الگوریتم خوشه بندی برای اپلیکیشن های سیستم حمل و نقل
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الگوریتم خوشه بندی برای اپلیکیشن های سیستم حمل و نقل

عنوان فارسی مقاله: یک الگوریتم خوشه بندی نظارت شده جدید برای اپلیکیشن های سیستم حمل و نقل
عنوان انگلیسی مقاله: A Novel Supervised Clustering Algorithm for Transportation System Applications
مجله/کنفرانس: نتایج بدست آمده در حوزه سیستم های حمل و نقل هوشمند - Transactions on Intelligent Transportation Systems
رشته های تحصیلی مرتبط: کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی الگوریتم ها و محاسبات، هوش مصنوعی، برنامه نویسی کامپیوتر
کلمات کلیدی فارسی: خوشه بندی نظارت شده، مجموعه داده هایی با ابعاد بالا و عملیات ترافیک، سیستم های دوچرخه اشتراک، محاسبات شهری، طبقه بندی
کلمات کلیدی انگلیسی: Supervised clustering، high dimensional datasets and traffic operations، bike-sharing systems، urban computing، classification
نمایه: Master Journal List - JCR - Scopus
شناسه دیجیتال (DOI): https://doi.org/10.1109/TITS.2018.2890588
دانشگاه: Charles E. Via, Jr. Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA 24061 USA, with the Center for Sustainable Mobility, Virginia Tech Transportation Institute, Blacksburg, VA 24061 USA, and also with the Civil Engineering Department, King Saud University, Riyadh 2890588, Saudi Arabia.
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 7/420 در سال 2018
شاخص H_index: 112 در سال 2019
شاخص SJR: 1/412 در سال 2018
شناسه ISSN: 1524-9050
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 11
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13118
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


I- INTRODUCTION


II- PROBLEM STATEMENT


III- RELATED WORK


IV- THE COLLEGE ADMISSION ALGORITHM


V- THE PROPOSED ALGORITHM


VI- DATASETS


VII- CLUSTERING RESULTS AND DISCUSSION


VIII- CONCLUSION


REFERENCES

نمونه متن انگلیسی مقاله

Abstract


This paper proposes a novel supervised clustering algorithm to analyze large datasets. The proposed clustering algorithm models the problem as a matching problem between two disjoint sets of agents, namely, centroids and data points. This novel view of the clustering problem allows the proposed algorithm to be multi-objective, where each agent may have its own objective function. The proposed algorithm is used to maximize the purity and similarity in each cluster simultaneously. Our algorithm shows promising performance when tested using two different transportation datasets. The first dataset includes speed measurements along a section of Interstate 64 in the state of Virginia, while the second dataset includes the bike station status of a bike sharing system (BSS) in the San Francisco Bay Area. We clustered each dataset separately to examine how traffic and bike patterns change within clusters and then determined when and where the system would be congested or imbalanced, respectively. Using a spatial analysis of these congestion states or imbalance points, we propose potential solutions for decision makers and agencies to improve the operations of I-64 and the BSS. We demonstrate that the proposed algorithm produces better results than classical kmeans clustering algorithms when applied to our datasets with respect to a time event. The contributions of our paper are: 1) we developed a multi-objective clustering algorithm; 2) the algorithm is scalable (polynomial order), fast, and simple; and 3) the algorithm simultaneously identifies a stable number of clusters and clusters the data.


INTRODUCTION


WITH the growth of new technologies, smart cities and urban areas are adapting advanced devices to control and monitor transportation networks and thus provide better service to the public and private sectors. These devices collect data through many sensors in the city’s infrastructure. Agencies and researchers exploring the massive amounts of collected data often find it challenging to draw meaningful conclusions due the sheer size of the datasets. One way to deal with such data is to use clustering approaches. In the transportation field, operating agencies (such as departments of transportation) have been collecting data to improve the efficiency of the transportation network and provide a better service for all transportation modes. Clustering the travel times or speeds of transportation modes could help operating agencies to better manage the transportation network. In particular, the collected data could be reduced to find the cluster centroids (i.e., the means of the clusters) that represent the entire data with respect to a time event such as time of day, day of month, and month of the year. This could help operating agencies answer several questions related to traffic operations such as, “Can we discriminate between recurrent congestion and outliers?” and “Can we identify how many time periods we need to plan for in terms of resource and congestion management?”

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