Dynamic Sensor Speed Measurement Algorithm and Influencing Factors of Traffic Safety With Wireless Sensor Network Nodes and RFID

Peng, Hua; Wang, Yongxin; Chen, Zhiyong; Lv, Zhihan

doi:10.1109/jsen.2020.3020643

Cited by 11 publications

(7 citation statements)

References 18 publications

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“…Furthermore, reliable traffic measurements and traffic monitoring to prevent network congestion and to prevent the depletion of the capacity of nodes in the transport network are very important. Some scholars have proposed traffic flow monitoring or prediction methods for road network nodes under emergencies [23][24][25][26]. In addition, many scholars have carried out research on traffic emergency disposal measures and methods under urban road network emergencies [27][28][29][30].…”

Section: It Is Necessary To Determine the Classification Parametersmentioning

confidence: 99%

A Grade Identification Method of Critical Node in Urban Road Network Based on Multi-Attribute Evaluation Correction

et al. 2022

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Accurately identifying the key nodes of the road network and focusing on its management and control is an important means to improve the robustness and invulnerability of the road network. In this paper, a classification and identification method of key nodes in urban road networks based on multi-attribute evaluation and modification was proposed. Firstly, the emergency function guarantee grade of road network nodes was divided by comprehensively considering the importance of road network nodes, the consequences of failure, and the degree of difficulty of recovery. The evaluation indexes were selected according to the local attributes, global attributes, and functional attributes of the road network topology. The spatial distribution patterns of the evaluation indexes of the nodes were analyzed. The dynamic classification method was used to cluster the attributes of the road network nodes, and the TOPSIS method was used to comprehensively evaluate the importance ranking of the road network nodes. Attribute clustering of road network nodes by dynamic classification method (DT) and the TOPSIS method was used to comprehensively evaluate the ranking of the importance of road network nodes. Then, combined with the modification of the comprehensive evaluation and ranking of the importance of the road network nodes, the emergency function support classification results of the road network nodes were obtained. Finally, the method was applied to the road network within the second Ring Road of Beijing. It was compared with the clustering method of self-organizing competitive neural networks. The results show that this method can identify the key nodes of the road network more accurately. The first-grade key nodes are all located at the more important intersections on expressways and trunk roads. The spatial distribution pattern shows a “center-edge” pattern, and the important traffic corridors of the road network show a “five vertical and five horizontal” pattern.

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Section: It Is Necessary To Determine the Classification Parametersmentioning

confidence: 99%

A Grade Identification Method of Critical Node in Urban Road Network Based on Multi-Attribute Evaluation Correction

et al. 2022

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“…A recent special issue of this journal [26] reports the cutting-edge advances and ICT technologies pertaining to the seamless integration of sensors with the transportation infrastructure. The main focus of the contributions in [26] is on sensing technologies for private transportation systems, oriented to autonomous driving [27,28], intelligent fault detection [29,30], electric charging optimization [31], road condition monitoring [32,33], precise fleet management [34], speed detection and accident avoidance [35,36]: in the same issue, less attention is dedicated to the integration of sensing technologies within PT systems. Moreover, sensing techniques for crowd monitoring are not specifically discussed.…”

Section: Introductionmentioning

confidence: 99%

Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review

et al. 2023

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Management of crowd information in public transportation (PT) systems is crucial, both to foster sustainable mobility, by increasing the user's comfort and satisfaction during normal operation, as well as to cope with emergency situations, such as pandemic crises, as recently experienced with COVID-19 limitations. This paper presents a taxonomy and review of sensing technologies based on Internet of Things (IoT) for real-time crowd analysis, which can be adopted in the different segments of the PT system (buses/trams/trains, railway/metro stations, and bus/tram stops). To discuss such technologies in a clear systematic perspective, we introduce a reference architecture for crowd management, which employs modern information and communication technologies (ICT) in order to: (i) monitor and predict crowding events; (ii) implement crowd-aware policies for real-time and adaptive operation control in intelligent transportation systems (ITSs); (iii) inform in real-time the users of the crowding status of the PT system, by means of electronic displays installed inside vehicles or at bus/tram stops/stations, and/or by mobile transport applications. It is envisioned that the innovative crowd management functionalities enabled by ICT/IoT sensing technologies can be incrementally implemented as an add-on to state-of-the-art ITS platforms, which are already in use by major PT companies operating in urban areas. Moreover, it is argued that, in this new framework, additional services can be delivered to the passengers, such as, e.g., on-line ticketing, vehicle access control and reservation in severely crowded situations, and evolved crowd-aware route planning.

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“…A recent special issue [24] reports the cutting-edge advances and ICT technologies pertaining to the seamless integration of sensors with the transportation infrastructure. The main focus of the contributions in [24] is on sensing technologies for private transportation systems, oriented to autonomous driving [25,26], intelligent fault detection [27,28], electric charging optimization [29], road condition monitoring [30,31], precise fleet management [32], speed detection and accident avoidance [33,34]: in the same issue, less attention is dedicated to the integration of sensing technologies within PT systems. Moreover, sensing techniques for crowd monitoring are not specifically discussed.…”

Section: Introductionmentioning

confidence: 99%

Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review

Darsena¹,

Gelli²,

Iudice³

et al. 2022

Preprint

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<p>Management of crowd information in public transportation (PT) systems is crucial, both to foster sustainable mobility, by increasing the user's comfort and satisfaction during normal operation, as well as to cope with emergency situations, such as pandemic crises, as recently experienced with COVID-19 limitations. This paper presents a taxonomy and review of sensing technologies based on Internet of Things (IoT) for real-time crowd analysis, which can be adopted in the different segments of the PT system (buses/trams/trains, railway/metro stations, and bus stops). To discuss such technologies in a clear systematic perspective, we introduce a reference architecture for crowd management, which employs modern information and communication technologies (ICT) in order to: (i) monitor and predict crowding events; (ii) implement crowd-aware policies for real-time and adaptive operation control in intelligent transportation systems (ITSs); (iii) inform in real-time the users of the crowding status of the PT system, by means of electronic displays installed inside vehicles or at bus stops/stations, and/or by mobile transport applications. It is envisioned that the innovative crowd management functionalities enabled by ICT/IoT sensing technologies can be incrementally implemented as an add-on to state-of-the-art ITS platforms, which are already in use by major PT companies operating in urban areas. Moreover, it is argued that, in this new framework, additional services can be delivered to the passengers, such as, e.g., on-line ticketing, vehicle access control and reservation in severely crowded situations, and evolved crowd-aware route planning.</p>

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Dynamic Sensor Speed Measurement Algorithm and Influencing Factors of Traffic Safety With Wireless Sensor Network Nodes and RFID

Cited by 11 publications

References 18 publications

A Grade Identification Method of Critical Node in Urban Road Network Based on Multi-Attribute Evaluation Correction

A Grade Identification Method of Critical Node in Urban Road Network Based on Multi-Attribute Evaluation Correction

Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review

Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review

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