Project Groups
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Project Groups
Please change Your Team name to your project topic and change student name to your own name
Project Team | Project Title/Description | Project Artifacts | Project Member |
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Understanding traffic patterns using network graph visualisations The project aims to illustrate the power of visual analytics in highlight patterns exhibited by vehicles when traversing through various traffic corridors. By linking the information captured in RFID tags when vehicles move through checkpoints, an interactive application is designed. This will help to unravel insights such as frequently travelled corridors, preferred routes amongst vehicles, traffic density, etc. The application will be primarily developed using R, and specifically the versatile ggraph package, which helps to develop powerful network visualisations. ggraph has been chosen as it is a recent release (Feb 2017), that exhibits the power of R and the ggplot architecture in incorporating network visualisations. Though ggplot tools have existed to visualise network patterns previously, the use of this package helps in making neater visualisations that help the user understand better. The motivation for this project stems from the traffic accumulation key problem found in most cities. Though the dataset pertains to a set of vehicles travelling through a wildlife preserve, the ideology can be applied to planning of roads and associated establishments. Urban planning needs to cater to robust planning of vehicle corridors to minimise disruptions in flow, and improve productivity. The interactive application helps the user understand linkages between various points in a predefined vicinity. The timestamp information of vehicle passages present in the data helps to understand various parameters such as traffic density, preferred corridors for vehicles and their speeds. The application devised here is also aimed to support the traffic authorities to identify what other alternative corridors might exist for reaching from Point A to point B. In addition, network measures such as the betweenness, the connectivity and closeness of various nodes, are also provided. |
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Team | Members | |||||||||||||||||
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Group 1 | GE BIN | LIM LIANG DANNY | RYAN CHIA | |||||||||||||||
Group 2 | Rachel Tong | Nurul Asyikeen Binte Azhar | Matilda Tan Ying Xuan | |||||||||||||||
Group 3 | CHEN ZHENGJIAN | XIAO ZHENYU | ZHENG MIANYI | |||||||||||||||
Group 4 | Yau Hon Tak | Deng Yuetong | ||||||||||||||||
Group 5 | Wang Rui | Wu Yuqing | Xing Siyuan | |||||||||||||||
Group 6 | Wang Yizhou | Zhou Chen | Zhang Lidan | |||||||||||||||
Group 7 | Zhang Peng | Wang Shang | ||||||||||||||||
Group 8 | Fam Guo Teng | Wang Yuchen | Xu Yanru | |||||||||||||||
Group 10 | Ma Xiaoliu | Deng Chunling | AISHWARYA MOHAN | |||||||||||||||
Group 11 | FOO CELONG RAYMOND | GOH JUN JIE ANTHONY | KARAN JYOTI KHANNA | |||||||||||||||
Group 12 | HE ZIWEN | GONGQIANG | KYONG HWAN KIM |