Article: An adaptive model for traffic flow optimisation in dynamic environments Journal: International Journal of Computational Science and Engineering (IJCSE) 2019 Vol.19 No.1 pp.25 - 35 Abstract: Formulating the solution as an optimisation problem has proven to be effective in developing solutions to many real world problems. We generally obtain the best possible solution using these methods. In this work, the traffic scheduling problem has been formulated as a waiting time minimisation problem, and appropriate cost functions have been developed, in pursuit of finding the optimal solution. A first-in, first-out queuing model is used, with the vehicles arriving in a Poisson process, and the service time being exponentially distributed. The key feature of this model is that it adapts to varying service and arrival rates of the lanes. These rates are forecast using a neural network model, and appear in the objective function. It was observed that the use of the neural network greatly improved the robustness of the model. Although the model has been developed for a four lane, two way architecture, it can be generalised to any architecture. Results have been analysed by comparing the proposed method to a proportional time distribution. It is shown that the proposed model performs relatively well, when there is rapid variation in the arrival and service rates. Inderscience Publishers - linking academia, business and industry through research

Title: An adaptive model for traffic flow optimisation in dynamic environments

Authors: M.V. Rahul; Rajashree Shettar; K.N. Subramanya

Addresses: Department of Computer Science, Rashtreeya Vidyalaya College of Engineering, Bangalore, Karnataka, India ' Department of Computer Science, Rashtreeya Vidyalaya College of Engineering, Bangalore, Karnataka, India ' Department of Industrial Engineering and Management, Rashtreeya Vidyalaya College of Engineering, Bangalore, Karnataka, India

Abstract: Formulating the solution as an optimisation problem has proven to be effective in developing solutions to many real world problems. We generally obtain the best possible solution using these methods. In this work, the traffic scheduling problem has been formulated as a waiting time minimisation problem, and appropriate cost functions have been developed, in pursuit of finding the optimal solution. A first-in, first-out queuing model is used, with the vehicles arriving in a Poisson process, and the service time being exponentially distributed. The key feature of this model is that it adapts to varying service and arrival rates of the lanes. These rates are forecast using a neural network model, and appear in the objective function. It was observed that the use of the neural network greatly improved the robustness of the model. Although the model has been developed for a four lane, two way architecture, it can be generalised to any architecture. Results have been analysed by comparing the proposed method to a proportional time distribution. It is shown that the proposed model performs relatively well, when there is rapid variation in the arrival and service rates.

Keywords: queuing model; isolated intersections; neural network; interior point optimisation; traffic scheduling; Poisson process; four lane intersection.

DOI: 10.1504/IJCSE.2019.099679

International Journal of Computational Science and Engineering, 2019 Vol.19 No.1, pp.25 - 35

Received: 24 Jan 2018
Accepted: 12 Jun 2018
Published online: 20 May 2019 *

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Title: An adaptive model for traffic flow optimisation in dynamic environments

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