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https://github.com/tmelliott/TransitNetworkModel

A realtime model of the Auckland Public Transport Network
https://github.com/tmelliott/TransitNetworkModel

cpp11 gtfs gtfs-realtime particle-filter public-transport transit

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A realtime model of the Auckland Public Transport Network

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README 

        

# 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



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## 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 (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 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