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The concept of vehicular networks utilizes vehicular nodes and roadside units to exchange data to achieve travel comfort and convenience. However, as the number of vehicles on the road increases exponentially, the data dissemination becomes inefficient and lossy due to communication factors, one of which is the availability of too much sources of information. In this paper, a data forwarding system (DFS) is proposed to address communication failure due to bit error during data transmission and in the presence of data collision. The proposed system enables the receiver to detect errors in the received data using Hamming codes and utilizes a retransmission scheme to correct bit errors. DFS also selects a limited number of vehicles, called leaders, that can communicate with the infrastructure to minimize data collision and flooding. DFS, under various number of vehicles, is tested in additive white gaussian noise (AWGN) and Rayleigh channels. Simulation results show that DFS reduces bit error rate (BER) to approximately 54% in the AWGN and 62% in the Rayleigh channel for the maximum number of retransmissions simulated, respectively. Also, with DFS, data upload to infrastructures is reduced by at most 99.5% depending on the maximum allowable number of clusters and vehicles inside the RSU coverage.

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With the increasing demand for taxis during peak hours, adding more vehicles is not always the optimal solution since this will also contribute to the increasing traffic congestion, noise, and air pollution. In this study, we propose a neural network-based ridesharing policy that will utilize the available seats in an occupied taxi to accommodate other queueing passengers. The proposed policy allows a single taxi to transport more people at the same time and reduces the passenger taxi fare. The ridesharing policy is extensively verified by studying empirical mobility trace datasets to determine its capability of reducing transportation costs for riders. Test results show that the proposed ridesharing policy can reduce the cost by approximately 120% with the implementation of ridesharing.