A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction
- Identification of the best performing, among well-known and generally used ones, AI/ML algorithm for the problem at hand;
- An analysis and evaluation of various ML/DL models (e.g., KNN, Random Forest, MLP, Decision Tree) for the problem of predicting parking space availability;
- An analysis/assessment of the Ensemble Learning approach and its comparison with other ML/DL models; and
- Recommendation of the most appropriate ML/DL model to predict parking space availability.
- Recommending top-k parking spots with respect to distance between the current position of vehicle and available parking spots;
- Application of the algorithms in order to demonstrate how satisfactory prediction of availability of parking spaces can be achieved using real data from Santander;
1.3. Impact of Our Parking Prediction Model on Smart Cities
2. Related Work
3. Overview of ML/DL Techniques
3.1. Multilayer Perceptron (MLP) Neural Network
3.2. K-Nearest Neighbors (KNN)
3.3. Decision Tree and Random Forest
3.4. Ensemble Learning Approach (Voting Classifier)
4. Results and Evaluation
4.1. Parking Space Data Set
- Parking ID: Refers to the unique ID associated with each parking space.
- Timestamp: The Timestamp of the parking space data collection.
- Start Time/End Time: Start Time and End Time refer to the time interval during which a parking space’s status remained the same, i.e., available or occupied.
- Duration: Refers to the total duration in seconds during which a specific parking space remained available or remained occupied.
- Status: This feature represents the status of a parking space, e.g., available or occupied.
4.2. Hyper-Parameters of ML/DL Techniques
4.3. Evaluation Metrics
- Precision can be defined as the fraction of all the samples labelled as positive and that are actually positive . It can be mathematically presented as follows:
- Recall, in contrast, is defined as the fraction of all the positive samples; they are also labeled as positive . Mathematical presentation of recall is given below:
- The F1-Score is defined as the harmonic mean of recall and precision , defined mathematically as:
- Accuracy is the measure of the correctly predicted samples among all the samples, expressed in an equation as:
- K-fold cross-validation is a method for checking the overfitting and evaluating how consistent a specific model is. In K-fold validation, a data set is divided into K equal sets. Among those K sets, each set is used once as testing data and the remaining sets are used as training data. In this paper, we used 5-fold cross-validation.
4.4. Performance Evaluation
4.4.1. 10-Min Prediction Validity (60% Threshold)
4.4.2. 10-Min Prediction Validity (80% Threshold)
4.4.3. 20-Min Prediction Validity (60% Threshold)
4.4.4. 20-Min Prediction Validity (80% Threshold)
4.4.5. Training Data Evaluation
4.4.6. Distance Based Recommendation
Conflicts of Interest
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|Parking Spot ID||Unique ID of Sensor|
|Day||1–7 (Day of the week)|
|Status||0–1 (Occupied or Free)|
|MLP||KNN||Decision Tree||Random Forest||Voting Classifier|
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Awan, F.M.; Saleem, Y.; Minerva, R.; Crespi, N. A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction. Sensors 2020, 20, 322. https://doi.org/10.3390/s20010322
Awan FM, Saleem Y, Minerva R, Crespi N. A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction. Sensors. 2020; 20(1):322. https://doi.org/10.3390/s20010322Chicago/Turabian Style
Awan, Faraz Malik, Yasir Saleem, Roberto Minerva, and Noel Crespi. 2020. "A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction" Sensors 20, no. 1: 322. https://doi.org/10.3390/s20010322