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Cited by 1Traffic Prediction and Management TechniquesTransportation Planning and OptimizationData Management and Algorithms

Predicting New York Taxi Trip Duration Based on Regression Analysis Using ML and Time Series Forecasting Using DL

Ramani, S., Ghiya, Anish, Aravind, Pusuluri Sidhartha, Karuppiah, Marimuthu, Pelusi, Danilo·2022·Lecture Notes in Networks and Systems
DOI 10.1007/978-981-19-2894-9_2
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Crossref · 10.1007/978-981-19-2894-9_2

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  • AI and ML can be used to analyze historical data to predict future demand for taxi services, helping taxi companies optimize fleet management, scheduling, and pricing strategies.

    “AI and ML can be used to analyze historical data to predict future demand for taxi services. This can help taxi companies optimize their fleet management, scheduling, and pricing strategies to ensure that they are providing the right number of taxis in the right areas at the right times [135] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [135]

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Authors
Ramani, S., Ghiya, A., Aravind, P. S., Karuppiah, M., & Pelusi, D.
Affiliations
  • Vellore Institute of Technology University (IN)
  • SRM Institute of Science and Technology (IN)
  • University of Teramo (IT)
Corresponding author
Pelusi, Danilo
ORCID
Venue
Lecture Notes in Networks and Systems
Publisher
Springer Nature Singapore
Published
2022
Pages
15-28
ISSN
  • 2367-3370 (ISSN-L)
  • 2367-3389
DOI
10.1007/978-981-19-2894-9_2
OpenAlex
W4292572490
Cited by
1
References
0
Citations by year
2023: 1
FWCI
0.97
Type
book chapter
Language
en
Open access
closed
Licence
Locations
  • Lecture notes in networks and systems (publishedVersion)
  • UniTERAMO Research Catalog (University of Teramo) (submittedVersion)
Topics
  • Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
  • Social Sciences › Transportation › Transportation Planning and Optimization
  • Physical Sciences › Computer Science › Signal Processing › Data Management and Algorithms
Keywords
New York City taxi data, time series forecasting, recurrent neural networks, XGBoost regression, feature engineering, data scaling, logarithmic transformation

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