Connectivity statistics of store-and-forward inter-vehicle communication

Abstract

Inter-vehicle communication (IVC) enables vehicles to exchange messages within a limited broadcast range and thus self-organize into dynamical vehicular ad hoc networks. For the foreseeable future, however, a direct connectivity between equipped vehicles in one direction is rarely possible. We therefore investigate an alternative mode in which messages are stored by relay vehicles traveling in the opposite direction, and forwarded to vehicles in the original direction at a later time. The wireless communication consists of two `transversal' message hops across driving directions. Since direct connectivity for transversal hops and a successful message transmission to vehicles in the destination region is only a matter of time, the quality of this IVC strategy can be described in terms of the distribution function for the total transmission time. Assuming a Poissonian distance distribution between equipped vehicles, we derive analytical probability distributions for message transmission times and related propagation speeds for a deterministic and a stochastic model of the maximum range of direct communication. By means of integrated microscopic simulations of communication and bi-directional traffic flows, we validated the theoretical expectation for multi-lane roadways. We found little deviation of the analytical result for multi-lane scenarios, but significant deviations for a single-lane. This can be explained by vehicle platooning. We demonstrate the efficiency of the transverse hopping mechanism for a congestion-warning application in a microscopic traffic simulation scenario. Messages are created on an event-driven basis by equipped vehicles entering and leaving a traffic jam. This application is operative for penetration levels as low as 1%.

References in corpus (4)

• Congested Traffic States in Empirical Observations and Microscopic Simulations
• Traffic and Related Self-Driven Many-Particle Systems
• Autonomous detection and anticipation of jam fronts from messages propagated by inter-vehicle communication
• Online traffic state estimation based on floating car data

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