[IEEE Xplore] Readings on Smart Cities -- [Editorial] Vol. 1, Issue 5, June 2015

Towards Smart Mobility

By Rosaldo J. F. Rossetti

Intelligent Transportation Systems (ITS) have revolutionised mobility over the past three decades, with most of recent progress stemming from early efforts of initiatives such as intelligent vehicle-highway systems, in America, transport telematics, in Europe, among other examples worldwide, such as in Japan and Australia, as well. Contemporary transportation heavily relies on information and communication technologies (ICT), allowing vehicle-to-vehicle as well as vehicle-to-infrastructure two-way interactions, and driverless cars operating in complex scenarios such as urban settings. With focus on improving traveller experience, efficiency and safety, ITS have achieved enormous progress, setting up the infrastructure and paving the road to a mobility paradigm shift towards Smart Mobility, in the context of Smart Cities. Besides being a central concern, users become active players to the development and maintenance of transportation solutions in Smart Mobility. This issue of our Readings on Smart Cities explores the potential of citizen participation in the development and innovation process of new mobility solutions in Smart Cities, as users become digitally included through ICT awareness and empowerment.

We start by revisiting traditional traffic management and control approaches, through the perspective shared by Kostakos, Ojala, and Juntunen [1]. Their work reports on how ubiquitous traffic sensing technologies and techniques can be incorporated into conventional traffic control and monitoring practices in the city of Oulu, Finland. Besides traditional sensors, such as inductive loops used to implement actuated traffic signal control, authors investigate how Wi-Fi scanning across the city centre might assist traffic operators in their managerial tasks. By collecting data from Wi-Fi access points, authors suggest appropriate techniques can be developed to infer mobility patterns from Wi-Fi devices throughout the city. Sassi and Zambonelli [2] argue contemporary ITS technologies are not as yet fully profiting from user interactions and engagement through smartphones and social networking, which could certainly contribute to more integrated, powerful, and flexible urban mobility solutions. Authors thus suggest the concept of Smart Social Mobility Services, in which the basic idea is to dynamically sense specific mobility needs of users and check them not only against the set of currently running mobility services, but also against potential mobility capabilities that could be turned into new mobility services. Finally, Jahangiri and Rakha [3] study different supervised learning methods from the field of machine learning to develop multiclass classifiers capable of identifying various transportation modes, including driving a car, riding a bicycle, riding a bus, walking, and running. This study considers transportation mode detection can be implemented through data collected from different sensors embedded in most smartphone devices, such as GPS, accelerometer, gyroscope, light sensor, etc. Knowledge of individuals’ transport mode options can have important practical implications on urban transportation planning, understanding carbon footprint, and improving the quality of real-time information provided to users.

Citizen participation in the context of Smart Cities is a key instrument towards the “smartification” of all sectors in society, naturally including mobility systems. Whereas ITS have greatly contributed to the installation of the appropriate infrastructure for contemporary and future transportation systems, ICT-aware and ICT-empowered populations have a word to say regarding their mobility needs and preferences, which must be accounted for in the implementation of new mobility solutions. Smart Mobility is still to be explored to its full potential, and opens up a vast range of new opportunities for research, development and innovation.

Good readings!


IEEE Xplore References

  1. V. Kostakos, T. Ojala and T. Juntunen, "Traffic in the Smart City: Exploring City-Wide Sensing for Traffic Control Center Augmentation," in IEEE Internet Computing, vol. 17, no. 6, pp. 22-29, Nov.-Dec. 2013.
  2. A. Sassi and F. Zambonelli, "Coordination Infrastructures for Future Smart Social Mobility Services," in IEEE Intelligent Systems, vol. 29, no. 5, pp. 78-82, Sept.-Oct. 2014.
  3. A. Jahangiri and H. A. Rakha, "Applying Machine Learning Techniques to Transportation Mode Recognition Using Mobile Phone Sensor Data," in IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 5, pp. 2406-2417, Oct. 2015.


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