A Cyber-Physical System and Graph-Based Approach for Transportation Management in Smart Cities

Rathore, M. Mazhar; Shah, Syed Attique; Awad, Ahmed; Shukla, Dhirendra; Vimal, Shanmuganathan; Paul, Anand

doi:10.3390/su13147606

Cited by 23 publications

(12 citation statements)

References 39 publications

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“…Rathore, M. M., Attique Shah et.al [9] transportation guarantees to provide the correct evidence at the proper period, location, and device to assist the public in making any transportation-related decisions. The vehicular network and the system of allow are two examples of these two components.…”

Section: Related Workmentioning

confidence: 99%

“…In a nutshell, traffic authorities are required to manage the traffic system wisely with the least amount of human intervention and resources. This clever transport infrastructure makes it easier for residents to access real-time transportation facts and has a big impact on how people live [1]. Task Execution Computation the number of computationally intensive apps.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Clustering Technique for Mobile Edge Computing To Detect Clumps in Transportation-Related Problems

Mayan

Manikanthan²,

Hussain³

et al. 2023

Int. J. Interact. Mob. Technol.

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The daily functioning of civilization depends heavily on transportation. In most cities, a sizable section of the working class consistently attends both employment and education. There are many different things you can do when commuting, such as unwinding, eating out, and other things. The most popular means of transportation in North Cyprus, particularly in developing cities, is island transportation, which includes the usage of both private cars and commercial vehicles. The advent of edge computing, which offers the opportunity to connect potent processing servers next to the mobile device, is a significant step toward improving user experience and reducing resource use. Mobile Edge Computing is the next trustworthy approach for how mobile devices consume communications and computing. Offloading computation is a key component developing mobile edge computing, which enables devices to get around clustering techniques' limitations and get around computing, storage, and energy constraints. However, computation offloading is not always the best strategy to use; making choosing unloading is a critical step that requires consideration of numerous factors. For instance, shifting the high-resource node to an edge server and granting similar capabilities to the low-resource nodes would delegate heavy duties to the external unit inside the network. The evaluations' results were noteworthy and substantial. Problems involving the vehicles of institutions and organizations can be resolved using the suggested solution to the school bus routing issue. We also test the impact of network latency on the delivery of a particular result using an Edge Computing simulator.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Clustering Technique for Mobile Edge Computing To Detect Clumps in Transportation-Related Problems

Mayan

Manikanthan²,

Hussain³

et al. 2023

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

show abstract

“…In Reference 17, Xie et al propose a two‐stage solution to ensure the reliability of automotive CPSs under energy and response‐time constraints. In addition, Rathore et al 18 propose a graph‐based approach for generating smart transportation management related decisions.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, Rathore et al 18 propose a graph-based approach for generating smart transportation management related decisions.…”

Section: Verification Of Cpssmentioning

confidence: 99%

Compatibility checking for cyber‐physical systems based on microservices

Dai

et al. 2022

Softw Pract Exp

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Microservices architecture provides a promising solution for developing sustainable cyber-physical systems (CPSs). However, checking the compatibility of CPSs over a set of microservices communicating asynchronously via unbounded buffers are undecidable due to their infinite state spaces. In this article, we propose a new approach for checking the compatibility of CPSs with infinite state spaces without restricting the size of buffers or the number of communication cycles. First, we integrate CPSs with microservice architecture and design the system architecture for building CPSs over a set of cyber-physical microservices with unbounded buffers. Second, we model CPSs composed of asynchronously communicating cyber-physical microservices via FIFO buffers as labelled transition systems. Third, we adopt the stability notion and present a sufficient condition for checking the unspecified receptions of CPSs through stability checking. Finally, we implement our approach in Process Analysis Toolkit for automatic compatibility checking and conduct experiments to show our approach is effective and efficient.

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“…This model discussed both spatial-temporal dependencies in predicting city wide spatial-temporal traffic flow volume. The authors also presented a hybrid LSTM and ResNet model that deals the spatial-temporal effects on a given volume of Ad Hoc Networks (VANET) [59], [60] that permit vehicles to communicate with each other and improve traffic safety.…”

Section: Acknowledgementmentioning

confidence: 99%

Traffic Prediction in Smart Cities Based on Hybrid Feature Space

et al. 2022

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In smart cities of the future, data will be generated, integrated, processed and utilized from heterogeneous sources and at varying levels of complexity. For urban traffic planning in smart cities, one of the biggest challenges is traffic congestion prediction and its avoidance. Traffic congestion is a complex phenomenon and it is a manifestation of various contributing factors. In addition to vehicular mobility, properties of road network, weather, holidays and peak hours play a significant role in traffic congestion especially on arterial roads within a city. In this paper, we proposed a hybrid GRU-LSTM based deep learning model and applied it on city-wide novel traffic data integrated from heterogeneous sources. We have devised our indigenous data pipeline that is composed of a set of algorithms dealing with map matching, sparsity handling, outlier removal, zero speed adjustments, Open Street Map (OSM) and segment mapping etc. Extensive experimentations have been carried out to demonstrate the improved performance of the proposed method. The comparative analysis reveals that our methodology yields 95 % accuracy that outperforms other deep neural network models.

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A Cyber-Physical System and Graph-Based Approach for Transportation Management in Smart Cities

Cited by 23 publications

References 39 publications

Clustering Technique for Mobile Edge Computing To Detect Clumps in Transportation-Related Problems

Clustering Technique for Mobile Edge Computing To Detect Clumps in Transportation-Related Problems

Compatibility checking for cyber‐physical systems based on microservices

Traffic Prediction in Smart Cities Based on Hybrid Feature Space

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