An open-source multi-modal trip planner for public transit, walking, bicycling, and mobility services.
OpenTripPlanner is an open-source multi-modal trip planner that enables users to plan journeys combining public transportation, walking, bicycling, and mobility services like bike share and ride hailing. It solves the problem of fragmented mobility options by integrating various transport modes into a single routing engine, using open data standards to represent transportation networks and providing real-time updates for accurate trip planning.
Transport agencies, urban planners, developers, and organizations looking to deploy or integrate a customizable trip planning solution for multi-modal transportation systems.
Developers choose OpenTripPlanner because it is a mature, open-source alternative to proprietary trip planners, supports a wide range of transport modes, leverages open data standards, and offers real-time integration, making it highly adaptable for global deployments.
An open source multi-modal trip planner
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Integrates public transit, walking, bicycling, bike share, and ride hailing into a single routing engine, as specified in the GitHub description, enabling comprehensive trip planning.
Uses open data formats like GTFS and OpenStreetMap, allowing easy integration with global transit datasets and fostering collaboration, per the project philosophy.
Applies real-time updates and alerts for immediate client visibility, ensuring accurate routing despite disruptions, as highlighted in the README.
Exposes GraphQL APIs for flexible client development, accessible by JavaScript components and native apps, as stated in the repository layout.
Over 16 years in development with active community contributions and global deployments, as noted in the development history and chat rooms.
Requires a Java virtual machine and configuration with GTFS/OSM data files, which can be time-consuming and resource-intensive, as indicated by the need for performance testing and custom client builds.
Includes a performance dashboard and speed tests, suggesting potential scalability challenges or optimization needs for large-scale transit networks.
The included Javascript client is primarily for testing, with most deployments building custom clients, adding to development effort and not providing a turnkey solution.