Using Metalearning for Prediction of Taxi Trip Duration Using Different Granularity Levels

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

Zarmehri, MN (Author) Other; Carlos Soares (Author) FEUP

Conference proceedings International

Title: Advances in Intelligent Data Analysis XIV

Pages: 205-216

14th International Symposium on Intelligent Data Analysis (IDA)

Saint Etienne, FRANCE, OCT 22-24, 2015

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 3 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

Other information

Authenticus ID: P-00G-SXV

DOI: 10.1007/978-3-319-24465-5_18

Abstract (EN): Trip duration is an important metric for the management of taxi companies, as it affects operational efficiency, driver satisfaction and, above all, customer satisfaction. In particular, the ability to predict trip duration in advance can be very useful for allocating taxis to stands and finding the best route for trips. A data mining approach can be used to generate models for trip time prediction. In fact, given the amount of data available, different models can be generated for different taxis. Given the difference between the data collected by different taxis, the best model for each one can be obtained with different algorithms and/or parameter settings. However, finding the configuration that generates the best model for each taxi is computationally very expensive. In this paper, we propose the use of metalearning to address the problem of selecting the algorithm that generates the model with the most accurate predictions for each taxi. The approach is tested on data collected in the Drive-In project. Our results show that metalearning can help to select the algorithm with the best accuracy.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12