An efficient smart parking pricing system for smart city environment: A machine-learning based approach
| Autor: | Neeraj Kumar, Seema Bawa, Sandeep Saharan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Pollution
Scheme (programming language) Occupancy Computer Networks and Communications Computer science media_common.quotation_subject Context (language use) 02 engineering and technology Machine learning computer.software_genre Smart city 0202 electrical engineering electronic engineering information engineering Parking space Overhead (computing) Smart parking media_common computer.programming_language business.industry 020206 networking & telecommunications Hardware and Architecture Order (business) 020201 artificial intelligence & image processing Artificial intelligence business computer Software |
| Zdroj: | Future Generation Computer Systems. 106:622-640 |
| ISSN: | 0167-739X |
| DOI: | 10.1016/j.future.2020.01.031 |
| Popis: | Now-a-days, with the ever increasing number of vehicles, getting parking space at right place and on time has become an inevitable necessity for all across the globe. In this context, finding an unoccupied parking slot by the interested vehicle owners with least overhead becomes an NP-Hard problem bounded by various constraints. In-advance availability of information regarding parking occupancy plays a major role in hassle free trip optimization for motorists. It also facilitates services-cum-profit management for the parking owners. It further helps in curbing congestion by reducing cruising time and hence, helps in controlling pollution of the smart cities. Thus, accurate and timely information regarding parking occupancy and availability has become the basic need in the evolution of the smart cities. Motivated by these facts, an occupancy-driven machine learning based on-street parking pricing scheme is proposed in this paper. The proposed scheme uses machine learning based approaches to predict occupancy of parking lots, which in turn is used to deduce occupancy driven prices for arriving vehicles. In order to train, test, and compare different machine learning models, on-street parking data of Seattle city has been used. To the best of our knowledge, this is the first time that parking occupancy prediction system is used to generate occupancy based parking prices for on-street parking system of the Seattle city. Results obtained using the proposed occupancy driven machine learning based on-street parking pricing scheme demonstrate its effectiveness over other existing state-of-the-art schemes. |
| Databáze: | OpenAIRE |
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