Sboing4Real: A real-time crowdsensing-based traffic management system

Toliopoulos, Theodoros; Nikolaidis, Nikodimos; Michailidou, Anna-Valentini; Seitaridis, Andreas; Nestoridis, Theodoros; Oikonomou, Chrysa; Temperekidis, Anastasios; Gioulekas, Fotios; Gounaris, Anastasios; Bassiliades, Nick; Katsaros, Panagiotis; Georgiadis, Apostolos; Liotopoulos, Fotis K.

doi:10.1016/j.jpdc.2022.01.017

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Static signal timings and historical traffic data have long been staples of conventional traffic control systems, intended to reduce these negative consequences. But a new era of traffic management has arrived with the development of data-intensive technologies and the spread of real-time data sources like security cameras, GPS units, and traffic sensors [11]- [15]. This shift is based on the idea that traffic efficiency and overall urban mobility may be significantly increased by the dynamic orchestration of traffic flows, guided by real-time analytics.…”

Section: Introductionmentioning

confidence: 99%

Data-Intensive Traffic Management: Real-Time Insights from the Traffic Management Simulation Test

Blinova,

Kumar,

Kansal

et al. 2024

BIO Web Conf.

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This research examined the effectiveness of data-intensive traffic management in urban settings using real-time insights from traffic management simulation experiments. The examination of data on traffic flow revealed a noteworthy decrease in congestion, with a 25% increase in traffic velocity during peak hours. Real-time information led to a 40% drop in the severity of traffic accidents and a 50% reduction in reaction times. Improved road safety was aided by a 30% decrease in accidents during inclement weather thanks to real-time weather data. To further optimize urban traffic flow, dynamic traffic management operations based on real-time information also resulted in a 20% reduction in congestion. These results highlight the revolutionary potential of data-intensive traffic management, offering safer and more effective urban transportation solutions by incorporating real-time information into traffic control plans.

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Section: Introductionmentioning

confidence: 99%

Data-Intensive Traffic Management: Real-Time Insights from the Traffic Management Simulation Test

Blinova,

Kumar,

Kansal

et al. 2024

BIO Web Conf.

View full text Add to dashboard Cite

show abstract

“…Through the integration with other power systems, the traffic system can better serve the whole power system and improve the operating efficiency and stability of the power system. At the same time, openness also enables the traffic system to better adapt to different scenarios and application requirements [14][15] . Among them, the traffic system based on IP PBX technology performs well in operation performance, but its application scope is limited; The research of traffic system based on big data has also made some achievements, but the technology is still not mature enough.…”

Section: Introductionmentioning

confidence: 99%

Design of Electric Power Intelligent Traffic System under Adaptive Dispatching Duty Mode

Wu,

Lu,

Sun

et al. 2024

Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2023, December

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Influenced by the multi-dimensional parallelism of the self-adaptive scheduling duty mode, the overload risk of the traffic system is high. Therefore, the design and research of the electric power intelligent traffic system under the self-adaptive scheduling duty mode are proposed. -SOM-3588, the high-performance core board of AI, is the development board of the system, and -FK-NSVU, a standard 6U 5HP VPX architecture product, is the switch of the system. After extracting the scene features of adaptive scheduling duty from the aspects of multi-batch and operation time of complex tasks, taking into account the objective needs of the actual adaptive scheduling duty mode scene, and taking the balance as the guide, the traffic resource allocation is designed pertinently. In the test results, the CPU occupancy rate of the design system is always below 30.0% in different task situations, and the overload risk is extremely low.

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Task recommendation method for fusion of multi-view social relationship learning and reasoning in the mobile crowd sensing system

Wang

Zhang

Zhao

2023

Computer Communications

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Sboing4Real: A real-time crowdsensing-based traffic management system

Cited by 3 publications

References 26 publications

Data-Intensive Traffic Management: Real-Time Insights from the Traffic Management Simulation Test

Data-Intensive Traffic Management: Real-Time Insights from the Traffic Management Simulation Test

Design of Electric Power Intelligent Traffic System under Adaptive Dispatching Duty Mode

Task recommendation method for fusion of multi-view social relationship learning and reasoning in the mobile crowd sensing system

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