Editor’s Note:
A group of students from SCIE presented their project OptiWay in “Mathematics and Computer Science Week”, which emerges as a game-changer for students at SCIE, offering a smart solution to the perennial challenge of navigating students to their next class through corridors and sprawling campuses, revolutionizing the daily hustle of commutes.
Existing methods (those presented in the previous students) for optimizing routes primarily focus on the shortest distances, often overlooking the practicality of these paths given student traffic.
OptiWay introduces an innovative algorithm that not only considers distance but also accounts for congestion, ensuring more efficient and practical routing.
The project stands out with its user-friendly, minimalistic interface, enhancing accessibility and ease of use.
Our work not only offers technical insights into algorithmic route planning but also offers substantial practical benefits, potentially transforming day-to-day navigation in the busy SCIE campus.
Project Description
Our software includes a few major parts: map data source conversion, timetable generation, timetable validation, cross-language communication, visual representations, route time minimization, and map-timetable input-output.
The most attractive ones are our dedicated User-Interface-and-Map designs with intuitive 3D sense, and optimization algorithm making use of mathematical theories to swiftly deal with large amounts of calculations.
Timetable validations also ensure the software to handle erroneous manipulation while cross-language communication maximizes the efficiency of the entire process, which also gives a chance for us to better divide our work based on the languages we are good at.
Project Development Process
Before the actual development of OptiWay, we first discussed the potential functionalities that users might find helpful, and narrowed down our focus to the functions that are most feasible to implement yet powerful.
With the help of an online collaboration platform, we assigned individual tasks such as diagram drawing and timetable generator to teammates most confident with them, and used a Gantt chart to ensure our project can steadily progress within the required time period.
Considering the fact that our teammates use diverse programming languages, we implemented an entry point which coordinates function calling between different languages such as C++ and Rust.
This allows all our teammates to participate fully and confidently in the project, without the need to learn and adapt to another language.
Technical Implementation
Taking timetable as input, OptiWay first finds out the shortest path for each student using Floyd’s algorithm, and also evaluates the congestion in the shortest-path scenario.
This leads to an overall performance index, which measures the overall time taken for an average student to change rooms between lessons, including the effect of congestion.
Then, OptiWay uses multithreading to iteratively attempt to reduce the performance index (which indicates better performance), and hence computes a better plan with less congestion for all 2000 students in SCIE for each period in a single week.
Learning Experience
Developing OptiWay was a transformative experience that extended our understanding beyond the AS Computer Science syllabus’s basics of programming languages like Python.
While the syllabus lays a foundational grasp of programming, it falls short in preparing students for real-world application development.
Our journey with OptiWay exposed us to advanced concepts such as multi-threading for enhanced performance, asynchronous operations for improved user experiences, and the necessity of integrating multiple languages – a realization we made when Python alone proved insufficient for algorithmic efficiency.
The project also underscored the distinct dynamics of frontend and backend development, teaching us the importance of aesthetic design in user interface creation.
This experience broadened our perspective of computer science from mere code-writing to encompassing vital aspects like UX (User Experience) design, app optimization, and demand identification.
Teamwork
Most importantly, OptiWay taught us the indispensable role of teamwork in application development. Although all of our team had prior experience in software development, most of it was through individual, full-stack projects.
While that helped broaden our skill sets, it also meant we couldn’t dive deep into a specific area due to time constraints, nor could we fully leverage individual strengths — some of us excel in design and aesthetics, others in logic and code optimization.
Therefore, before writing the first line of OptiWay’s code, we discussed each member’s strengths and areas of interest within the team. We then collectively decided how to allocate responsibilities such as algorithm design, backend, and frontend development to different members.
This allowed everyone to play to their strengths and complement one another, leading to efficient module development and seamless integration.
It was this rational division of labour and our effective, open, and continuous communication that enabled us to build an elegant frontend and a feature-rich backend and algorithm system, all within a tight post-class development schedule.
Successes and Challenges
OptiWay boasts a minimalistic, modern UI (User Interface), with a robust app design that includes thorough testing of edge cases.
It leverages multi-threading and asynchronous operations for optimization, and its visualization of congestion and paths is both straightforward and impressive.
The app incorporates both a baseline and a customized route-finding algorithm, offering clear insights into the advancements of our approach. We maintain detailed technical documentation for further study and demonstrations.
Challenges include the lack of access to actual SCIE student timetables, which affects real-world applicability. To mitigate this, we simulated timetable allocation patterns to generalize our algorithm. Initially, the app may seem complex, but this is addressed through specialized training for teacher users, our primary audience.
A key assumption in our congestion model is students follow the calculated paths; while this might not always reflect real-life scenarios, it is valid for demonstrating our algorithm and can be refined in the future by incorporating data outliers.
Future Improvements and Recommendations
OptiWay’s model for addressing the problem of SCIE students’ daily transportation on campus is comprehensive and detailed. However, there are areas where the model can be improved.
The model can be enhanced in three dimensions: software performance, algorithm design and UI/UX. To improve software performance and stability, we need to define our requirements in more detail to align with our goals and the problem we are trying to solve.
This will allow us to test our prototypes more thoroughly with real-world scenarios.
We can also consider using more advanced algorithms to improve the model’s accuracy and efficiency. We can also consider gathering feedback from users and stakeholders to identify areas for improvement. This feedback can be used to enhance the system further.
For future research directions related to the project’s domain, we can explore the use of machine learning algorithms to optimize the model’s performance.
We can also investigate the feasibility of integrating the model with other systems to provide a more comprehensive solution for students’ daily transportation needs.
Conclusion
In conclusion, our project is well-developed and user-friendly. By testing in real scenes, we find it highly useful and significantly reduce the time we need to reach our next class, although the timetables are simulated ones.
In future we hope we can cooperate with the school to utilize the program.
We can also consider using more advanced algorithms to improve the model’s accuracy and efficiency. We can also consider gathering feedback from users and stakeholders to identify areas for improvement. This feedback can be used to enhance the system further.
For future research directions related to the project’s domain, we can explore the use of machine learning algorithms to optimize the model’s performance.
We can also investigate the feasibility of integrating the model with other systems to provide a more comprehensive solution for students’ daily transportation needs.
- Article / Michael Zhang
