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Connected Cars Traffic Flow Balancing Based on Classification and Calibration

  • Ioan Stan
  • Rodica Potolea
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11308)

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.

Keywords

Connected cars Calibration Traffic flow Balancing Metrics 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Computer Science DepartmentTechnical University of Cluj-NapocaCluj-NapocaRomania

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