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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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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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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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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DOI: https://doi.org/10.1007/s13177-024-00420-5