Personalized Route Suggestions
BCNByBike stands at the forefront of transforming urban cycling in Barcelona. This mobile application harnesses the power of AI to guide cyclists through the city's streets, offering the most efficient and safe bike routes. Our development approach centers on creating a seamless user experience, where cyclists can effortlessly find, save, and share their preferred routes.
Development - Research - UX/UI Design - Dedicated Team - AI integration
The primary challenge in developing BCNByBike was to create an intuitive and engaging application that delivered accurate, personalized, and predictive route information while maintaining visual appeal. Our development team harnessed the power of technologies such as Mapbox SDK, React Native, Ruby on Rails, and cutting-edge AI models to create an incredibly functional and visually engaging application.
UI/UX and Development
The app combines intuitive UI/UX design with robust development techniques to ensure a seamless user experience. The UI/UX is crafted for easy navigation and interaction, accessible to users of all levels, and is visually engaging. React Native is used for frontend development, ensuring consistency across various devices with its responsive design. For the backend, Ruby on Rails is employed to manage data processing and API integrations effectively, providing a stable and scalable infrastructure that supports the app's extensive functionality.
We focused on integrating AI technologies into the app, starting with selecting and integrating key data sources for route optimization. Real-time traffic data was accessed through APIs like Google Maps and OpenStreetMap, complemented by historical user route data and environmental factors like weather and air quality. The consulting emphasized applying machine learning for predictive route analysis and personalizing user experience. We chose models based on their effectiveness in handling complex data, crucial for route prediction and user preference learning. A significant part of our approach involved combining supervised learning for route prediction based on historical data with unsupervised learning to adapt to user preferences. Model deployment involved using platforms like AWS SageMaker or Azure Machine Learning for seamless integration into the app, enabling efficient real-time data processing.
Machine Learning Models and Data Sources
The app employs machine learning models for predictive analysis, forecasting the fastest and safest routes by analyzing multi-dimensional data. Additionally, it learns user preferences over time through clustering algorithms and recommendation systems, leading to increasingly personalized route suggestions. To provide the most efficient and safe cycling routes, BCNByBike integrates real-time traffic information from Google Maps and OpenStreetMap, analyzes anonymized historical data from user journeys, and considers various environmental factors such as weather conditions, air quality, and changes in urban infrastructure.
Model Deployment and Integration
The application's machine learning models are integrated seamlessly as APIs, utilizing AWS SageMaker. This integration facilitates real-time data processing and enables the app to offer immediate route suggestions. Moreover, these models are not static; they are designed for continuous learning and adaptation. As new data is received, the models update their parameters, constantly improving the accuracy and personalization of route suggestions over time.
AI-Powered Customized Route Planning
Optimized routes considering user’s location, historical data, travel time, public notifications, and other road conditions are suggested, thanks to our AI-enhanced algorithm developed with Ruby on Rails.
We implemented AWS Lambda and Amazon SNS to enable real-time updates and push notifications, keeping users informed with the most up-to-date and accurate route information.
By leveraging native sharing capabilities provided by React Native, we integrated social media platforms and facilitated route sharing through email and direct messaging within the app.
We built an offline mode that allows users to access certain data at any time, from anywhere.
Ruby on Rails and Amazon Cognito are used to enable users to create and customize their profiles, with the AI learning and adapting to individual user preferences over time, making the experience increasingly personalized.
Custom Route Optimization
In order to find the best and safest routes for users depending on their input and current conditions, we developed an internal algorithm in Ruby on Rails.
Enhanced UI/UX Design
Our team skillfully designed a visually appealing and user-friendly interface with both light and dark themes, ensuring compatibility across a wide range of devices and demonstrating our expertise in User Interface (UI) design.
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