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.
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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,Luis, Ferreira,Michel, Mendes-Moreira,Joao, L.,L., and J.,J.. (2015). Taxi Service Trajectory - Prediction Challenge, ECML PKDD 2015. UCI Machine Learning Repository. https://doi.org/10.24432/C55W25.
@misc{misc_taxi_service_trajectory_-_prediction_challenge,_ecml_pkdd_2015_339,
author = {Moreira-Matias,Luis, Ferreira,Michel, Mendes-Moreira,Joao, L.,L., and J.,J.},
title = {{Taxi Service Trajectory - Prediction Challenge, ECML PKDD 2015}},
year = {2015},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: 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.