Center for Machine Learning and Intelligent Systems
About  Citation Policy  Donate a Data Set  Contact


Repository Web            Google
View ALL Data Sets

Taxi Service Trajectory - Prediction Challenge, ECML PKDD 2015 Data Set
Download: Data Folder, Data Set Description

Abstract: An accurate dataset describing trajectories performed by all the 442 taxis running in the city of Porto, in Portugal.

Data Set Characteristics:  

Multivariate, Sequential, Time-Series, Domain-Theory

Number of Instances:

1710671

Area:

Computer

Attribute Characteristics:

Real

Number of Attributes:

9

Date Donated

2015-07-11

Associated Tasks:

Clustering, Causal-Discovery

Missing Values?

Yes

Number of Web Hits:

33300


Source:

Challenge Chair: Luis Moreira-Matias

Steering Committee:
- Michel Ferreira
- Joao Mendes-Moreira

tst.challenge '@' ecmlpkdd2015.org

http://www.geolink.pt/ecmlpkdd2015-challenge/whoweare.html


Data Set Information:

For complete information see the official challenge page:
[Web Link]


Attribute 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.
[Web Link]
[Web Link]

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.


Relevant Papers:

Moreira-Matias L., Gama J., Ferreira M., Mendes-Moreira J. and Damas L.,: "Time-Evolving OD Matrix Estimation using high-speed GPS data streams". In: Expert Systems with Applications, vol. 44, pp. 275-288, February (2016)

Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L., ”Predicting Taxi–Passenger Demand Using Streaming Data”. In: IEEE Transactions on Intelligent Transportation Systems, vol.14, no.3, pp.1393-1402, September (2013)



Citation Request:

Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L., ”Predicting Taxi–Passenger Demand Using Streaming Data”. In: IEEE Transactions on Intelligent Transportation Systems, vol.14, no.3, pp.1393-1402, September (2013)


Supported By:

 In Collaboration With:

About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML