[RuntimeErrors] SingDiscover

Description:

Post-COVID, a cycling app remains valuable for individuals seeking health and fitness support, outdoor recreation opportunities, social interaction, sustainable transportation options, mental well-being support, motivation, and exploration. It promotes physical fitness, enables users to plan enjoyable outdoor experiences, find cycling buddies, and participate in community events. Additionally, it assists in alternative commuting, enhances mental well-being, and encourages users to set and achieve cycling goals. It also serves as a valuable tool for tourists, offering information on local cycling trails and points of interest.

SingDiscover is an innovative cycling route app designed to enhance the cycling experience for enthusiasts of all levels. Our app combines fitness tracking, route planning, social interaction, and rewards to create a comprehensive platform for cyclists in Singapore. With SingDiscover, you can join different groups currently doing a cycling route based on your current location. Whether you're a casual rider or a seasoned athlete, SingDiscover has something to offer for everyone.

What can you do with SingDiscover?

Connect with a Thriving Cycling Community on SingDiscover and Share the Journey Together!
Embark on Your Cycling Adventure, Track Your Progress, and Gain Valuable Insights with SingDiscover's Trip Analysis Feature.
Discover Exciting New Routes Tailored to Your Preferences and Previous Cycling History with SingDiscover's Smart Recommendation System.
Take on Challenges, Earn Rewards, and Unlock Exciting Credits and Vouchers as You Conquer Milestones on SingDiscover.

Key Features:

Calorie Counting and Time Optimization: Input your desired calorie count and cycling duration, and SingDiscover will suggest the best routes based on elevation, scenery, and estimated calorie burn, ensuring an optimal workout experience. Mapbox offers powerful routing services that can be utilized in SingDiscover for efficient route planning and turn-by-turn directions. Users can input their starting point and destination, and Mapbox's routing algorithms can generate optimal cycling routes based on factors such as distance, elevation, and traffic conditions.

Route Recommendations: Leveraging machine learning algorithms, SingDiscover analyzes your past routes and those of other cyclists to suggest the most popular and enjoyable routes in your area based on the reviews and ratings given by previous cyclists on that route. Discover hidden gems and uncover new cycling adventures. Utilize existing datasets like DataMall by LTA containing information about popular cycling routes, their ratings, user reviews, and scenic spots

Food Spot Integration: Our app incorporates a comprehensive database of food spots along the cycling routes. Discover local cafes, restaurants, and food stalls where you can refuel and indulge in delicious meals, ensuring a delightful cycling journey.

Social Interaction: Connect with like-minded cyclists in your vicinity. Our app allows you to find people with similar interests, chat with them, organize group rides, and share your cycling experiences.

Partnerships with local cycle providers: Partnering with local cycle providers allows SingDiscover to offer users the convenience of seamless bike rentals directly through the app. By integrating with these providers' systems, users can easily access and book rental bikes, ensuring a smooth and hassle-free experience. This integration eliminates the need for users to navigate multiple platforms or visit physical rental locations, making it more convenient for them to engage in cycling activities.

Challenges and Leaderboards: Engage in exciting cycling challenges and compete against other riders. Complete designated trails, earn points, and climb the leaderboards. The top cyclists will be rewarded with cycling credits, vouchers, and free cycling goodies.

Development Process:

SingDiscover was developed using a combination of modern technologies and frameworks to ensure a seamless user experience and robust functionality. Our development team followed an agile methodology, allowing for iterative improvements and quick adaptation to changing requirements.

The front-end of the app was built using Flutter, a cross-platform framework, ensuring compatibility with both iOS and Android devices. This enabled us to reach a wider user base and provide a consistent user interface across different platforms. The back-end was powered by Firebase, providing a scalable and efficient server infrastructure to handle user requests and data processing.

To implement the machine learning algorithms for route recommendations, we utilized Python libraries such as TensorFlow and scikit-learn. These frameworks allowed us to analyze and compare various route parameters, such as elevation, scenery ratings, and user preferences, to provide personalized suggestions.

KNN for Finding Nearby Cyclists: SingDiscover can utilize KNN to help users find other cyclists with similar interests or cycling preferences in their vicinity. The algorithm would consider attributes like cycling experience, preferred distance, average speed, and favorite routes as features for each user. When a user searches for nearby cyclists, KNN can identify users with similar feature values and recommend them as potential cycling buddies.

KNN for Route Similarity: SingDiscover can also utilize KNN to find routes with similar characteristics based on elevation, distance, scenery ratings, or other relevant features. By representing each route as a vector of these attributes, SingDiscover can compute the similarity between routes using the KNN algorithm. When a user selects a specific route, KNN can search for and suggest similar routes that other cyclists have enjoyed.

Regression Analysis: SingDiscover uses regression analysis to estimate calorie burn based on cycling duration, route characteristics, and user attributes. Regression analysis is a supervised learning technique that models the relationship between dependent and independent variables. By analyzing historical data of cyclists' calorie burn and corresponding features, the ML models in SingDiscover can predict the approximate calorie expenditure for a given cycling duration and route.

Collaborative Filtering: Collaborative filtering is a technique used in SingDiscover to provide personalized route recommendations based on similarities among users. It analyzes the preferences and patterns of a user and compares them to other users with similar tastes and behaviors. By leveraging collaborative filtering, SingDiscover can recommend routes that are popular among users with similar cycling interests, thereby enhancing the user experience and ensuring relevant suggestions.

Challenges Faced:

While developing SingDiscover, we encountered several challenges that required innovative solutions and extensive testing. Some of the key challenges include:

Route Recommendation Accuracy: Ensuring accurate and relevant route recommendations based on user preferences and past routes was a complex task. It required refining our machine learning models and continuously improving the algorithms to provide the best suggestions.

Data Integration: Incorporating a comprehensive database of food spots and maintaining their accuracy and relevancy posed a challenge. We employed a combination of crowdsourcing, data scraping, and partnerships with local businesses to ensure an up-to-date and diverse selection of food spots.

Privacy and Security: Protecting user data and ensuring privacy was a top priority. We implemented strict security measures, including encryption of sensitive information and secure authentication protocols, to safeguard user accounts and data.

Benefits and Impact:

Encourages a healthy and active lifestyle by providing personalized cycling routes and calorie tracking. Promotes exploration and appreciation of Singapore's scenic landscapes and hidden culinary delights. Fosters a vibrant cycling community through social interaction and group rides. Encourages users to push their limits and achieve their fitness goals through challenges and rewards.

Future Enhancements:

Integration with wearables and fitness devices for seamless tracking and data synchronization. Incorporation of user-generated content, allowing cyclists to share their favorite routes, photos, and reviews. Partnership with local businesses to offer exclusive discounts and promotions for SingDiscover users. Expansion to other cities and countries, catering to the global cycling community.

SingDiscover aims to revolutionize the way cyclists discover, explore, and connect. Join us on this exciting journey and experience cycling like never before!