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The purpose of this paper is to propose a distributed smartphone sensing-enabled system, which assumes an intelligent transport signaling (ITS) infrastructure that operates traffic lights in a smart city (SC). The system is able to handle priorities between groups of cyclists (crowd-cycling) and traffic when approaching traffic lights at road junctions.

The system takes into consideration normal probability density function (PDF) and analytics computed for a certain group of cyclists (i.e. crowd-cycling). An inference model is built based on real-time spatiotemporal data of the cyclists. As the system is highly distributed – both physically (i.e. location of the cyclists) and logically (i.e. different threads), the problem is treated under the umbrella of multi-agent systems (MAS) modeling. The proposed model is experimentally evaluated by incorporating a real GPS trace data set from the SC of Melbourne, Australia. The MAS model is applied to the data set according to the quantitative and qualitative criteria adopted. Cyclists’ satisfaction (CS) is defined as a function, which measures the satisfaction of the cyclists. This is the case where the cyclists wait the least amount of time at traffic lights and move as fast as they can toward their destination. ITS system satisfaction (SS) is defined as a function that measures the satisfaction of the ITS system. This is the case where the system serves the maximum number of cyclists with the fewest transitions between the lights. Smart city satisfaction (SCS) is defined as a function that measures the overall satisfaction of the cyclists and the ITS system in the SC based on CS and SS. SCS defines three SC policies (SCP), namely, CS is maximum and SS is minimum then the SC is cyclist-friendly (SCP1), CS is average and SS is average then the SC is equally cyclist and ITS system friendly (SCP2) and CS is minimum and SS is maximum then the SC is ITS system friendly (SCP3).

Results are promising toward the integration of the proposed system with contemporary SCs, as the stakeholders are able to choose between the proposed SCPs according to the SC infrastructure. More specifically, cyclist-friendly SCs can adopt SCP1, SCs that treat cyclists and ITS equally can adopt SCP2 and ITS friendly SCs can adopt SCP3.

The proposed approach uses internet connectivity available in modern smartphones, which provide users control over the data they provide to us, to obviate the installation of additional sensing infrastructure. It extends related study by assuming an ITS system, which turns traffic lights green by considering the normal PDF and the analytics computed for a certain group of cyclists. The inference model is built based on the real-time spatiotemporal data of the cyclists. As the system is highly distributed – both physically (i.e. location of the cyclists) and logically (i.e. different threads), the system is treated under the umbrella of MAS. MAS has been used in the literature to model complex systems by incorporating intelligent agents. In this study, the authors treat agents as proxy threads running in the cloud, as they require high computation power not available to smartphones.

The purpose of this paper is to understand the emotional state of a human being by capturing the speech utterances that are used during common conversation. Human beings except of thinking creatures are also sentimental and emotional organisms. There are six universal basic emotions plus a neutral emotion: happiness, surprise, fear, sadness, anger, disgust and neutral.

It is proved that, given enough acoustic evidence, the emotional state of a person can be classified by an ensemble majority voting classifier. The proposed ensemble classifier is constructed over three base classifiers: k nearest neighbors, C4.5 and support vector machine (SVM) polynomial kernel.

The proposed ensemble classifier achieves better performance than each base classifier. It is compared with two other ensemble classifiers: one-against-all (OAA) multiclass SVM with radial basis function kernels and OAA multiclass SVM with hybrid kernels. The proposed ensemble classifier achieves better performance than the other two ensemble classifiers.

The current paper performs emotion classification with an ensemble majority voting classifier that combines three certain types of base classifiers which are of low computational complexity. The base classifiers stem from different theoretical background to avoid bias and redundancy. It gives to the proposed ensemble classifier the ability to be generalized in the emotion domain space.

Although research on the impacts of the Olympic Games on Athens addressed the impact of the Games on economy, generic tourism, and urban restructuring, there has not been given to date attention on the prospects for sport tourism development in Athens as a result of hosting the Olympics, especially if it is considered that the construction of Olympic facilities was legitimized by the government's intention to use them for sport. To address this omission, the purpose of this study is to draw attention to examining the challenges and potential of post‐Olympic Athens to exploit its Olympic legacy for the development of sport tourism.

A qualitative approach was employed by conducting nine semi‐structured interviews with Athens’ tourism/administrative officials and analyzing them in line with pertinent literature.

Results show that the city's tourism officials respond with ad‐hoc policies in their effort to capitalize on Athens’ Olympic legacy. Consequently, Athens’ potential is constrained by the absence of a comprehensive tourism policy aimed at enriching and diversifying the city's post‐Olympic tourism product. In this context, the study shows that there is limited awareness by the city's tourism administration for sport tourism development and for establishing appropriate coordination mechanisms, which could foster mutually beneficial links between sport and tourism stakeholders. This leaves unexploited the potential for utilizing effectively Athens’ Olympic facilities and destination capitals in developing a competitive sport tourism product mix.

A limitation of the study is that it examines Athens’ sport tourism prospects through the lens of tourism policy. Future studies are needed to examine also sport policy. On a broader level, it is suggested that future research should extend the focus on the study of post‐event leverage to find the best means for fostering post‐Games Olympic tourism from a sustainability perspective.

To redress post‐Olympic Athens’ inertia and associated structural problems that affect its tourism policy, the study presents a framework for the strategic planning and sustainable development of sport tourism in Athens.

The study by examining Athens’ neglected legacy for sport tourism, attempts to synthesize a common ground for sport and tourism development in Olympic cities. This inquiry suggests the need for a broader planning and leveraging framework to extend the study of Olympic tourism in the post‐Games period as it relates to the use of Olympic legacy and post‐Olympic assets, which can, in turn, reveal the conditions for synergistic development of sport and tourism. Also, such an examination may shed light on what and how can be corrected in order to mitigate the sources and consequences of problems, while providing lessons for future Olympic cities. Finally, by focusing on sport tourism as it is induced by the Olympics knowledge can be advanced on how to effectively leverage the Olympic legacy and develop sustainable post‐Olympic tourism products.