Data analysis and data prediction is the field of informatics and mathematics, en-gaged in calculation of algorithms and mathematical models that are able to extract practical data from analyzed data. Data analysis has many aspects and approaches, covers different methods in various fields of science and everyday human life.
Data prediction and forecasting has interested people for thousands of years, with the new stage of human civilization development - expenditure of computers and different computing machines, data prediction methods and techniques tremendously change. New field of "Big Data" and machine learning, which research data sets that are too large to deal with by traditional data analysis techniques and applications are expanding. In our days "Big Data" are widely used in areas of internet search, economics, business, urban informatics and etc.
The urban informatics is one of the most interesting and applicable fields of "Big Data" usage. This field uses information and data sets in the context of smart cities and urban environments with purpose to make quality of life of pedestrians better and improve urban environment.
The aim of this project is to create a model, which would predict behavior of one of the most visible part of every urban area - crossroad. Provided information from traffic light controllers (detectors) on the crossroad at "Makro" Zlin is being registered, stored with equal periods of time and analyzed. Data analysis is implemented through usage of different statistical and computation models in a free and open-source integrated development envi-ronment "RStudio" and spreadsheet program for data storage "Microsoft Excel". The project is aimed to predict traffic data on the crossroad.
Annotation in English
Data analysis and data prediction is the field of informatics and mathematics, en-gaged in calculation of algorithms and mathematical models that are able to extract practical data from analyzed data. Data analysis has many aspects and approaches, covers different methods in various fields of science and everyday human life.
Data prediction and forecasting has interested people for thousands of years, with the new stage of human civilization development - expenditure of computers and different computing machines, data prediction methods and techniques tremendously change. New field of "Big Data" and machine learning, which research data sets that are too large to deal with by traditional data analysis techniques and applications are expanding. In our days "Big Data" are widely used in areas of internet search, economics, business, urban informatics and etc.
The urban informatics is one of the most interesting and applicable fields of "Big Data" usage. This field uses information and data sets in the context of smart cities and urban environments with purpose to make quality of life of pedestrians better and improve urban environment.
The aim of this project is to create a model, which would predict behavior of one of the most visible part of every urban area - crossroad. Provided information from traffic light controllers (detectors) on the crossroad at "Makro" Zlin is being registered, stored with equal periods of time and analyzed. Data analysis is implemented through usage of different statistical and computation models in a free and open-source integrated development envi-ronment "RStudio" and spreadsheet program for data storage "Microsoft Excel". The project is aimed to predict traffic data on the crossroad.
Data analysis and data prediction is the field of informatics and mathematics, en-gaged in calculation of algorithms and mathematical models that are able to extract practical data from analyzed data. Data analysis has many aspects and approaches, covers different methods in various fields of science and everyday human life.
Data prediction and forecasting has interested people for thousands of years, with the new stage of human civilization development - expenditure of computers and different computing machines, data prediction methods and techniques tremendously change. New field of "Big Data" and machine learning, which research data sets that are too large to deal with by traditional data analysis techniques and applications are expanding. In our days "Big Data" are widely used in areas of internet search, economics, business, urban informatics and etc.
The urban informatics is one of the most interesting and applicable fields of "Big Data" usage. This field uses information and data sets in the context of smart cities and urban environments with purpose to make quality of life of pedestrians better and improve urban environment.
The aim of this project is to create a model, which would predict behavior of one of the most visible part of every urban area - crossroad. Provided information from traffic light controllers (detectors) on the crossroad at "Makro" Zlin is being registered, stored with equal periods of time and analyzed. Data analysis is implemented through usage of different statistical and computation models in a free and open-source integrated development envi-ronment "RStudio" and spreadsheet program for data storage "Microsoft Excel". The project is aimed to predict traffic data on the crossroad.
Annotation in English
Data analysis and data prediction is the field of informatics and mathematics, en-gaged in calculation of algorithms and mathematical models that are able to extract practical data from analyzed data. Data analysis has many aspects and approaches, covers different methods in various fields of science and everyday human life.
Data prediction and forecasting has interested people for thousands of years, with the new stage of human civilization development - expenditure of computers and different computing machines, data prediction methods and techniques tremendously change. New field of "Big Data" and machine learning, which research data sets that are too large to deal with by traditional data analysis techniques and applications are expanding. In our days "Big Data" are widely used in areas of internet search, economics, business, urban informatics and etc.
The urban informatics is one of the most interesting and applicable fields of "Big Data" usage. This field uses information and data sets in the context of smart cities and urban environments with purpose to make quality of life of pedestrians better and improve urban environment.
The aim of this project is to create a model, which would predict behavior of one of the most visible part of every urban area - crossroad. Provided information from traffic light controllers (detectors) on the crossroad at "Makro" Zlin is being registered, stored with equal periods of time and analyzed. Data analysis is implemented through usage of different statistical and computation models in a free and open-source integrated development envi-ronment "RStudio" and spreadsheet program for data storage "Microsoft Excel". The project is aimed to predict traffic data on the crossroad.
At the beginning, meet and discuss with the traffic data managers from the assigned company, prepare the data and gain insights into the formulated problem.
Then, start with the data processing and simultaneously work on the next point.
Focus on the literature review of existing problems and the needed theory. Utilize gained knowledge.
Analyze the prepared data series using a selected software.
Discuss all the (even if) particular problem insights and results with the company. Provide als a discussion of applicability of the results.
Last but not least, make an overview of problems within the so-called samrt city problems where the results of the master thesis are applicable. Discuss future research challeneges.
Clearly discuss the main findings of the work.
Research Plan
At the beginning, meet and discuss with the traffic data managers from the assigned company, prepare the data and gain insights into the formulated problem.
Then, start with the data processing and simultaneously work on the next point.
Focus on the literature review of existing problems and the needed theory. Utilize gained knowledge.
Analyze the prepared data series using a selected software.
Discuss all the (even if) particular problem insights and results with the company. Provide als a discussion of applicability of the results.
Last but not least, make an overview of problems within the so-called samrt city problems where the results of the master thesis are applicable. Discuss future research challeneges.
Clearly discuss the main findings of the work.
Recommended resources
Boon, MAA (2011). Polling models : from theory to traffic intersections. Doctor of Philosophy, TUE: Department of Mathematics and Computer Science, Eindhoven. DOI: 10.6100/IR702638.
Montgomery DC, Jennings CL, Kulahci M (2008). Introduction to Time Series Analysis and Forecasting. Wiley Series in Probability and Statistics. ISBN: 978-1-118-74511-3.
De Gooijer (2017). Elements of Nonlinear Time Series Analysis and Forecasting. ISBN: 978-3-319-43251-9.
Adhikari R, Agrawal RK (2013). An introductory study on time series modeling and forecasting. ISBN: 978-3-659-33508-2.
Fam S-K, Su C-J, Nien H-T, Tsai P-F, Cheng C-Y (2018). Using machine learning and big data to predict travel time based on historical and real-time data from Taiwan electronic toll collection. Soft Computing 22, 5707-5718. DOI: 10.1007/s00500-017-2610-y
Raj J, Bahuleyan H, Vanajakshi LD (2016). Application of data mining techniques for traffic density estimation and prediction. Transportation Research Procedia 17, 321-33. DOI: 10.1016/j.trpro.2016.11.102.
Recommended resources
Boon, MAA (2011). Polling models : from theory to traffic intersections. Doctor of Philosophy, TUE: Department of Mathematics and Computer Science, Eindhoven. DOI: 10.6100/IR702638.
Montgomery DC, Jennings CL, Kulahci M (2008). Introduction to Time Series Analysis and Forecasting. Wiley Series in Probability and Statistics. ISBN: 978-1-118-74511-3.
De Gooijer (2017). Elements of Nonlinear Time Series Analysis and Forecasting. ISBN: 978-3-319-43251-9.
Adhikari R, Agrawal RK (2013). An introductory study on time series modeling and forecasting. ISBN: 978-3-659-33508-2.
Fam S-K, Su C-J, Nien H-T, Tsai P-F, Cheng C-Y (2018). Using machine learning and big data to predict travel time based on historical and real-time data from Taiwan electronic toll collection. Soft Computing 22, 5707-5718. DOI: 10.1007/s00500-017-2610-y
Raj J, Bahuleyan H, Vanajakshi LD (2016). Application of data mining techniques for traffic density estimation and prediction. Transportation Research Procedia 17, 321-33. DOI: 10.1016/j.trpro.2016.11.102.
