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{ "item_title" : "Efficient Traffic Management with IoT Technology", "item_author" : [" M. Arun Kumar "], "item_description" : "In the past, numerous automobiles were impacted by traffic congestion, primarily due to mechanical failures in the engine and accidents. This can now be addressed through the following techniques. We employ sensors to identify traffic congestion, which will also decrease travel time. The primary benefit of technology is to eliminate the need for human intervention and to automatically alleviate traffic congestion. An autonomous traffic jam avoidance strategy is employed, demonstrating greater efficiency than the manual approach. This system proposes a novel context-aware methodology to ascertain the present traffic status and density, alongside the dynamic management of traffic signals in relation to environmental conditions. The system employs advanced technologies for the real-time collection, organization, and transfer of information to deliver an efficient and precise assessment of traffic density, which may be utilized by traffic-aware apps.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/6/20/799/723/6207997239_b.jpg", "price_data" : { "retail_price" : "51.00", "online_price" : "51.00", "our_price" : "51.00", "club_price" : "51.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Efficient Traffic Management with IoT Technology|M. Arun Kumar

Efficient Traffic Management with IoT Technology

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Overview

In the past, numerous automobiles were impacted by traffic congestion, primarily due to mechanical failures in the engine and accidents. This can now be addressed through the following techniques. We employ sensors to identify traffic congestion, which will also decrease travel time. The primary benefit of technology is to eliminate the need for human intervention and to automatically alleviate traffic congestion. An autonomous traffic jam avoidance strategy is employed, demonstrating greater efficiency than the manual approach. This system proposes a novel context-aware methodology to ascertain the present traffic status and density, alongside the dynamic management of traffic signals in relation to environmental conditions. The system employs advanced technologies for the real-time collection, organization, and transfer of information to deliver an efficient and precise assessment of traffic density, which may be utilized by traffic-aware apps.

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Details

  • ISBN-13: 9786207997237
  • ISBN-10: 6207997239
  • Publisher: LAP Lambert Academic Publishing
  • Publish Date: July 2025
  • Dimensions: 9 x 6 x 0.17 inches
  • Shipping Weight: 0.24 pounds
  • Page Count: 72

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