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 on-demand vehicle sampling 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 inner-relaying and outer-relaying procedures. To effectively adapt the level of granularity of traffic density depending on the remaining survey time, we present an adaptive cell sizing 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.
Bibliographical noteFunding Information:
Manuscript received January 9, 2020; revised June 7, 2020; accepted August 19, 2020. Date of publication August 26, 2020; date of current version October 22, 2020. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) under Grant NRF-2018R1A2B6004006. The review of this article was coordinated by Prof. T. Hara. (Corresponding author: HyungJune Lee.) Christina Suyong Shin is with the Department of Computer Science, University of Southern California, Los Angeles, CA 90007 USA (e-mail: email@example.com).
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- Vehicle traffic density estimation
- vehicle-to-vehicle communication
- vehicular ad-hoc networks (VANETs)