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Predicting Curb Side Parking Availability for Commercial Vehicle Loading Zones

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Abstract

Commercial fleet management and operations pose distinct challenges compared to regular passenger vehicles. These challenges stem from the varying sizes, shapes, and parking demands of commercial vehicles, requiring specific curbside accommodations. Despite extensive research on smart-parking management for personal vehicles, there has been limited focus on improving parking outcomes for urban freight systems. To address this gap, we have developed a framework that utilizes sensors installed in parking areas to collect occupancy information. This framework predicts parking space availability for commercial vehicles in 10-minute intervals. The current states and the predictions are relayed to the drivers in near real-time through a web-based interface, empowering them to find suitable parking spaces and reducing search time. Our framework incorporates a suite of machine-learning models for predicting curbside parking availability based on real-time sensor data from commercial vehicle loading zones. We evaluated these models in a busy commercial district in the Seattle area, focusing on prediction accuracy and real-world performance. Our study concludes that, for practical use, the convolutional neural network (CNN) model outperforms other architectures, including Spatial Temporal Graph Convolutional Networks (ST-GCN) and Transformer.

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Availability of Data and Materials

The code for the web app framework has been published and is available on our GitHub page: https://github.com/pnnl/parking. Restrictions apply to the availability of data. Data was obtained using the sensors and APIs provided by Frontier Communications Parent Inc (Fybr) and LACUNA (lacuna.ai) and is owned by the Seattle Department of Transportation (SDOT). Data can be made available from the corresponding author upon reasonable request.

Abbreviations

PLZ:

Personal Vehicle Loading Zone

CVLZ:

Commercial Vehicle Loading Zone

NPZ:

No-Parking Zone

SPMS:

Smart Parking Management System

RFID:

Radio Frequency Identification

AI:

Artificial Intelligence

ML:

Machine Learning

DL:

Deep Learning

KNN:

k-Nearest Neighbors

DT:

Decision Tree

RF:

Random Forest

RNN:

Recurrent Neural Network

LSTM:

Long-Short Term Memory

CNN:

Convolution Neural Network

GCN:

Graph Convolution Network

ST-GCN:

Spatio Temporal Graph Convolution Network

DGL:

Deep Graph Library

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Funding

This research was supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) through the Pacific Northwest National Laboratory (PNNL) under the DOE-VTO award 73074 and contract number DE-AC05-76RL01830. (Corresponding Author: Milan Jain).

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For research articles with several authors, a short paragraph specifying their individual contributions must be provided. The following statements should be used “Conceptualization, M.J., V.A., S.V., J.F., and K.W..; methodology, M.J., V.A., and S.V.; software, A.B., M.J., and V.A.; validation, M.J. and S.V.; formal analysis, M.J.; investigation, M.J., V.A., and S.V.; data curation, M.J., V.A., and S.V.; writing—original draft preparation, M.J., V.A., and S.V.; writing—review and editing, M.J., V.A., and S.V.; visualization, A.B.; supervision, J.F., and K.W.; project administration, J.F., and K.W.; funding acquisition, J.F., and K.W. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Milan Jain.

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This research was supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) through the Pacific Northwest National Laboratory (PNNL) under the DOE-VTO award 73074 and contract number DE-AC05-76RL01830.

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Jain, M., Amatya, V.C., Bleeker, A. et al. Predicting Curb Side Parking Availability for Commercial Vehicle Loading Zones. Int. J. ITS Res. 22, 614–628 (2024). https://doi.org/10.1007/s13177-024-00420-5

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