Sparse Vehicular Sensor Networks for Traffic Dynamics Reconstruction

PROJECT TITLE :

Sparse Vehicular Sensor Networks for Traffic Dynamics Reconstruction

ABSTRACT:

During this paper, we propose the employment of an advertisement-hoc wireless network shaped by a fraction of the passing vehicles (sensor vehicles) to periodically recover their positions and speeds. A static roadside unit (RSU) gathers knowledge from passing sensor vehicles. Finally, the speed/position information or area-time velocity (STV) field is then reconstructed in a very information fusion center with simple interpolation techniques. We use widely accepted theoretical traffic models (i.e., automobile-following, multilane, and overtake-enabled models) to replicate the nonlinear characteristics of the STV field in representative things (congested, free, and transitional traffic). To obtain realistic packet losses, we simulate the multihop ad-hoc wireless network with an IEEE 802.11p PHY layer. We conclude that: 1) for relevant configurations of both sensor vehicle and RSU densities, the wireless multihop channel performance does not critically have an effect on the STV reconstruction error, 2) the system performance is marginally tormented by transmission errors for realistic traffic conditions, three) the STV field can be recovered with minimal mean absolute error for a very tiny fraction of sensor vehicles (FSV) ≈ 9p.c, and 4) for that FSV worth, the chance that a minimum of one sensor vehicle transits the spatiotemporal regions that contribute the foremost to reduce the STV reconstruction error sharply tends to 1. Thus, a random and sparse choice of wireless sensor vehicles, in realistic traffic conditions, is sufficient to urge an accurate reconstruction of the STV field.

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