Struct sim::Analytics[][src]

pub struct Analytics {
Show 16 fields pub road_thruput: TimeSeriesCount<RoadID>, pub intersection_thruput: TimeSeriesCount<IntersectionID>, pub traffic_signal_thruput: TimeSeriesCount<CompressedMovementID>, pub demand: BTreeMap<MovementID, usize>, pub bus_arrivals: Vec<(Time, CarID, TransitRouteID, TransitStopID)>, pub passengers_boarding: BTreeMap<TransitStopID, Vec<(Time, TransitRouteID, Duration)>>, pub passengers_alighting: BTreeMap<TransitStopID, Vec<(Time, TransitRouteID)>>, pub started_trips: BTreeMap<TripID, Time>, pub finished_trips: Vec<(Time, TripID, TripMode, Option<Duration>)>, pub problems_per_trip: BTreeMap<TripID, Vec<(Time, Problem)>>, pub trip_log: Vec<(Time, TripID, Option<PathRequest>, TripPhaseType)>, pub intersection_delays: BTreeMap<IntersectionID, Vec<(u8, Time, Duration, AgentType)>>, pub parking_lane_changes: BTreeMap<LaneID, Vec<(Time, bool)>>, pub parking_lot_changes: BTreeMap<ParkingLotID, Vec<(Time, bool)>>, pub(crate) alerts: Vec<(Time, AlertLocation, String)>, record_anything: bool,
}
Expand description

As a simulation runs, different pieces emit Events. The Analytics object listens to these, organizing and storing some information from them. The UI queries Analytics to draw time-series and display statistics.

For all maps whose weekday scenario fully runs, the game’s release includes some “prebaked results.” These are just serialized Analytics after running the simulation on a map without any edits for the full day. This is the basis of A/B testing – the player can edit the map, start running the simulation, and compare the live Analytics to the prebaked baseline Analytics.

Fields

road_thruput: TimeSeriesCount<RoadID>intersection_thruput: TimeSeriesCount<IntersectionID>traffic_signal_thruput: TimeSeriesCount<CompressedMovementID>demand: BTreeMap<MovementID, usize>

Most fields in Analytics are cumulative over time, but this is just for the current moment in time.

bus_arrivals: Vec<(Time, CarID, TransitRouteID, TransitStopID)>passengers_boarding: BTreeMap<TransitStopID, Vec<(Time, TransitRouteID, Duration)>>

For each passenger boarding, how long did they wait at the stop?

passengers_alighting: BTreeMap<TransitStopID, Vec<(Time, TransitRouteID)>>started_trips: BTreeMap<TripID, Time>finished_trips: Vec<(Time, TripID, TripMode, Option<Duration>)>

Finish time, ID, mode, trip duration if successful (or None if cancelled)

problems_per_trip: BTreeMap<TripID, Vec<(Time, Problem)>>

Record different problems that each trip encounters.

trip_log: Vec<(Time, TripID, Option<PathRequest>, TripPhaseType)>intersection_delays: BTreeMap<IntersectionID, Vec<(u8, Time, Duration, AgentType)>>

Only for traffic signals. The u8 is the movement index from a CompressedMovementID.

parking_lane_changes: BTreeMap<LaneID, Vec<(Time, bool)>>

Per parking lane or lot, when does a spot become filled (true) or free (false)

parking_lot_changes: BTreeMap<ParkingLotID, Vec<(Time, bool)>>alerts: Vec<(Time, AlertLocation, String)>record_anything: bool

For benchmarking, we may want to disable collecting data.

Implementations

Ignores the current time. Returns None for cancelled trips.

Returns pairs of trip times for finished trips in both worlds. (ID, before, after, mode)

If calling on prebaked Analytics, be careful to pass in an unedited map, to match how the simulation was originally run. Otherwise the paths may be nonsense.

Returns the free spots over time

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