[IEEE Xplore] Readings on Smart Cities -- [Editorial] Vol. 2, Issue 2, February 2016

Parking Smartly

By Rosaldo J. F. Rossetti

Mobility in major contemporary urban areas is vehicle-centric and suffers from the scarcity of parking lots and related facilities as the number of vehicles on roads continues to increase. Drivers usually face a tremendous challenge trying to find an available place, preferably for free and close to the final destination. Depending on the total length of the journey and the time spent while searching for a vacant place, this task may reveal to be very frustrating, leading to considerable delays, fuel consumption, and stress. In this issue of our Readings on Smart Cities, we look into the parking lot problem from the perspective of smart cities. The three selected papers stir up important issues and identify innovative ways leveraged on low-cost, low-power sensing and communication technologies offered by the Internet of Things (IoT) so as to make parking management systems smarter.

In the first work by Barone and colleagues [1], parking is seen as an expensive resource within urban settings. Authors also emphasise the fact that curb parking spots cannot be reserved beforehand, as an integrating part of the itinerary planning, which greatly contributes to the deterioration of the network service. Their paper proposes an intelligent parking assistant (IPA) architecture that leverages on IoT so as to overcome current public parking management drawbacks. The IPA conceptualisation is discussed in detail and illustrated through simulation. In a rather practical perspective, Rhodes and others [2] propose a collaborative path-finding mechanism to improve efficiency of parking systems and therefore reduce traffic congestion in metropolitan areas. Their approach leverages on information conveyed to drivers on whereabouts of available spaces, possibly reserving it and providing direction to reach the spot. Contrarily to other similar systems, their approach suggests multiple agents planning paths collaboratively can considerably reduce traffic congestion on routes leading to already busy areas. Another positive outcome of this work is the increased parking revenue as access to vacant places is managed more efficiently. However, providing drivers with accurate real-time information requires the road infrastructure features appropriate sensors and implements efficient surveillance instruments. In this direction, Zheng, Rajasegarar, and Leckie [3] present a prediction mechanism for the parking occupancy rate using three feature sets with selected parameters to illustrate the utility of these features. Their approach considers two scenarios based on real-time car parking information that has been collected and disseminated by the City of San Francisco, USA and the City of Melbourne, Australia. Authors also analyse the relative strengths of different machine learning methods for prediction purposes, so as to improve the efficiency of parking management systems.

Parking is one of the most important factors in urban mobility, directly influencing road safety, operating efficiency and traffic order. Understanding the complexities and importance of planning for vehicle parking is vital in the smart city context. Indeed, and according to Todd Litman, “although often taken for granted, the details of parking regulations can actually have wide-ranging impacts on city life, from reducing traffic and pollution to increasing local revenues.” Parking management systems can greatly benefit from all technological advances proportionated by IoT, fostering innovation through data-intensive services so as to improve sustainable urban mobility.

Good readings!

 

IEEE Xplore References

  1. R. E. Barone, T. Giuffre, S. M. Siniscalchi, M. A. Morgano and G. Tesoriere, "Architecture for parking management in smart cities," in IET Intelligent Transport Systems, vol. 8, no. 5, pp. 445-452, August 2014.
  2. C. Rhodes, W. Blewitt, C. Sharp, G. Ushaw and G. Morgan, "Smart Routing: A Novel Application of Collaborative Path-Finding to Smart Parking Systems," 2014 IEEE 16th Conference on Business Informatics, Geneva, 2014, pp. 119-126.
  3. Yanxu Zheng, S. Rajasegarar and C. Leckie, "Parking availability prediction for sensor-enabled car parks in smart cities," Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2015 IEEE Tenth International Conference on, Singapore, 2015, pp. 1-6.

 

Access to articles is complimentary to members of the IEEE Smart Cities Technical Community for a period of 30 days. Please subscribe to receive our selected articles each month via email.