Big data analytics for smart transportation
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...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project |
| Language: | English |
| Published: |
2019
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/76961 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
|---|