<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Advances in machine learning technology and the availability of big data from GPS systems have led to the development of effective methods for modelling transportation demand and forecasting the future. Most previous research concentrated on demand prediction using a variety of machine learning and deep learning models that took into account spatial and temporal relationships. This paper investigates the impact of spaces and time granularity for a Spatio-temporal demand modelling framework. Using taxi demand data from New York City, our study compares the prediction performance of deep learning models such as Long Short-Term Memory (LSTM), Convolution Neural Networks (CNN) and Temporal-Guided Networks (TGNet), modelled with a grid-based tessellation strategy. The findings of this study could assist researchers in better understanding how the granularity of space and time helps deep learning models perform better for demand forecasting problems.</p>
</abstract>

Advances in machine learning technology and the availability of big data from GPS systems have led to the development of effective methods for modelling transportation demand and forecasting the future. Most previous research concentrated on demand prediction using a variety of machine learning and deep learning models that took into account spatial and temporal relationships. This paper investigates the impact of spaces and time granularity for a Spatio-temporal demand modelling framework. Using taxi demand data from New York City, our study compares the prediction performance of deep learning models such as Long Short-Term Memory (LSTM), Convolution Neural Networks (CNN) and Temporal-Guided Networks (TGNet), modelled with a grid-based tessellation strategy. The findings of this study could assist researchers in better understanding how the granularity of space and time helps deep learning models perform better for demand forecasting problems.

Effect of Spatio-Temporal Granularity on Demand Prediction for Deep Learning Models

mahdaviabbasabad.1852942@studenti.uniroma1.it

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Advances in machine learning technology and the availability of big data from GPS systems have led to the development of effective methods for modelling...

Effect of Spatio-Temporal Granularity on Demand Prediction for Deep Learning Models

Data publikacji: 28 lut 2023

Zakres stron: 22 - 32

DOI: https://doi.org/10.2478/ttj-2023-0003

Słowa kluczowe
Taxi Demand, Demand Forecasting, Spatiotemporal Granularity, Deep Learning, Grid Clustering

© 2023 Ken Koshy Varghese et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Effect of Spatio-Temporal Granularity on Demand Prediction for Deep Learning Models

Data publikacji: 28 lut 2023

Zakres stron: 22 - 32

DOI: https://doi.org/10.2478/ttj-2023-0003

Słowa kluczoweTaxi Demand, Demand Forecasting, Spatiotemporal Granularity, Deep Learning, Grid Clustering

© 2023 Ken Koshy Varghese et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Taxi Demand, Demand Forecasting, Spatiotemporal Granularity, Deep Learning, Grid Clustering