Exploiting User Behavior to Predict Parking Availability through Machine Learning
Cruising-for-parking in an urban area is a time-consuming and frustrating activity. We present four machine learning-based models to predict the parking availability of street segments in an urban area on a three-level scale, which navigator and smart-parking apps can exploit to ease and reduce the...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Smart Cities |
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| Online Access: | https://www.mdpi.com/2624-6511/5/4/64 |
| _version_ | 1827636889790709760 |
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| author | Enrico Bassetti Andrea Berti Alba Bisante Andrea Magnante Emanuele Panizzi |
| author_facet | Enrico Bassetti Andrea Berti Alba Bisante Andrea Magnante Emanuele Panizzi |
| author_sort | Enrico Bassetti |
| collection | DOAJ |
| description | Cruising-for-parking in an urban area is a time-consuming and frustrating activity. We present four machine learning-based models to predict the parking availability of street segments in an urban area on a three-level scale, which navigator and smart-parking apps can exploit to ease and reduce the cruising phase. The models were trained with data generated by a cruising-for-parking simulator that we developed, replicating four parking behavior types (workers, residents, buyers, and visitors). The generated data is comparable to that collectible with smartphones’ sensors. We simulated 40 users moving for 200 weeks in the city area of San Giovanni in Rome. We collected information about users’ parking, unparking, and cruising actions over considered road segments at different time slots. Once a significant amount of trips were collected, we extracted ten features for each road segment at a given time slot. With the obtained dataset, which contained 761 samples, we trained and compared four supervised machine learning models that receive the history of a segment and, in return, classify the Parking Availability Level of the segment as Green, Yellow or Red. The four models were further evaluated in a different city area, San Lorenzo, and obtained very accurate results. We can predict parking availability with an accuracy above 97% for all the street segments where we collected 30 or more user actions, confirming the robustness of the simulator in generating synthetic cruising-for-parking data and the suitability of designing a Parking Availability Classifier (PAC) based on data collectible by smartphones. |
| first_indexed | 2024-03-09T15:51:49Z |
| format | Article |
| id | doaj.art-ab9b871c46c2465098531940d52a9633 |
| institution | Directory Open Access Journal |
| issn | 2624-6511 |
| language | English |
| last_indexed | 2024-03-09T15:51:49Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Smart Cities |
| spelling | doaj.art-ab9b871c46c2465098531940d52a96332023-11-24T18:00:50ZengMDPI AGSmart Cities2624-65112022-09-01541243126610.3390/smartcities5040064Exploiting User Behavior to Predict Parking Availability through Machine LearningEnrico Bassetti0Andrea Berti1Alba Bisante2Andrea Magnante3Emanuele Panizzi4Department of Computer Science, Sapienza University of Rome, 00161 Rome, ItalyDepartment of Computer Science, Sapienza University of Rome, 00161 Rome, ItalyDepartment of Computer Science, Sapienza University of Rome, 00161 Rome, ItalyDepartment of Computer Science, Sapienza University of Rome, 00161 Rome, ItalyDepartment of Computer Science, Sapienza University of Rome, 00161 Rome, ItalyCruising-for-parking in an urban area is a time-consuming and frustrating activity. We present four machine learning-based models to predict the parking availability of street segments in an urban area on a three-level scale, which navigator and smart-parking apps can exploit to ease and reduce the cruising phase. The models were trained with data generated by a cruising-for-parking simulator that we developed, replicating four parking behavior types (workers, residents, buyers, and visitors). The generated data is comparable to that collectible with smartphones’ sensors. We simulated 40 users moving for 200 weeks in the city area of San Giovanni in Rome. We collected information about users’ parking, unparking, and cruising actions over considered road segments at different time slots. Once a significant amount of trips were collected, we extracted ten features for each road segment at a given time slot. With the obtained dataset, which contained 761 samples, we trained and compared four supervised machine learning models that receive the history of a segment and, in return, classify the Parking Availability Level of the segment as Green, Yellow or Red. The four models were further evaluated in a different city area, San Lorenzo, and obtained very accurate results. We can predict parking availability with an accuracy above 97% for all the street segments where we collected 30 or more user actions, confirming the robustness of the simulator in generating synthetic cruising-for-parking data and the suitability of designing a Parking Availability Classifier (PAC) based on data collectible by smartphones.https://www.mdpi.com/2624-6511/5/4/64parking availabilitymachine learningartificial neural networkuser-centered artificial intelligenceHCI |
| spellingShingle | Enrico Bassetti Andrea Berti Alba Bisante Andrea Magnante Emanuele Panizzi Exploiting User Behavior to Predict Parking Availability through Machine Learning Smart Cities parking availability machine learning artificial neural network user-centered artificial intelligence HCI |
| title | Exploiting User Behavior to Predict Parking Availability through Machine Learning |
| title_full | Exploiting User Behavior to Predict Parking Availability through Machine Learning |
| title_fullStr | Exploiting User Behavior to Predict Parking Availability through Machine Learning |
| title_full_unstemmed | Exploiting User Behavior to Predict Parking Availability through Machine Learning |
| title_short | Exploiting User Behavior to Predict Parking Availability through Machine Learning |
| title_sort | exploiting user behavior to predict parking availability through machine learning |
| topic | parking availability machine learning artificial neural network user-centered artificial intelligence HCI |
| url | https://www.mdpi.com/2624-6511/5/4/64 |
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