Recent Advances in Intelligent Transportation Systems for Cloud-Enabled Smart Cities

Tsai, Sang‐Bing; Gupta, Brij B.; Agrawal, Dharma P.; Wu, Wenqing; Liu, Aijun

doi:10.1155/2021/9792543

Cited by 4 publications

(3 citation statements)

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“…The peaks in highway traffic and nonstationary situations may be predicted using an ERS-ELM technique. The results of the experiments indicated that ERS-ELM had a high prediction accuracy and a short training period [28].…”

Section: Density Estimation and Traffic Flowmentioning

confidence: 96%

“…Their experiments found a reduction of 7 to 24% over the current procedures. Deep-travel models were used in the study of route-based methods for predicting journey times in both forward and backward directions [28]. The authors provided an attention mechanism for taxi-carpool platforms to collect background from local trajectories extracted features created by an LLSTM network.…”

Section: Routing and Planningmentioning

confidence: 99%

“…The convolutional models of the DNN framework allow it to learn distributed representation of category data quickly. Entity integration beat other neural networks and tree-based methods in their studies on the London trip dataset [28].…”

Section: Routing and Planningmentioning

confidence: 99%

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Sustainable Smart City Planning: Advances in Intelligent Transportation and E-Transportation Management

Almatar

2024

IJTDI

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The study is carried out with the aim to investigate the barriers impacting the sustainable smart city planning and implementation of Intelligent transportation systems (ITS). This research study will contribute to the current knowledge by highlighting the main challenges that prevent the implementation of ITS for smart urban mobility. The findings demonstrate that various challenges, including management, resource-related, technical, economic, personal, interoperability, and individual factors hinders the successful implementation of ITS. The outcome of the findings holds few managerial implications. Policymakers and urban planners need to develop strategies that help resolve the identified challenges to guarantee the successful execution of ITS. Because of the proliferation of big data and the interconnectedness of vehicular, infrastructural, and pedestrian settings, acquiring, storing, and analyzing multi-source data has become easier and less expensive. The linked model brings new techniques to adaptable coordination and monitoring in real-time to better monitor and regulates transportation systems. IoT is developed, and it is now possible to integrate complicated solutions into existing frameworks and procedures for city administration. As the number of people who possess a vehicle rises, the difficulty of finding a parking spot and the consequent impact on air quality. New smart parking solutions must be developed to save time and minimize greenhouse gas emissions. Smart parking solutions are the primary focus of this article, which emphasizes the available systems and sensors, as reported in the literature. This analysis aims to provide an in-depth detail at the development of smart transportation solutions. The inclusion of large vehicle detection technologies in a complete examination of the present state of smart parking systems should be a top priority. As a result, the communication modules are provided clearly and concisely.

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Section: Density Estimation and Traffic Flowmentioning

confidence: 96%

Section: Routing and Planningmentioning

confidence: 99%