mosstool.trip.generator.generate_from_od

Module Contents

Classes

TripGenerator

generate trip from OD matrix.

Functions

geo_coords

_get_mode

_get_mode_with_distribution

_match_aoi_unit

_generate_unit

_process_agent_unit

_fill_sch_unit

_fill_person_schedule_unit

Data

__all__

API

mosstool.trip.generator.generate_from_od.geo_coords(geo)
mosstool.trip.generator.generate_from_od._get_mode(p1, p2)
mosstool.trip.generator.generate_from_od._get_mode_with_distribution(partial_args: tuple[list[str], tuple[float, float, float, float, float, float, float, float, float, float, float]], p1: tuple[float, float], p2: tuple[float, float], profile: dict, seed: int = 0)
mosstool.trip.generator.generate_from_od._match_aoi_unit(partial_args: tuple[list[Any]], aoi)
mosstool.trip.generator.generate_from_od._generate_unit(partial_args: tuple[mosstool.map._map_util.const.np.ndarray, mosstool.map._map_util.const.np.ndarray, mosstool.map._map_util.const.np.ndarray, mosstool.map._map_util.const.np.ndarray, dict[int, list[dict[str, typing.Any]]], dict[int, dict[str, typing.Any]], dict[str, list[int]], int, typing.Any, list[float], int], get_mode_partial_args: tuple[list[str], tuple[float, float, float, float, float, float, float, float, float, float, float]], a_home_region: int, a_profile: dict[str, typing.Any], modes: list[typing.Union[typing.Literal[mosstool.trip.generator.generate_from_od._generate_unit.H], typing.Literal[mosstool.trip.generator.generate_from_od._generate_unit.W], typing.Literal[mosstool.trip.generator.generate_from_od._generate_unit.E], typing.Literal[mosstool.trip.generator.generate_from_od._generate_unit.O], typing.Literal[+]]], p_mode: list[float], seed: int)
mosstool.trip.generator.generate_from_od._process_agent_unit(partial_args_tuple: tuple[tuple[mosstool.map._map_util.const.np.ndarray, mosstool.map._map_util.const.np.ndarray, mosstool.map._map_util.const.np.ndarray, mosstool.map._map_util.const.np.ndarray, dict[int, list[dict[str, typing.Any]]], dict[int, dict[str, typing.Any]], dict[str, list[int]], int, typing.Any, list[float], int], tuple[list[str], tuple[float, float, float, float, float, float, float, float, float, float, float]]], arg: tuple[int, int, int, dict[str, typing.Any], list[typing.Union[typing.Literal[H], typing.Literal[W], typing.Literal[E], typing.Literal[O], typing.Literal[+]]], list[float]])
mosstool.trip.generator.generate_from_od._fill_sch_unit(partial_args: tuple[mosstool.map._map_util.const.np.ndarray, dict[int, list[dict[str, typing.Any]]], dict[int, dict[str, typing.Any]], dict[str, list[int]], int, typing.Any, list[float], int], get_mode_partial_args: tuple[list[str], tuple[float, float, float, float, float, float, float, float, float, float, float]], p_home: int, p_home_region: int, p_work: int, p_work_region: int, p_profile: dict[str, typing.Any], modes: list[typing.Union[typing.Literal[H], typing.Literal[W], typing.Literal[E], typing.Literal[mosstool.trip.generator.generate_from_od._fill_sch_unit.O], typing.Literal[+]]], p_mode: list[float], seed: int)
mosstool.trip.generator.generate_from_od._fill_person_schedule_unit(partial_args_tuple: tuple[tuple[mosstool.map._map_util.const.np.ndarray, dict[int, list[dict[str, typing.Any]]], dict[int, dict[str, typing.Any]], dict[str, list[int]], int, typing.Any, list[float], int], tuple[list[str], tuple[float, float, float, float, float, float, float, float, float, float, float]]], arg: tuple[int, int, int, int, int, dict[str, typing.Any], list[typing.Union[typing.Literal[H], typing.Literal[W], typing.Literal[E], typing.Literal[O], typing.Literal[+]]], list[float], int])
mosstool.trip.generator.generate_from_od.__all__

[‘TripGenerator’]

class mosstool.trip.generator.generate_from_od.TripGenerator(m: mosstool.type.Map, pop_tif_path: Optional[str] = None, activity_distributions: Optional[dict] = None, driving_speed: float = 30 / 3.6, parking_fee: float = 20.0, driving_penalty: float = 0.0, subway_speed: float = 35 / 3.6, subway_penalty: float = 600.0, subway_expense: float = 10.0, bus_speed: float = 15 / 3.6, bus_penalty: float = 600.0, bus_expense: float = 5.0, bike_speed: float = 10 / 3.6, bike_penalty: float = 0.0, template_func: collections.abc.Callable[[], mosstool.type.Person] = default_person_template_generator, add_pop: bool = False, multiprocessing_chunk_size: int = 500, workers: int = cpu_count())

generate trip from OD matrix.

Initialization

Args:

  • m (Map): The Map.

  • pop_tif_path (str): path to population tif file.

  • activity_distributions (dict): human mobility mode and its probability. e.g. {“HWH”: 18.0, “HWH+”: 82.0,}. H for go home, W for go to work, O or + for other activities

  • driving_speed (float): vehicle speed(m/s) for traffic mode assignment.

  • parking_fee (float): money cost(￥) of parking a car for traffic mode assignment.

  • driving_penalty (float): extra cost(s) of vehicle for traffic mode assignment.

  • subway_speed (float): subway speed(m/s) for traffic mode assignment.

  • subway_penalty (float): extra cost(s) of subway for traffic mode assignment.

  • subway_expense (float): money cost(￥) of subway for traffic mode assignment.

  • bus_speed (float): bus speed(m/s) for traffic mode assignment.

  • bus_penalty (float): extra cost(s) of bus for traffic mode assignment.

  • bus_expense (float): money cost(￥) of bus for traffic mode assignment.

  • bike_speed (float): extra cost(s) of bike for traffic mode assignment.

  • bike_penalty (float): money cost(￥) of bike for traffic mode assignment.

  • template_func (Callable[[],Person]): The template function of generated person object, whose schedules, home will be replaced and others will be copied.

  • add_pop (bool): Add population to aois.

  • multiprocessing_chunk_size (int): the maximum size of each multiprocessing chunk

  • workers (int): number of workers.

_read_aois()
_read_regions()
_read_od_matrix()
_match_aoi2region()
_generate_mobi(agent_num: int = 10000, area_pops: Optional[list] = None, person_profiles: Optional[list] = None, seed: int = 0, max_chunk_size: int = 500)
generate_persons(od_matrix: mosstool.map._map_util.const.np.ndarray, areas: geopandas.geodataframe.GeoDataFrame, available_trip_modes: list[str] = ['drive', 'walk', 'bus', 'subway', 'taxi'], departure_time_curve: Optional[list[float]] = None, area_pops: Optional[list] = None, person_profiles: Optional[list[dict]] = None, seed: int = 0, agent_num: Optional[int] = None) → list[mosstool.type.Person]

Args:

  • od_matrix (numpy.ndarray): The OD matrix.

  • areas (GeoDataFrame): The area data. Must contain a ‘geometry’ column with geometric information and a defined crs string.

  • available_trip_modes (list[str]): available trip modes for person schedules.

  • departure_time_curve (Optional[list[float]]): The departure time of a day (24h). The resolution must >=1h.

  • area_pops (list): list of populations in each area. If is not None, # of the persons departs from each home position is exactly equal to the given pop num.

  • person_profiles (Optional[list[dict]]): list of profiles in dict format.

  • seed (int): The random seed.

  • agent_num (int): number of agents to generate.

Returns:

  • list[Person]: The generated person objects.

_get_driving_pos_dict() → dict[tuple[int, int], mosstool.type.LanePosition]
generate_public_transport_drivers(template_func: Optional[collections.abc.Callable[[], mosstool.type.Person]] = None, stop_duration_time: float = 30.0, seed: int = 0) → list[mosstool.type.Person]

Args:

  • template_func (Optional[Callable[[],Person]]): The template function of generated person object, whose schedules, home will be replaced and others will be copied. If not provided, the temp_func provided in __init__ will be utilized.

  • stop_duration_time (float): The duration time (in second) for bus at each stop.

  • seed (int): The random seed.

Returns:

  • list[Person]: The generated driver objects.

_generate_schedules(input_persons: list[mosstool.type.Person], seed: int)
fill_person_schedules(input_persons: list[mosstool.type.Person], od_matrix: mosstool.map._map_util.const.np.ndarray, areas: geopandas.geodataframe.GeoDataFrame, available_trip_modes: list[str] = ['drive', 'walk', 'bus', 'subway', 'taxi'], departure_time_curve: Optional[list[float]] = None, seed: int = 0) → list[mosstool.type.Person]

Generate person schedules.

Args:

  • input_persons (list[Person]): Input Person objects.

  • od_matrix (numpy.ndarray): The OD matrix.

  • areas (GeoDataFrame): The area data. Must contain a ‘geometry’ column with geometric information and a defined crs string.

  • available_trip_modes (Optional[list[str]]): available trip modes for person schedules.

  • departure_time_curve (Optional[list[float]]): The departure time of a day (24h). The resolution must >=1h.

  • seed (int): The random seed.

Returns:

  • list[Person]: The person objects with generated schedules.

generate_taxi_drivers(template_func: Optional[collections.abc.Callable[[], mosstool.type.Person]] = None, parking_positions: Optional[list[Union[mosstool.type.LanePosition, mosstool.type.AoiPosition]]] = None, agent_num: Optional[int] = None, seed: int = 0) → list[mosstool.type.Person]

Args:

  • template_func (Optional[Callable[[],Person]]): The template function of generated person object, whose schedules, home will be replaced and others will be copied. If not provided, the temp_func provided in __init__ will be utilized.

  • parking_positions (Optional[list[Union[LanePosition,AoiPosition]]]): The parking positions of each taxi.

  • agent_num (Optional[int]): The taxi driver num.

  • seed (int): The random seed.

Returns:

  • list[Person]: The generated driver objects.