Integrating computer vision and traffic modeling for near-real-time signal timing optimization of multiple intersections

Zhou, Shenghua; Ng, S. Thomas; Yang, Yifan; Xu, Jiazhu

doi:10.1016/j.scs.2021.102775

Cited by 15 publications

(4 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With more people living in cities throughout the globe, efficient traffic control is becoming essential. With its foundation in real-time insights from traffic management simulations, Data-Intensive Traffic Management offers a revolutionary way to tackle the intricate and diverse problems caused by urban congestion [1]- [5]. The dynamics of data-intensive traffic management are examined in this research, along with the importance of using realtime insights from traffic management simulation experiments to improve safety, optimize traffic flow, and lessen congestion in metropolitan areas.…”

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

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.

show abstract

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

“…The last two decades have brought development and implementation of image recognition methods using classifiers (Kandil and Atwan, 2012; Anitha and Deepa, 2014), databases of selected object features (Li, Liu and School, 2013; Pamuła, 2014), and neural networks (Maddalena and Petrosino, 2008;Zhou et al, 2021). It is not uncommon for a combination of many of these methods to be used in augmented reality systems available directly to drivers and road transport management systems.…”

Section: Introductionmentioning

confidence: 99%

Method of Improving Incomplete Spatial-Temporal Data in Inland Navigation, on the Basis of Industrial Camera Images – West Oder River Case Study

Stępień

Kujawski

Tomczak

et al. 2022

Transport and Telecommunication Journal

View full text Add to dashboard Cite

Main aim of the paper is to use a single non-metric camera to support the determination of the position of. Authors propose to use the existing infrastructure of CCTV cameras mounted on bridges and wharves to determine the position of inland waterway vessels. Image from cameras giving the pixel coordinates of moving object is transformed to the geodetic data domain using a modified projective transformation method. Novel approach is to use of Sequential Projection Transformation (SPT) which additionally uses virtual reference points. The transformation coefficients calculated using the virtual points are used to determine the position of the vessels and are also simultaneously used to calibrate the industrial camera. The method has been verified under real conditions, and the results obtained are average 30% more accurate compared to the traditionally used projective transformation using a small number of real points.

show abstract

“…The most effective solution to this problem is to introduce traffic signal synchronization at intersections (Saha et al, 2019;Gorodokin et al, 2020;Patel et al, 2015). The development and widespread use of artificial intelligence methods, fuzzy logic, and computer vision has made it possible to obtain real-time data on the parameters of the TF (speed, types of vehicles, and their number) (Dai et al, 2020;Gu et al, 2020;Shen et al, 2020;Shepelev et al, 2020;Shepelev et al, 2021;Wang et al, 2022;Zhou et al, 2021). Models to control traffic at intersections are being developed based on empirical data to minimize delays and, as a result, to reduce harmful traffic-related emissions and save fuel consumption (Araghi et al, 2015;Biswas et al, 2018;Jeon et al, 2018;Ren et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Assessing the Traffic Capacity of Urban Road Intersections

Shepelev

Glushkov

Gritsenko

et al. 2022

Front. Built Environ.

View full text Add to dashboard Cite

Integrating computer vision and traffic modeling for near-real-time signal timing optimization of multiple intersections

Cited by 15 publications

References 57 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

Method of Improving Incomplete Spatial-Temporal Data in Inland Navigation, on the Basis of Industrial Camera Images – West Oder River Case Study

Assessing the Traffic Capacity of Urban Road Intersections

Contact Info

Product

Resources

About