Internet of Things data analytics for parking availability prediction and guidance

Abstract

Cutting-edge sensors and devices are increasingly deployed within urban areas to make-up the fabric of TCP/IP connectivity 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 paper 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 process is triggered to identify the expected arrival-time periods to all parking lots in the targeted Central Business District or CBD area. This process utilizes smart-pole data-streams reporting congestion rates across parking-area junctions. Then, a predictive analytics phase uses consolidated historical-data about past parking-dynamics, to infer a statetransition 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 figure out the actual vacancy-expectation of a lot. The performance evaluation over an actual busy CBD area in Stockholm (Sweden) shows increased scalability capabilities, 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.

Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities

“…Network infrastructure data (e.g., GIS data) is becoming increasingly available within open-access traffic datasets due to its importance in representing both spatial and temporal correlations (Liao et al, 2018a;Pan et al, 2019a;Peng et al, 2020a;James, 2021). More specifically, in applications such as map matching, network infrastructure data can be leveraged to map trajectory data sequences to actual positions on the roadway infrastructure, improving traffic prediction capabilities (Atif et al, 2020;James, 2021). This data includes information about the topology of roadway, subway, and bus networks.…”

Value of incorporating geospatial information into the prediction of on-street parking occupancy – A case study

“…Mostly applied to off-street parking facilities, it is based on the assumption that vehicles arrive at parking spaces following a Poisson distribution. A number of studies (Atif et al 2020;Caliskan et al 2007;Klappenecker, Lee, and Welch 2014;Peng and Li 2016;Wu et al 2014) made parking occupancy predictions using a continuous-time Markov Chain. Lu et al (2009) used advanced technologies for the provision of arrival and departure rates, whereas Caicedo, Blazquez, and Miranda (2012) made parking availability predictions based on request allocations.…”

Privacy Enhancing Technologies (PETs) for connected vehicles in smart cities

“…In another study, [93] have discussed about intravehicle resource sharing model to provide a range of cloud services such as on-demand entertainment and speech recognition for driver assistance. The proposed solution forms nearly low-latency vehicular service cloud (VSC) on-the-fly as per the need of vehicular users [94].…”

“…The proposed solution forms nearly low-latency vehicular service cloud on-the-fly as per the need of vehicular users. 94 Multitenancy is another concern in the cloud IoT. Multitenancy is one of the differences between locally managed computing and cloud computing in which some of the tenants can share resources and delegate the management of the data and process to the cloud service provider.…”