Game Theoretic Approach for a Multi-Mode Transportation in Smart Cities

Hariz, Mohammed Bin; Said, Dhaou; Mouftah, Hussein T.

doi:10.1109/isncc49221.2020.9297295

Cited by 3 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The unceasing demand for computer games and the fact that more and more people are involved in this segment, creating, improving, researching, and analyzing computer games from a scientific point of view are behind the emergence of scientific directions oriented to computer games. Their mutual interaction with computer science reaches the stage where part of the results of this interaction is usable and is used in practice in areas such as medicine, therapy [7], services, transportation [8], or education [9]. Its further potential is enormous, which is good news for players, developers, and scientists [10].…”

Section: Author Detailsmentioning

confidence: 99%

Introductory chapter: Game Development and Computer Science

Sobota,

Pietriková

2023

Computer Science for Game Development and Game Development for Computer Science

View full text Add to dashboard Cite

Nothing affects a person's life more than the games he played in his childhood. Games open a wonderful world of colors, light, secrets, materials, and relationships. The feeling of the game carries with us even into adulthood, and our desire to play remains in the subconscious and regularly emerges. In the colossal change of the world caused by computers, it remains unchanged, but its modification appearscomputer games [1]. In recent years, they have incredibly influenced children and adults.The development of computer technology and its impact on our lives are striking. Entertainment, especially computer games, is no exception in this sense. Playing computer (including electronic) games is a substantial percentage of this area. Of course, the development of these games is closely related to this. The development of computer games requires in-depth knowledge in the field of computer science, starting with programming, software engineering, or computer graphics (in some cases, in this sense, also virtual reality), through databases, artificial intelligence, and ending with, for example, the physics of games [2]. In short, as computer games become more complex, sophisticated, and immersive, the role of computer science in game development becomes increasingly important.In the secondary view, computer science is integral to every phase of computer game development. First, it provides computer game developers with the necessary tools to create, optimize and debug computer games, develop their game mechanics and gameplay, design interactive environments and characters, and create visual and sound effects [3]. Secondly, there is a close connection with the business aspect of computer games, including game marketing, distribution, or advertising [4].Following that, from the view of current game systems, it is necessary to address several areas (Figure 1). This applies mainly to the following areas (in no order of importance): graphics, sound, game logic and design, user interface (depending on the type of game also 3D interface, including player motion tracking or, e.g., his gesture sensing), sometimes different kinds of simulations or training modes, including the possible use of digital twins technology (if there is a game session with a potential real world), possible in-game physics, dynamics processing including scripting, the most immersive gaming experience possible, single-user or multi-user game mode including a possible cooperative mode, educational or research potential of the game or possibly data processing, artificial intelligence, and of course the economic aspect.

show abstract

Section: Author Detailsmentioning

confidence: 99%

Introductory chapter: Game Development and Computer Science

Sobota,

Pietriková

2023

Computer Science for Game Development and Game Development for Computer Science

View full text Add to dashboard Cite

show abstract

“…and enables dynamic collaborations among users, such as crowdshipping [4] or crowdsensing [5,6] scenarios. In a multimodal context [7,8], real-time recommendation systems may suggest any type of appropriate means of transport (not just bikes) that fits the particular needs and preferences of a user at a particular moment. In some cases, this may alleviate the quality of service problems within a BSS due to imbalances, as mentioned above.…”

Section: Introductionmentioning

confidence: 99%

Smart Recommendations for Renting Bikes in Bike-Sharing Systems

2021

View full text Add to dashboard Cite

Vehicle-sharing systems—such as bike-, car-, or motorcycle-sharing systems—have become increasingly popular in big cities in recent years. On the one hand, they provide a cheaper and environmentally friendlier means of transportation than private cars, and on the other hand, they satisfy the individual mobility demands of citizens better than traditional public transport systems. One of their advantages in this regard is their availability, e.g., the possibility of taking (or leaving) a vehicle almost anywhere in a city. This availability obviously depends on different strategic and operational management decisions and policies, such as the dimension of the fleet or the (re)distribution of vehicles. Agglutination problems—where, due to usage patterns, available vehicles are concentrated in certain areas, whereas no vehicles are available in others—are quite common in such systems, and need to be dealt with. Research has been dedicated to this problem, specifying different techniques to reduce imbalanced situations. In this paper, we present and compare strategies for recommending stations to users who wish to rent or return bikes in station-based bike-sharing systems. Our first contribution is a novel recommendation strategy based on queuing theory that recommends stations based on their utility to the user in terms of lower distance and higher probability of finding a bike or slot. Then, we go one step further, defining a strategy that recommends stations by combining the utility of a particular user with the utility of the global system, measured in terms of the improvement in the distribution of bikes and slots with respect to the expected future demand, with the aim of implicitly avoiding or alleviating balancing problems. We present several experiments to evaluate our proposal with real data from the bike sharing system BiciMAD in Madrid.

show abstract