Automatic Real-Time River Traffic Monitoring Based on Artificial Vision Techniques

Artificial vision techniques derived from computer vision and autonomous robotic systems have been successfully employed for river traffic monitoring and management. For this purpose, ARGOS and HYDRA systems have been developed by Achimedes Logica in collaboration with Sapienza University of Rome under the EU initiatives URBAN and MOBILIS for the monitoring of the boat traffic in Venice on the Gran Canal and the harbour area. These advanced systems provide an efficient automatic traffic monitoring to guarantee navigation safety and regular flow while producing and distributing information about the traffic. The systems are based on the processing of digital images that are gathered by survey cell stations distributed throughout the supervised area providing a visual platform on which the system displays recent and live traffic conditions in a synthetic way similar to radar view. ARGOS and HYDRA systems are programmed to automatically recognize and notice situations of great interest in whatever sea or land-targeted security applications including environmental, perimeter, and security control. This article describes the wide spectrum of applications of these two systems, that is, monitoring traffic and automatically tracking position, speed and direction of all vehicles.

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A DISTRIBUTED VISION SYSTEM FOR BOAT TRAFFIC MONITORING IN THE VENICE GRAND CANAL

Proceedings of the Second International Conference on Computer Vision Theory and Applications ◽

10.5220/0002060605490556 ◽

2007 ◽

Keyword(s):

Vision System ◽

Traffic Monitoring ◽

Grand Canal ◽

Boat Traffic ◽

Distributed Vision System ◽

Distributed Vision

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Reconstruction of False and Missing Data with First-Order Traffic Flow Model

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1802-18 ◽

2002 ◽

Vol 1802 (1) ◽

pp. 155-165 ◽

Cited By ~ 8

Author(s):

H. Haj-Salem ◽

J. P. Lebacque

Keyword(s):

Flow Model ◽

Data Cleaning ◽

Wide Spectrum ◽

Real Data ◽

Mathematical Structure ◽

Traffic Data ◽

First Order ◽

Traffic Conditions ◽

High Level ◽

Real Time Application

In previous studies, two traffic data-cleaning algorithms were developed at the Institut National de Recherche sur les Transports on the basis of filtering techniques and statistical approaches. Because of their mathematical structure (linearity of the process), both algorithms present a high level of inaccuracy in the case of nonhomogeneous traffic conditions at the location of the measurement stations (for example, free flow upstream and congestion downstream, or vice versa). A new algorithm for solving the traffic data-cleaning problem on the basis of real-time application of a dynamic first-order modeling approach was devised to take into account the nonlinearity of the traffic phenomenon. The developed algorithm, named PROPAGE, was tested using real data measurements, including a wide spectrum of traffic conditions. Compared with results from previous algorithms, the results obtained were more accurate.

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IoT based Real Time Traffic Monitoring System Using M/G/1 Queuing Research

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9397.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 3818-3822

Keyword(s):

Intelligent Transportation Systems ◽

Traffic Congestion ◽

Queuing Theory ◽

Traffic Monitoring ◽

Transportation Systems ◽

Theory Approach ◽

The Road ◽

Traffic Conditions ◽

Performance Framework ◽

Real Time Traffic

Vehicular Traffic crowding is paramount worry in urban cities. The use of technologies like Intelligent Transportation systems and Internet of Things can solve the problem of traffic congestion to some extent. The paper analyses the traffic conditions on a particular urban highway using queuing theory approach. It researches on performance framework such as time for waiting and queue length. The results can provide significant analysis to predict traffic congestion during peak hours. A congestion controlling action can be generated to utilize the road capacity fully during peak hours by using these results

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A Privacy-Preserving Traffic Monitoring Scheme via Vehicular Crowdsourcing

Sensors ◽

10.3390/s19061274 ◽

2019 ◽

Vol 19 (6) ◽

pp. 1274 ◽

Cited By ~ 3

Author(s):

Chuan Zhang ◽

Liehuang Zhu ◽

Chang Xu ◽

Xiaojiang Du ◽

Mohsen Guizani

Keyword(s):

Traffic Congestion ◽

Road Safety ◽

Security Analysis ◽

Traffic Monitoring ◽

Privacy Preserving ◽

Average Speed ◽

Traffic Conditions ◽

Roadside Unit ◽

Paillier Cryptosystem ◽

Effectiveness And Efficiency

The explosive number of vehicles has given rise to a series of traffic problems, such as traffic congestion, road safety, and fuel waste. Collecting vehicles’ speed information is an effective way to monitor the traffic conditions and avoid vehicles’ congestion, however it may threaten vehicles’ location and trajectory privacy. Motivated by the fact that traffic monitoring does not need to know each individual vehicle’s speed and the average speed would be sufficient, we propose a privacy-preserving traffic monitoring (PPTM) scheme to aggregate vehicles’ speeds at different locations. In PPTM, the roadside unit (RSU) collects vehicles’ speed information at multiple road segments, and further cooperates with a service provider to calculate the average speed information for every road segment. To preserve vehicles’ privacy, both homomorphic Paillier cryptosystem and super-increasing sequence are adopted. A comprehensive security analysis indicates that the proposed PPTM can preserve vehicles’ identities, speeds, locations, and trajectories privacy from being disclosed. In addition, extensive simulations are conducted to validate the effectiveness and efficiency of the proposed PPTM scheme.

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ARGOS — A VIDEO SURVEILLANCE SYSTEM FOR BOAT TRAFFIC MONITORING IN VENICE

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001409007594 ◽

2009 ◽

Vol 23 (07) ◽

pp. 1477-1502 ◽

Cited By ~ 21

Author(s):

DOMENICO BLOISI ◽

LUCA IOCCHI

Keyword(s):

Video Surveillance ◽

Surveillance System ◽

Water Channel ◽

Traffic Monitoring ◽

Visual Surveillance ◽

Video Surveillance System ◽

Surveillance Systems ◽

Real Performance ◽

Boat Traffic ◽

Active Research

Visual surveillance in dynamic scenes is currently one of the most active research topics in computer vision, many existing applications are available. However, difficulties in realizing effective video surveillance systems that are robust to the many different conditions that arise in real environments, make the actual deployment of such systems very challenging. In this article, we present a real, unique and pioneer video surveillance system for boat traffic monitoring, ARGOS. The system runs continuously 24 hours a day, 7 days a week, day and night in the city of Venice (Italy) since 2007 and it is able to build a reliable background model of the water channel and to track the boats navigating the channel with good accuracy in real-time. A significant experimental evaluation, reported in this article, has been performed in order to assess the real performance of the system.

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Road Traffic Monitoring System Based on Mobile Devices and Bluetooth Low Energy Beacons

Wireless Communications and Mobile Computing ◽

10.1155/2018/3251598 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 11

Author(s):

Marcin Lewandowski ◽

Bartłomiej Płaczek ◽

Marcin Bernas ◽

Piotr Szymała

Keyword(s):

Mobile Devices ◽

Data Aggregation ◽

Experimental Evaluation ◽

Road Traffic ◽

Traffic Monitoring ◽

Majority Voting ◽

Detection Methods ◽

Crowd Sourcing ◽

Traffic Conditions ◽

Real Traffic

The paper proposes a method, which utilizes mobile devices (smartphones) and Bluetooth beacons, to detect passing vehicles and recognize their classes. The traffic monitoring tasks are performed by analyzing strength of radio signal received by mobile devices from beacons that are placed on opposite sides of a road. This approach is suitable for crowd sourcing applications aimed at reducing travel time, congestion, and emissions. Advantages of the introduced method were demonstrated during experimental evaluation in real-traffic conditions. Results of the experimental evaluation confirm that the proposed solution is effective in detecting three classes of vehicles (personal cars, semitrucks, and trucks). Extensive experiments were conducted to test different classification approaches and data aggregation methods. In comparison with state-of-the-art RSSI-based vehicle detection methods, higher accuracy was achieved by introducing a dedicated ensemble of random forest classifiers with majority voting.

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The potential of monitoring traffic conditions up to 15 times a day using sub-meter resolution EO images

10.5194/egusphere-egu21-12831 ◽

2021 ◽

Author(s):

Refiz Duro ◽

Georg Neubauer ◽

Alexandra-Ioana Bojor

Keyword(s):

Real Time ◽

Traffic Management ◽

Traffic Monitoring ◽

Movement Direction ◽

Time Lags ◽

Time Data ◽

Traffic Lights ◽

Traffic Conditions ◽

Real Time Traffic ◽

Spatial Coverage

Urbanization and the trend of people moving to cities often leads to problematic traffic conditions, which can be very challenging for traffic management. It can hamper the flow of people and goods, negatively affecting businesses through delays and the inability to estimate travel times and thus plan, as well as the environment and health of population due to increased fuel consumption and subsequent air pollution. Many cities have a policy and rules to manage traffic, ranging from standard traffic lights to more dynamic and adaptable solutions involving in-road sensors or cameras to actively modify the duration of traffic lights, or even more sophisticated IoT solutions to monitor and manage the conditions on a city-wide scale. The core to these technologies and to decision making processes is the availability of reliable data on traffic conditions, and better yet real-time data. Thus, a lot of cities are still coping with the lack of good spatial and temporal data coverage, as many of these solutions are requiring not only changes to the infrastructure, but also large investments.One approach is to exploit the current and the forthcoming advancements made available by Earth Observation (EO) satellite technologies. The biggest advantage is EOs great spatial coverage ranging from a few km&#178; to 100 km&#178; per image on a spatial resolution down to 0.3m, thus allowing for a quick, city-spanning data collection. Furthermore, the availability of imaging sensors covering specific bands allows the constituent information within an image to be separated and the information to be leveraged.In this respect, we present the findings of our work on multispectral image sets collected on three occasions in 2019 using very high resolution WorldView-3 satellite. We apply a combination of machine learning and PCA methods to detect vehicles and devise their kinematic properties (e.g., movement, direction, speed), only possible with satellites with a specific design allowing for short time lags between imaging in different spectral bands. As these data basically constitute a time-series, we will discuss how the results presented fully apply to the forthcoming WorldView-Legion constellation of satellites providing up to 15 revisits per day, and thus near-real time traffic monitoring and its impact on the environment.

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