کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
524805 868859 2014 19 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Urban activity pattern classification using topic models from online geo-location data
ترجمه فارسی عنوان
طبقه بندی الگو شهری با استفاده از مدل های موضوعی از داده های جغرافیایی آنلاین
کلمات کلیدی
طبقه بندی الگوی فعالیت، مدل سازی مبتنی بر فعالیت، محاسبات اجتماعی، داده های مبتنی بر مکان، اطلاعات بزرگ، رسانه های اجتماعی، مدل سازی موضوع فراگیری ماشین
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی


• Geo-location data from social media has potential for activity-travel analysis.
• Identifies the limitations of geo-location data for activity-based modeling.
• Adopts a data-driven approach (topic model) for activity pattern classification.
• Extends the topic model to capture user-specific activity patterns.
• Extends to account for missing activities – a major issue of social media data.

Location-based check-in services in various social media applications have enabled individuals to share their activity-related choices providing a new source of human activity data. Although geo-location data has the potential to infer multi-day patterns of individual activities, appropriate methodological approaches are needed. This paper presents a technique to analyze large-scale geo-location data from social media to infer individual activity patterns. A data-driven modeling approach, based on topic modeling, is proposed to classify patterns in individual activity choices. The model provides an activity generation mechanism which when combined with the data from traditional surveys is potentially a useful component of an activity-travel simulator. Using the model, aggregate patterns of users’ weekly activities are extracted from the data. The model is extended to also find user-specific activity patterns. We extend the model to account for missing activities (a major limitation of social media data) and demonstrate how information from activity-based diaries can be complemented with longitudinal geo-location information. This work provides foundational tools that can be used when geo-location data is available to predict disaggregate activity patterns.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Transportation Research Part C: Emerging Technologies - Volume 44, July 2014, Pages 363–381
نویسندگان
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