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{ "item_title" : "Data-Driven Solutions to Transportation Problems", "item_author" : [" Yinhai Wang", "Ziqiang Zeng "], "item_description" : "Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/0/12/817/026/0128170263_b.jpg", "price_data" : { "retail_price" : "100.00", "online_price" : "100.00", "our_price" : "100.00", "club_price" : "100.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Data-Driven Solutions to Transportation Problems|Yinhai Wang

Data-Driven Solutions to Transportation Problems

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Overview

Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more.

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Details

  • ISBN-13: 9780128170267
  • ISBN-10: 0128170263
  • Publisher: Elsevier
  • Publish Date: December 2018
  • Dimensions: 9 x 6 x 0.63 inches
  • Shipping Weight: 0.89 pounds
  • Page Count: 299

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