| # 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](https://github.com/LabIA-UFBA/SUNT#). | |
| ### Available datasets: | |
| - [boarding_03-05_2024.csv](boarding_03-05_2024.csv): Estimated boardings per stop | |
| - [landing_03-05_2024.csv](landing_03-05_2024.csv): Estimated alightings per stop | |
| - [loader_03-05_2024.csv](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](Visualize_series.ipynb). | |
| ## 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: | |
| | Stop ID | Location | Observations | | |
| |-------------|--------------------------------------------------------------------------|------------------------------------------------------------------------------| | |
| | 44783654 | In front of the Federal University of Bahia (UFBA) | High volume of students and university staff | | |
| | 43768720 | Lapa Station | One of the city's main public transport terminals | | |
| | 230565994 | Itapuã Lighthouse Beach | Passenger flow varies throughout the day; higher on weekends and holidays | | |
| | 125960550 | Arena Fonte Nova surroundings | Demand influenced by sports and cultural events | | |
| | 45833547 | Near Manoel Barradas Stadium | Sharp increases in demand during game days | | |
| | 44784438 | Near the Ferry Boat terminal | Primarily serves intercity and cross-bay travelers | | |
| | 47568123 | Near a major shopping mall | High commercial traffic throughout the day | | |
| | 44072192 | Close to Castro Alves Theater | Passenger flow increases during performance and event hours | | |
| | 258781031 | Salvador Bus Station | Central hub for urban and intercity bus routes | | |
| | 44783914 | Lacerda Elevator tourist area | Located in a busy commercial and tourist district | | |
| ## 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 | |
|  | |
| ### Transport network - Stops | |
|  | |
| To more plots, [Jupyter Notebook Overview](gtfs/overview.ipynb) | |
| ### Description | |
| Below is a brief overview of how to use these GTFS files and what each file represents in the `gtfs` folder: | |
| 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. | |
| 2. **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. | |
| 3. **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. | |
| 4. **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. | |
| 5. **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. | |
| 6. **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. | |
| 7. **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. | |
| 8. **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. | |
| 9. **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. | |
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| **License:** [CC BY-ND 4.0](https://creativecommons.org/licenses/by-nd/4.0/) |