Dynamic Space Allocation Based on Internal Demand for Optimizing Release of Shared Parking
The size of cities has been continuously increasing because of urbanization. The number of public and private transportation vehicles is rapidly increasing, thus resulting in traffic congestion, traffic accidents, and environmental pollution. Although major cities have undergone considerable develop...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/1/235 |
| _version_ | 1797497552916971520 |
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| author | Shuo-Yan Chou Anindhita Dewabharata Ferani Eva Zulvia |
| author_facet | Shuo-Yan Chou Anindhita Dewabharata Ferani Eva Zulvia |
| author_sort | Shuo-Yan Chou |
| collection | DOAJ |
| description | The size of cities has been continuously increasing because of urbanization. The number of public and private transportation vehicles is rapidly increasing, thus resulting in traffic congestion, traffic accidents, and environmental pollution. Although major cities have undergone considerable development in terms of transportation infrastructure, problems caused by a high number of moving vehicles cannot be completely resolved through the expansion of streets and facilities. This paper proposes a solution for the parking problem in cities that entails a shared parking system. The primary concept of the proposed shared parking system is to release parking lots that are open to specific groups for public usage without overriding personal usage. Open-to-specific-groups parking lots consist of parking spaces provided for particular people, such as parking buildings at universities for teachers, staff, and students. The proposed shared parking system comprises four primary steps: collecting and preprocessing data by using an Internet of Things system, predicting internal demand by using a recurrent neural network algorithm, releasing several unoccupied parking lots based on prediction results, and continuously updating the real-time data to improve future internal usage prediction. Data collection and data forecasting are performed to ensure that the system does not override personal usage. This study applied several forecasting algorithms, including seasonal ARIMA, support vector regression, multilayer perceptron, convolutional neural network, long short-term memory recurrent neural network with a many-to-one structure, and long short-term memory recurrent neural network with a many-to-many structure. The proposed system was evaluated using artificial and real datasets. Results show that the recurrent neural network with the many-to-many structure generates the most accurate prediction. Furthermore, the proposed shared parking system was evaluated for some scenarios in which different numbers of parking spaces were released. Simulation results show that the proposed shared parking system can provide parking spaces for public usage without overriding personal usage. Moreover, this system can generate new income for parking management and/or parking lot owners. |
| first_indexed | 2024-03-10T03:20:51Z |
| format | Article |
| id | doaj.art-2be2be7122564b7e871cd920d42e75a3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:20:51Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2be2be7122564b7e871cd920d42e75a32023-11-23T12:19:06ZengMDPI AGSensors1424-82202021-12-0122123510.3390/s22010235Dynamic Space Allocation Based on Internal Demand for Optimizing Release of Shared ParkingShuo-Yan Chou0Anindhita Dewabharata1Ferani Eva Zulvia2Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, TaiwanDepartment of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, TaiwanDepartment of Logistics Engineering, Universitas Pertamina, Jakarta 12220, IndonesiaThe size of cities has been continuously increasing because of urbanization. The number of public and private transportation vehicles is rapidly increasing, thus resulting in traffic congestion, traffic accidents, and environmental pollution. Although major cities have undergone considerable development in terms of transportation infrastructure, problems caused by a high number of moving vehicles cannot be completely resolved through the expansion of streets and facilities. This paper proposes a solution for the parking problem in cities that entails a shared parking system. The primary concept of the proposed shared parking system is to release parking lots that are open to specific groups for public usage without overriding personal usage. Open-to-specific-groups parking lots consist of parking spaces provided for particular people, such as parking buildings at universities for teachers, staff, and students. The proposed shared parking system comprises four primary steps: collecting and preprocessing data by using an Internet of Things system, predicting internal demand by using a recurrent neural network algorithm, releasing several unoccupied parking lots based on prediction results, and continuously updating the real-time data to improve future internal usage prediction. Data collection and data forecasting are performed to ensure that the system does not override personal usage. This study applied several forecasting algorithms, including seasonal ARIMA, support vector regression, multilayer perceptron, convolutional neural network, long short-term memory recurrent neural network with a many-to-one structure, and long short-term memory recurrent neural network with a many-to-many structure. The proposed system was evaluated using artificial and real datasets. Results show that the recurrent neural network with the many-to-many structure generates the most accurate prediction. Furthermore, the proposed shared parking system was evaluated for some scenarios in which different numbers of parking spaces were released. Simulation results show that the proposed shared parking system can provide parking spaces for public usage without overriding personal usage. Moreover, this system can generate new income for parking management and/or parking lot owners.https://www.mdpi.com/1424-8220/22/1/235shared parkingshared economypredictionrecurrent neural networkintelligent transportation systemsmart cities |
| spellingShingle | Shuo-Yan Chou Anindhita Dewabharata Ferani Eva Zulvia Dynamic Space Allocation Based on Internal Demand for Optimizing Release of Shared Parking Sensors shared parking shared economy prediction recurrent neural network intelligent transportation system smart cities |
| title | Dynamic Space Allocation Based on Internal Demand for Optimizing Release of Shared Parking |
| title_full | Dynamic Space Allocation Based on Internal Demand for Optimizing Release of Shared Parking |
| title_fullStr | Dynamic Space Allocation Based on Internal Demand for Optimizing Release of Shared Parking |
| title_full_unstemmed | Dynamic Space Allocation Based on Internal Demand for Optimizing Release of Shared Parking |
| title_short | Dynamic Space Allocation Based on Internal Demand for Optimizing Release of Shared Parking |
| title_sort | dynamic space allocation based on internal demand for optimizing release of shared parking |
| topic | shared parking shared economy prediction recurrent neural network intelligent transportation system smart cities |
| url | https://www.mdpi.com/1424-8220/22/1/235 |
| work_keys_str_mv | AT shuoyanchou dynamicspaceallocationbasedoninternaldemandforoptimizingreleaseofsharedparking AT anindhitadewabharata dynamicspaceallocationbasedoninternaldemandforoptimizingreleaseofsharedparking AT feranievazulvia dynamicspaceallocationbasedoninternaldemandforoptimizingreleaseofsharedparking |