TBTR Functions

Module contains function related to TBTR, rTBTR, One-To-Many rTBTR, HypTBTR

Algorithms.TBTR.TBTR_functions.enqueue(connection_list: list, nextround: int, predecessor_label: tuple, R_t: dict, Q: list, stoptimes_dict: dict) → None[source]

Main enqueue function used in TBTR to add trips segments to next round and update first reached stop of each trip.

Parameters

  • connection_list (list) – list of connections to be added. Format: [(to_trip_id, to_trip_id_stop_index)].

  • nextround (int) – next round/transfer number to which trip-segments are added.

  • predecessor_label (tuple) – used for backtracking journey ( To be developed ).

  • R_t (dict) – dict with keys as trip id. Format {trip_id : first reached stop}.

  • Q (list) – list of trips segments.

  • stoptimes_dict (dict) – preprocessed dict. Format {route_id: [[trip_1], [trip_2]]}.

Returns

None

Algorithms.TBTR.TBTR_functions.enqueue_range(connection_list: list, nextround: int, predecessor_label: tuple, R_t: dict, Q: list, stoptimes_dict: dict, MAX_TRANSFER: int) → None[source]

Adds trips-segments to next round and update R_t. Used in range queries

Parameters

  • connection_list (list) – list of connections to be added. Format: [(to_trip_id, to_trip_id_stop_index)].

  • nextround (int) – next round/transfer number to which trip-segments are added

  • predecessor_label (tuple) – predecessor_label for backtracking journey ( To be developed ).

  • R_t (nested dict) – Nested_Dict with primary keys as trip id and secondary keys as number of transfers. Format {trip_id: {[round]: first reached stop}}

  • Q (list) – list of trips segments

  • stoptimes_dict (dict) – preprocessed dict. Format {route_id: [[trip_1], [trip_2]]}.

  • MAX_TRANSFER (int) – maximum transfer limit.

Returns: None

Algorithms.TBTR.TBTR_functions.initialize_from_desti(routes_by_stop_dict: dict, stops_dict: dict, DESTINATION: int, footpath_dict: dict, idx_by_route_stop_dict: dict) → dict[source]

Initialize routes/footpath to leading to destination stop.

Parameters

  • routes_by_stop_dict (dict) – preprocessed dict. Format {stop_id: [id of routes passing through stop]}.

  • stops_dict (dict) – preprocessed dict. Format {route_id: [ids of stops in the route]}.

  • DESTINATION (int) – stop id of destination stop.

  • footpath_dict (dict) – preprocessed dict. Format {from_stop_id: [(to_stop_id, footpath_time)]}.

  • idx_by_route_stop_dict (dict) – preprocessed dict. Format {(route id, stop id): stop index in route}.

Returns

A dict to track routes/leading to destination stop. Format {route_id: (from_stop_idx, travel time, stop id)}

Return type

L (dict)

Examples

>>> output = initialize_from_desti(routes_by_stop_dict, stops_dict, 1482, footpath_dict, idx_by_route_stop_dict)
>>> print(output)
Algorithms.TBTR.TBTR_functions.initialize_from_desti_onemany(routes_by_stop_dict: dict, stops_dict: dict, DESTINATION_LIST: list, footpath_dict: dict, idx_by_route_stop_dict: dict) → dict[source]

Initialize routes/footpath to leading to destination stop in case of one-to-many rTBTR

Parameters

  • routes_by_stop_dict (dict) – preprocessed dict. Format {stop_id: [id of routes passing through stop]}.

  • stops_dict (dict) – preprocessed dict. Format {route_id: [ids of stops in the route]}.

  • DESTINATION_LIST (list) – list of stop ids of destination stop.

  • footpath_dict (dict) – preprocessed dict. Format {from_stop_id: [(to_stop_id, footpath_time)]}.

  • idx_by_route_stop_dict (dict) – preprocessed dict. Format {(route id, stop id): stop index in route}.

Returns

A dict to track routes/leading to destination stops. Key: route_id, value: {destination_stop_id: [(from_stop_idx, travel time, stop id)]}

Return type

L (nested dict)

Examples

>>> output = initialize_from_desti_onemany(routes_by_stop_dict, stops_dict, [1482], footpath_dict, idx_by_route_stop_dict)
>>> print(output)
Algorithms.TBTR.TBTR_functions.initialize_from_source(footpath_dict: dict, SOURCE: int, routes_by_stop_dict: dict, stops_dict: dict, stoptimes_dict: dict, D_TIME, MAX_TRANSFER: int, WALKING_FROM_SOURCE: int, idx_by_route_stop_dict: dict) → tuple[source]

Initialize trips segments from source stop.

Parameters

  • footpath_dict (dict) – preprocessed dict. Format {from_stop_id: [(to_stop_id, footpath_time)]}.

  • SOURCE (int) – stop id of source stop.

  • routes_by_stop_dict (dict) – preprocessed dict. Format {stop_id: [id of routes passing through stop]}.

  • stops_dict (dict) – preprocessed dict. Format {route_id: [ids of stops in the route]}.

  • stoptimes_dict (dict) – preprocessed dict. Format {route_id: [[trip_1], [trip_2]]}.

  • D_TIME (pandas.datetime) – departure time.

  • MAX_TRANSFER (int) – maximum transfer limit.

  • WALKING_FROM_SOURCE (int) – 1 or 0. 1 means walking from SOURCE is allowed.

  • idx_by_route_stop_dict (dict) – preprocessed dict. Format {(route id, stop id): stop index in route}.

Returns

dict to store first reached stop of every trip. Format {trip_id: first reached stop} Q (list): list of trips segments

Return type

R_t (dict)

Examples

>>> output = initialize_from_source(footpath_dict, 20775, routes_by_stop_dict, stops_dict, stoptimes_dict, pd.to_datetime('2019-06-10 00:00:00'), 4, 1, idx_by_route_stop_dict)
>>> print(output)
Algorithms.TBTR.TBTR_functions.initialize_from_source_range(dep_details: list, MAX_TRANSFER: int, stoptimes_dict: dict, R_t: dict) → list[source]

Initialize trips segments from source in rTBTR

Parameters

  • dep_details (list) – list of format [trip id, departure time, source index]

  • MAX_TRANSFER (int) – maximum transfer limit.

  • stoptimes_dict (dict) – preprocessed dict. Format {route_id: [[trip_1], [trip_2]]}.

  • R_t (nested dict) – Nested_Dict with primary keys as trip id and secondary keys as number of transfers. Format {trip_id: {[round]: first reached stop}}

Returns

list of trips segments

Return type

Q (list)

Algorithms.TBTR.TBTR_functions.initialize_onemany(MAX_TRANSFER: int, DESTINATION_LIST: list) → tuple[source]

Initialize values for one-to-many TBTR.

Parameters

  • MAX_TRANSFER (int) – maximum transfer limit.

  • DESTINATION_LIST (list) – list of stop ids of destination stop.

Returns

dict to store arrival timestamps. Keys: number of transfer, Values: arrival time. inf_time (pandas.datetime): Variable indicating infinite time.

Return type

J (dict)

Examples

>>> output = initialize_onemany(4, [1482])
>>> print(output)
Algorithms.TBTR.TBTR_functions.initialize_tbtr(MAX_TRANSFER: int) → dict[source]

Initialize values for TBTR.

Returns

dict to store arrival timestamps. Keys: number of transfer, Values: arrival time. inf_time (pandas.datetime): Variable indicating infinite time.

Return type

J (dict)

Examples

>>> output = initialize_tbtr(4)
>>> print(output)
Algorithms.TBTR.TBTR_functions.post_process(J: dict, Q: list, DESTINATION: int, SOURCE: int, footpath_dict: dict, stops_dict: dict, stoptimes_dict: dict, PRINT_ITINERARY: int, D_TIME, MAX_TRANSFER: int, trip_transfer_dict: dict) → list[source]

Contains post-processing features for TBTR. Currently supported functionality:

Collect pareto-optimal arrival timestamps.

Parameters

  • J (dict) – dict to store arrival timestamps. Keys: number of transfer, Values: arrival time

  • Q (list) – list of trips segments.

  • DESTINATION (int) – stop id of destination stop.

  • SOURCE (int) – stop id of source stop.

  • footpath_dict (dict) – preprocessed dict. Format {from_stop_id: [(to_stop_id, footpath_time)]}.

  • stops_dict (dict) – preprocessed dict. Format {route_id: [ids of stops in the route]}.

  • stoptimes_dict (dict) – preprocessed dict. Format {route_id: [[trip_1], [trip_2]]}.

  • PRINT_ITINERARY (int) – 1 or 0. 1 means print complete path.

  • D_TIME (pandas.datetime) – departure time.

  • MAX_TRANSFER (int) – maximum transfer limit.

  • trip_transfer_dict (nested dict) – keys: id of trip we are transferring from, value: {stop number: list of tuples

Returns

pareto-optimal arrival timestamps.

Return type

TBTR_out (list)

Algorithms.TBTR.TBTR_functions.post_process_range(J: dict, Q: list, rounds_desti_reached: list, PRINT_ITINERARY: int, DESTINATION: int, SOURCE: int, footpath_dict: dict, stops_dict: dict, stoptimes_dict: dict, d_time, MAX_TRANSFER: int, trip_transfer_dict: dict) → set[source]

Contains all the post-processing features for rTBTR. Currently supported functionality:

Collect list of trips needed to cover pareto-optimal journeys.

Parameters

  • J (dict) – dict to store arrival timestamps. Keys: number of transfer, Values: arrival time

  • Q (list) – list of trips segments.

  • rounds_desti_reached (list) – Rounds in which DESTINATION is reached.

  • PRINT_ITINERARY (int) – 1 or 0. 1 means print complete path.

  • DESTINATION (int) – stop id of destination stop.

  • SOURCE (int) – stop id of source stop.

  • footpath_dict (dict) – preprocessed dict. Format {from_stop_id: [(to_stop_id, footpath_time)]}.

  • stops_dict (dict) – preprocessed dict. Format {route_id: [ids of stops in the route]}.

  • stoptimes_dict (dict) – preprocessed dict. Format {route_id: [[trip_1], [trip_2]]}.

  • D_TIME (pandas.datetime) – departure time.

  • MAX_TRANSFER (int) – maximum transfer limit.

  • trip_transfer_dict (nested dict) – keys: id of trip we are transferring from, value: {stop number: list of tuples

Returns

trips needed to cover pareto-optimal journeys.

Return type

necessory_trips (set)

Algorithms.TBTR.TBTR_functions.post_process_range_onemany(J: dict, Q: list, rounds_desti_reached: list, PRINT_ITINERARY: int, desti: int, SOURCE: int, footpath_dict: dict, stops_dict: dict, stoptimes_dict: dict, d_time, MAX_TRANSFER: int, trip_transfer_dict: dict) → set[source]

Contains all the post-processing features for One-To-Many rTBTR. Currently supported functionality:

Collect list of trips needed to cover pareto-optimal journeys.

Parameters

  • J (dict) – dict to store arrival timestamps. Keys: number of transfer, Values: arrival time

  • Q (list) – list of trips segments.

  • rounds_desti_reached (list) – Rounds in which DESTINATION is reached.

  • PRINT_ITINERARY (int) – 1 or 0. 1 means print complete path.

  • desti (int) – stop id of destination stop.

  • SOURCE (int) – stop id of source stop.

  • footpath_dict (dict) – preprocessed dict. Format {from_stop_id: [(to_stop_id, footpath_time)]}.

  • stops_dict (dict) – preprocessed dict. Format {route_id: [ids of stops in the route]}.

  • stoptimes_dict (dict) – preprocessed dict. Format {route_id: [[trip_1], [trip_2]]}.

  • d_time (pandas.datetime) – departure time.

  • MAX_TRANSFER (int) – maximum transfer limit.

  • trip_transfer_dict (nested dict) – keys: id of trip we are transferring from, value: {stop number: list of tuples

Returns

Trips needed to cover pareto-optimal journeys.

Return type

TBTR_out (set)

Algorithms.TBTR.TBTR_functions.update_label(label, no_of_transfer: int, predecessor_label: tuple, J: dict, MAX_TRANSFER: int) → dict[source]

Updates and returns destination pareto set.

Parameters

  • label (pandas.datetime) – optimal arrival time .

  • no_of_transfer (int) – number of transfer.

  • predecessor_label (tuple) – predecessor_label for backtracking (To be developed)

  • J (dict) – dict to store arrival timestamps. Keys: number of transfer, Values: arrival time

  • MAX_TRANSFER (int) – maximum transfer limit.

Returns

dict to store arrival timestamps. Keys: number of transfer, Values: arrival time

Return type

J (dict)