Infrastructure-Less Vehicle Traffic Density Estimation via Distributed Packet Probing in V2V Network

Christina Suyong Shin, Jiho Lee, HyungJune Lee

Abstract: In this paper, we address the problem of vehicle traffic density estimation without relying on infrastructure cameras or sensors on the road. Previous infrastructure-less approaches still require some prior knowledge on the road infrastructure, e.g., via road topology map. We seek a lightweight estimation method based only on vehicle-to-vehicle (V2V) communication, i.e., without using any prior knowledge. The main objective of this paper is to examine traffic density through simple yet efficient packet probing within a survey time period and obtain a snapshot of the traffic density distribution map. We propose an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on-demand vehicle sampling</i> algorithm that makes a probing packet at a vehicle (i.e., sampler) keep sampling to explore the local traffic density on a cell basis. If a current sampler does not operate as an efficient carrier, the packet selects another one as the next sampler via <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inner-relaying</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">outer-relaying</i> procedures. To effectively adapt the level of granularity of traffic density depending on the remaining survey time, we present an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptive cell sizing</i> algorithm. Further, we extend the sampling activity to multiple vehicle samplers by making them aggregate their collected information and also negotiate their future areas to explore. Within a designated deadline, multiple samplers collaborate for more accurate and fast traffic density estimation. By doing so by iterations till the given survey deadline, we can gather a complete view of traffic density estimates based on multiple sources where some areas have more detailed information, whereas others do less. Experiments with a real trace-driven simulation demonstrate that our proposed algorithm effectively estimates the distribution of traffic density considering local traffic conditions compared to other counterpart algorithms, with a factor of up to 9.5.