Smart cities are struggling with using public space efficiently and decreasing pollution at the same time. For this governments have embraced smart parking initiatives, which should result in a high utilization of public space and minimization of the driving, in this way reducing the emissions of cars. Yet, simply opening data about the availability of public spaces results in more congestions as multiple cars might be heading for the same parking space. In this work, we propose a Multiple Criteria based Parking space Reservation (MCPR) algorithm, for reserving a space for a user to deal with parking space in a fair way. Users' requirements are the main driving factor for the algorithm and used as criteria in MCPR. To evaluate the algorithm, simulations for three set of user preferences were made. The simulation results show that the algorithm satisfied the users' request fairly for all the three preferences. The algorithm helps users automatically to find a parking space according to the users' requirements. The algorithm can be used in a smart parking system to search for a parking space on behalf of user and send parking space information to the user.

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Smart cities have been heralded for improving traffic management by utilizing data for making better traffic management decisions. Multi-sided platforms collect data from sensors and citizen-generated data on one side and can provide input for decision-making using data analytics by governments and the public on the other side. However, there is no guidance for creating developing Intelligent Traffic Management Systems (ITMS) platforms. The involvement of various actors having different interest and heterogeneous datasets hampers development. In this article, the authors design a reference architecture (RA) to support intelligent traffic management systems for providing better a commute, and safety and security during travel based on real-time information. The main three layers of this RA are datasets, processes, and actors. The RA for ITMS provides guidance for designing and overcoming the challenges with: 1) heterogeneous datasets; 2) data gathering; 3) data processing; 4) data management; and 5) supporting various types of data users. The illustration and evaluation of the architecture shows possible solutions of the aforementioned challenges. The RA helps to integrate the activities performed by the various actors. In this way it can be used to reduce traffic queues, increase the efficient use of resources, smooth and safe commute of the citizens.

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In the last few years, the smart city concept resulted in the development and deployment of platforms for providing innovative services to improve sustainability and the living standards. These platforms integrate data collected from devices and citizen-generated data and thereafter employ big data analytics to create insights from the data. These platform enable the creation of context-aware Intelligent traffic management systems (ITMS), however the involvement of various actors at different stages hampers development. In this paper, we propose a framework to support sustainable traffic management system for providing better commute, safety and security during travel based on real-time information. The framework should help to integrate the activities performed by the various actors. The main key elements of this framework are Datasets, Traffic Management Analytics, Actors and Actions which are taken by these users. The framework helps to create an overall overview of the activities needed. In this way it can be used to improve the quality of the traffic flow, increase efficient use of resources, smooth and safe commute of the citizens.@en

Governments are releasing their data to the public to accomplish benefits like the creation of transparency, accountability, citizen engagement and to enable business innovation. At the same time, decision-makers are reluctant to open their data due to some potential risks like misuse, sensitivity, ownership, and inaccuracy of the data. The goal of the study presented in this paper is to develop a Fuzzy Multi-Criteria Decision Making (FMCDM) approach to analyze the risks and benefits to determine the decision to open a dataset. FMCDM is chosen due to its capability to measure and weight the relative importance of the criteria. FMCDM need the weighting of criteria as input. For this Fuzzy Analytical Hierarchy Process (FAHP) is utilized by collecting input from experts’ knowledge and expertise. The scores for each criterion are summed up to rank the importance of the alternatives. Four main criteria are used, e.g. data sensitivity and data ownership representing risks criteria, and data availability and data trustworthy as benefits criteria. For each criterion, there were two sub-criteria identified. Four types of decisions to open data can be made: completely open, maintain suppression, provide limited access, and remain closed. A health patient record dataset is used to illustrate the approach. In further research, we recommend to develop automated approaches that take a dataset as an input and can provide an advice.

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