data in a big city and propose a multivariate autoregressive model that takes into account both temporal and spatial correlations of parking availability. The model is used to predict parking availability with high accuracy. The prediction errors are used to recommend the parking location with the highest probability of having at least one parking spot available at the estimated arrival time. The results are demonstrated using real-time parking data in the areas of San Francisco and Los Angeles.

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References

2003Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results 1984Dynamic prediction of traffic volume through Kalman filtering theory 1997The High Cost of Free Parking 2002Comparison of parametric and nonparametric models for traffic flow forecasting 1996Combining kohonen maps with arima time series models to forecast traffic flow 2004Short‐term traffic forecasting: Overview of objectives and methods 2004A Simple and Effective Method for Predicting Travel Times on Freeways 1999Spectral Basis Neural Networks for Real-Time Travel Time Forecasting

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2017A Survey of Smart Parking Solutions 2019A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources 2016iParker—A New Smart Car-Parking System Based on Dynamic Resource Allocation and Pricing 2017Smart Parking Guidance, Monitoring and Reservations: A Review 2020Improving parking availability prediction in smart cities with IoT and ensemble-based model

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