Jing-Doo Wang¹, Chayadi Oktomy Noto Susanto^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1711-1728, 2024, DOI:10.32604/cmes.2024.048955 - 20 May 2024

Abstract A significant obstacle in intelligent transportation systems (ITS) is the capacity to predict traffic flow. Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately. However, accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors. This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory (Conv-BiLSTM) with attention mechanisms. Prior studies neglected to include data pertaining to factors such as holidays, weather conditions, and More >

SangSeok Lee¹, HaeWon Moon¹, Lee Sael^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 319-336, 2024, DOI:10.32604/cmes.2023.031150 - 30 December 2023

Abstract How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data? Much of the multidimensional dynamic data in the real world is generated in the form of time-growing tensors. For example, air quality tensor data consists of multiple sensory values gathered from wide locations for a long time. Such data, accumulated over time, is redundant and consumes a lot of memory in its raw form. We need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand… More > Graphic Abstract

Perez Joan¹, Fusco Giovanni¹, Sadahiro Yukio²

Revue Internationale de Géomatique, Vol.31, No.2, pp. 303-328, 2022, DOI:10.3166/rig31.303-328

Abstract This paper presents different methods implemented with the aim of studying urban dynamics at the building level. Building types are identified within a comprehensive vector-based building inventory, spanning over at least two time points. First, basic morphometric indicators are computed for each building: area, floor-area, number of neighbors, elongation, and convexity. Based on the availability of expert knowledge, different types of classification and clustering are performed: supervised tree-like classificatory model, expert-constrained k-means and combined SOM-HCA. A grid is superimposed on the test region of Osaka (Japan) and the number of building types per cell and More >

Shun Wang¹, Lin Qiao², Wei Fang³, Guodong Jing⁴, Victor S. Sheng⁵, Yong Zhang^1,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 673-687, 2022, DOI:10.32604/cmc.2022.028411 - 18 May 2022

Abstract PM2.5 concentration prediction is of great significance to environmental protection and human health. Achieving accurate prediction of PM2.5 concentration has become an important research task. However, PM2.5 pollutants can spread in the earth’s atmosphere, causing mutual influence between different cities. To effectively capture the air pollution relationship between cities, this paper proposes a novel spatiotemporal model combining graph attention neural network (GAT) and gated recurrent unit (GRU), named GAT-GRU for PM2.5 concentration prediction. Specifically, GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities, and GRU is to extract the… More >

Chamseddine Zaki¹ , Mohamed Ayet^1,2, Allah Bilel Soussi²

Revue Internationale de Géomatique, Vol.29, No.3, pp. 255-262, 2019, DOI:10.3166/rig.2019.00086

Abstract A conceptual spatiotemporal data model must be able to offer users a semantic richness of expression to meet their diverse needs concerning the modeling of spatio-temporal data. The conceptual spatiotemporal data model must be able to represent the objects, relationships and events that can occur in a field of study, track data history, support the multirepresentation of these data, and represent temporal and spatial data with two and three dimensions features. The model must also allow the assignment of different types of constraints to relations and provide a complete orthogonality between dimensions and concepts. The More >