Taxi Service Trajectory - Prediction Challenge, ECML PKDD 2015
Donated on 7/10/2015
An accurate dataset describing trajectories performed by all the 442 taxis running in the city of Porto, in Portugal.
Dataset Characteristics
Multivariate, Sequential, Time-Series, Domain-Theory
Subject Area
Computer Science
Associated Tasks
Clustering, Causal-Discovery
Feature Type
Real
# Instances
1710671
# Features
9
Dataset Information
Additional Information
For complete information see the official challenge page: http://www.geolink.pt/ecmlpkdd2015-challenge/dataset.html
Has Missing Values?
Yes
Variable Information
Each data sample corresponds to one completed trip. It contains a total of 9 (nine) features, described as follows: TRIP_ID: (String) It contains a unique identifier for each trip; CALL_TYPE: (char) It identifies the way used to demand this service. It may contain one of three possible values: - 'A' if this trip was dispatched from the central; - 'B' if this trip was demanded directly to a taxi driver at a specific stand; - 'C' otherwise (i.e. a trip demanded on a random street). ORIGIN_CALL: (integer) It contains a unique identifier for each phone number which was used to demand, at least, one service. It identifies the trip's customer if CALL_TYPE='A'. Otherwise, it assumes a NULL value; ORIGIN_STAND: (integer): It contains a unique identifier for the taxi stand. It identifies the starting point of the trip if CALL_TYPE='B'. Otherwise, it assumes a NULL value; TAXI_ID: (integer): It contains a unique identifier for the taxi driver that performed each trip; TIMESTAMP: (integer) Unix Timestamp (in seconds). It identifies the trip's start; DAYTYPE: (char) It identifies the daytype of the trip's start. It assumes one of three possible values: - 'B' if this trip started on a holiday or any other special day (i.e. extending holidays, floating holidays, etc.); - 'C' if the trip started on a day before a type-B day; - 'A' otherwise (i.e. a normal day, workday or weekend). IMPORTANT NOTICE: This field has not been correctly calculated. Please see the following links as reliable sources for official holidays in Portugal. http://calendar.retira.eu/public-holidays/portugal/2013/ http://calendar.retira.eu/public-holidays/portugal/2014/ MISSING_DATA: (Boolean) It is FALSE when the GPS data stream is complete and TRUE whenever one (or more) locations are missing; POLYLINE: (String): It contains a list of GPS coordinates (i.e. WGS84 format) mapped as a string. The beginning and the end of the string are identified with brackets (i.e. [ and ], respectively). Each pair of coordinates is also identified by the same brackets as [LONGITUDE, LATITUDE]. This list contains one pair of coordinates for each 15 seconds of trip. The last list item corresponds to the trip's destination while the first one represents its start.
Dataset Files
File | Size |
---|---|
train.csv.zip | 508.9 MB |
Porto_taxi_data_test_partial_trajectories.csv | 436.9 KB |
solution_fixed.csv | 8.3 KB |
solution_challengeII.csv | 3.8 KB |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset taxi_service_trajectory_prediction_challenge_ecml_pkdd_2015 = fetch_ucirepo(id=339) # data (as pandas dataframes) X = taxi_service_trajectory_prediction_challenge_ecml_pkdd_2015.data.features y = taxi_service_trajectory_prediction_challenge_ecml_pkdd_2015.data.targets # metadata print(taxi_service_trajectory_prediction_challenge_ecml_pkdd_2015.metadata) # variable information print(taxi_service_trajectory_prediction_challenge_ecml_pkdd_2015.variables)
Moreira-Matias, L., Ferreira, M., Mendes-Moreira, J., L., L., & J., J. (2013). Taxi Service Trajectory - Prediction Challenge, ECML PKDD 2015 [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C55W25.
Creators
Luis Moreira-Matias
Michel Ferreira
Joao Mendes-Moreira
L. L.
J. J.
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.