Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/76961
Title: Big data analytics for smart transportation
Authors: Wong, Zhen Wei
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2019
Abstract: In recent years, big data has provided valuable insights for businesses to make informed decisions. Analyzing big data is particularly important to optimize transport routes and frequencies. Through the initiative to develop a Smart Nation City, in cities like Singapore, data from various sources are mined to find out the travel behavior and mobility patterns. This information obtained can assist transit authorities in evaluating their current services and forecast demands with greater accuracy. The purpose of this project is to process, visualize and analyze the real-world crowdsensing data from the Singapore National Science Experiment (NSE). It aims to identify the trajectory of each student from the large and noisy mobility data and visualize them on a Django web application. As a result, the trajectories of 27,473 students from home to school were identified from the mobility dataset. We verified the trajectory of 1,580 students from 6 different schools and the results suggest over 90% accuracy in identifying the student’s school based on the processed trajectory data. In this report, we elaborate on how the objectives are achieved through the processing of mobility data obtained from NSE and implementing a web application using Django Framework, MySQL Database and Mapbox GL.
URI: http://hdl.handle.net/10356/76961
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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