Awesome resources for Open-Source Software for (mainly public) Transport.
Open Data Standards are like a common language for different types of transportation, making everything work together smoothly. Think of them as rules that everyone follows to share information easily. These standards help different systems understand each other, allowing for things like real-time updates and better planning.
- Standards:
- Open Data portals:
- Transitland - collection of GTFS worldwide.
- Transport Gov FR - Transport in FR.
Transitland, collections public transport data.
The OpenStreetMap ecosystem is a global collaboration where volunteers contribute to create free and open maps for various purposes. This collaborative effort makes OpenStreetMap an invaluable resource for transportation, offering detailed, up-to-date information that facilitates route planning, navigation, and community engagement while promoting accessibility and inclusivity.
- Maps:
- Main: OpenStreetMap
- Transport Modes: OpenHumanitarianMap, OpenRailwayMaps, OpenSeaMap
- Historical mapping: OpenHistoryMap, OpenHistoricalMap
- Imagery: Kartaview, OpenAerialMap
- Tools:
- List: OSM List of Services (GREAT!!)
- Mapping: iD, JOSM, ...
- Querying: Overpass Turbo
OpenStreetMap, the collaboration platform for mapping.
The OSGeo Ecosystem is like a toolbox for making maps and understanding transportation. It's like having all the tools you need to figure out and improve how we move around, making it super useful for anyone working with maps and transportation.
- Tools:
- All-in-one: OSGeo Live
OSGeo, find your tools here.
Choosing open-source tools for movement analytics is a smart move for several reasons. The best part is that you can look at and even change the code if you want to customize things, for example changing some kind of indicator. This open-source approach makes analytics more accessible and flexible for everyone.
- Libraries
- Python analytics: geopy, photon, geopandas, osmnx, geoviews
- R analytics: R-spatial, lwgeom, stars, sfnetworks
- Mobility analytics: MobilityDB, MoveTK, MOVE, movingpandas, scikit-mobility
- GTFS Analytics: gtfs_manager, gtfspy, gtfsdb, gtfstools, GTFS-GO
- Routing:
- Engines: OSRM, ORS, Valhalla, Graphhopper, Routino, per pedes routing
- Multimodal routing: OTP, Navitia, MOTIS, digitransit
- Rail routing: OpenRailRouting, osrd
- Simulators:
- Demand estimation: TransiTion, grid2demand
- Traffic micro-simulator: SUMO
Transition, a simulation tool for demand estimation.
Mapping public infrastructure using open-source tools provides professionals with a suite of digital resources. These open-source tools serve as effective digital aids, empowering professionals to efficiently explore and enhance transportation systems across various modes.
- Tools:
- Infrastructure status: StreetComplete, SmartRoadSense,
tsmv, Mapswipe, MapComplete - Cycling: CyIPT, BikeDNA
- Rail: CloudCompare, railroad
- Infrastructure status: StreetComplete, SmartRoadSense,
- Infrastructure change simlators:
- Main: streetmix, matsim
- OSM based: abstreet, osm2streets, abstr
- EV Infrastructure simulator: ELVIS, spice_ev, RAMP Mobility, TUMFTM
StreetComplete, collecting data using OSM.
Various open-source apps contribute significantly to transport analytics, each offering unique features to enhance the understanding and optimization of transportation systems. For example, Kitenerary provides a comprehensive itinerary solution, aiding in the seamless organization and management of travel plans. Similarly, Trufi specializes in public transit information, facilitating the efficient planning of routes and schedules.
- Apps:
- OSM Apps: OSMAnd, OrganicMaps, JungleBus
- Different: Kitenerary, TrackIt,MyOSMatic,
- Public Transport: Trufi, TransitWand
- Display Info:
- General: datatools-ui, Tavla
- Timetable: timetable_kit, hsl-map-*, GTFStoHTML
- Management:
- Others:
Trufi App, an application for public transport.
AI applications in transport, particularly those built on open-source frameworks, offer innovative solutions for enhancing efficiency and intelligence in transportation systems. Open-source versions of AI tools provide accessibility and transparency, allowing users to understand, modify, and contribute to the underlying code. From traffic prediction to route optimization, these applications leverage machine learning algorithms and neural networks to analyze vast datasets, improving decision-making processes and overall system performance.
- Geospatial analysis
- Usecase - Near crash prediction based on street curvature
- Usecase - Demand estimation based on demographical data
- Usecase - Road network stats
- Usecase - Distribution of services in the city
- Usecase - Distribution of public transport in a city
- Usecase - EV charging station distribution
- Usecase - Landuse and transport relation
- Usecase - Safety assessment based on mapped amenities and infrastructure
- Operational optimization
- Usecase - Delay forecasting, ETA estimations
- Usecase - Predicting failure of a vehicle (predictive maintenance)
- Usecase - Historical analysis for defection type detection
- Usecase - Best distribution of service for optimizing charging time for EV fleet
- Usecase - Optimize route for logistics
- Usecase - Notification system for predictive maintenance
- Legal documentation
- Technical check
- Electrical equipment failure
- Operational runs
- Image analysis
- Usecase - Safety assessment through combination of factors
- Usecase - Monitor infrastructure using image recognition of traffic signs
- Usecase - Infrastructure change through drone images
- Video analysis
- Models:
- Applications:
- Usecase - Traffic monitoring
- Usecase - Modal share counting
- Usecase - Parking spots detections
- Usecase - Compliance with traffic lights
- Usecase - Near crash prediction based on video stream
- Usecase - Automated goods tracing (in logistics)
- Satellite imagery
- Usecase - Road graph extraction from satellite imagery
- Usecase - Track spatial changes using satellite imaging
- Usecase - Urban sprawl using historical data
- Usecase - Climate impact comparison based on historical data
- Usecase - Demographical distribution based on historical data
- Usecase - Vehicle detection from space (e.g. for parking)
- List - (code) Road Segmentation
- Volume Forecasting
- Autonomous vehicles
- Usecase - Segment the infrastructure
- Usecase - Route finding
- Generative AI
- Usecase - Regenerate a part of the city using common-sensical suggestions
- Usecase - Redesign a street using technically possible changes
- Usecase - Geospatial LLM for mobility
- Customer facing
- Usecase - Chatbot for taxi company
- Usecase - Measuring sentiment on transport topics in social media
- Disaster risk management
- Usecase - HOTOSM detecting buildings destroyed pre-disaster
- Usecase - Intervention relation with mobility
- Usecase - Climate change projections for infrastructure degradation
- Usecase - Weather impact on infrastructure material
- Datasets:
Why OS AI?
These tools encompass various aspects such as version control, collaborative development, data analysis, database management, visualization, and machine learning. Depending on specific needs, users can leverage these tools to tailor and build a customized infrastructure that suits their transportation requirements.
- DIY Infra Setup
- Essentials: git, github, docker
- Reporting: jupyter notebooks
- Databases: postgresql, mariadb
- Dashboards: Grafana, Metabase
- ML Infra
- Base Models: scikit-learn, xgboost
- Deep Models: pytorch, tensorflow, keras
- Metrics: sklearn metrics
- Explainability: FreeMove Space, shap, lime
- Deployment: MLFlow
- Resources consumptions
A plethora of online resources is available for those delving into geospatial analysis and transportation-related fields. Initiatives like OpenStreetMap and OSGeo contribute to the open-source geospatial community. Academic research is accessible through journals like GeoAI MDPI and IEEE Transactions on Intelligent Transportation Systems, while groups like TransitLab MIT and Mobility MIT foster collaborative research environments.
- Mapping:
- 30 Day Challenge: Example 30MDCh
- Basic Geospatial Analysis:
- Medium Tutorials: AI4SM, GeoAI, NAGorg
- Books: Geographic Data Science, Sustainability GIS
- Model list: Kaggle Transport
- Dataset List: Transit Land, TUMI Data, HuggingFace, AWESOME Public Datasets
- Academic Research
- Disaster Risk Management: HOT-OSM, Crisis Ready, Flood susceptibility, Disaster response, Evacuation plan
- Transit:
- Remote Sensing:
- Logistics:
- Autonomous:
- General:
- Technical:
- Reinforcement Learning:
- Timeseries:
- Logistics
- Industry:
- GUIDELINES
- SOTM - conference on OpenStreetMap and related ecosystem
- List of Events, present in Africa, Asia, EU, and more
- FOSS4G - conference on OSGeo and related ecosystem
- FOSDEM - general conference on Open-Source - lately has a devroom for transport.