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Recent Developments in Machine Learning and Data Analytics pp 341–348Cite as

Analysis and Prediction of City-Scale Transportation System Using XGBOOST Technique

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 740))

Abstract

Recent research has been carried out in predicting the taxi rides that can be shared with different customers with similar pickup point also called as taxi pooling by taking into consideration parameters such as pickup latitude mean waiting time and distance between the pickup points. The influence/impact of weather is vital in deciding the taxis that needed to be present in an area for successful pooling transportation. This paper proposes a methodology incorporating the impact of weather in taxi pooling using XGBOOST technique. The proposed method is tested with New York City taxi dataset and New York City weather dataset. The simulation results show that there is a significant improvement in analysis and prediction of delays after incorporating weather details.

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Author information

Authors and Affiliations

  1. National Institute of Technology Trichy, Trichy, India

    Sai Prabanjan Kumar Kalvapalli & Mala Chelliah

Authors
  1. Sai Prabanjan Kumar Kalvapalli
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  2. Mala Chelliah
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Corresponding author

Correspondence to Mala Chelliah .

Editor information

Editors and Affiliations

  1. College of Engineering and Applied Science, University of Colorado Colorado Springs, Colorado Springs, CO, USA

    Jugal Kalita

  2. Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Department of Computer Applications, Sikkim Manipal University, Sikkim, India

    Samarjeet Borah

  4. Department of Computer Applications, Sikkim Manipal University, Sikkim, India

    Ratika Pradhan

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© 2019 Springer Nature Singapore Pte Ltd.

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Cite this paper

Kalvapalli, S.P.K., Chelliah, M. (2019). Analysis and Prediction of City-Scale Transportation System Using XGBOOST Technique. In: Kalita, J., Balas, V., Borah, S., Pradhan, R. (eds) Recent Developments in Machine Learning and Data Analytics. Advances in Intelligent Systems and Computing, vol 740. Springer, Singapore. https://doi.org/10.1007/978-981-13-1280-9_32

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