It has been proposed that the use of speed and position information from a subset of vehicles in the traffic (probe vehicles) can provide for accurate traffic information. Furthermore, it can be seen as an economic and scalable alternative to the use of inductive loop detectors, cameras and radars. However, the impact of the communications channel performance in the estimation of the traffic states has been insufficiently studied.In this work we propose the use of the Wireless Sensor Network (WSN) paradigm to develop a Vehicular Sensor Network (VSN) in order to obtain accurate traffic information from a few probe vehicles. The problems that plague WSNs are 1) the deployment is to cope with the variations of the measured field, the Space-Time-Velocity (STV) field and 2) the communications channel influences data collection reliability. In order to assess these two issues, we perform 1) accurate simulation of discrete vehicular traffic in order to obtain spatiotemporal patterns that closely mimic traffic congestion and 2) accurate simulation of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) wireless communications.We obtain experimental evidence that with a Fraction of Sensor Vehicles (FSV) as low as 1% we are able to obtain accurate measurements of traffic congestion accounting for Packet Loss from Rayleigh fading, Doppler spreading and multihop relaying. We present results for different FSVs and RoadSide wireless bridge (RSU) densities and assess that, for FSVs of 10% and RSU densities half the usual detector densities, the reconstructed STV fields are virtually indistinguishable from the ground truth.
In this paper, we propose the use of an ad-hoc wireless network formed by a fraction of the passing vehicles (sensor vehicles) to periodically recover their positions and speeds. A static roadside unit (RSU) gathers data from passing sensor vehicles. Finally, the speed/position information or space-time velocity (STV) field is then reconstructed in a data fusion center with simple interpolation techniques. We use widely accepted theoretical traffic models (i.e., car-following, multilane, and overtake-enabled models) to replicate the nonlinear characteristics of the STV field in representative situations (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 affect the STV reconstruction error, 2) the system performance is marginally affected by transmission errors for realistic traffic conditions, 3) the STV field can be recovered with minimal mean absolute error for a very small fraction of sensor vehicles (FSV) ≈ 9%, and 4) for that FSV value, the probability that at least one sensor vehicle transits the spatiotemporal regions that contribute the most to reduce the STV reconstruction error sharply tends to 1. Thus, a random and sparse selection of wireless sensor vehicles, in realistic traffic conditions, is sufficient to get an accurate reconstruction of the STV field.Index Terms-Vehicular ad hoc networks, space-time velocity, geospatial analysis, combinatorial optimization.
1524-9050 include bodysensor networks with medical applications, physiological signal processing, dynamic routing, distributed signal processing, and wireless sensor networks for energy efficiency.
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