Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid
2. Materials and Methods
2.1. Dataset Processing
- The stationary period, p, was calculated as the set of full minutes between the end_time of and the start_time of
- The co-ordinates of the end location of were retrieved, i.e., end_latv and end_lngv
- Vehicle availability, av, for period p was calculated using Equation (2)
- Each half-hour period, , for which all 30 min fell within p was added to set
- Where , the vehicle was deemed to be available for each period within
- The day number (d); from 0 to 6, i.e., Sunday to Saturday
- Half-hour (hh); the index of the half-hour period from 1 to 48
- Public holidays (ph); i.e., national holidays
- University holidays (uh); other days—when the University was closed—that were typically contiguous to public holidays
- Holidays (hol); days that were either a public holiday or a University holiday
- Term days (term), i.e., whether the day fell within a University term period
2.2. Learning Approaches
2.2.1. Automated Machine Learning
- AutoML on Microsoft Azure : At the time of writing, this implementation supported the automated evaluation of up to 16 different algorithms for classification problems, including variations of popular approaches, such as decision trees and gradient boosting. Accuracy was chosen as the primary metric for the optimiser, i.e., the percentage of the training dataset for which availability was correctly predicted, and a typical AutoML run evaluated around 100 different frameworks to produce the final optimised classifier. For the problem explored in this work, the eXtreme Gradient Boosting (XGBoost) classifier was consistently the best performer . This approach is based on gradient boosted decision trees, which is a fast and efficient technique that creates a strong classifier from an ensemble of weak decision tree classifiers.
- AutoML Tables on the Google Cloud Platform : In addition to considering standard machine learning algorithms, this technique also used neural architecture search (NAS)  to assess the efficacy of artificial neural networks. As for other types of machine learning, design of an appropriate neural network for a given problem often requires much trial and error with the number of hidden layers, the number of nodes within each layer, network connectivity and other hyperparameters being key decisions. Best results were achieved by the adaptive structural learning of artificial neural Networks (AdaNet) technique, which progressively builds a network architecture form an ensemble of subnetworks .
2.2.2. Cumulative Moving Average
2.2.3. Exponential Moving Average
3. Results and Discussion
3.1. Model Analysis
3.2. Vehicle Analysis
3.3. Fleet Analysis
Conflicts of Interest
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|vid||Unique identifier for this vehicle||12|
|start_lat||Latitude at the start of the journey||52.95282|
|start_lng||Longitude at the start of the journey||−1.18652|
|start_time||Timestamp at the start of the journey||2019-11-21T13:53:10+00:00|
|end_lat||Latitude at the end of the journey||52.94025|
|end_lng||Longitude at the end of the journey||−1.192132|
|end_time||Timestamp at the end of the journey||2019-11-21T14:00:16+00:00|
|Availability (av = 1)||57.4%||55.7%|
|Term days (term = 1)||54.4% (129 days)||47.4% (27 days)|
|Public Holiday (ph = 1)||3.0% (7 days)||1.8% (1 day)|
|University Holiday (uh = 1)||1.7% (1 day)||1.8% (1 day)|
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Shipman, R.; Waldron, J.; Naylor, S.; Pinchin, J.; Rodrigues, L.; Gillott, M. Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid. Energies 2020, 13, 1933. https://doi.org/10.3390/en13081933
Shipman R, Waldron J, Naylor S, Pinchin J, Rodrigues L, Gillott M. Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid. Energies. 2020; 13(8):1933. https://doi.org/10.3390/en13081933Chicago/Turabian Style
Shipman, Rob, Julie Waldron, Sophie Naylor, James Pinchin, Lucelia Rodrigues, and Mark Gillott. 2020. "Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid" Energies 13, no. 8: 1933. https://doi.org/10.3390/en13081933