Yellow Taxi Demand Prediction for New York City Based on VMD-SSA-LSTM

Haodong Wang

2024

Abstract

Taxis have always been a crucial component of urban public transportation systems. Efficient dispatching and improved operational efficiency are essential for enhancing taxi services. Therefore, accurate prediction of taxi demand in urban areas is imperative. This paper utilizes a Coupled Network model based on Variational Mode Decomposition, Sparrow Search Algorithm, and Long Short-Term Memory (VMD-SSA-LSTM) to predict the demand for yellow taxis in New York City from January to February 2023. The integration of VMD and SSA proves to be a potent solution to the limitations encountered by traditional LSTM models in time series analysis, specifically addressing issues of inadequate precision and the intricate nature of parameter determination. Results from the VMD-SSA-LSTM coupled model show higher accuracy compared to both traditional LSTM and VMD-LSTM approaches. This indicates that optimized coupled models, such as VMD-SSA-LSTM, are well-suited for short-term traffic flow predictions. Accurate prediction of taxi demand facilitates improved scheduling, reduced passenger wait times, increased taxi company revenue, and contributes to the advancement of smart city initiatives.

Paper Citation

in Harvard Style

Wang H. (2024). Yellow Taxi Demand Prediction for New York City Based on VMD-SSA-LSTM. In Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-690-3, SciTePress, pages 434-440. DOI: 10.5220/0012810400004547

in Bibtex Style

@conference{icdse24,
author={Haodong Wang},
title={Yellow Taxi Demand Prediction for New York City Based on VMD-SSA-LSTM},
booktitle={Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2024},
pages={434-440},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012810400004547},
isbn={978-989-758-690-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Yellow Taxi Demand Prediction for New York City Based on VMD-SSA-LSTM
SN - 978-989-758-690-3
AU - Wang H.
PY - 2024
SP - 434
EP - 440
DO - 10.5220/0012810400004547
PB - SciTePress