labiaufba/SSA_StopBusTimeSeries_5

Public Transport Time Series

Overview

This dataset provides time series data on estimated boardings, landings, and bus occupancy (loader) across selected stops in the public transport network. The data is aggregated in 5-minute intervals and was generated through the integration and analysis of multiple transport data sources.

• —Boarding data was extracted using data mining techniques from Automatic Vehicle Location (AVL), Automatic Fare Collection (AFC), and General Transit Feed Specification (GTFS) systems.
• —Alighting data was estimated using the trip chaining method.
• —Bus occupancy was calculated based on the ratio of boardings and alightings per vehicle.

The full SUNT dataset is available on GITHUB.

Available datasets:

• —boarding_03-05_2024.csv: Estimated boardings per stop
• —landing_03-05_2024.csv: Estimated alightings per stop
• —loader_03-05_2024.csv: Estimated bus occupancy

Time Series plots

Boarding

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Landing

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Loader

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This example illustrates how to use a dataset and plot the series: Visualize.

Description of Selected Stops

From over 2,700 bus stops in the network, ten were selected based on diverse and representative passenger flow patterns. The table below summarizes the characteristics of each location:

GTFS

GTFS (General Transit Feed Specification) is a widely adopted, standardized format for sharing public transit schedules and related geographic data. By providing a set of simple text files (CSV-style), GTFS makes it easy for developers, researchers, and planners to analyze route structure, service frequency, stop locations, and trip timing for any transit agency.

Transport network - Shapes

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Transport network - Stops

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To more plots, Jupyter Notebook Overview

Description

Below is a brief overview of how to use these GTFS files and what each file represents in the gtfs folder:

1. 1.agency.txt – Contains basic information about the transit agency (name, URL, time zone, language). – Useful for identifying which agency the feed belongs to, especially when multiple agencies share a single dataset.

1. 1.stops.txt – Lists all individual stops or stations, with columns such as stop_id, stop_name, stop_lat, stop_lon. – Use this file to plot stop locations on a map or to calculate distances between stops.

1. 1.routes.txt – Describes each route (e.g., bus line) using route_id, route_short_name, route_long_name, and optional route colors. – Ideal for grouping trips and distinguishing one service corridor from another.

1. 1.trips.txt – Defines each trip as a single run of a vehicle along a route on a given service day. – Key columns: route_id, service_id, trip_id, shape_id. – Combine with stop_times.txt to reconstruct a vehicle’s schedule.

1. 1.stop\_times.txt – Contains the scheduled departure and arrival times at each stop for every trip (using trip_id, arrival_time, departure_time, stop_id, stop_sequence). – Enables analysis of headways, dwell times, and temporal patterns.

1. 1.calendar.txt – Specifies on which days of the week each service (service_id) operates, along with a start and end date. – Use this to filter trips by weekday, weekend, or holiday schedules.

1. 1.calendar\_dates.txt (optional) – Lists exceptions to the regular calendar.txt schedule, such as added or removed service on specific dates (holidays or special events). – Important for correctly modeling service on days when regular schedules are modified.

1. 1.shapes.txt – Defines the exact path (“shape”) of each trip via a sequence of latitude/longitude points (shape_id, shape_pt_lat, shape_pt_lon, shape_pt_sequence). – Use this to draw each route on a map in its real-world alignment.

1. 1.frequencies.txt (optional) – Specifies headways (time between vehicles) for frequency-based scheduling instead of exact stop times. – Contents include trip_id, start_time, end_time, headway_secs. – Useful for feeds that define service in terms of “every 10 minutes” rather than fixed departure times.

License: CC BY-ND 4.0