Sustainable Urban Transport Planning using Big Data from Mobile PhonesDaniel Emaasit
In the past decades, there has been rapid urbanization as more and more people migrate into cities. The World Health Organization (WHO) estimates that by 2017, a majority of people will be living in urban areas. By 2030, 5 billion people—60 percent of the world’s population—will live in cities, compared with 3.6 billion in 2013. Developing nations must cope with this rapid urbanization. Transportation and urban planners must estimate travel demand for transportation facilities and use this to plan transportation infrastructure. Presently, the technique used for transportation planning uses data inputs from local and national household travel surveys. However, these surveys are expensive to conduct, cover smaller areas of cities and the time between surveys range from 5 to 10 years. This calls for new and innovative ways for Transportation Planning using new data sources.
In recent years, we have witnessed the proliferation of ubiquitous mobile computing devices in developing countries. These mobile phones capture the movement of vehicles and people in near real time and generate massive amounts of new data. My PhD research investigates how we can utilize anonymized mobile phone data ( i.e. Call Detail Records) and probabilistic machine learning to infer travel/mobility patterns. One of the objectives of this research is to demonstrate that these new “big” data sources are cheaper alternatives for transport modeling and travel behavior studies.
I upload this PPT because I want to say thankfully my group that do the best and hardest to make it very good, especially my group leader!!!
The last one I want to say thank for viewing and downloading.
Introduction to Model-Based Machine LearningDaniel Emaasit
The field of machine learning has seen the development of thousands of learning algorithms. Typically, scientists choose from these algorithms to solve specific problems. Their choices often being limited by their familiarity with these algorithms. In this classical/traditional framework of machine learning, scientists are constrained to making some assumptions so as to use an existing algorithm. This is in contrast to the model-based machine learning approach which seeks to create a bespoke solution tailored to each new problem.
Sustainable Urban Transport Planning using Big Data from Mobile PhonesDaniel Emaasit
In the past decades, there has been rapid urbanization as more and more people migrate into cities. The World Health Organization (WHO) estimates that by 2017, a majority of people will be living in urban areas. By 2030, 5 billion people—60 percent of the world’s population—will live in cities, compared with 3.6 billion in 2013. Developing nations must cope with this rapid urbanization. Transportation and urban planners must estimate travel demand for transportation facilities and use this to plan transportation infrastructure. Presently, the technique used for transportation planning uses data inputs from local and national household travel surveys. However, these surveys are expensive to conduct, cover smaller areas of cities and the time between surveys range from 5 to 10 years. This calls for new and innovative ways for Transportation Planning using new data sources.
In recent years, we have witnessed the proliferation of ubiquitous mobile computing devices in developing countries. These mobile phones capture the movement of vehicles and people in near real time and generate massive amounts of new data. My PhD research investigates how we can utilize anonymized mobile phone data ( i.e. Call Detail Records) and probabilistic machine learning to infer travel/mobility patterns. One of the objectives of this research is to demonstrate that these new “big” data sources are cheaper alternatives for transport modeling and travel behavior studies.
I upload this PPT because I want to say thankfully my group that do the best and hardest to make it very good, especially my group leader!!!
The last one I want to say thank for viewing and downloading.
Introduction to Model-Based Machine LearningDaniel Emaasit
The field of machine learning has seen the development of thousands of learning algorithms. Typically, scientists choose from these algorithms to solve specific problems. Their choices often being limited by their familiarity with these algorithms. In this classical/traditional framework of machine learning, scientists are constrained to making some assumptions so as to use an existing algorithm. This is in contrast to the model-based machine learning approach which seeks to create a bespoke solution tailored to each new problem.
Sebuah buku foto yang berjudul Lensa Kampung Ondel-Ondelferrydmn1999
Indonesia, negara kepulauan yang kaya akan keragaman budaya, suku, dan tradisi, memiliki Jakarta sebagai pusat kebudayaan yang dinamis dan unik. Salah satu kesenian tradisional yang ikonik dan identik dengan Jakarta adalah ondel-ondel, boneka raksasa yang biasanya tampil berpasangan, terdiri dari laki-laki dan perempuan. Ondel-ondel awalnya dianggap sebagai simbol budaya sakral dan memainkan peran penting dalam ritual budaya masyarakat Betawi untuk menolak bala atau nasib buruk. Namun, seiring dengan bergulirnya waktu dan perubahan zaman, makna sakral ondel-ondel perlahan memudar dan berubah menjadi sesuatu yang kurang bernilai. Kini, ondel-ondel lebih sering digunakan sebagai hiasan atau sebagai sarana untuk mencari penghasilan. Buku foto Lensa Kampung Ondel-Ondel berfokus pada Keluarga Mulyadi, yang menghadapi tantangan untuk menjaga tradisi pembuatan ondel-ondel warisan leluhur di tengah keterbatasan ekonomi yang ada. Melalui foto cerita, foto feature dan foto jurnalistik buku ini menggambarkan usaha Keluarga Mulyadi untuk menjaga tradisi pembuatan ondel-ondel sambil menghadapi dilema dalam mempertahankan makna budaya di tengah perubahan makna dan keterbatasan ekonomi keluarganya. Buku foto ini dapat menggambarkan tentang bagaimana keluarga tersebut berjuang untuk menjaga warisan budaya mereka di tengah arus modernisasi.