Internet of Things data analytics for parking availability prediction and guidance

Abstract

AbstractCutting‐edge sensors and devices are increasingly deployed within urban areas to make‐up the fabric of transmission control protocol/internet protocol 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 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 process 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 junctions. 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 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.