Dynamic pricing techniques for Intelligent Transportation System in smart cities: A systematic review

Saharan, Sandeep; Bawa, Seema; Kumar, Neeraj

doi:10.1016/j.comcom.2019.12.003

Cited by 144 publications

(57 citation statements)

References 93 publications

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“…We believe that the findings presented here might provide a more fundamental basis upon which to build these more elaborate networked CPR models, which incorporate additional social processes which interact with agents' myopic self-interest to facilitate or hinder effective CPR self-regulation by reallocation. Finally, we expect that it will be fruitful to explore potential links between the present work, which is rooted in a more abstract, evolutionary perspective, and the wider body of ongoing applied research that applies the tools of rational queueing theory 33,34 , dynamic pricing [35][36][37] , and social choice theory 38 to address issues of resource allocation in networked systems [39][40][41][42] . Although the CPR literature may seem to suggest that regulation by a centralized decision-maker is likely doomed to failure, a study of the mechanisms underlying CPR self-regulation may help to inform research undertaken from an optimal control perspective, with the aim of identifying potential targeted interventions that respect and leverage a community's innate capacity for bottom-up self-regulation rather than disrupt it.…”

Section: Discussionmentioning

confidence: 99%

Myopic reallocation of extraction improves collective outcomes in networked common-pool resource games

Schauf

2021

Sci Rep

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When individuals extract benefits from multiple resources, the decision they face is twofold: besides choosing how much total effort to exert for extraction, they must also decide how to allocate this effort. We focus on the allocation aspect of this choice in an iterated game played on bipartite networks of agents and common-pool resources (CPRs) that degrade linearly in quality as extraction increases. When CPR users attempt to reallocate their extraction efforts among resources to maximize their own payoffs in the very next round (that is, myopically), collective wealth is increased. Using a heterogeneous mean-field approach, we estimate how these reallocations affect the payoffs of CPR users of different degrees within networks having different levels of degree heterogeneity. Focusing specifically on Nash equilibrium initial conditions, which represent the patterns of over-exploitation that result from rational extraction, we find that networks with greater heterogeneity among CPR degrees show greater improvements over equilibrium due to reallocation. When the marginal utility of extraction diminishes, these reallocations also reduce wealth inequality. These findings emphasize that CPR users’ adaptive reallocations of effort—a behavior that previously-studied network evolutionary game models typically disallow by construction—can serve to direct individuals’ self-interest toward the collective good.

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Section: Discussionmentioning

confidence: 99%

Myopic reallocation of extraction improves collective outcomes in networked common-pool resource games

Schauf

2021

Sci Rep

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“…For training, they used different inductive learning models such as Decision Trees, k-Nearest Neighbor, Naive Bayes, and Bayes Net and evaluated their approach using five-fold cross-validation. Authors in [40][41][42][43][44][45][46] discussed the network protocol to use and process the sensors and wearable devices data over the network. The research [13] proposed an assisted approach that helps the participants to live healthily.…”

Section: Literature Reviewmentioning

confidence: 99%

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Javed

Sarwar

Khan

et al. 2020

Sensors

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Recognizing human physical activities from streaming smartphone sensor readings is essential for the successful realization of a smart environment. Physical activity recognition is one of the active research topics to provide users the adaptive services using smart devices. Existing physical activity recognition methods lack in providing fast and accurate recognition of activities. This paper proposes an approach to recognize physical activities using only2-axes of the smartphone accelerometer sensor. It also investigates the effectiveness and contribution of each axis of the accelerometer in the recognition of physical activities. To implement our approach, data of daily life activities are collected labeled using the accelerometer from 12 participants. Furthermore, three machine learning classifiers are implemented to train the model on the collected dataset and in predicting the activities. Our proposed approach provides more promising results compared to the existing techniques and presents a strong rationale behind the effectiveness and contribution of each axis of an accelerometer for activity recognition. To ensure the reliability of the model, we evaluate the proposed approach and observations on standard publicly available dataset WISDM also and provide a comparative analysis with state-of-the-art studies. The proposed approach achieved 93% weighted accuracy with Multilayer Perceptron (MLP) classifier, which is almost 13% higher than the existing methods.2 of 18 emotional issues, and depression as well. Physical fitness can be tracked and analyzed by monitoring daily life physical activities.Physical activity recognition was initiated back in 2004 using on-body sensors. Researchers in [2] used the accelerometer's annotated data to recognize the physical activities. They made an Android-based system that collects raw data from the accelerometer and applied machine learning algorithms to predict physical activities. Authors in [3] recognized six basic activities, i.e., walking, jogging, sitting, standing, upstairs and downstairs. Authors in [4] used on-body sensors for activity recognition but found that it is very difficult to carry the sensors all the time. Many authors suggested that a smartphone is a non-obtrusive option for activity recognition [3,[5][6][7][8][9].The smartphone is playing a vital role in modern life. It provides services and applications such as health monitoring, early-stage disease detection, sports analysis, fitness tracking, and behavior analysis. Android-based smartphones have a built-in motion sensor that provides accurate and precise acceleration readings against physical activities. In early conditions, dedicated sensors were used for activity recognition. There exist several techniques for physical activity recognition such as on-body obtrusive and non-obtrusive sensors [10][11][12][13]. Non-obtrusive sensors are used in smart homes and smartphones. In smart homes, different motion and door sensors are installed at different locations and the primary objective is to recognize...

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“…Although, drones having countless applications in various areas, but its ungoverned deployment causes serious security problems [19] and require intelligent transportation techniques [20]. It can be used for transfer of unlawful material like explosives or can violate the boundary of security sensitive organizations.…”

Section: Ref No Year Of Publicationmentioning

confidence: 99%

Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference

Uddin

Altaf

Bilal

et al. 2020

Computer Communications

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Owing to small size, sensing capabilities and autonomous nature, the Unmanned Air Vehicles (UAVs) have enormous applications in various areas e.g., remote sensing, navigation, archaeology, journalism, environmental science, and agriculture. However, the un-monitored deployment of UAVs called the amateur drones (AmDr) can lead to serious security threats and risk to human life and infrastructure. Therefore, timely detection of the AmDr is essential for the protection and security of sensitive organizations, human life and other vital infrastructure. Am-Drs can be detected using different techniques based on sound, video, thermal, and radio frequencies. However, the performance of these techniques is limited in * sever atmospheric conditions. In this paper, we propose an efficient un-supervise machine learning approach of independent component analysis (ICA) to detect various acoustic signals i.e., sounds of bird, airplanes, thunderstorm, rain, wind and the UAVs in practical scenario. After unmixing the signals, the features like Mel Frequency Cepstral Coefficients (MFCC), the power spectral density (PSD) and the Root Mean Square Value (RMS) of the PSD are extracted by using ICA. The PSD and the RMS of PSD signals are extracted by first passing the signals from octave band filter banks. Based on the above features the signals are classified using Support Vector Machines (SVM)and K Nearest Neighbor (KNN)to detect the presence or absence of AmDr. Unique feature of the proposed technique is the detection of a single or multiple AmDrs at a time in the presence of multiple acoustic interfering signals. The proposed technique is verified through extensive simulations and it is observed that the RMS values of PSD with KNN performs better than the MFCC with KNN and SVM.

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Dynamic pricing techniques for Intelligent Transportation System in smart cities: A systematic review

Cited by 144 publications

References 93 publications

Myopic reallocation of extraction improves collective outcomes in networked common-pool resource games

Myopic reallocation of extraction improves collective outcomes in networked common-pool resource games

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference

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