Persistent congestions which are varying in strength and duration in the dense traffic networks are the most prominent obstacle towards sustainable mobility. Those types of congestions cannot be adequately resolved by the traditional Adaptive Traffic Signal Control (ATSC). The introduction of Reinforcement Learning (RL) in ATSC as tackled those types of congestions by using on-line learning, which is based on the trial and error approach. Furthermore, RL is prone to the dimensionality curse related to the state–action space size based on which a non-linear quality function is derived. The Deep Reinforcement Learning (DRL) framework uses Deep Neural Networks (DNN) to digest raw traffic data to approximate the quality function of RL. This paper provides a comprehensive analysis of the most recent DRL approaches used for the ATSC algorithm design. Special emphasis is set to overview of the traffic state representation and multi-agent DRL frameworks applied for the large traffic networks. Best practices are provided for choosing the adequate DRL model, hyper-parameters tuning, and model architecture design. Finally, this paper provides a discussion about the importance of the open traffic data concept for the extensive application of DRL in the real world ATSC.

The opening of government data, in order to have both social and economic value generated from them, has attracted the attention and interest of both researchers and practitioners from various disciplines, such as information systems, management sciences, political and social sciences, and law. Despite the rapid growth of this multidisciplinary research domain, which has led to the emergence and continuous evolution of technologies and management approaches for open government data (OGD), a detailed analysis of the specific areas and topics of this research is still missing. In this article, a detailed taxonomy of research areas and corresponding research topics of the OGD domain is presented: it includes four main research areas (ODG management and policies, infrastructures, interoperability and usage and value), which are further analyzed into 35 research topics. An important advantage of this taxonomy, beyond its high level of detail, is that it has been developed through extraction and a combination of relevant knowledge from three different sources: important relevant government policy documents, research literature, and experts. For each of the 35 research topics we have identified, its research literature is summarized and main research objectives and directions are highlighted. Based on the taxonomy, an extension of the extant OGD lifecycle is advanced; also, under-researched topics that require further research are identified.

Current open data systems lag behind in their promised value creation and sustainability. The objective of the current study is twofold: 1) to investigate whether existing open data systems meet the requirements of open data ecosystems, and 2) to develop a research agenda that discusses the gaps between current open data systems on the one hand and participatory, value-creating, sustainable open data ecosystems on the other hand. The literature reveals that the main characteristics of value-creating, sustainable open data ecosystems are user-drivenness, inclusiveness, circularity, and skill-based. Our comparative case study of five open data systems in various application domains and countries highlighted that none of these systems are real open data ecosystems: they often do not balance open data supply and demand, exclude specific user groups and domains, are linear, and lack skill-training. We elaborate on a research agenda that discusses how research should address the challenge of making open data ecosystems more value-generating and sustainable.

Nowadays, Blockchain Technologies (BCT) could be characterized as one of the most promising trends. We are currently witnessing a plethora of implementations basically in the economic sector with the creation of cryptocurrencies. The majority of researchers and practitioners argues that many benefits could be derived from the use of this innovative technology with the most significant one being the improved sense of trust to BCT applications. At the same time governments pursue amplified trust from their citizens and BCT is gaining momentum since it addresses this of utmost importance problem based on its unique characteristics. More and more governments realize the advances of this technology and participate in pilot applications in different vertical governmental sectors. Even though there are several implementations in the Government sector, there is no comprehensive study towards the analysis of the major characteristics of these developments. This paper moves towards the fulfilment of this gap conducting a thorough analysis of e-Government pilot applications of BCT in a European level. Furthermore, this study discusses the key benefits and main barriers coming from the application of this technology in different domains with BCT experts.