app.state.schedule

The planned schedule: every vehicle’s stop visits for the day, indexed for arrival detection.

schedule_plan.csv rows are planned visits: tt_action_item_id identifies one visit (the same id the ML contract calls target_stop_id), building_address is the only stop name there is. A planned gap longer than TRIP_GAP_S between consecutive visits is a terminal: a new trip begins.

Functions

load_schedule(path)

Classes

StopVisit(pos, stop_id, plan, lat, lon, ...)
VehicleSchedule(tr_id, visits)

class app.state.schedule.StopVisit(pos: 'int', stop_id: 'int', plan: 'datetime', lat: 'float', lon: 'float', manual_fill: 'bool', name: 'str', geom: 'str', trip: 'int', idx_in_trip: 'int')

Базовые классы: object

__init__(pos: int, stop_id: int, plan: datetime, lat: float, lon: float, manual_fill: bool, name: str, geom: str, trip: int, idx_in_trip: int) → None

geom: str

idx_in_trip: int

property label: str

the address, or a placeholder where the dataset has none.

Type:

What the dispatcher sees

lat: float

lon: float

manual_fill: bool

name: str

plan: datetime

pos: int

stop_id: int

trip: int

class app.state.schedule.VehicleSchedule(tr_id: 'int', visits: 'list[StopVisit]')

Базовые классы: object

__init__(tr_id: int, visits: list[StopVisit]) → None

cum_dist_m: numpy.ndarray

first_after(t_s: int) → int | None

Position of the first visit planned strictly after t_s.

lat: numpy.ndarray

lon: numpy.ndarray

mf: numpy.ndarray

plan_s: numpy.ndarray

tr_id: int

visits: list[StopVisit]

app.state.schedule.load_schedule(path: Path) → dict[int, VehicleSchedule]