ritis-inrix-api 1.0.0

Automated download of XD Segment data from RITIS or INRIX API, for daily or real time processing.

RITIS INRIX API

A Python package for automated retrieval of traffic data from RITIS and INRIX APIs for INRIX XD Segments.

Installation

pip install ritis-inrix-api

Core Functionality

This package provides tools to:

• Download Historical Data: Retrieve historical XD segment data from the RITIS API for specific date ranges or on a daily schedule.
• Fetch Real-Time Data: Get current traffic speeds from the INRIX API.
• Scrape Segment Geometries: Identify and retrieve XD segment geometries based on a list of latitude/longitude coordinates.

Examples

Here are some examples of how to use the package.

RITIS API: Download Historical Data

You can download data for a specific date range or set up a recurring daily download.

Download data for a single period

import os
from ritis_inrix_api import RITIS_Downloader

segments = [1236893704, 1236860943]

updater = RITIS_Downloader(
    api_key=os.environ.get('RITIS_API_KEY'),
    segments=segments, # can be list of IDs or path to a .txt file
    columns=['speed', 'travel_time_seconds'], # Specify desired columns
    start_time='06:00:00', #default is '00:00:00
    end_time='06:15:00', #default is '23:59:00'
    bin_size=5, #Enter 1, 5, 10, 15(default), or 60
    units='seconds', #'seconds' or 'minutes'
    #download_path='Data', #where to save data

) 

# Returns a pandas DataFrame (unless download_path is specified)
df = updater.single_download('2025-09-01', '2025-09-02', 'test')

Sample Output

	xd_id	measurement_tstamp	speed	travel_time_seconds
0	1236860943	2025-09-01 06:00:00	25	11.95
1	1236860943	2025-09-01 06:05:00	25	11.95
2	1236860943	2025-09-01 06:10:00	25	11.95

Set up automated daily downloads

This will download data from the last run date through yesterday one day at a time and save it as Parquet files at the specified download path. This option is intended to run daily via a scheduler like cron or Windows Task Scheduler.

import os
from ritis_inrix_api import RITIS_Downloader

updater = RITIS_Downloader(
    api_key=os.environ.get('RITIS_API_KEY'),
    download_path='Data', # Data will be saved in this directory
    segments='sample_XD_segments.txt', # Path to a file with segment IDs
    last_run_path='last_run.txt' # Path for text file containing last run datetime
) 

updater.daily_download()

INRIX API: Fetch Real-Time Speeds

import os
from ritis_inrix_api import INRIX_Downloader

inrix_downloader = INRIX_Downloader(
    app_id=os.environ.get('INRIX_APP_ID'),
    hash_token=os.environ.get('INRIX_HASH_TOKEN'),
    segments_path='sample_XD_segments.txt'
)

# Returns a pandas DataFrame
speed_data = inrix_downloader.get_speed_data()

Sample Output

	code	type	speed	average
0	1236893704	XDS	53	52
1	1236860943	XDS	21	19

Geometry Scraper: Find Segments and Geometries

This tool finds XD segment geometries within a specified radius of given latitude/longitude points.

How to get the authentication cookie

The Geometry Scraper requires a valid browser cookie from a logged-in RITIS session to authenticate its requests. Open network tab in dev tools look for a request that has a cookie, copy and paste the cookie when prompted in the console.

Example Usage

from ritis_inrix_api import GeometryScraper

# A list of (latitude, longitude) tuples
locations = [
    (42.34072155027376, -122.89930147132378),
    (44.3029045246138, -120.842181329508),
    # ... more locations
]

# The scraper will prompt you to paste the cookie into the console
scraper = GeometryScraper()
geometry_data = scraper.process_locations(locations, buffer_size=50) # buffer size in yards

# Save the new segment IDs to a file
segments = list(geometry_data['segID'])
with open('Map_Data/XD_Segments.txt', 'w') as f:
    f.write(','.join(map(str, segments)))

Sample Output

The script will print progress messages as it runs:

Processing 1002 locations in batches of 500
Processing batch 1
Processing batch 2
Processing batch 3
Combining all batches into a single DataFrame
There were 5008 segments found for the provided signals.

The final geometry_data DataFrame will look like this:

	zip	country	segID	bearing	county	...	coordinates
0	97051	USA	1237027426	E	COLUMBIA	...	[[-122.8316, 45.84865], [-122.83134, 45.84852], ...]
1	97756	USA	1237004066	S	DESCHUTES	...	[[-121.19391, 44.24292], [-121.1941, 44.2427], ...]
2	97527	USA	125164532	E	JOSEPHINE	...	[[-123.32078, 42.42779], [-123.32054, 42.42772], ...]