Modeling cooperative behavior for resilience in cyber-physical systems using SDN and NFV

Cyber-Physical Systems (CPSs) are increasingly important in everyday applications including the latest mobile devices, power grids and intelligent buildings. CPS functionality has intrinsic characteristics including considerable heterogeneity, variable dynamics, and complexity of operation. These systems also typically have insufficient resources to satisfy their full demand for specialized services such as data edge storage, data fusion, and reasoning. These novel CPS characteristics require new management strategies to support the resilient global operation of CPSs. To reach this goal, we propose a Software Defined Networking based solution scaled out by Network Function Virtualization modules implemented as distributed management agents. Considering the obvious need for orchestrating the distributed agents towards the satisfaction of a common set of global CPS functional goals, we analyze distinct incentive strategies to enact a cooperative behavior among the agents. The repeated operation of each agent’s local algorithm allows that agent to learn how to adjust its behavior following both its own experience and observed behavior in neighboring agents. Therefore, global CPS management can evolve iteratively to ensure a state of predictable and resilient operation

The impact of ride-hailing services on travel behaviour

The introduction of Ride- hailing Services into our transport systems has rapidly transformed travel behavior. Ride-hailing services provide multi-modality and fill transit gaps, but they also impact the modal share of other modes such as public transit and car ownership. This study delves into links between ride-hailing services and private vehicles ownership. It also questions the impact of ridesharing services on public transport use and the role neighborhood context plays on the link between ride-hailing and car-ownership. This is studied through a Path Analysis implemented through Structural Equation Modelling (SEM). It describes the relationships the relationship between ‘ride-hailing usage’, ‘public transit usage’, ‘car ownership’, ‘sociodemographic characteristics’, ‘urban form’ and ‘technology dependence’. It factors in Ewing’s sprawl index to represent urban form and takes into consideration the number of years since rideshare was introduced in specific cities as part of calculations. The primary database for the research is the NHTS 2017 survey. Data compilation is done to establish a dataset of cities with TNCs operating in them and the duration of operation. The first of the three key questions analyzed in this study is the relationship between public transit and ridesharing. Although the statistical model shows an insignificant covariance, initial findings suggest that ride sharing services complement public transit more in small towns and less in urban areas. The second topic analyzed through this model was the link between ridesharing and car ownership. The model estimates that ride-hailing has a significant and comparatively large impact on car ownership. Due to the bidirectional nature of the model, we were able to study the reverse relationship as well. The model did not show car-ownership having a significant impact on frequency of rideshare use. The final relationship to be studied was the impact of neighborhood context on the links between car ownership and ride-hailing. It was suggested through a moderation estimation that urban form does play a significant role in impacting the role of rideshare on car ownership. The length of duration since the introduction of TNCs in a city plays an important role on car ownership. The longer TNCs have been around, the smaller the value of car ownership is. Denser Urban forms deepen this relationship while sprawled neighborhoods weaken the correlation. Based on this research a few areas have been identified as areas with critical data deficiency which are needed to understand and properly manage the ever-changing travel behavior. These areas include the links between city types, public transit and rideshare

Oil Security Short- and Long-Term Policies

Increasing oil security represents one of the most important policy actions, especially within IEA countries. Short and long term mechanisms could help such goal. On the short term side, revision of IEA emergency response oil stock system has been discussed. The attention is mainly focused on three issues: the high costs of stock management for private industries, the possible use of strategic reserves to smooth price when no high supply disruption has taken, the extension of IEA emergency system to non-OECD countries. The main actions specifically proposed by the European Commission are: an harmonisation of national storage systems, with the institution of public and private agency, a wider co-ordinated use of security stocks, and an increase in the physical amount of oil stocks. Long term measures for enhancing oil supply security can be seen on the demand-side and the supply-side. Main demand-side policies could be the following: energy saving and efficiency, investments in research and technology, and reduction of oil price inelasticity especially for transport sector. Main supply-side policies can be summarized into co-operation and institutional promotion for supply diversification of suppliers/routes. Main factors that could affect described policies could be the liberalization of international trade even in the energy sector and the increasing role of oil demand from developing countries.Oil, Security, Energy

A Machine Learning Recommender Model for Ride Sharing Based on Rider Characteristics and User Threshold Time

In the present age, human life is prospering incredibly due to the 4th Industrial Revolution or The Age of Digitization and Computing. The ubiquitous availability of the Internet and advanced computing systems have resulted in the rapid development of smart cities. From connected devices to live vehicle tracking, technology is taking the field of transportation to a new level. An essential part of the transportation domain in smart cities is Ride Sharing. It is an excellent solution to issues like pollution, traffic, and the rapid consumption of fuel. Even though Ride Sharing has several benefits, the current usage is significantly low due to limitations like social barriers and long rider waiting times. The thesis proposes a novel Ride Sharing model with two matching layers to eliminate most of the observed issues in the existing Ride Sharing applications like UberPool and LyftLine. The first matching layer matches riders based on specific human characteristics, and the second matching layer provides riders the option to restrict the waiting time by using personalized threshold time. At the end of trips, the system collects user feedback according to five characteristics. Then, at most, two main characteristics that are the most important to riders are determined based on the collected feedback. The registered characteristics and the two main determined characteristics are fed as the inputs to a Machine Learning classification module. For newly registering users, the module predicts the two main characteristics of riders, and that assists in matching with other riders having similar determined characteristics. The thesis includes subjecting the proposed model to an extensive simulation for measuring system efficiency. The model simulations have utilized the real-time New York City Cab traffic data with real-traffic conditions using Google Maps Application Programming Interface (API). Results indicate that the proposed Ride Sharing model is feasible, and efficient as the number of riders increases while maintaining the rider threshold time. The expected outcome of the thesis is to help service providers increase the usage of Ride Sharing, complete the pool for the maximum number of trips in minimal time and perform maximum rider matches based on similar characteristics, thus providing an energy-efficient and a social platform for Ride Sharing

Market-based Options for Security of Energy Supply

Energy market liberalization and international economic interdependence have affected governments’ ability to react to security of supply challenges. On the other side, whereas in the past security of supply was largely seen as a national responsibility, the frame of reference has increasingly become the EU in which liberation increases security of supply mainly by increasing the number of markets participants and improving the flexibility of energy systems. In this logic, security of supply becomes a risk management strategy with a strong inclination towards cost effectiveness, involving both the supply and the demand side. Security of supply has two major components that interrelate: cost and risk. This paper focus the attention on costs in the attempt to develop a market compatible approach geared towards security of supply.Energy supply, Market-based options

Dynamic pricing or not?:pricing models of Finnish taxi dispatch centers under the act on transport services

Abstract. In 1.7.2018, Finnish government liberalized Finnish taxi markets to create possibilities to introduce new technology, digitalization and new business models into transport sector. Also allowing usage of dynamic pricing in Finnish taxi markets was specifically mentioned. Before the Act on Transport Services came into effect, Finnish regulations specified maximum limits for fares, taxi licenses and operational area where dispatch centers were allowed to operate. In this study, I will look into how pricing models have evolved after the Act on Transport Services came into effect by collecting data from internet and conducting interviews to understand purposes of the changes more in-depth. Also, I looked into how pricing models have become more dynamic compared to old pricing model, and how dynamic pricing is described in literature. Lastly, I combined list of different aspects that affect to implementation of dynamic pricing. Currently, none of the Finnish dispatch centers have implemented similar dynamic pricing based on demand and supply in real-time as what Uber uses. But based on results, Finnish dispatch center’s pricing models have evolved to be more dynamic even though Finnish dispatch center’s do not consider them to be dynamic. Also, there are obstacles related to willingness, technology and regulations why Finnish dispatch centers do not consider dynamic pricing similar to what Uber uses to be currently possible to implement

The Allure of Technology: How France and California Promoted Electric Vehicles to Reduce Urban Air Pollution

All advanced industrialized societies face the problem of air pollution produced by motor vehicles. In spite of striking improvements in internal combustion engine technology, air pollution in most urban areas is still measured at levels determined to be harmful to human health. Throughout the 1990s and beyond, California and France both chose to improve air quality by means of technological innovation, adopting legislation that promoted clean vehicles, prominently among them, electric vehicles (EVs). In California, policymakers chose a technology-forcing approach, setting ambitious goals (e.g., zero emission vehicles), establishing strict deadlines and issuing penalties for non-compliance. The policy process in California called for substantial participation from the public, the media, the academic community and the interest groups affected by the regulation. The automobile and oil industries bitterly contested the regulation, in public and in the courts. In contrast, in France the policy process was non-adversarial, with minimal public participation and negligible debate in academic circles. We argue that California's stringent regulation spurred the development of innovative hybrid and fuel cell vehicles more effectively than the French approach. However, in spite of the differences, both California and France have been unable to put a substantial number of EVs on the road. Our comparison offers some broad lessons about how policy developments within a culture influence both the development of technology and the impact of humans on the environment.Environmental policy, Electric vehicles, Air pollution, Technology policy, Sustainable transport

Towards cooperative urban traffic management: Investigating voting for travel groups

In den letzten Jahrzehnten haben intelligente Verkehrssysteme an Bedeutung gewonnen. Wir betrachten einen Teilbereich des kooperativen Verkehrsmanagements, nämlich kollektive Entscheidungsfindung in Gruppen von Verkehrsteilnehmern. In dem uns interessierenden Szenario werden Touristen, die eine Stadt besuchen, gebeten, Reisegruppen zu bilden und sich auf gemeinsame Besuchsziele (Points of Interest) zu einigen. Wir konzentrieren uns auf Wählen als Gruppenentscheidungsverfahren. Unsere Fragestellung ist, wie sich verschiedene Algorithmen zur Bildung von Reisegruppen und zur Bestimmung gemeinsamer Reiseziele hinsichtlich der System- und Benutzerziele unterscheiden, wobei wir als Systemziel große Gruppen und als Benutzerziele hohe präferenzbasierte Zufriedenheit und geringen organisatorischen Aufwand definieren. Wir streben an, einen Kompromiss zwischen System- und Benutzerzielen zu erreichen. Neu ist, dass wir die inhärenten Auswirkungen verschiedener Wahlregeln, Wahlprotokolle und Gruppenbildungsalgorithmen auf Benutzer- und Systemziele untersuchen. Altere Arbeiten zur kollektiven Entscheidungsfindung im Verkehr konzentrieren sich auf andere Zielgrößen, betrachten nicht die Gruppenbildung, vergleichen nicht die Auswirkungen mehrerer Wahlalgorithmen, benutzen andere Wahlalgorithmen, berücksichtigen nicht klar definierte Gruppen von Verkehrsteilnehmern, verwenden Wahlen für andere Anwendungen oder betrachten andere Algorithmen zur kollektiven Entscheidungsfindung als Wahlen. Wir untersuchen in der Hauptsimulationsreihe verschiedene Gruppenbildungsalgorithmen, Wahlprotokolle und Komiteewahlregeln. Wir betrachten sequentielle Gruppenbildung vs. koordinierte Gruppenbildung, Basisprotokoll vs. iteratives Protokoll und die Komiteewahlregeln Minisum-Approval, Minimax-Approval und Minisum-Ranksum. Die Simulationen wurden mit dem neu entwickelten Simulationswerkzeug LightVoting durchgef¨uhrt, das auf dem Multi-Agenten-Framework LightJason basiert. Die Experimente der Hauptsimulationsreihe zeigen, dass die Komiteewahlregel Minisum-Ranksum in den meisten Fällen bessere oder ebenso gute Ergebnisse erzielt wie die Komiteewahlregeln Minisum-Approval und Minimax-Approval. Das iterative Protokoll tendiert dazu, eine Verbesserung hinsichtlich der präferenzbasierten Zufriedenheit zu erbringen, auf Kosten einer deutlichen Verschlechterung hinsichtlich der Gruppengröße. Die koordinierte Gruppenbildung tendiert dazu, eine Verbesserung hinsichtlich der präferenzbasierten Zufriedenheit zu erbringen bei relativ geringen Kosten in Bezug auf die Gruppengröße. Dies führt uns dazu, die Komiteewahlregel Minisum-Ranksum, das Basisprotokoll und die koordinierte Gruppenbildung zu empfehlen, um einen Kompromiss zwischen System- und Benutzerzielen zu erreichen. Wir demonstrieren auch die Auswirkungen verschiedener Kombinationen von Gruppenbildungsalgorithmen und Wahlprotokollen auf die Reisekosten. Hier bietet die Kombination aus Basisprotokoll und koordinierter Gruppenbildung einen Kompromiss zwischen der präferenzbasierten Zufriedenheit und den Reisekosten. Zusätzlich zur Hauptsimulationsreihe bieten wir ein erweitertes Modell an, das die Präferenzen der Reisenden generiert, indem es die Attraktivität der möglichen Ziele und Distanzkosten, basierend auf den Entfernungen zwischen den möglichen Zielen, kombiniert. Als weiteren Anwendungsfall von Wahlverfahren betrachten wir ein Verfahren zur Treffpunktempfehlung, bei dem eine Bewertungs-Wahlregel und eine Minimax-Wahlregel zur Bestimmung von Treffpunkten verwendet werden. Bei kleineren Gruppen ist die durchschnittliche maximale Reisezeit unter der Bewertungs-Wahlregel deutlich höher. Bei größeren Gruppen nimmt der Unterschied ab. Bei kleineren Gruppen ist die durchschnittliche Verspätung für die Gruppe unter der Minimax-Wahlregel hoch, bei größeren Gruppen nimmt sie ab. Es ist also sinnvoll für kleinere Gruppen, die Minimax-Wahlregel zu verwenden, wenn man eine fairere Verteilung der Reisezeiten anstrebt, und die Bewertungs-Wahlregel zu verwenden, wenn das Ziel stattdessen ist, Verzögerungen für die Gruppe zu vermeiden. Für zukünftige Arbeiten wäre es sinnvoll, das Simulationskonzept anzupassen, um reale Bedingungen und Anforderungen berücksichtigen zu können. Weitere Möglichkeiten für zukünftige Arbeiten wären die Betrachtung zusätzlicher Algorithmen und Modelle, wie zum Beispiel die Betrachtung kombinatorischer Wahlen oder die Durchführung von Simulationen auf der Grundlage des erweiterten Modells, die Berücksichtigung der Rolle finanzieller Anreize zur Förderung von Ridesharing oder Platooning und die Nutzung des LightVoting-Tools für weitere Forschungsanwendungen.In the last decades, intelligent transport systems have gained importance. We consider a subarea of cooperative traffic management, namely collective decision-making in groups of traffic participants. In the scenario we are studying, tourists visiting a city are asked to form travel groups and to agree on common points of interest. We focus on voting as a collective decision-making process. Our question is how different algorithms for the formation of travel groups and for determining common travel destinations differ with respect to system and user goals, where we define as system goal large groups and as user goals high preference satisfaction and low organisational effort. We aim at achieving a compromise between system and user goals. What is new is that we investigate the inherent effects of different voting rules, voting protocols and grouping algorithms on user and system goals. Older works on collective decision-making in traffic focus on other target quantities, do not consider group formation, do not compare the effects of several voting algorithms, use other voting algorithms, do not consider clearly defined groups of vehicles, use voting for other applications or use other collective decision-making algorithms than voting. In the main simulation series, we examine different grouping algorithms, voting protocols and committee voting rules. We consider sequential grouping vs. coordinated grouping, basic protocol vs. iterative protocol and the committee voting rules Minisum-Approval, Minimax-Approval and Minisum-Ranksum. The simulations were conducted using the newly developed simulation tool LightVoting, which is based on the multi-agent framework LightJason. The experiments of the main simulation series show that the committee voting rule Minisum-Ranksum in most cases yields better than or as good results as the committee voting rules Minisum-Approval and Minimax-Approval. The iterative protocol tends to yield an improvement regarding preference satisfaction, at the cost of strong deterioriation regarding the group size. The coordinated grouping tends to yield an improvement regarding the preference satisfaction at relative small cost regarding the group size. This leads us to recommend the committee voting rule Minisum-Ranksum, the basic protocol and coordinated grouping in order to achieve a compromise between system and user goals. We also demonstrate the effect of different combinations of grouping algorithms and voting protocols on travel costs. Here, the combination of the basic protocol and coordinated grouping yields a compromise between preference satisfaction and traveller costs. Additionally to the main simulation series, we provide an extended model which generates traveller preferences by combining attractiveness of the points of interest and distance costs based on the distances between the points of interest. As further application of voting, we consider a meeting-point scenario where a range voting rule and a minimax voting rule are used to agree on meeting points. For smaller groups, the average maximum travel time is clearly higher for range voting. For larger groups, the difference decreases. For smaller groups, the average lateness for the group using minimax voting is high, for larger groups it decreases. Hence, it makes sense for smaller groups to use the minimax voting rule if one aims at fairer distribution of travel times, and to use the range voting rule if the goal is instead to avoid delay for the group. For future work, it would be useful to adapt the simulation concept to take real-world conditions and requirements into account. Further possibilities for future work would be considering additional algorithms and models, such as considering combinatorial voting or running simulations based on the extended model, considering the role of financial incentives to encourage ridesharing or platooning and using the LightVoting tool for further research applications