Title: A fractional variation estimation algorithm for smart road traffic control network

Authors: Zhichao Wang; Jun Steed Huang; Qi Chen

Addresses: Suqian College, 399 South Huanghe, Jiangsu 223800, China ' Suqian College, Jiangsu University, 399 South Huanghe, Jiangsu 223800, China ' Suqian College, Jiangsu University, 399 South Huanghe, Jiangsu 223800, China

Abstract: Mean and variation models are of traditional model. It is increasingly used to explain the stochastic phenomenon such as fluctuations of freeway traffic from Monday to Sunday. Different from the existing method, this paper proposes a novel fractional dimension derivation along with the estimation algorithm. It involves the fractional variation definitions with respect to the prediction of the vehicle speed, which are used to warn upcoming collisions between cars or with pedestrians. The proposed real time algorithm provides additional granularity in terms of the complex fractional variation, that eventually leading to the revealing of the driver's behaviour, from both real and imaginary angles. More precisely, the real part reflects the ways of changing speed, and the imaginary part is related to the ways of switching lanes. The calculation results demonstrate that the complex model is able to distinguish the subtle difference between the offensive and the polite drivers. We take the preliminary road measurement as an input to the MATLAB simulation, and show that the new method is able to see the differences of the speeding habit. It has potential for predicting the collisions in real time.

Keywords: fractional variation estimation; complex deviation; smart roads; intelligent vehicles; road traffic control networks; vehicle speed; collision warnings; vehicle collisions; road safety; driver behaviour; lane changing; collision prediction.

DOI: 10.1504/IJRIS.2016.080061

International Journal of Reasoning-based Intelligent Systems, 2016 Vol.8 No.1/2, pp.31 - 36

Available online: 27 Oct 2016 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article