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 / Varghese, KEN KOSHY; Mahdaviabbasabad, Sajjad; Gentile, Guido; Eldafrawi, MOHAMED MOHAMED AHMED. - In: TRANSPORT AND TELECOMMUNICATION. - ISSN 1407-6179. - 24:1(2023), pp. 22-32. [10.2478/ttj-2023-0003]

Effect of spatio-temporal granularity on demand prediction for deep learning models

Ken Koshy Varghese
;
Sajjad Mahdaviabbasabad
;
Guido Gentile
;
Mohamed Eldafrawi
2023

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.
2023
taxi demand; demand forecasting; spatiotemporal granularity; deep learning; grid clustering
01 Pubblicazione su rivista::01a Articolo in rivista
Effect of spatio-temporal granularity on demand prediction for deep learning models / Varghese, KEN KOSHY; Mahdaviabbasabad, Sajjad; Gentile, Guido; Eldafrawi, MOHAMED MOHAMED AHMED. - In: TRANSPORT AND TELECOMMUNICATION. - ISSN 1407-6179. - 24:1(2023), pp. 22-32. [10.2478/ttj-2023-0003]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1673524
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