Eagle-YOLO : An Eagle-Inspired YOLO for Object Detection in Unmanned Aerial Vehicles Scenarios

Funding Information: This research was funded by National Natural Science Foundation of China OF FUNDER grant number 41471333, 61304199. This research was funded by Fujian Provincial Department of Science and Technology OF FUNDER grant number 2021Y4019, 2020D002, 2020L3014, 2019I0019. This research was funded by Fujian University of Technology OF FUNDER grant number KF-J21012. This research was funded by Shenzhen Science and Technology Innovation Program OF FUNDER grant number JCYJ20220530160408019. This research was funded by Basic and Applied Basic Research Foundation of Guangdong Province OF FUNDER grant number 2023A1515011915. This research was funded by the Key Research and Development Project of Hunan Province of China OF FUNDER grant number 2022GK2020. This research was funded by Hunan Natural Science Foundation of China OF FUNDER grant number 2022JJ30171. Publisher Copyright: © 2023 by the authors.Peer reviewedPublisher PD

A Multi-Sensory Stimulating Attention Model for Cities’ Taxi Service Demand Prediction

Taxi demand forecasting is crucial to building an efficient transportation system in a smart city. Accurate taxi demand forecasting could help the taxi management platform to allocate taxi resources in advance, alleviate traffic congestion, and reduce passenger waiting time. Thus, more efforts in industrial and academic circles have been directed towards the cities’ taxi service demand prediction (CTSDP). However, the complex nonlinear spatio-temporal relationship in demand data makes it challenging to construct an accurate forecasting model. There remain challenges in perceiving the micro spatial characteristics and the macro periodicity characteristics from cities’ taxi service demand data. What’s more, the existing methods are significantly insufficient for exploring the potential multi-time patterns from these demand data. To meet the above challenges, and also stimulated by the human perception mechanism, we propose a Multi-Sensory Stimulus Attention (MSSA) model for CTSDP. Specifically, the MSSA model integrates a detail perception attention and a stimulus variety attention for capturing the micro and macro characteristics from massive historical demand data, respectively. The multiple time resolution modules are employed to capture multiple potential spatio-temporal periodic features from massive historical demand data. Extensive experiments on the yellow taxi trip records data in Manhattan show that the MSSA model outperforms the state-of-the-art baselines

Short-Term Load Forecasting with an Ensemble Model Based on 1D-UCNN and Bi-LSTM

Short-term load forecasting (STLF), especially for regional aggregate load forecasting, is essential in smart grid operation and control. However, the existing CNN-based methods cannot efficiently extract the essential features from the electricity load. The reason is that the basic requirement of using CNNs is space invariance, which is not satisfied by the actual electricity data. In addition, the existing models cannot extract the multi-scale input features by representing the tendency of the electricity load, resulting in a reduction in the forecasting performance. As a solution, this paper proposes a novel ensemble model, which is a four-stage framework composed of a feature extraction module, a densely connected residual block (DCRB), a bidirectional long short-term memory layer (Bi-LSTM), and ensemble thinking. The model first extracts the basic and derived features from raw data using the feature extraction module. The derived features comprise hourly average temperature and electricity load features, which can capture huge randomness and trend characteristics in electricity load. The DCRB can effectively extract the essential features from the above multi-scale input data compared with CNN-based models. The experiment results show that the proposed method can provide higher forecasting performance than the existing models, by almost 0.9–3.5%

Short-Term Load Forecasting with an Ensemble Model Using Densely Residual Block and Bi-LSTM Based on the Attention Mechanism

Short-term load forecasting (STLF) is essential for urban sustainable development. It can further contribute to the stable operation of the smart grid. With the development of renewable energy, improving STLF accuracy has become a vital task. Nevertheless, most models based on the convolutional neural network (CNN) cannot effectively extract the crucial features from input data. The reason is that the fundamental requirement of adopting the convolutional neural network (CNN) is space invariance, which cannot be satisfied by the received data, limiting the forecasting performance. Thus, this paper proposes an innovative ensemble model that comprises a densely residual block (DRB), bidirectional long short-term memory (Bi-LSTM) layers based on the attention mechanism, and ensemble thinking. Specifically, the DRB is adopted to extract the potential high-dimensional features from different types of data, such as multi-scale load data, temperature data, and calendar data. The extracted features are the input of the Bi-LSTM layer. Then, the adopted attention mechanism can assign various weights to the hidden state of Bi-LSTM and focus on the crucial factors. Finally, the proposed two-stage ensemble thinking can further improve model generalization. The experimental results show that the proposed model can produce better forecasting performance compared to the existing ones, by almost 3.37–5.94%

Fourier Graph Convolution Network for Time Series Prediction

The spatio-temporal pattern recognition of time series data is critical to developing intelligent transportation systems. Traffic flow data are time series that exhibit patterns of periodicity and volatility. A novel robust Fourier Graph Convolution Network model is proposed to learn these patterns effectively. The model includes a Fourier Embedding module and a stackable Spatial-Temporal ChebyNet layer. The development of the Fourier Embedding module is based on the analysis of Fourier series theory and can capture periodicity features. The Spatial-Temporal ChebyNet layer is designed to model traffic flow’s volatility features for improving the system’s robustness. The Fourier Embedding module represents a periodic function with a Fourier series that can find the optimal coefficient and optimal frequency parameters. The Spatial-Temporal ChebyNet layer consists of a Fine-grained Volatility Module and a Temporal Volatility Module. Experiments in terms of prediction accuracy using two open datasets show the proposed model outperforms the state-of-the-art methods significantly

Deep ResNet-Based Ensemble Model for Short-Term Load Forecasting in Protection System of Smart Grid

Short-term load forecasting is a key digital technology to support urban sustainable development. It can further contribute to the efficient management of the power system. Due to strong volatility of the electricity load in the different stages, the existing models cannot efficiently extract the vital features capturing the change trend of the load series. The above problem limits the forecasting performance and creates the challenge for the sustainability of urban development. As a result, this paper designs the novel ResNet-based model to forecast the loads of the next 24 h. Specifically, the proposed method is composed of a feature extraction module, a base network, a residual network, and an ensemble structure. We first extract the multi-scale features from raw data to feed them into the single snapshot model, which is modeled with a base network and a residual network. The networks are concatenated to obtain preliminary and snapshot labels for each input, successively. Also, the residual blocks avoid the probable gradient disappearance and over-fitting with the network deepening. We introduce ensemble thinking for selectively concatenating the snapshots to improve model generalization. Our experiment demonstrates that the proposed model outperforms exiting ones, and the maximum performance improvement is up to 4.9% in MAPE

Deep ResNet-Based Ensemble Model for Short-Term Load Forecasting in Protection System of Smart Grid

Short-term load forecasting is a key digital technology to support urban sustainable development. It can further contribute to the efficient management of the power system. Due to strong volatility of the electricity load in the different stages, the existing models cannot efficiently extract the vital features capturing the change trend of the load series. The above problem limits the forecasting performance and creates the challenge for the sustainability of urban development. As a result, this paper designs the novel ResNet-based model to forecast the loads of the next 24 h. Specifically, the proposed method is composed of a feature extraction module, a base network, a residual network, and an ensemble structure. We first extract the multi-scale features from raw data to feed them into the single snapshot model, which is modeled with a base network and a residual network. The networks are concatenated to obtain preliminary and snapshot labels for each input, successively. Also, the residual blocks avoid the probable gradient disappearance and over-fitting with the network deepening. We introduce ensemble thinking for selectively concatenating the snapshots to improve model generalization. Our experiment demonstrates that the proposed model outperforms exiting ones, and the maximum performance improvement is up to 4.9% in MAPE

Multi-View Travel Time Prediction Based on Electronic Toll Collection Data

The travel time prediction of vehicles is an important part of intelligent expressways. It can not only provide the vehicle distribution trend of each section for the expressway management department to assist the fine management of the expressway, but it can also provide owners with dynamic and accurate travel time prediction services to assist the owners to formulate more reasonable travel plans. However, there are still some problems in the current travel time prediction research (e.g., different types of vehicles are not processed separately, the proximity of the road network is not considered, and the capture of important information in the spatial-temporal perspective is not considered in depth). In this paper, we propose a Multi-View Travel Time Prediction (MVPPT) model. First, the travel times of different types of vehicles of each section in the expressway are analyzed, and the main differences in the travel times of different types of vehicles are obtained. Second, multiple travel time features are constructed, which include a novel spatial proximity feature. On this basis, we use CNN to capture the spatial correlation and the spatial attention mechanism to capture key information, the BiLSTM to capture the time correlation of time series, and the time attention mechanism capture key time information. Experiments on large-scale real traffic data demonstrate the effectiveness of our proposal over state-of-the-art methods

Deep Reinforcement Learning for Intersection Signal Control Considering Pedestrian Behavior

Using deep reinforcement learning to solve traffic signal control problems is a research hotspot in the intelligent transportation field. Researchers have recently proposed various solutions based on deep reinforcement learning methods for intelligent transportation problems. However, most signal control optimization takes the maximization of traffic capacity as the optimization goal, ignoring the concerns of pedestrians at intersections. To address this issue, we propose a pedestrian-considered deep reinforcement learning traffic signal control method. The method combines a reinforcement learning network and traffic signal control strategy with traffic efficiency and safety aspects. At the same time, the waiting time of pedestrians and vehicles passing through the intersection is considered, and the Discrete Traffic State Encoding (DTSE) method is applied and improved to define the more comprehensive states and rewards. In the training of the neural network, the multi-process operation method is adopted, and multiple environments are run for training simultaneously to improve the model’s training efficiency. Finally, extensive simulation experiments are conducted on actual intersection scenarios using the simulation software Simulation of Urban Mobility (SUMO). The results show that compared to Dueling DQN, the waiting time due to our method decreased by 58.76% and the number of people waiting decreased by 51.54%. The proposed method can reduce both the number of people waiting and the waiting time at intersections

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

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 [email protected] of 92.2% and a [email protected]:0.95 of 68.3%. Compared to the baseline model YOLOv5, our proposed model achieved improvements of 1.9% and 4.7% in [email protected] and [email protected]: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