Converting GTFS-RT Trip Updates Data to a Pandas DataFrame

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In this article, we will explore how to convert the GTFS-RT trip updates data from a dictionary format to a pandas DataFrame. The GTFS-RT (General Transit Feed Specification Real-time) protocol is used by many transit agencies around the world to provide real-time information about bus and train positions, as well as stops and schedules.

Introduction

The GTFS-RT protocol uses Protocol Buffers, a language-neutral, platform-neutral, extensible way of serializing structured data. The protocol defines a message format that describes various aspects of public transportation services, including trip updates. These trip updates provide information about the location and status of buses or trains at any given time.

The provided Python script fetches GTFS-RT Trip Updates data from a URL using the requests library and parses it into a dictionary format using the protobuf_to_dict function. The resulting dictionary contains nested dictionaries representing individual bus trip updates, which need to be converted into a pandas DataFrame for easier analysis and manipulation.

GTFS-RT Protocol Basics

To understand how to convert the data, we first need to grasp the basics of the GTFS-RT protocol. Here’s a brief overview:

• The GTFS-RT message format consists of several fields, including:
  • id: A unique identifier for the trip update.
  • trip: Describes the trip being updated (e.g., trip ID, start date, schedule relationship).
  • stop_time_update: Provides information about specific stops along the route, such as arrival and departure times, delays, and uncertainty levels.
  • vehicle: Specifies the vehicle associated with the trip update (id, label).
• The protocol also includes additional fields for various purposes, like timestamps and stop IDs.

Converting GTFS-RT Trip Updates Data to a Pandas DataFrame

The conversion process involves flattening the nested dictionary into a pandas DataFrame. Here’s how you can achieve it:

Python Code

from google.transit import gtfs_realtime_pb2
import requests
import pandas as pd

# Fetch GTFS-RT Trip Updates Data from URL
feed = gtfs_realtime_pb2.FeedMessage()
response = requests.get('link')
feed.ParseFromString(response.content)

# Convert dictionary to pandas DataFrame
buses_dict = {}
for entry in feed.entries:
    for trip_update in entry.trip_updates:
        bus_id = trip_update.vehicle.id
        if bus_id not in buses_dict:
            buses_dict[bus_id] = {'trip': {}, 'stop_times': [], 'vehicle': {}}

        # Extract Trip Data
        trip_id = trip_update.trip.trip_id
        trip_data = {
            'trip_id': trip_id,
            'start_time': trip_update.trip.start_time,
            'start_date': trip_update.trip.start_date,
            'schedule_relationship': trip_update.trip.schedule_relationship,
            'route_id': trip_update.trip.route_id,
            'direction_id': trip_update.trip.direction_id
        }
        buses_dict[bus_id]['trip'] = trip_data

        # Extract Stop Time Data
        for stop_time_update in trip_update.stop_time_updates:
            stop_sequence = stop_time_update.stop_sequence
            arrival = {
                'delay': stop_time_update.arrival.delay,
                'time': stop_time_update.arrival.time,
                'uncertainty': stop_time_update.arrival.uncertainty
            }
            departure = {
                'delay': stop_time_update.departure.delay,
                'time': stop_time_update.departure.time,
                'uncertainty': stop_time_update.departure.uncertainty
            }
            buses_dict[bus_id]['stop_times'].append({
                'stop_sequence': stop_sequence,
                'arrival': arrival,
                'departure': departure
            })

        # Extract Vehicle Data
        vehicle_data = {
            'id': trip_update.vehicle.id,
            'label': trip_update.vehicle.label,
            'occupancy_status': trip_update.vehicle.occupancy_status
        }
        buses_dict[bus_id]['vehicle'] = vehicle_data

buses_df = pd.json_normalize(buses_dict)

print(buses_df)

Explanation

The Python script starts by fetching the GTFS-RT Trip Updates data from a URL using the requests library and parsing it into a dictionary format. Then, it iterates through each trip update in the dictionary and performs the following tasks:

1. Trip Data: Extracts relevant information about the trip being updated (e.g., trip ID, start date, schedule relationship) and stores it under the corresponding key in the buses_dict dictionary.
2. Stop Time Data: Iterates through each stop time update and extracts arrival and departure data for each stop along the route. Stores these values as separate dictionaries within the stop_times list for the respective bus ID.
3. Vehicle Data: Retrieves vehicle-related information (e.g., id, label) and stores it in a dictionary under the corresponding key.

After processing all trip updates, the script uses the pd.json_normalize() function to convert the nested dictionary into a pandas DataFrame (buses_df). This DataFrame provides an efficient structure for analyzing and manipulating transit data.

Conclusion

Converting GTFS-RT Trip Updates Data from a dictionary format to a pandas DataFrame is essential for leveraging this valuable dataset in various applications, such as route optimization, trip planning, or real-time passenger information systems. By following the steps outlined above and understanding the underlying GTFS-RT protocol basics, developers can unlock the full potential of their transit data.

Last modified on 2023-06-14