smart.lab.tunisia@gmail.com +216 71 588 443
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Salah Ghodhbani

Informations générales

Salah Ghodhbani
Grade

Maître Technologue

Axes de recherche

Artificial Intelligence
Big Data
Data Mining
Business Intelligence

Publications

  • 2024
    Salah Ghodhbani

    A New Multimodal and Spatio-Temporal Dataset for Traffic Control: Development, Analysis, and Potential Applications

    The dataset provides a comprehensive view of traffic behavior at specific junctions, enabling detailed analysis and real-world applications. By integrating previously disparate data sources, this dataset offers a valuable resource for understanding and op, 2024

    Résumé

    Multimodal data, which includes various data formats such as image, video, text, and sensor data, is essential for urban traffic management. The lack of proven multimodal transportation data has been a significant challenge for urban planners, leading to biased or incomplete estimates of travel demand, mode choice, and network performance. Multimodal data integration offers a valuable resource for understanding and optimizing traffic control and management. However, the heterogeneity of the data, various kinds of noise, alignment of modalities, and techniques to handle missing data are some of the challenges that arise. This paper presents a novel multimodal dataset which is the first of its kind, its scraped from England Highways, incorporating speed, flow, and camera images for the M60, M25, and M1 motorways. The dataset provides a comprehensive view of traffic behavior at specific junctions, enabling detailed analysis and real-world applications. By integrating previously disparate data sources, this dataset offers a valuable resource for understanding and optimizing traffic control and management. The paper outlines the dataset's development, including the gathering of speed and flow data, and the use of image scraping techniques to capture CCTV images. The potential applications of the dataset for traffic control, planning, and optimization are also discussed. Overall, this multimodal dataset represents a significant contribution to the field, with implications for the development of advanced traffic management systems and the improvement of transportation infrastructure

    Salah Ghodhbani, Sabeur Elkosantini

    A Spatial-Temporal DLApproach for Traffic Flow Prediction Using Attention Fusion Method

    The proposed model can extract comprehensive features from various transportation data and effectively capture the spatial-temporal dependencies. By merging these features, it aims to generate more accurate and robust traffic flow predictions. This method, 2024

    Résumé

    in recent years, traffic flow prediction has presented challenges in the management of transportation systems. It is a crucial part of Intelligent Transportation Systems (ITS). The complexities of various transportation data, spatial and temporal dependencies on road networks, and multimodalities, such as public transit, pedestrian flow, and bike sharing, make it a challenging task to forecast traffic flow accurately. Numerous works have been introduced to address these challenges, but few have simultaneously considered these factors, resulting in limited success. In this study, a model is proposed to integrate Graph Convolutional Networks (GCN) and Bidirectional Long Short-Term Memory (BiLSTM). This model utilizes the advantages of GCN in handling spatial data and capturing dependencies in road networks, combined with BiLSTM's capability in learning temporal dynamics. The proposed model can extract comprehensive features from various transportation data and effectively capture the spatial-temporal dependencies. By merging these features, it aims to generate more accurate and robust traffic flow predictions. This method addresses the limitations of existing methods that fail to consider spatial-temporal dependencies and multimodalities, leading to improved prediction accuracy and efficiency

  • Salah Ghodhbani, Sabeur Elkosantini

    ADL based Framework For Multimodal Data Fusion in Traffic Jam prediction

    In this paper, we propose new Hybrid method based on Deep Learning combine two independent model such as CNN, LSTM models to fuse multimodal and spatial temporal data. The proposed model uses Extended Kalman Filter (EKF) to combine result of the proposed, 2023

    Résumé

    Recently, intelligent transportation system (ITS) is considered as one of the most important issues in smart city applications. Its supports urban and regional development and promotes economic growth, social development, and enhances human well-being. ITS integrates new technologies of information and communication including sensors, social media IoT devices which can generate a massive amount of heterogeneous and multimodal data known as big data term. In this context, Data Fusion techniques (DF) seem promising and have emerged from transportation applications and hold a promising opportunity to deal with imperfect raw data for capturing reliable, valuable and accurate information. In literature many DF techniques based on machine learning remarkably renovates fusion techniques by offering the strong ability of computing and predicting. In this paper, we propose new Hybrid method based on Deep Learning combine two independent model such as CNN, LSTM models to fuse multimodal and spatial temporal data. The proposed model uses Extended Kalman Filter (EKF) to combine result of the proposed DL classifiers. In the other side, the proposed approach uses CBOA algorithm for feature selection in order to provide effective exploration of significant features with faster convergence

  • Salah Ghodhbani, Sabeur Elkosantini

    Transfer Learning Based Architecture for Urban Transportation Big Data Fusion

    the propose method use CBOA algorithm for feature selection in to order to provide effective exploration of significant features with faster convergence. The proposed model demonstrated its effective results on the applied dataset by offering good results, 2022

    Résumé

Projets