| 1. |
- Atif, Yacine, 1967-, et al.
(författare)
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Internet of Things data analytics for parking availability prediction and guidance
- 2020
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Ingår i: European transactions on telecommunications. - : Wiley-Blackwell Publishing Inc.. - 1124-318X .- 2161-3915 .- 2161-5748. ; 31
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Tidskriftsartikel (refereegranskat)abstract
- Cutting-edge sensors and devices are increasingly deployed within urban areas to make-up the fabric of transmission control protocol/internet protocol con- nectivity driven by Internet of Things (IoT). This immersion into physical urban environments creates new data streams, which could be exploited to deliver novel cloud-based services. Connected vehicles and road-infrastructure data are leveraged in this article to build applications that alleviate notorious parking and induced traffic-congestion issues. To optimize the utility of parking lots, our proposed SmartPark algorithm employs a discrete Markov-chain model to demystify the future state of a parking lot, by the time a vehicle is expected to reach it. The algorithm features three modular sections. First, a search pro- cess is triggered to identify the expected arrival-time periods to all parking lots in the targeted central business district (CBD) area. This process utilizes smart-pole data streams reporting congestion rates across parking area junc- tions. Then, a predictive analytics phase uses consolidated historical data about past parking dynamics to infer a state-transition matrix, showing the transfor- mation of available spots in a parking lot over short periods of time. Finally, this matrix is projected against similar future seasonal periods to figure out the actual vacancy-expectation of a lot. The performance evaluation over an actual busy CBD area in Stockholm (Sweden) shows increased scalability capa- bilities, when further parking resources are made available, compared to a baseline case algorithm. Using standard urban-mobility simulation packages, the traffic-congestion-aware SmartPark is also shown to minimize the journey duration to the selected parking lot while maximizing the chances to find an available spot at the selected lot.
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| 2. |
- Kharrazi, Sogol, 1980-, et al.
(författare)
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Sustainable smart-parking management for connected and autonomous vehicles
- 2020
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Traffic induced by parking-spot seekers is a growing challenge and constitutes a considerable portion of the traffic in city centers. New opportunities to solve this problem are emerging by connected vehicles and infrastructure. For instance, ultrasonic and magnetic sensors are already mounted on the ceiling of many parking lots to detect the availability of a parking spot. These sensors can provide parking spot availability information in real-time. Further, traffic-aware smart sensors which can detect the movement of individual vehicles are also available in many city and highway areas. This report suggests an algorithm for a cloud-based parking service that exploits these streams of data to choose the best parking lot in a given parking area.The parking seeking problem is subject to a range of criteria that may include user, municipality and parking operator preferences. Users may have some preferences with respect to walking distance to destination. Municipalities prefer to spread the traffic to reduce congestion in the urban core. Parking operators seek to maximize parking lot utilization in order to increase the revenue on real-estate investments. To solve this problem, an optimization algorithm based on multicriteria decision making process is used.The proposed SmartPark algorithm employs a discrete Markov-chain model to demystify the future state of a parking lot. The algorithm features three modular sections:• First, a search process is triggered to identify the expected arrival time periods to all parking lots in the targeted parking area. This process utilizes smart pole data streams reporting congestion rates across the targeted parking area.• Then, a predictive analytics phase uses consolidated historical data about past parking dynamics to infer a state transition matrix, showing the transformation of available spots in a parking lot over short periods of time.• Finally, this matrix is projected against similar future seasonal periods to predict the actual vacancy of a parking lot at the arrival time.
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