Evidence-driven policy-making using heterogeneous data sources—The case of a controlled parking system in Thessaloniki

Abstract

Policy-making in local public administrations is still largely based on intuition rather than being backed up by data and evidence. The goal of this work is to introduce the methodology and software tools for contributing toward transforming the existing intuition-based paradigm of policy-making into an evidence-driven approach enabled by heterogeneous sources of data already available in the city. More specifically, methods for data collection, efficient data storage, and data analysis are implemented to measure the economic activity, assess the environmental impact and evaluate the social consequences of certain policy decisions. Subsequently, the extracted pieces of evidence are used to inform, advise, monitor, evaluate, and revise the decisions made by policy planners. Our contribution in this work is on outlining and deploying an easily extendable system architecture to harmonize and analyze heterogeneous data sources in ways that are found to be useful for policy-makers. For evaluating this architecture, we examine the case of a controlled parking system in the city of Thessaloniki and try to optimize its operation by balancing effectively between economic growth, environmental protection, and citizen satisfaction.

A Novel Edge Architecture and Solution for Detecting Concept Drift in Smart Environments

“…project called CUTLER to provide evidence-based policy-making focusing on coastal urban development by utilising big data from five different city pilots (Thessaloniki, Antalya, Vicenza, Cork, Antwerp) [49]. The project involved multiple use cases, equally contributing towards better urban management policies, e.g., controlled parking systems and flood risk reduction [9,50,51]. Finally, smart campuses, historically known as catalysts for nurturing emerging technologies, have undergone substantial changes in the education market in recent years.…”

Concept Drift Adaptation Techniques in Distributed Environment for Real-World Data Streams

“…For example, the Smart London Project [27] focuses on utilizing the city wide collected data to connect different communities, enhance environment protection, provide improved public security, and enable smarter transport network. Another EU funded project called CUTLER provided evidence-based policy making solution [28] for coastal urban development using big data. Five pilot cities (Thessaloniki, Antalya, Antwerp, Cork, and Vicenza) [19] were involved in the project each with different use case scenario.…”

“…The CUTLER smart city platform connected heterogeneous data that enabled the users to characterise geographical landscapes, parking scans, parking spot income, vehicle emission, user contributed content, and others. The data from the CUTLER platform was further used by the municipalities in designing new policies (e.g., controlled parking system), considering various urban infrastructure limitations [28]. Similarly, Tokyo plans to be the greenest city in Asia by efficiently managing their transportation, reducing energy consumption, and improving other sectors of the city [29].…”

“…Initiatives in both research and industry have been taken to mitigate the challenges of data collection, data processing, and storage of data. A hybrid cloud infrastructure consisting of big data technology solutions such as, Apache Hadoop [33], Apache Spark [34], and Elasticsearch [35] was proposed in the CUTLER project [28] to store, process, and analyze large-scale data from smart cities. Similarly, different architectures have been deployed for example, Padova Smart city project that uses a server tier to sense environmental and traffic situations, making the data available for city offices [36].…”