A Systematic Review on Scheduling Public Transport Using IoT as Tool

Patel, Dharmendrakumar A.; Narmawala, Zunnun; Tanwar, Sudeep; Singh, Pradeep Kumar

doi:10.1007/978-981-10-8971-8_4

Cited by 26 publications

(6 citation statements)

References 9 publications

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“…The computers powered systems with artificial intelligence vision have progressively become essential for a secure and prosperous part of smart cities. To advance the performance and quality of various services in a city, IoT technology services must be incorporated 62‐67 . The autonomous monitoring of the powered systems is based on the deep learning architecture's performance of glean data.…”

Section: Discussionmentioning

confidence: 99%

Deep neural network based anomaly detection in Internet of Things network traffic tracking for the applications of future smart cities

Reddy

Behera

Nayak

et al. 2020

Trans Emerging Tel Tech

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An anomaly exposure system's foremost objective is to categorize the behavior of the system into normal and untruthful actions. To estimate the possible incidents, the administrators of smart cities have to apply anomaly detection engines to avert data from being jeopardized by errors or attacks. This article aims to propose a novel deep learning‐based framework with a dense random neural network approach for distinguishing and classifying anomaly from normal behaviors based on the type of attack in the Internet of Things. Machine learning algorithms have the improbability to explore the performance, compared with deep learning models. Distinctively, the examination of deep learning neural network architectures achieved enhanced computation performance and deliver desired results for categorical attacks. This article focuses on the complete study of experimentation performance and evaluations on deep learning neural network architecture for the recognition of seven categorical attacks found in the Distributed Smart Space Orchestration System traffic traces data set. The empirical results of the simulation model report that deep neural network architecture performs well through noticeable improvement in most of the categorical attack.

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Section: Discussionmentioning

confidence: 99%

Deep neural network based anomaly detection in Internet of Things network traffic tracking for the applications of future smart cities

Reddy

Behera

Nayak

et al. 2020

Trans Emerging Tel Tech

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“…UTS Data: it is related to datasets regarding mobility and transport aspects typically involved in a smart city [ 56 , 57 ], such as: Traffic Manager to track the status of the traffic in the city Public Transport schedule plans and real time status; Road network status: roads, bridges, underpasses, etc. ; Parking position and status, car and bike sharing, movements of public vehicles, cycling paths, etc.…”

Section: U-bmd Architecturementioning

confidence: 99%

An IoE and Big Multimedia Data Approach for Urban Transport System Resilience Management in Smart Cities

Bellini

Cenni

et al. 2021

Sensors

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Today, the complexity of urban systems combined with existing and emerging threats constrains administrations to consider smart technologies and related huge amounts of data generated as a means to take timely and informed decisions. The smart city needs to be prepared for both expected and unexpected situations, and the possibility to mitigate the effect of the uncertainty behind the causes of disruptions through the analysis of all the possible data generated by the city open new possibility for resilience operationalization. This article aims at introducing a new conceptualization for resilience and presenting an innovative full stack solution to exploit Internet of Everything (IoE) and big multimedia data in smart cities to manage resilience of urban transport systems (UTS), which is one of the most critical infrastructures of the city. The approach is based on a novel data driven approach to resilience engineering and functional resonance analysis method (FRAM), to understand and model an UTS in the context of smart cities and to support evidence driven decision making. The paper proposes an architecture taking into account: (a) different kinds of available data generated in the smart city, (b) big data collection and semantic aggregation and enrichment; (c) data sense-making process composed by analytics of different data sources like social media, communication networks, IoT, user behavior; (d) tools for knowledge driven decisions able to combine different information generated by analytics, experience, and structural information of the city into a comprehensive and evidence driven decision model. The solution has been applied in Florence metropolitan city in the context of RESOLUTE H2020 research project of the European Commission.

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“…Yang et al 46 analyze various traffic patterns based on multiple attributes like road hierarchies and occupancy, traffic volume and speed, and functional and social activity zones with the help of dimension reduction spectral clustering. This future traffic prediction pattern can be further inculcated to traffic management systems for scheduling and route‐guidance in ITS 57 . K‐Means is the most common, simple, empirically efficient algorithm used in ITS to extract traffic patterns.…”

Section: Taxonomymentioning

confidence: 99%

Machine learning‐based traffic scheduling techniques for intelligent transportation system: Opportunities and challenges

Nama

Nath

Bechra

et al. 2021

Int J Communication

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Summary The rise in traffic congestion has become a significant concern in the urban city environment. The conventional traffic control systems with inefficient human resources management fail to control the traffic discipline, leading to increased traffic density and road offenses. However, the intelligent transportation system (ITS) can provide safety, efficiency, and sustainability for large‐scale vehicular traffic dilemmas. ITS unites machine learning with the available traffic control force and performs real‐time police scheduling to ensure the smooth flow of traffic. Many researchers have demonstrated notable work in intelligent traffic police scheduling and deployment using various optimization algorithms. However, the compilation of such praiseworthy work as a whole is still missing. Motivated by these facts, we provide a comprehensive review of the machine learning‐based state‐of‐the‐art technologies that can be used to form a three‐tier solution taxonomy. The first tier describes various tools and technologies that can be utilized to collect traffic data. The second tier highlights the machine learning algorithms and their accuracy, which forms a pattern in the collected data and then yields some crucial information about traffic flow, congestion levels, and so forth. In the third tier, the most vital taxonomy layer, various traffic police scheduling strategies are discussed. The proposed survey also presents the use of cases of traffic police scheduling that elaborates this review's applicability in various domains. Finally, some of the key challenges in the subject being reviewed are discussed, which initiates a further scope of improvement.

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A Systematic Review on Scheduling Public Transport Using IoT as Tool

Cited by 26 publications

References 9 publications

Deep neural network based anomaly detection in Internet of Things network traffic tracking for the applications of future smart cities

Deep neural network based anomaly detection in Internet of Things network traffic tracking for the applications of future smart cities

An IoE and Big Multimedia Data Approach for Urban Transport System Resilience Management in Smart Cities

Machine learning‐based traffic scheduling techniques for intelligent transportation system: Opportunities and challenges

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