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Vehicular Ad Hoc Network (VANET) is an emerging field of wireless networks that facilitates different applications such traffic information for participant vehicles and related authorities. However, deploying of such applications is mainly depending on the market penetration rate of this technology. In this paper, we propose a new 3-steps approach for estimation of traffic volume in a road segment based on actual volume of wireless-equipped vehicles. For this propose, we fist collect the traffic information for different groups of vehicles using a new clustering algorithm. Then, a chaining technique between the clusters transmits this information to the roadside cloud in the next step. Finally, we employ a machine learning method to generalization of the total traffic volume from the collected data. Performance of the proposed approach is evaluated using extensive simulation for different traffic densities, and the estimation accuracy of the proposed approach is shown through comparing to a state-of-the-art existing approach.

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