Developing advanced traffic violation detection system with RFID technology for smart city

Wong, Seng Fat; Mak, H. C.; Ku, Cheng-Yuan; Ho, Wai-Yiu

doi:10.1109/ieem.2017.8289907

Cited by 16 publications

(5 citation statements)

References 4 publications

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“…The RFID technology can be used to support the management of urban infrastructure, in particular related to traffic [ 54 ]. Two options are worth considering: RFID transponders installed in vehicles, and RWD devices in the road infrastructure [ 22 ], RWD devices installed in vehicles and transponders in the road infrastructure.…”

Section: Improving Traffic Management With Rfid Solutionsmentioning

confidence: 99%

Dynamic RFID Identification in Urban Traffic Management Systems

Pawłowicz

Trybus

Salach

et al. 2020

Sensors

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The paper covers the application of Radio Frequency IDentification (RFID) technology in road traffic management with regard to vehicle identification. Various infrastructure configurations for Automated Vehicle Identification (AVI) have been presented, including configurations that can be used in urban traffic as part of the Smart City concept. In order to describe the behavior of multiple identifications of moving vehicles, an operation model of the dynamic identification using RFID is described. While it extends the definition of the correct work zone, this paper introduces the concept of dividing the zone into sections corresponding to so-called inventory rounds. The system state is described using a set of matrices in which unread, read, and lost transponders are recorded in subsequent rounds and sections. A simplified algorithm of the dynamic object identification system was also proposed. The results of the simulations and lab experiments show that the efficiency of mobile object identification is conditioned by the parameters of the communication protocol, the speed of movement, and the number of objects.

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Section: Improving Traffic Management With Rfid Solutionsmentioning

confidence: 99%

Dynamic RFID Identification in Urban Traffic Management Systems

Pawłowicz

Trybus

Salach

et al. 2020

Sensors

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“…Some of the conveniences offered cannot be separated from the role of technology, especially ICT to the Internet of Things [25]. Various applications have been developed to support smart cities, such as the development of intelligent systems for optimization of energy allocations [26], [27] monitoring landfills capable of reducing urban waste management costs [28], electronic public service-based complaints services [29] lighting control with remote control systems [30], [31], intelligent control of city traffic lights [32], [33], [34], analysis and development of intelligent systems in the field of transportation [35], [36], [ 37], [38] as well as the development of a city bike rental sharing system that is able to minimize city noise [39].…”

Section: Smart City Current Implementationmentioning

confidence: 99%

A Literature Study: The Concept of Smart City between Municipal vs. Regency (Between Efficiency, Habitability, and Local Wisdom)

Munandar¹,

Harsiti²,

Malik³

et al. 2018

Proceedings of the the 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus

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The smart city concept is one of the important alternatives in the effort to solve various problems in the urban area. However, provincial administration in several countries including Indonesia is divided into two categories: municipality and regency. There are problems arising when smart city concept implemented in the regency because the municipality and the regency have the significant of different characteristics. The blueprint preparation of smart city concept should be prepared by taking into the local wisdom possesed by the region, especially for regency that is different from the municipality. Thus, the implementation of the concept is adapted to the characteristics and needs of the region. The result shows that smart cities can be carried out as efficiently as possible in terms of technology implementation. Besides that the smart city that is built must prioritize the concept of livable for its inhabitants without ignoring the local wisdom they have.

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“…In addition to the traditional SVM model, variant SVM algorithms, such as seasonal SVM [18], which considers traffic data seasonality, and Online-SVR [19], which deals with special events, have also been applied in traffic flow prediction and good results are obtained. e development and wide applications of traffic information collection technology, such as inductive detector, geomagnetic detectors, radio frequency identification technology, radar detection, video detection, and floating car detection [20][21][22][23][24], provide a large amount of data for traffic flow prediction. At the same time, with the rapid development of artificial intelligence technology, deep learning, which has powerful data feature mining and nonlinear data fitting capabilities, has been successfully applied in many fields, such as image processing and speech recognition [25][26][27], and gradually used in traffic parameter forecasting [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Traffic Flow Prediction: A Method of Combined Deep Learnings

Ren

Chai

Yin

et al. 2021

Journal of Advanced Transportation

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Short-term traffic flow prediction can provide a basis for traffic management and support for travelers to make decisions. Accurate short-term traffic flow prediction also provides necessary conditions for the sustainable development of the traffic environment. Although the application of deep learning methods for traffic flow prediction has achieved good accuracy, the problem of combining multiple deep learning methods to improve the prediction accuracy of a single method still has a margin for in-depth research. In this article, a combined deep learning prediction (CDLP) model including two paralleled single deep learning models, CNN-LSTM-attention model and CNN-GRU-attention model, is established. In the model, a one-dimensional convolutional neural network (1DCNN) is used to extract traffic flow local trend features and RNN variants (LSTM and GRU) with attention mechanism are used to extract long temporal dependencies trend features. Moreover, a dynamic optimal weighted coefficient algorithm (DOWCA) is proposed to calculate the dynamic weights of CNN-LSTM-attention and CNN-GRU-attention with the goal of minimizing the sum of squared errors of the CDLP model. Then, the neuron number, loss function, optimization algorithm, and other parameters of the CDLP model are discussed and set through experiments. Finally, the training set and test set for the CDLP model are established through the processing of traffic flow data collected from the field. The CDLP model is trained and tested, and the prediction results of traffic flow are obtained and analyzed. It indicates that the CDLP model can fit the change trend of traffic flow very well and has better performance. Furthermore, under the same dataset, the results from the CDLP model are compared with baseline models. It is found that the CDLP model has higher prediction accuracy than baseline models.

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Developing advanced traffic violation detection system with RFID technology for smart city

Cited by 16 publications

References 4 publications

Dynamic RFID Identification in Urban Traffic Management Systems

Dynamic RFID Identification in Urban Traffic Management Systems

A Literature Study: The Concept of Smart City between Municipal vs. Regency (Between Efficiency, Habitability, and Local Wisdom)

Short-Term Traffic Flow Prediction: A Method of Combined Deep Learnings

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