استخراج ویژگی از خطوط سیر به منظور شناسایی حالت حمل و نقل در شهر هوشمند با استفاده از یادگیری ماشین

نوع مقاله : علمی - پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد سیستم‌های اطلاعات مکانی، پردیس دانشکده فنی، دانشگاه تهران، تهران، ایران

2 دانشیار دانشکده مهندسی نقشه برداری و اطلاعات مکانی، پردیس دانشکده‌های فنی، دانشگاه تهران، تهران، ایران

3 استادیار دانشکده مهندسی معدن، دانشگاه صنعتی سهند، تبریز، ایران

چکیده

با پیشرفت فناوری­ و اهمیت حمل و نقل هوشمند در شهر­هوشمند، پیش ­بینی و شناسایی استفاده از حالات حمل و نقلی بخش اولیه و اساسی در حمل­ و نقل هوشمند نظیر مسائل کاربردی کنترل ترافیک، تجزیه و تحلیل تقاضای سفر و برنامه ­ریزی حمل ­و نقل به شمار می­ آید. با فراگیر شدن دستگاه ­های تعیین موقعیت و همچنین استفاده از تلفن­ های هوشمند، مسیرهای موقعیتی ثبت شده یک رویکرد اقتصادی و سریع به منظور شناسایی حالات حمـل ­و نقل به حساب می ­آید. در این پژوهش با استفاده از داده­ های موقعیتی ثبت شده توسط مرکز تحقیقاتی مایکروسافت آسیا (GeoLife) که بیشتر در شهر پکن انجام پذیرفته و استخراج ویژگی­ هـای حرکتی به پیش ­بینی حالات حمل ­و نقلی پیاده ­روی، استفاده از دوچرخه، استفاده از اتوبوس، استفاده از اتومبیل و استفاده از قطار پرداخته می ­شود. رویکرد پیشنهادی استفاده از کلاسه ­بندی­ های یادگیری ماشین، شامل: کلاسه ­بندی ماشین بردار پشتیبان، جنگل تصادفی، تقویت گرادیان و تقویت بیش ­از حد گرادیان می ­باشد. در بین مدل ­ها، مدل تقویت بیش ­از حد گرادیان توانسته با کسب دقت 18/95 درصد و پیچیدگی زمانی بهتر با توجه به سایر مدل­ ها، حالات حمل­ و نقلی را با دقت بالاتری پیش ­بینی کند.

کلیدواژه‌ها


عنوان مقاله [English]

Feature Extraction from Trajectories for Transportation Mode Detection in Smart City using Machine Learning

نویسندگان [English]

  • Sajjad Sowlati 1
  • Rahim Ali Abbaspour 2
  • Alireza Chehreghan 3
1 School of Surveying and Geospatial Engineering, University of Tehran, Tehran, Iran
2 School of Surveying and Geospatial Engineering, University of Tehran, Tehran, Iran
3 Sahand University of Technology
چکیده [English]

Nowadays, with the advancement of technology and the importance of intelligent transportation in the smart city, predicting and identifying the use of transportation modes is the primary and essential part of intelligent transportation such as traffic control, travel demand analysis, and transportation planning. With the pervasiveness of positioning devices as well as the pervasive use of smartphones, recorded location routes are an economical and rapid approach to identifying modes of transport. In this study, using situational data recorded by the Microsoft Research Asia (Geolife) and extracting dynamic characteristics, walking, biking, use of bus, driving, and use of train are predicted. The proposed approach to using machine learning classifications includes support vector machine classification, random forest classification, gradient boosting classification, and extreme gradient boosting classification. Among the models, the extreme gradient boosting model was able to predict transport modes with higher accuracy by obtaining 95.18% accuracy and less time complexity.

کلیدواژه‌ها [English]

  • Intelligent Transportation System (ITS)
  • Predicting Transportation Modes
  • Geolife dataset
  • Machine Learning (ML)
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