Development of road traffic flow model for nighttime conditions

Ouessai, Asmâa; Keche, Mokhtar

doi:10.1109/ceit.2015.7233107

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…(3), namely the free speed and the density at capacity , were calibrated by minimizing the dispersion between the model outputs and the sensor measurements, using the Complex based optimization algorithm, developed by Box (1956). This algorithm is widely used in the literature, especially for road traffic modelling (Kotsialos and Papageorgiou, 2002;Ouessai and Keche, 2015). The modelling task was achieved by using the measured parameters of the exit section obtained from the real time video traffic data set, presented in section 5.2.1.…”

Section: Evaluation Using Simulated Datamentioning

confidence: 99%

Real-Time Freeway Traffic State Estimation Based on the Second-Order Divided Difference Kalman Filter

Ouessai

Keche

2019

Transport and Telecommunication Journal

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Reliable road traffic state identification systems should be designed to provide accurate traffic state information anywhere and anytime. In this paper we propose a road traffic classification system, based on traffic variables estimated using the second order Divided Difference Kalman Filter (DDKF2). This filter is compared with the Extended Kalman Filter (EKF) using both simulated and real-world dataset of highway traffic. Monte-Carlo simulations indicate that the DDKF2 outperforms the EKF filter in terms of parameters estimation error. The real-word evaluation of the DDKF2 filter in terms of classification rate confirms that this filter is promising for real-world traffic state identification systems.

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Section: Evaluation Using Simulated Datamentioning

confidence: 99%

Real-Time Freeway Traffic State Estimation Based on the Second-Order Divided Difference Kalman Filter

Ouessai

Keche

2019

Transport and Telecommunication Journal

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“…To detect the traffic intensity, there are 3 critical approach in the modelling of the traffic flows in roads namely the spatial modelling [6]- [9], time prediction modelling [10]- [12] and non-parametric modelling. Spatial modelling refers to using the physical space on real time to estimate the motion of the vehicles based on the physical parameters such as road distance, car length, road curvature and other key elements.…”

Section: Figure 1 Traffic Congestion In Kuala Lumpur Malaysiamentioning

confidence: 99%

Measuring the Road Traffic Intensity using Neural Network with Computer Vision

Hamdan¹,

Khalifah²,

Gunawan³

2018

IJEECS

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Traffic congestion plagues all driver around the world. To solve this problem computer vision can be used as a tool to develop alternative routes and eliminate traffic congestions. In the current generation with increasing number of cameras on the streets and lower cost for Internet of Things(IoT) this solution will have a greater impact on current systems. In this paper, the Macroscopic Urban Traffic model is used using computer vision as its source and traffic intensity monitoring system is implemented. The input of this program is extracted from a traffic surveillance camera and another program running a neural network classification which can classify and distinguish the vehicle type is on the road. The neural network toolbox is trained with positive and negative input to increase accuracy. The accuracy of the program is compared to other related works done and the trends of the traffic intensity from a road is also calculated.

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“…To detect the traffic intensity, there are 3 critical approach in the modelling of the traffic flows in roads namely the spatial modelling [7], [8] [9] [10], time prediction modelling [11] [12] [13] and nonparametric modelling. Spatial modelling refers to using the physical space on real time to estimate the motion of the vehicles based on the physical parameters such as road distance, car length, road curvature and other key elements.…”

Section: Related Workmentioning

confidence: 99%

Traffic intensity monitoring using multiple object detection with traffic surveillance cameras

Gani

Khalifa

Gunawan

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. Object detection and tracking is a field of research that has many applications in the current generation with increasing number of cameras on the streets and lower cost for Internet of Things(IoT). In this paper, a traffic intensity monitoring system is implemented based on the Macroscopic Urban Traffic model is proposed using computer vision as its source. The input of this program is extracted from a traffic surveillance camera which has another program running a neural network classification which can identify and differentiate the vehicle type is implanted. The neural network toolbox is trained with positive and negative input to increase accuracy. The accuracy of the program is compared to other related works done and the trends of the traffic intensity from a road is also calculated. relevant articles in literature searches, great care should be taken in constructing both. Lastly the limitation and the future work is concluded.

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Development of road traffic flow model for nighttime conditions

Cited by 6 publications

References 9 publications

Real-Time Freeway Traffic State Estimation Based on the Second-Order Divided Difference Kalman Filter

Real-Time Freeway Traffic State Estimation Based on the Second-Order Divided Difference Kalman Filter

Measuring the Road Traffic Intensity using Neural Network with Computer Vision

Traffic intensity monitoring using multiple object detection with traffic surveillance cameras

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