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Intelligent Traffic Management Systems for Next Generation IoV in Smart City Scenario

  • V. VijayaraghavanEmail author
  • J. Rian Leevinson
Chapter

Abstract

With ever-growing number of vehicles on roads, traffic congestion is becoming a major problem in big cities around the world. Traffic congestion leads to pollution, time delays, excessive fuel consumption, financial losses and can severely disrupt normal human life. Conventional traffic management systems that rely on predecided traffic signal timings and pneumatic actuators are woefully inadequate in handling current traffic scenarios. Therefore, there is a pertinent need for a modern and intelligent overhaul of conventional traffic management systems and the introduction of such systems in modern smart cities. Intelligent traffic management systems are an ensemble of networks and systems integrated with each other to ensure optimum user commuting experience. They use a variety of advanced techniques such as reinforcement learning, Q theory, RFID tagging, IoT, IoV and local context awareness. Intelligent traffic management systems also help with safety issues, route optimization, delay reduction, and pollution control. This chapter explores conventional traffic management systems and their drawbacks, intelligent traffic management systems and recent advancements in them such as the introduction of reinforcement learning and local context-aware systems. This chapter aims to provide a glimpse of how intelligent traffic management systems can help drive smart cities of the future.

Keywords

IoV Traffic management IoT Q theory RFID Reinforcement learning Context awareness Intelligent traffic management systems 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Infosys LimitedBengaluruIndia
  2. 2.Infosys LimitedChennaiIndia

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