dc.contributor.authorKhin Yadana Oo
dc.date.accessioned2017-02-01T09:15:45Z
dc.date.available2017-02-01T09:15:45Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10356/69521
dc.description.abstractIntelligent Transportation Systems (ITS) represent a major transition in transportation in many aspects. Advancements in sensor technologies have helped to improve the efficiency of existing transportation and safety of traffic operations. A detailed set of traffic information plays an important role of modern ITS system. With more reliable up-to-date data which can be easily collected using ITS systems, transportation systems become more and more advanced and comfortable. Having a convenient public transit system is one of the most important factors for metropolitan cities. The increase in modes of public transports, urbanization, and productivity in the economy sector aggravate the problem Therefore, to avoid the huge crowd in the low-cost public transports and save travel-time, especially during peak hours, people often prefer taxis in a faster, more comfortable and private way. Further, as Singapore is a city with plenty of tourist attractions, international business corporations, and eminent universities, taxis are very useful for the tourists or people on business trip or educational conferences. Therefore, an efficient management system for taxi fleet distribution over the island is required so that traffic congestion problem does not get beyond control due to the increased numbers of taxis on the roads. The Land Transport Authority (LTA) of Singapore have studied a lot of possible ways to control the traffic flow and collected road traffic data for further research. Based on tracked taxi data provided by the LTA, in this dissertation, traffic data of taxis is studied and analyzed to find the most common travel patterns using MATLAB software. In future, the results obtained from this project can be used to improve taxi hiring/booking/tracking applications.en_US
dc.format.extent68 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleIntelligent transportation system : traffic patterns analysis using taxi populationen_US
dc.typeThesis
dc.contributor.supervisorJustin Dauwelsen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Computer Control and Automation)en_US


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