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SMT483: Project Experience
Welcome to this course wiki for SMT483: Project Experience.
You can access the Project Groups page here, where you will write your group projects.
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Course Information
| Faculty | TAN Hwee Pink |
| Course | Project Experience |
| Course code | SMT483 |
| Term | Your input |
| Section | Your input |
| Teaching Assistant | Your Name |
Potential Projects
| Project Topic | Project Description and Deliverable | Skill Requirements | |
|---|---|---|---|
| 1 | Quantifying the ‘car-lite’ness of precincts | Going car-lite brings tremendous benefits to the environment and liveability of the residents. There has been much effort and interest in recent years to transform Singapore into a more car-lite city. In order for us to understand how different towns are performing in terms of their ‘car-lite’ qualities, it is important to quantify the ‘car-lite’ characteristics of particular built environment elements, to complement our existing public transport network. The objectives of this project are to:
|
ArcGIS, R/Python |
| 2 | Modelling vehicular flows into Pick-Up Drop-Off (PUDO) areas | With the increase in demand for private hire cars today, existing Pick-Up Drop-Off (PUDO) at various developments could have difficulty coping with increased flows, resulting in traffic spill overs. There are limited studies on the factors that influence vehicular flows into PUDOs. We are interested to quantify current and project future PUDO volumes to inform the future-proofing of PUDO design. The objective of this project is to design a user-friendly model to predict vehicle flows and spill-overs for several typologies of PUDOs. |
Basic modelling skills, R/Python (Fieldwork may be necessary to collect additional data) |
| 3 | Developing a Video Analytics algorithm to detect conflict between active mobility users | As part of our move towards car-lite, active mobility modes are highly encouraged. Pedestrians, cyclists and PMD users often meet along shared paths and traffic junctions. Thus, there is a need to ensure that we provide comfortable and safe walkways for all users. An understanding of user interaction and conflict will be important to translate into useful applications. Using footage collected along a footpath, the study would tag all interactions between active mobility users observed. It will then measure the angular change in direction and change in speed to derive some threshold values that determine conflicts. Applying machine learning, an algorithm should be designed to automatically pick out conflict cases in video and categorise these cases. |
Machine learning/ Neural network, Python |
