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Park-n-Ride

Introduction​

Park-n-Ride is a digital service that connects parking lots with public transportation systems, enabling commuters to park their vehicles near transit hubs and transfer to rail or bus services for longer commutes.
This service improves the commuting experience by offering last-mile connectivity options while reducing congestion and parking challenges.
By integrating parking facilities into transit systems, the service promotes public transport adoption and increases the utilization rate of parking spaces.

Why Build a Park-n-Ride Service?​

Urban commuters often struggle with first-mile connectivity and limited parking near transit hubs. A Park-n-Ride system improves this by enabling commuters to:

  • Park vehicles near transit points and switch to public transport.
  • Reduce congestion in city centers and lower the demand for inner-city parking.
  • Increase public transport usage by simplifying access to stations.
  • Optimize infrastructure by integrating digital tools with parking and transit data.

Core Functional Capabilities​

The Park & Ride system is designed to help users efficiently transition from private vehicles to public transport by offering location-aware, and convenience-focused features.

Search Results​

  • Displays nearby parking options in proximity to metro or transit stations.
  • Provides:
    • Availability of parking lots
    • Distance from parking to nearest station
    • Parking types and pricing information

Commute Integration View​

  • Offers a visual guide from parking location to transit boarding point.
  • Helps users clearly understand how to transition from parking to public transport.

Parking Details Page​

  • Lists availability based on vehicle type (e.g., two-wheeler, EV, standard car).
  • Shows detailed pricing structure.
  • Provides exact address and map coordinates.
  • Enables smooth redirection to parking locations using native maps or in-app routing for real-time turn-by-turn directions.

Core Features​

  • Parking Availability: Filterable by vehicle type (car, two-wheeler, bicycle).
  • Probability Prediction: Based on historical patterns for forecasting occupancy.

System Overview​

The Park-n-Ride system digitally integrates parking facilities with public transport apps and platforms to enable seamless planning of journeys that begin with private vehicles and continue via public transit.

Key Components​

  • Commuter App Integration: Search for parking, view availability, and plan journeys including parking.
  • Scalable Architecture: Easily extensible to include more parking lots, live navigation, and reservation features.

Data Management​

  • Static Parking Data: Lot names, types (e.g., car, bike), location coordinates, capacities.
  • Real-Time Parking Data: Live slot availability through integration with smart parking systems.
  • Historical Data: Used for predicting future parking availability using trends and patterns.

Infrastructure​

  • Cloud Deployment: Hosted on scalable platforms such as AWS.
  • Containerization: Uses Docker and Kubernetes (e.g., EKS) for flexibility and resilience.
  • Auto-Scaling: Dynamically scales based on system load and demand.

Database Management​

  • Relational DBs: For structured data like slot counts and locations (e.g., MySQL, PostgreSQL).
  • Caching: Redis or similar tools for real-time read-heavy queries.

API Endpoints​

  • API Gateway: Handles request management, security, rate limiting, and analytics.
  • Public APIs:
    • /api/get_parking_lots?<args>
    • /api/<version>/get_park_and_ride_journey/?<args>

1. Get Parking Lots​

  • Endpoint: /api/get_parking_lots?<args>
  • Method: GET
  • Arguments:
    • near_station: ID or coordinates of transit station
    • vehicle_type: car, two-wheeler, bicycle, ev
  • Example:
    /api/get_parking_lots?near_station=metro123&vehicle_type=car

2. Get Park and Ride Journey​

  • Endpoint: /api/<version>/get_park_and_ride_journey/?<args>
  • Method: GET
  • Required Arguments:
    • src_type, src, dst_type, dest, vehicle_type, transit_mode
  • Optional Arguments:
    • time, src_name, dst_name, max_parking_distance
  • Example:
    /api/v2/get_park_and_ride_journey/?src=[28.7041,77.1025]&src_type=place&dst=28.7041,77.1025&dst_type=place&vehicle_type=car&transit_mode=metro&max_parking_distance=500

3. Get Parking Availability​

  • Endpoint: /api/get_parking_availability?<args>
  • Method: GET
  • Arguments:
    • lot_id: ID of the parking lot
    • vehicle_type: car, two-wheeler, bicycle, ev (optional)
  • Example:
    /api/get_parking_availability?lot_id=park123&vehicle_type=car

API Development​

  • RESTful Architecture: Standard endpoints for modular communication between services.
  • High Availability: Includes load balancing, rate-limiting, and retry mechanisms.

Performance & Security Considerations​

Scalability​

  • Support peak-hour demand through auto-scaling and optimized caching.

Reliability​

  • Redundant infrastructure and failover mechanisms ensure high availability.

Efficiency​

  • Real-time APIs and queries are optimized for low latency and fast data access.
  • Parking availability updates are synced with minimal delay from smart systems.

Security​

  • All sensitive data is encrypted both at rest and during transmission.
  • Regular audits are conducted to detect and prevent unauthorized data access.
  • Access is secured via token-based authentication for both API and UI.
  • Role-based access control restricts system functions based on user roles.
  • Data handling complies with privacy regulations such as GDPR.
  • Only essential user data is retained, with clear and consent-based collection policies.

Open Integration​

Park-n-Ride can integrate with broader Mobility-as-a-Service (MaaS) ecosystems, offering APIs to both government and private apps. Examples include smart city dashboards, multimodal planners, and transit operator platforms.

Planned Future Enhancements​

  • Integration with EV charging station networks
  • Support for dynamic pricing based on demand and availability
  • Gamified rewards to encourage regular usage
  • AI-driven forecasting to predict parking demand patterns

Open Source Repository​

The source code and documentation for the Park-n-Ride system are available on GitLab:
Park-n-Ride GitLab Repository

References​