Abstract
Most of the vehicular traffic flow challenges happens because of the roads infrastructure or route planning process in a navigation system. This results in longer time spent in traffic by many people in the world.
In this paper we classified and synthesized comprehensive traffic scenarios in order to improve drivers daily experience thorough connected cars navigation model calibration. The proposed solution systematically calibrates connected cars parameters in order to balance the traffic flow in a simulated connected cars ecosystem based on real map data.
The experimental results and measurement metrics prove that our classification and synthesis of comprehensive traffic scenarios is a favorable infrastructure that supports connected cars navigation model calibration for efficiently balance the vehicular traffic flow in urban areas.
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Stan, I., Potolea, R. (2018). Connected Cars Traffic Flow Balancing Based on Classification and Calibration. In: Groza, A., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2018. Lecture Notes in Computer Science(), vol 11308. Springer, Cham. https://doi.org/10.1007/978-3-030-05918-7_16
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DOI: https://doi.org/10.1007/978-3-030-05918-7_16
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