PRD
Product Requirements Document (PRD)
B-Scooter Urban Mobility Platform
1. App Overview and Objectives
Vision Statement
Build safer, smarter cities through an integrated urban mobility platform that combines micro-mobility safety systems with intelligent parking management, leveraging AI and computer vision technology to create seamless urban transportation experiences.
Core Objectives
- Primary Goal: Integrate AI-powered scooter safety systems with real-time parking detection to solve dual urban mobility challenges
- Secondary Goals: Scale nationwide through strategic partnerships, establish international market presence, and create unified urban mobility management platform
- Success Metrics: Deploy in 10+ Israeli cities by Q4 2024, achieve $10M+ ARR by Q3 2025, maintain 99%+ platform uptime
Business Context
- Company: B-Scooter - Automotive Safety Systems
- Leadership: Daniel Rosenzweig (Founder & CEO)
- Current Status: Operational units deployed to major Israeli micro-mobility operators, active partnerships with Haifa and Ramat Gan municipalities
2. Target Audience
Primary Users
Municipal Administrators
- Profile: City planners, traffic management officials, urban mobility coordinators
- Pain Points: Inefficient parking management, lack of micro-mobility safety oversight, fragmented urban mobility data
- Goals: Improve traffic flow, enhance citizen safety, optimize urban resource allocation
- Usage: Daily monitoring through unified dashboard, monthly reporting and analytics
Micro-mobility Operators
- Profile: Fleet managers, operations directors, safety coordinators at scooter companies
- Pain Points: Accident liability, inefficient fleet monitoring, regulatory compliance challenges
- Goals: Reduce accidents, optimize fleet deployment, improve operational efficiency
- Usage: Real-time fleet monitoring, safety incident management, performance analytics
Urban Drivers
- Profile: Daily commuters, city residents, visitors seeking parking
- Pain Points: Wasted time searching for parking, unpredictable availability, unsafe scooter interactions
- Goals: Quick parking solutions, predictable travel times, safe urban navigation
- Usage: Mobile app for parking availability, route optimization, safety alerts
Secondary Users
City Engineers & Urban Planners
- Long-term urban development insights, infrastructure optimization data
Regulatory Bodies
- Compliance monitoring, safety standard enforcement, policy development support
3. Core Features and Functionality
3.1 Micro-mobility Safety System
Real-time Safety Monitoring
- Description: AI-powered visual sensors and embedded intelligence preventing scooter accidents
- Core Components:
- Computer vision accident detection
- Real-time hazard identification
- Automatic emergency response triggering
- Speed and route optimization alerts
- Acceptance Criteria:
- Sub-100ms response time for accident prevention
- 99%+ accuracy in hazard detection
- Integration with existing scooter hardware
- Operator dashboard with real-time fleet status
Safety Analytics Dashboard
- Description: Comprehensive safety metrics and incident reporting for operators
- Features:
- Accident prediction modeling
- Risk area heat mapping
- Fleet safety performance tracking
- Regulatory compliance reporting
- User Access: Operator administrators, fleet managers, safety coordinators
3.2 Intelligent Parking Management
Real-time Parking Detection
- Description: Computer vision platform using existing city cameras for parking availability
- Core Components:
- Camera feed processing and analysis
- Parking space occupancy detection
- Real-time availability updates
- Historical utilization patterns
- Technical Requirements:
- Integration with existing city camera infrastructure
- 99%+ detection accuracy across weather conditions
- Real-time processing with minimal latency
- Support for various camera types and angles
Parking Intelligence Platform
- Description: Predictive parking analytics and citizen-facing availability information
- Features:
- AI-driven parking availability forecasting
- Mobile app with real-time parking maps
- Route optimization with parking integration
- Dynamic pricing recommendations for cities
- User Access: Citizens via mobile app, municipal administrators via web dashboard
3.3 Unified Municipal Dashboard
Integrated Urban Mobility Analytics
- Description: Single interface combining parking and micro-mobility data for city administrators
- Core Features:
- Real-time city-wide mobility metrics
- Cross-platform safety and efficiency reporting
- Policy impact analysis and recommendations
- Resource allocation optimization insights
- Data Integration: Parking utilization rates, scooter safety incidents, traffic patterns, citizen usage metrics
Multi-tenant Administration
- Description: Scalable platform supporting multiple cities and operators
- Features:
- Role-based access control
- Customizable reporting and alerts
- Multi-language support
- Regional compliance management
- Scalability Requirements: Support 100+ cities by Phase 2, cloud-based architecture
3.4 Mobile Application (Consumer-Facing)
Integrated Mobility App
- Description: Consumer application combining parking and safe micro-mobility options
- Core Features:
- Real-time parking availability map
- Safe scooter route recommendations
- Multi-modal trip planning
- Safety alert notifications
- Technical Implementation: Native mobile development for iOS and Android
- Integration Points: City parking systems, scooter operator APIs, mapping services
4. Technical Stack Recommendations
Backend Architecture
- Cloud Platform: AWS or Azure for scalability and global presence
- Microservices: Node.js or Python for API services, containerized with Docker
- Database: PostgreSQL for transactional data, Redis for caching, InfluxDB for time-series data
- Message Queue: Apache Kafka for real-time data streaming
- API Gateway: Kong or AWS API Gateway for service orchestration
AI/ML Infrastructure
- Computer Vision: TensorFlow or PyTorch for image processing models
- Edge Computing: NVIDIA Jetson for on-device processing
- ML Pipeline: MLflow for model management, Apache Airflow for data workflows
- Real-time Processing: Apache Spark for large-scale data processing
Frontend Technologies
- Web Dashboard: React.js with TypeScript for municipal and operator interfaces
- Mobile Apps: React Native for cross-platform development with native modules for performance-critical features
- Data Visualization: D3.js, Chart.js for analytics dashboards
- Mapping: Mapbox or Google Maps API for location services
Integration & APIs
- Camera Systems: Custom adapters for various city camera infrastructures
- Scooter Hardware: IoT integration using MQTT protocol
- Third-party Services: Payment processing, notification services, weather APIs
- Security: OAuth 2.0 for authentication, JWT for session management, end-to-end encryption
5. Conceptual Data Model
Core Entities
City
- city_id (UUID, primary key)
- name (string)
- country (string)
- timezone (string)
- camera_systems (array of camera_config)
- parking_zones (array of zone_id references)
- created_at (timestamp)
- updated_at (timestamp)
Parking Space
- space_id (UUID, primary key)
- city_id (UUID, foreign key)
- zone_id (UUID, foreign key)
- location (GPS coordinates)
- space_type (enum: street, garage, reserved)
- occupancy_status (enum: occupied, available, unknown)
- camera_id (UUID, foreign key)
- last_updated (timestamp)
- confidence_score (float)
Scooter Safety Event
- event_id (UUID, primary key)
- scooter_id (UUID, foreign key)
- operator_id (UUID, foreign key)
- event_type (enum: near_miss, accident, hazard_detected)
- location (GPS coordinates)
- severity (enum: low, medium, high, critical)
- response_time_ms (integer)
- resolved (boolean)
- timestamp (timestamp)
- sensor_data (JSON)
Operator
- operator_id (UUID, primary key)
- company_name (string)
- contact_info (JSON)
- cities (array of city_id references)
- fleet_size (integer)
- safety_rating (float)
- subscription_tier (enum: basic, premium, enterprise)
- created_at (timestamp)
Relationships
- Cities have many parking zones and operator partnerships
- Parking spaces belong to zones and are monitored by cameras
- Scooters belong to operators and generate safety events
- Users can be associated with multiple roles (citizen, operator_admin, city_admin)
6. UI Design Principles
Municipal Dashboard Design
- Layout: Clean, data-dense interface prioritizing real-time metrics
- Navigation: Tab-based structure separating parking and mobility views with unified overview
- Visualization: Color-coded maps, trend charts, and alert notifications
- Responsive: Desktop-first design with tablet compatibility
Operator Interface Design
- Layout: Fleet-centric dashboard with safety metrics prominence
- Real-time Updates: Live status indicators, alert notifications, incident management
- Mobile Responsive: Tablet and mobile optimization for field operations
- Customization: Configurable KPI widgets and report generation
Consumer Mobile App Design
- Map-First Interface: Large map view with overlay information
- Simple Navigation: Bottom tab navigation with quick access to key features
- Accessibility: WCAG 2.1 AA compliance, voice navigation support
- Offline Capability: Cached map data and basic functionality without connectivity
Design System Standards
- Color Palette: Safety-focused with clear status indicators (red for alerts, green for safe/available)
- Typography: Sans-serif fonts optimized for screen reading and data density
- Icons: Universal mobility symbols with custom safety and parking iconography
- Spacing: 8px grid system for consistent layouts across platforms
7. Security Considerations
Data Protection
- Encryption: AES-256 for data at rest, TLS 1.3 for data in transit
- Privacy Compliance: GDPR, CCPA compliance with data anonymization for analytics
- Data Retention: Configurable retention policies, automated data purging
- Backup & Recovery: Automated encrypted backups with 99.9% recovery SLA
Access Control
- Authentication: Multi-factor authentication for administrative users
- Authorization: Role-based access control (RBAC) with principle of least privilege
- API Security: Rate limiting, API key management, request signing for sensitive operations
- Audit Logging: Comprehensive access logs with tamper-evident storage
Infrastructure Security
- Network Security: VPC with private subnets, WAF for web applications
- Container Security: Image scanning, runtime protection, secrets management
- Monitoring: 24/7 SOC monitoring, automated threat detection and response
- Compliance: SOC 2 Type II, ISO 27001 certification target
8. Development Phases/Milestones
Phase 1: Partnership & Market Consolidation (Q3-Q4 2024)
Sprint 1-3: Core Platform Foundation
- Duration: 6 weeks
- Deliverables:
- Unified backend architecture and APIs
- Basic municipal dashboard with dual-product view
- Operator safety monitoring interface
- Database schema implementation and migration tools
- Success Criteria: Support existing Israeli operators, process real-time data from 100+ scooters
Sprint 4-6: Integration & Scalability
- Duration: 6 weeks
- Deliverables:
- Camera system integration framework
- Parking detection API with 95%+ accuracy
- Mobile app MVP for citizens
- Partnership integration tools and documentation
- Success Criteria: Deploy in 5 Israeli cities, close 3+ strategic partnerships
Phase 2: International Expansion Preparation (Q1-Q2 2025)
Sprint 7-10: Multi-Market Platform
- Duration: 8 weeks
- Deliverables:
- Multi-tenant SaaS architecture
- Compliance engine for international regulations
- Advanced analytics and predictive capabilities
- Automated deployment and onboarding systems
- Success Criteria: Platform ready for international deployment, 100+ city capacity
Sprint 11-14: Market Entry Tools
- Duration: 8 weeks
- Deliverables:
- Localization framework and multi-language support
- Regional partnership integration tools
- Enhanced customer success and support systems
- Performance optimization for global scale
- Success Criteria: 2+ international pilot programs initiated
Phase 3: Global Market Entry (Q3+ 2025)
Sprint 15-18: International Launch
- Duration: 8 weeks
- Deliverables:
- Full international platform deployment
- Regional customer success teams and processes
- Advanced AI/ML capabilities and predictive analytics
- Enterprise-grade SLA and support infrastructure
- Success Criteria: 5+ international markets, $10M+ ARR target
9. Potential Challenges and Solutions
Technical Challenges
Challenge: Computer Vision Accuracy Across Varying Conditions
- Impact: Parking detection reliability in different weather, lighting, camera angles
- Solution: Multi-model ensemble approach with weather-specific training data, continuous learning pipeline
- Mitigation: Confidence scoring system, manual validation workflows for low-confidence detections
Challenge: Real-time Processing at Scale
- Impact: Sub-100ms safety response requirements across thousands of devices
- Solution: Edge computing deployment, regional processing centers, optimized AI models
- Mitigation: Graceful degradation, offline capability, automatic failover systems
Challenge: Integration with Diverse City Infrastructure
- Impact: Varying camera systems, data formats, API standards across municipalities
- Solution: Abstraction layer architecture, standardized adapter framework, automated discovery tools
- Mitigation: Professional services team, comprehensive testing suite, pilot program methodology
Business Challenges
Challenge: Single Point of Leadership Risk
- Impact: CEO dependency creating operational bottlenecks and scaling limitations
- Solution: Immediate executive team expansion (CTO, VP Sales), advisory board formation, succession planning
- Timeline: Executive hires within 60 days, advisory board by Q4 2024
Challenge: Competitive Market Entry
- Impact: Established players in parking and scooter safety markets
- Solution: Unique dual-solution positioning, strategic partnerships, rapid innovation cycles
- Differentiator: Unified platform approach, existing infrastructure leverage, proven deployment capability
Challenge: International Regulatory Complexity
- Impact: Varying safety standards, privacy laws, municipal procurement processes
- Solution: Regulatory compliance framework, local partnership strategy, legal expertise expansion
- Mitigation: Pilot program approach, conservative expansion timeline, regulatory affairs team
10. Future Expansion Possibilities
Product Line Extensions
- Traffic Optimization System: Third urban solution using same AI/computer vision platform
- Smart City Integration: APIs connecting with broader urban management systems (traffic lights, public transit)
- Sustainability Metrics: Carbon footprint reduction tracking for ESG-conscious municipalities
- AR-Enhanced Safety: Augmented reality components for enhanced scooter safety awareness
Market Expansion
- Consumer Direct Models: Direct-to-citizen parking assistance applications
- Technology Licensing: License core AI platform to complementary urban tech companies
- Integration Marketplace: Third-party developer ecosystem for extended functionality
- Regional Distributor Program: Partner-led expansion in secondary markets
Advanced Capabilities
- Predictive Urban Planning: Long-term mobility pattern analysis for city development
- Autonomous Vehicle Integration: Platform extension for autonomous parking and mobility systems
- IoT Ecosystem: Comprehensive urban sensor network management
- Machine Learning as a Service: AI capabilities for other urban technology providers
This PRD provides a comprehensive foundation for engineering teams to understand, develop, and scale the B-Scooter urban mobility platform while maintaining alignment with business objectives and market opportunities.