A Game-Theory Based Incentive Framework for an Intelligent Traffic System as Part of a Smart City Initiative

Mei, Haibo; Poslad, Stefan; Du, Shuang

doi:10.3390/s17122874

Cited by 25 publications

(11 citation statements)

References 29 publications

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“…Serious games that involve driving and mobility have been designed to support a range of objectives in intelligent transport systems, such as encouraging the use of different transport modes and supporting route planning [31]. Gamification solutions have been applied towards more fuel-efficient driving (e.g., [32]) and safer driver behavior (e.g., [33]).…”

Section: Literature Reviewmentioning

confidence: 99%

IoT Sensing for Reality-Enhanced Serious Games, a Fuel-Efficient Drive Use Case

Massoud

Berta

Poslad

et al. 2021

Sensors

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Internet of Things technologies are spurring new types of instructional games, namely reality-enhanced serious games (RESGs), that support training directly in the field. This paper investigates a key feature of RESGs, i.e., user performance evaluation using real data, and studies an application of RESGs for promoting fuel-efficient driving, using fuel consumption as an indicator of driver performance. In particular, we propose a reference model for supporting a novel smart sensing dataflow involving the combination of two modules, based on machine learning, to be employed in RESGs in parallel and in real-time. The first module concerns quantitative performance assessment, while the second one targets verbal recommendation. For the assessment module, we compared the performance of three well-established machine learning algorithms: support vector regression, random forest and artificial neural networks. The experiments show that random forest achieves a slightly better performance assessment correlation than the others but requires a higher inference time. The instant recommendation module, implemented using fuzzy logic, triggers advice when inefficient driving patterns are detected. The dataflow has been tested with data from the enviroCar public dataset, exploiting on board diagnostic II (OBD II) standard vehicular interface information. The data covers various driving environments and vehicle models, which makes the system robust for real-world conditions. The results show the feasibility and effectiveness of the proposed approach, attaining a high estimation correlation (R2 = 0.99, with random forest) and punctual verbal feedback to the driver. An important word of caution concerns users’ privacy, as the modules rely on sensitive personal data, and provide information that by no means should be misused.

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Section: Literature Reviewmentioning

confidence: 99%

IoT Sensing for Reality-Enhanced Serious Games, a Fuel-Efficient Drive Use Case

Massoud

Berta

Poslad

et al. 2021

Sensors

Self Cite

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“…Orlowski et al [66] design business models to measure air quality for smart cities for using IoT (Internet of Things) and SaaS (software as a service). Mei et al [67] design an incentive framework for an intelligent traffic system based on initiative game-theory. Vuppalapati et al [68] design a smart dairy model using IoT sensor network measuring cattle's health issues, milk production prediction, and productivity improvement.…”

Section: Tree-based Models (Decision Trees)mentioning

confidence: 99%

“…Mei et al [67] Designing an incentive framework for an intelligent traffic system based on initiative game-theory Urban transport Vuppalapati et al [68] Designing a smart dairy model using IoT sensor network measuring cattle's health issues, milk production prediction and productivity improvement Food…”

Section: Air Pollutionmentioning

confidence: 99%

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

Nosratabadi

Mosavi

Keivani

et al. 2020

Lecture Notes in Networks and Systems

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Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neurofuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems.

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“…How to choose the right location of charging station and how to configure the reasonable number of charging piles are the hot issues concerned by the current society and many scholars. The in-depth development of high-tech technologies such as smart cities and the Internet of Things provides information research ideas for the construction of smart city charging station resource allocation optimization technology based on big data [5].…”

Section: Introductionmentioning

confidence: 99%

Research on Resource Allocation Optimization of Smart City Based on Big Data

2020

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The resource allocation of charging stations is an important part of promoting the development of renewable energy in modern cities. It can promote the scientific and modern construction of urban resource allocation and promote the intelligent transformation of cities. In view of the existing problems in the resource allocation process of urban charging stations, such as a single planning method, considering the actual travel demand. Based on the smart city transportation network information, this article will consider the impact of charging station construction costs, user driving and waiting costs on the location of charging stations, construct a charging station configuration optimization model, and introduce charging convenience coefficients to modify the model. Secondly, this paper establishes a systematic clustering model based on principal component analysis, selecting factors such as per capita GDP, population, and civilian car ownership as indicators, clustering analysis of different regions and assigning different charging convenience coefficients. Finally, the shortest distance matrix between any two nodes is calculated by the Voronoi diagram to concentrate the regional charging load to the traffic node, and the Floyd algorithm is used to analyze and evaluate the effect of the established charging station configuration optimization model. This technology provides a basis for promoting the modernization of urban green transportation.

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A Game-Theory Based Incentive Framework for an Intelligent Traffic System as Part of a Smart City Initiative

Cited by 25 publications

References 29 publications

IoT Sensing for Reality-Enhanced Serious Games, a Fuel-Efficient Drive Use Case

IoT Sensing for Reality-Enhanced Serious Games, a Fuel-Efficient Drive Use Case

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

Research on Resource Allocation Optimization of Smart City Based on Big Data

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