Awesome

Transit Network Model

Three parts, running in real time:

1. particle filter for vehicle location and speed
2. Kalman filter for transit road network state (speed)
3. travel- and arrival-time predictions for each vehicle/stop combination in the network

1. Particle Filter

IN: GTFS realtime protobuf feed

OUT: (updated) vehicle objects with updated particle states

2. Kalman filter

IN: particle filter state estimates, road state at time now - delta

OUT: road state at time now

3. Predictions

IN: particle filter state estimates, road state estimates

OUT: ETA to remaining stops along route

Dependencies

• apt-get install build-essential cmake unzip libboost-all-dev libprotobuf-dev libsqlite3-dev cxxtest sqlite3
• (optional) Doxygen (for making the Documentation)
• (optional) Google protobuf compiler protoc: https://github.com/google/protobuf/blob/master/src/README.md (install with make protoc)

To-do

• Application to run indefinitely
• Use a Vehicle object concept with
  • vector<Particle> (N)
  • void update (gtfs::VehiclePosition, gtfs::TripUpdate): adjust the position, arrival/departure times etc, trigger particle transitions
  • void resample (N): perform particle filter weighted resample
  • properties vehicle_id, timestamp, trip_id, route_id, position, stop_sequence, arrival_time, departure_time
• And the particles work in memory only
  • Particle
    • void initialize ()
    • void transition ()
    • void calc_likelihood (): uses parent Vehicle
    • void calc_weight ()
    • properties distance, velocity, stop_index, arrival_time, departure_time, segment_index, queue_time, begin_time, likelihood, weight
• Similar concept for network route segments
  • Segment
    • vector<Path> shape: the GPS coordinates and cumulative distance of segment shape
    • double speed
    • void update (): perform Kalman filter update, using particle summaries (?)
• The GTFS information can either be
  • loaded into an SQLite database, or
  • loaded into a MEMORY table via MySQL
• Vehicle state summaries can be written to a file (?)
• Making information available (via server) - road segment speeds + arrival time predictions
  • database (with no foreign key checks, and no transaction?)

(?) best way of collecting vehicle/segment data

• sequentially append speed estimates to Segment, then periodically update and clear?
• write to file? (makes keeping history easier?)

Project Structure

• docs: documentation (HTML and LaTeX)
• gps: a library containing methods for dealing with GPS coordinates
• gtfs: a library with GTFS object classes, and methods for modeling them
  • Vehicle: Class representing a physical vehicle
  • Particle: Class representing a single vehicle state estimate
  • Segment: Class representing a road segment
• include: header files for programs
• protobuf: GTFS Realtime protobuf description and classes
• src
  • transit_network_model.cpp: mostly just a wrapper for while (TRUE) { ... }
  • load_gtfs.cpp: a program that imports the latest GTFS data and segments it