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- W2208112544 abstract "One of the most ubiquitous AI applications is vehicle route planning. While state-of-the-art systems take into account current traffic conditions or historic traffic data, current planning approaches ignore the impact of their own plans on the future traffic conditions. We present a novel algorithm for self-aware route planning that uses the routes it plans for current vehicle traffic to more accurately predict future traffic conditions for subsequent cars. Our planner uses a roadmap with stochastic, time-varying traffic densities that are defined by a combination of historical data and the densities predicted by the planned routes for the cars ahead of the current traffic. We have applied our algorithm to large-scale traffic route planning, and demonstrated that our self-aware route planner can more accurately predict future traffic conditions, which results in a reduction of the travel time for those vehicles that use our algorithm." @default.
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- W2208112544 date "2011-08-04" @default.
- W2208112544 modified "2023-10-02" @default.
- W2208112544 title "Self-Aware Traffic Route Planning" @default.
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- W2208112544 doi "https://doi.org/10.1609/aaai.v25i1.7984" @default.
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