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A Multi-Criteria Framework for Smart Parking Recommender System
Parking has become a real challenge in cities, especially in metros, due to exponential increase in number of vehicles. A significant amount of time wasted in locating the parking space results in traffic congestion, pollution and fuel consumption. Recommending parking spot is an important service towards intelligent transportation system. Evolution in Internet of Things (IoT), Fog and Cloud Computing, and Sensor technologies can be better utilized to explore parking details such as parking occupancy, traffic congestion in parking path etc. in real time and an efficient and effective Parking Recommender System (PRS) can be designed. Parkers may have different expectations from PRS such as walking distance between the destination and the parking spot, pricing, safety etc. Therefore, a personalized recommender system is warranted in which a parker specifies its preference to various quality parameters related to parking. Considering parkers as human being, uncertainty in decision making over the preference cannot be ruled out. This work proposes a framework for multiple quality parameters/criteria based smart parking spot recommender system. It also provides various quality parameters, related to parking, to help parkers to express their need which helps in recommendation. As the boundaries between the parametric values are not crisp, fuzzy logic is utilized in parking recommender method to handle the uncertainty in human decision making. A case study, along with sensitivity analysis, demonstrates the effectiveness of the proposed model.
A Multi-Criteria Framework for Smart Parking Recommender System
Parking has become a real challenge in cities, especially in metros, due to exponential increase in number of vehicles. A significant amount of time wasted in locating the parking space results in traffic congestion, pollution and fuel consumption. Recommending parking spot is an important service towards intelligent transportation system. Evolution in Internet of Things (IoT), Fog and Cloud Computing, and Sensor technologies can be better utilized to explore parking details such as parking occupancy, traffic congestion in parking path etc. in real time and an efficient and effective Parking Recommender System (PRS) can be designed. Parkers may have different expectations from PRS such as walking distance between the destination and the parking spot, pricing, safety etc. Therefore, a personalized recommender system is warranted in which a parker specifies its preference to various quality parameters related to parking. Considering parkers as human being, uncertainty in decision making over the preference cannot be ruled out. This work proposes a framework for multiple quality parameters/criteria based smart parking spot recommender system. It also provides various quality parameters, related to parking, to help parkers to express their need which helps in recommendation. As the boundaries between the parametric values are not crisp, fuzzy logic is utilized in parking recommender method to handle the uncertainty in human decision making. A case study, along with sensitivity analysis, demonstrates the effectiveness of the proposed model.
A Multi-Criteria Framework for Smart Parking Recommender System
Baranwal, Gaurav (author) / Kumar, Dinesh (author) / Vidyarthi, Deo Prakash (author)
2020-09-28
481252 byte
Conference paper
Electronic Resource
English