MFGCN: A Multimodal Fusion Graph Convolutional Network for Online Car-Hailing Demand Prediction

Liao, Lyuchao; Li, Ben; Zou, Fumin; Huang, Dejuan

doi:10.1109/mis.2023.3250600

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…It provides people with safer, more convenient, and more efficient means of transportation. In autonomous driving technology, not only is traffic condition prediction technology [2][3][4] a component, but a more directly related and crucial technique is object detection. Object detection is a crucial task that helps vehicles identify and track surrounding objects, thereby achieving autonomous driving.…”

Section: Introductionmentioning

confidence: 99%

BiGA-YOLO: A Lightweight Object Detection Network Based on YOLOv5 for Autonomous Driving

Cai

Zou

et al. 2023

Electronics

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Object detection in autonomous driving scenarios has become a popular task in recent years. Due to the high-speed movement of vehicles and the complex changes in the surrounding environment, objects of different scales need to be detected, which places high demands on the performance of the network model. Additionally, different driving devices have varying performance capabilities, and a lightweight model is needed to ensure the stable operation of devices with limited computing power. To address these challenges, we propose a lightweight network called BiGA-YOLO based on YOLOv5. We design the Ghost-Hardswish Conv module to simplify the convolution operations and incorporate spatial coordinate information into feature maps using Coordinate Attention. We also replace the PANet structure with the BiFPN structure to enhance the expression ability of features through different weights during the process of fusing multi-scale feature maps. Finally, we conducted extensive experiments on the KITTI dataset, and our BiGA-YOLO achieved a mAP@0.5 of 92.2% and a mAP@0.5:0.95 of 68.3%. Compared to the baseline model YOLOv5, our proposed model achieved improvements of 1.9% and 4.7% in mAP@0.5 and mAP@0.5:0.95, respectively, while reducing the model size by 15.7% and the computational cost by 16%. The detection speed was also increased by 6.3 FPS. Through analysis and discussion of the experimental results, we demonstrate that our proposed model is superior, achieving a balance between detection accuracy, model size, and detection speed.

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Section: Introductionmentioning

confidence: 99%

BiGA-YOLO: A Lightweight Object Detection Network Based on YOLOv5 for Autonomous Driving

Cai

Zou

et al. 2023

Electronics

Self Cite

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HPDE: A dynamic Hierarchical Population based Differential Evolution with novel diversity metric

Meng,

Zhang

2023

Engineering Applications of Artificial Intelligence

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Prediction on Demand for Regional Online Car-Hailing Travel Based on Self-Attention Memory and ConvLSTM

Li,

Zeng,

Liu

et al. 2024

Sustainability

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High precision in forecasting travel demand for online car-hailing is crucial for traffic management to schedule vehicles, hence reducing energy consumption and achieving sustainable development. Netflix demand forecasting relies on the capture of spatiotemporal correlations. To extract the spatiotemporal information more fully, this study designs and develops a novel spatiotemporal prediction model with multidimensional inputs (MSACL) by embedding a self-attention memory (SAM) module into a convolutional long short-term memory neural network (ConvLSTM). The SAM module can extract features with long-range spatiotemporal dependencies. The experimental data are derived from the Chengdu City online car-hailing trajectory data set and the external factors data set. Comparative experiments demonstrate that the proposed model has higher accuracy. The proposed model outperforms the Sa-ConvLSTM model and has the highest prediction accuracy, shows a reduction in the mean absolute error (MAE) by 1.72, a reduction in the mean squared error (MSE) by 0.43, and an increase in the R-squared (R2) by 4%. In addition, ablation experiments illustrate the effectiveness of each component, where the external factor inputs have the least impact on the model accuracy, but the removal of the SAM module results in the most significant decrease in model accuracy.

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MFGCN: A Multimodal Fusion Graph Convolutional Network for Online Car-Hailing Demand Prediction

Cited by 5 publications

References 18 publications

BiGA-YOLO: A Lightweight Object Detection Network Based on YOLOv5 for Autonomous Driving

BiGA-YOLO: A Lightweight Object Detection Network Based on YOLOv5 for Autonomous Driving

HPDE: A dynamic Hierarchical Population based Differential Evolution with novel diversity metric

Prediction on Demand for Regional Online Car-Hailing Travel Based on Self-Attention Memory and ConvLSTM

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