TripGo Routing API Parser
A simple parser for use with the TripGo routing API. This package should be used to obtain trip information based on Origin-Destination pairs.
Getting Started
Installation
Windows
If using an Anaconda environment, enter the following in the Anaconda command prompt.
conda install pip
pip install tripgo-parserMac/Linux
pip install tripgo-parserUsage
All Modes
Note: The TripGo routing API does not give all possible modes for a given O-D pair. Instead, the API returns the trips for modes (and mode combinations) it deems reasonable.
For example:
import tripgo_parser as tgp
key = 'fakekey123' # TripGo Authentication Key
olt = '-37.83724'
oln = '145.201767'
dlt = '-37.817800'
dln = '145.018510'
mpm = '1110' # Minutes past midnight
dot = '3/1/20' # Date of travel: d/m/yy
# Request the routing data from TripGo
data = tgp.get.Response(key, olt, oln, dlt, dln, mpm, dot).fetch()
parsedData = tgp.parse.Parse(data).getCompiledData()This will return:
code output
{'car': 1, # 1 = Information for this mode exists
'car_calorieCost': 30, # TripGo calculated calorie cost
'car_distances': {'me_car': 23851}, # Dict of distances for each mode used
'car_fuelCost': 2.12, # TripGo estimated fuel cost
'car_hassleCost': 4, # TripGo estimated hassle cost
'car_modeCount': 1, # Number of modes used in trip
'car_numberOfTransfers': 0, # Number of public transport transfers
'car_parkingCost': 0, # If exists, cost of parking
'car_parkingExists': False, # Does off-street parking exist?
'car_travelTime': 1816, # Total travel time
'car_tripModes': {0: 'me_car'}, # Dict of ordered modes used
'car_walkingDistance': 0, # If exists, walking distance within trip
'car_weightedScore': 44.1, # Tripgo calculated weighted score (ranking)
'cycling': 1,
'cycling_Calorie Cost': 375,
'cycling_distances': {'cy_bic': 20002},
'cycling_hassleCost': 8.7,
'cycling_modes': 'cy_bic',
'cycling_travelTime': 4175,
'cycling_weightedScore': 106.8,
'startime': '',
'taxi': 1,
'taxi_hassleCost': 2,
'taxi_numberOfTransfers': 0,
'taxi_travelTime': 2205,
'taxi_waitTime': 0,
'taxi_walkingDistance': 0,
'taxi_weightedScore': 69.3,
'transit': 1,
'transit_allModes': {0: 'me_car',
1: 'stationary_parking-onstreet',
2: 'stationary_transfer',
3: 'pt_pub_train',
4: 'wa_wal'},
'transit_distances': {'me_car': 3828,
'pt_pub_train': 0,
'stationary_parking-onstreet': 0,
'stationary_transfer': 0,
'wa_wal': 999},
'transit_end': {'pt_pub_train': {'Hawthorn Railway Station': 1609661820}},
'transit_hassleCost': 6,
'transit_mainMode': 'pt_pub_train',
'transit_modes': ['me_car',
'stationary_parking-onstreet',
'stationary_transfer',
'pt_pub_train',
'wa_wal'],
'transit_numberOfTransfers': 0,
'transit_start': {'pt_pub_train': {'Heatherdale Railway Station': 1609660080}},
'transit_timeOnEachMode': {'me_car': 289,
'pt_pub_train': 1740,
'stationary_parking-onstreet': 240,
'stationary_transfer': 180,
'wa_wal': 872},
'transit_timeOnMainMode': 1740,
'transit_timeOnOtherModes': 1581,
'transit_totalTravelTime': 3339,
'transit_waitTime': 0,
'transit_weightedScore': 50.7,
'walking': 0}For a large OD datasets, it is recommended to save the JSON data. The 'save' function takes one required parameter and one optional parameter. These are respectively:
- Destination Folder ('myFolder') - written in the current working directory
- Unique ID (..,unique_id='myuniqueid') - name of file
This can be done using the TripGo Parser as follows:
data = tgp.get.Response(key, olt, oln, dlt, dln, mpm, dot).save('MyFolder', unique_id='trip1')And the resulting file will be saved as ../MyFolder/trip1.json. This file can then be parsed using the following:
import json
with open('MyFolder/trip1.json') as file:
data = json.loads(file.read())
parsedData = tgp.parse.Parse(data)This will yield the same result as the above example.
Specific Modes
Specific modes can be specified to be used with the API. Please note that if multiple modes are listed, the trip will be classified as the dominant mode. For example:
modes = ['me_car', 'pt_pub']
data = tgp.get.Response(key, olt, oln, dlt, dln, mpm, dot, modes=modes).save('MyFolder', unique_id='trip2')VISTA Dataset
For use with the VISTA dataset, significant area (SA1) values must be converted to coordinates using their centroids. Using these coordinates labelled (e.g. 'origlat' for latitude origin coordinate) and the associated trip ID and start time (minutes past midnight) we are able to use the TripGo routing API to obtain trip attributes.
import pandas as pd
import json
vista_dataset = pd.read_csv('vista.csv')
i = 0
key = 'fakekey123'
olt = vista_dataset.origlat[i] # All parameters should be strings
oln = vista_dataset.origlon[i]
dlt = vista_dataset.destlat[i]
dln = vista_dataset.destlon[i]
startime = vista_dataset.startime[i] # For example: '1020'
travdate = vista_dataset.travdate[i]
modes = ['pt_pub']
tripid = vista_dataset.tripid[i] # For example: 'Y14H1050101P01T02'
data = tgp.get.Response(key, olt, oln, dlt, dln, mpm, dot, tripid=tripid, modes=modes).save('VISTA_trips')This will result in the file being saved as:
../VISTA_trips/Y14H1050101P01T02-1020.json
