Search

Drug-drug interactions (DDIs) aim at describing the effect relations produced by a combination of two or more drugs. It is an important semantic processing task in the field of bioinformatics such as pharmacovigilance and clinical research. Recently, graph neural networks are applied on dependency graph to promote the performance of DDI extraction with better semantic representations. However, current method concentrates more on first-order dependency relations and cannot discriminate the connected nodes properly. To better incorporate the dependency relations and improve the representations, we propose a novel DDI extraction method named Drug-drug Interactions extRaction with Enhanced Dependency Graph and Attention Mechanism in this work. Specifically, the dependency graph is enhanced with some potential long-range words to complete the semantic information and fit the aggregation process of graph neural networks. And graph attention mechanism is adopted to further improve word representation by discriminating the connected nodes according to the specific task. Numerical experiments on DDIExtraction 2013 corpus, the benchmark corpus for this domain, demonstrate the superiority of our proposed method.

Mental illnesses are one of the most prevalent public health problems worldwide, which negatively influence people's lives and society's health. With the increasing popularity of social media, there has been a growing research interest in the early detection of mental illness by analysing user-generated posts on social media. According to the correlation between emotions and mental illness, leveraging and fusing emotion information has developed into a valuable research topic. In this article, we provide a comprehensive survey of approaches to mental illness detection in social media that incorporate emotion fusion. We begin by reviewing different fusion strategies, along with their advantages and disadvantages. Subsequently, we discuss the major challenges faced by researchers working in this area, including issues surrounding the availability and quality of datasets, the performance of algorithms and interpretability. We additionally suggest some potential directions for future research., Accepted manuscript

Detecting rotated ships is difficult in optical remote sensing images due to the challenges of complex scenes. Existing advanced rotated ship detectors are typically anchor-based algorithms that require plenty of predefined anchors. However, the use of anchors brings three critical problems: 1) a large number of anchors bring a huge amount of calculation; 2) the attributes (e.g., size and aspect ratios) of anchors are designed via ad hoc heuristics; and 3) only a tiny fraction of anchors that overlap with ground-truth bounding boxes of ships tightly can be considered as positive samples, which causes an extreme imbalance between positive and negative samples. As a result, the detection accuracy will be influenced seriously when the design of anchors is not suitable. To address the above problems, this article proposes a novel anchor-free rotated ship detection framework, called SKNet, which detects rotated ships as keypoints in optical remote sensing images. In SKNet, a ship target is modeled as its center keypoint and morphological sizes, including the width, height, and rotation angle. Accordingly, we design two customized modules: orthogonal pooling and soft-rotate-nonmaximum suppression (NMS), where the former is to improve the prediction accuracy of the center keypoint and the morphological size, and the latter is to effectively remove redundant rotated ship detection results. Extensive experiments are conducted to demonstrate the effectiveness of SKNet on three optical remote sensing image data sets: HRSC2016, DOTA-ship, and HPDM-OSOD, which is collected by ourselves and published in this article. Empirical studies show that SKNet achieves state-of-the-art detection performance while being time-efficient. Overall, SKNet achieves the best speed–accuracy tradeoff.

As an environmentally friendly oxidant, H2O2 is widely utilized in various fields; however, its production methods remain limited to the chemical anthraquinone process. Alternatively, electrocatalytic oxygen reduction possesses numerous notable advantages (e.g., cost-effectiveness, small-scale, and distributed nature). As electrocatalytic oxygen reduction has been widely investigated in the fields of fuel cells and metal-air batteries, the mechanism of the 2e−-ORR pathway for producing H2O2 is not sufficiently clear. Herein, we explore the effect of the cobalt (Co) coordination environment on the electrochemical production of H2O2. The detailed investigation on N-, P-, and S-coordinated Co catalysts (Co1N1N3, Co1P1N3, and Co1S1N3) demonstrates that changing the coordination environment evidently affects the H2O2 selectivity, and the S-coordinated Co exhibits the best catalytic performance. This finding would lead to the design and selection of catalysts at atomic level for producing H2O2 via electrocatalytic oxygen reduction.

In dynamic scene deblurring, recent neural network–based methods have been very successful. But with the improvement of deep deblurring performance, network structure and learning become more complicated. Compared with large‐scale network parameters and complex network structures, an iterative multi‐scale residual network to achieve a more effective parameter sharing scheme is proposed. In each iterative unit, fast multi‐scale residual blocks to replace superimposed convolutional layers or classic residual blocks are used. On the basis of preventing model overfitting, the receptive field of the network is increased. At the same time, the gated recurrent unit is introduced to connect modules of different stages. The model does not rely on the estimation of the blur kernel and directly generates sharp images in an end‐to‐end manner. The experimental structure on the benchmark dataset and real‐world images showed that this method has better quality than the existing methods in terms of large‐scale blur and subjective perception effects, both in quantitative and qualitative terms.

Urbanization has adverse environmental effects, such as rising surface temperatures. This study analyzes the relationship between the urban heat island (UHI) intensity and Tianjin city’s land cover characteristics. The land use cover change (LUCC) effects on the green areas and the land surface temperature (LST) were also studied. The land cover characteristics were divided into five categories: a built-up area, an agricultural area, a bare area, a forest, and water. The LST was calculated using the thermal bands of spatial images taken from 2005 to 2020. The increase in the built-up area was mainly caused by the agricultural area decreasing by 11.90%. The average land surface temperature of the study area increased from 23.50 to 36.51 °C, and the region moved to a high temperature that the built-up area’s temperature increased by 1.5%. Still, the increase in vegetation cover was negative. From 2020 to 2050, the land surface temperature is expected to increase by 9.5 °C. The high-temperature areas moved into an aerial distribution, and the direction of urbanization determined their path. Urban heat island mitigation is best achieved through forests and water, and managers of urban areas should avoid developing bare land since they may suffer from degradation. The increase in the land surface temperature caused by the land cover change proves that the site is becoming more urbanized. The findings of this study provide valuable information on the various aspects of urbanization in Tianjin and other regions. In addition, future research should look into the public health issues associated with rapid urbanization.

With the emergence and development of blockchain technology, due to its innovation in the underlying technology, many potential innovations have been created in the financial aspects of different industries, and even disruptive changes have occurred. Especially mature applications in supply chain finance are more extensive, and at the same time, the technology also effectively promotes the development of finance-related technology. We proposed a blockchain-based framework and used an accessory technology, namely smart contracts. The credit mechanism can be reformed to promote the flow of credit value and make it highly coupled with financial scenarios to obtain the feasibility of supply chain process design.

With the continuous in-depth implementation of the ”Internet +” development strategy, supply chain finance relying on Internet technology is gradually becoming the main way for small and medium-sized enterprises (SMEs) to finance. Supply chain finance is closely integrated with the physical industry and finance, which has greatly promoted the continuous development of the main bodies of the supply chain, especially the small and medium-sized enterprises. Therefore, by integrating the components and characteristics of the blockchain into the various application links of supply chain finance, it can effectively solve the dilemma of SME financing in supply chain finance.

Co 5,10,15,20-tetrakis(4-sulfonatophenyl) porphyrin (CoTPPS) anions were successfully immobilized into the lamellar space of Mg–Al LDH through the spontaneous flocculation method by using exfoliated LDH nanosheets and guest molecules as building blocks. A variety of techniques including XRD, IR, UV–Vis, SEM, TEM and TG–DSC have been applied to characterize the morphology and microstructure of the obtained hybrids. It was found that the ratio between the metalloporphyrin molecules and LDH nanosheets has an important effect on the structure of restacking products. The interlayer spacing determined from XRD result suggests that a perpendicular orientation of the CoTPPS anions in the lamellar space. The surface charge changes associated with the flocculation process were traced through zeta potential measurements. The immobilized CoTPPS could act as an efficient and reusable heterogeneous catalyst for selective epoxidation of various alkenes including different cycloalkenes, styrene derivatives and long-chain alkenes. Cyclohexene undergoes up to 99% conversion within 2 h and 90% selectivity with molecular oxygen as environmentally friendly oxidant and isobutyraldehyde as co-reductant at room temperature. The CoTPPS/LDH2.0 can be easily recovered and reused for at least 5 times without obvious decrease of activity. This work provides a facile route to synthesize a highly efficient catalyst for the green epoxidation of olefins.

This paper proposes a robust obstacle detection and recognition method for driver assistance systems. Unlike existing methods, our method aims to detect and recognize obstacles on the road rather than all the obstacles in the view. The proposed method involves two stages aiming at an increased quality of the results. The first stage is to locate the positions of obstacles on the road. In order to accurately locate the on-road obstacles, we propose an obstacle detection method based on the U-V disparity map generated from a stereo vision system. The proposed U-V disparity algorithm makes use of the V-disparity map that provides a good representation of the geometric content of the road region to extract the road features, and then detects the on-road obstacles using our proposed realistic U-disparity map that eliminates the foreshortening effects caused by the perspective projection of pinhole imaging. The proposed realistic U-disparity map greatly improves the detection accuracy of the distant obstacles compared with the conventional U-disparity map. Second, the detection results of our proposed U-V disparity algorithm are put into a context-aware Faster-RCNN that combines the interior and contextual features to improve the recognition accuracy of small and occluded obstacles. Specifically, we propose a context-aware module and apply it into the architecture of Faster-RCNN. The experimental results on two public datasets show that our proposed method achieves state-of-the-art performance under various driving conditions.

Drug–drug interactions (DDIs) are crucial for drug research and pharmacovigilance. These interactions may cause adverse drug effects that threaten public health and patient safety. Therefore, the DDIs extraction from biomedical literature has been widely studied and emphasized in modern biomedical research. The previous rules-based and machine learning approaches rely on tedious feature engineering, which is labourious, time-consuming and unsatisfactory. With the development of deep learning technologies, this problem is alleviated by learning feature representations automatically. Here, we review the recent deep learning methods that have been applied to the extraction of DDIs from biomedical literature. We describe each method briefly and compare its performance in the DDI corpus systematically. Next, we summarize the advantages and disadvantages of these deep learning models for this task. Furthermore, we discuss some challenges and future perspectives of DDI extraction via deep learning methods. This review aims to serve as a useful guide for interested researchers to further advance bioinformatics algorithms for DDIs extraction from the literature.

Mental illness is highly prevalent nowadays, constituting a major cause of distress in people’s life with impact on society’s health and well-being. Mental illness is a complex multi-factorial disease associated with individual risk factors and a variety of socioeconomic, clinical associations. In order to capture these complex associations expressed in a wide variety of textual data, including social media posts, interviews, and clinical notes, natural language processing (NLP) methods demonstrate promising improvements to empower proactive mental healthcare and assist early diagnosis. We provide a narrative review of mental illness detection using NLP in the past decade, to understand methods, trends, challenges and future directions. A total of 399 studies from 10,467 records were included. The review reveals that there is an upward trend in mental illness detection NLP research. Deep learning methods receive more attention and perform better than traditional machine learning methods. We also provide some recommendations for future studies, including the development of novel detection methods, deep learning paradigms and interpretable models.

This paper considers a problem that visual servo control for an aerial manipulator removes foreign objects of power transmission lines. A position-based visual servoing (PBVS) combing a foreign objects locating method based on the point cloud with a hierarchical task-priority control method is employed to drive the aerial manipulator to remove the foreign object. Firstly, the RGB-D camera mounted on the drone obtains the point cloud of the environment, and the foreign object will be localized by the detection and localization algorithm. Then, a new visual servo error is proposed to decouple linear speed and angular speed, allowing the aerial manipulator to grasp accurately in the dangerous environment. In addition, the redundant characteristics of the aerial manipulator will be fully used by the hierarchical task priority control scheme. Finally, experimental results of a drone equipped with a 4-DOF delta manipulator removing foreign objects of power transmission line are provided to demonstrate the effectiveness of the control method.

Background In recent years, the COVID-19 pandemic has brought great changes to public health, society, and the economy. Social media provide a platform for people to discuss health concerns, living conditions, and policies during the epidemic, allowing policymakers to use this content to analyze the public emotions and attitudes for decision-making. Objective The aim of this study was to use deep learning–based methods to understand public emotions on topics related to the COVID-19 pandemic in the United Kingdom through a comparative geolocation and text mining analysis on Twitter. Methods Over 500,000 tweets related to COVID-19 from 48 different cities in the United Kingdom were extracted, with the data covering the period of the last 2 years (from February 2020 to November 2021). We leveraged three advanced deep learning–based models for topic modeling to geospatially analyze the sentiment, emotion, and topics of tweets in the United Kingdom: SenticNet 6 for sentiment analysis, SpanEmo for emotion recognition, and combined topic modeling (CTM). Results We observed a significant change in the number of tweets as the epidemiological situation and vaccination situation shifted over the 2 years. There was a sharp increase in the number of tweets from January 2020 to February 2020 due to the outbreak of COVID-19 in the United Kingdom. Then, the number of tweets gradually declined as of February 2020. Moreover, with identification of the COVID-19 Omicron variant in the United Kingdom in November 2021, the number of tweets grew again. Our findings reveal people’s attitudes and emotions toward topics related to COVID-19. For sentiment, approximately 60% of tweets were positive, 20% were neutral, and 20% were negative. For emotion, people tended to express highly positive emotions in the beginning of 2020, while expressing highly negative emotions over time toward the end of 2021. The topics also changed during the pandemic. Conclusions Through large-scale text mining of Twitter, our study found meaningful differences in public emotions and topics regarding the COVID-19 pandemic among different UK cities. Furthermore, efficient location-based and time-based comparative analysis can be used to track people’s thoughts and feelings, and to understand their behaviors. Based on our analysis, positive attitudes were common during the pandemic; optimism and anticipation were the dominant emotions. With the outbreak and epidemiological change, the government developed control measures and vaccination policies, and the topics also shifted over time. Overall, the proportion and expressions of emojis, sentiments, emotions, and topics varied geographically and temporally. Therefore, our approach of exploring public emotions and topics on the pandemic from Twitter can potentially lead to informing how public policies are received in a particular geographical area.

To detect the joint angle of the manipulator accurately, a measuring method based on an IMU sensor is proposed. The sensor's attitude angles and corresponding rotation matrix are obtained according to the data of three-axis gyroscope and three-axis accelerometer. During the installation, the sensor's Z-axis direction is kept along with the motor's rotation axis direction, so that the angle of the sensor rotating around its Z-axis, which is the rotation angle of the motor, can be calculated by comparing the rotation relationship between the sensor's initial position and the sensor's position after the motor rotating. The largest source of result error derives from the inconsistent between the sensor's Z-axis direction and the motor's rotation axis direction. Consequently, an installation's error compensation method is designed to correct the sensor's Z-axis to the motor's rotation axis by rotating the related motor. The experiments show that the method can measure the joint angle of the manipulator accurately and calibrate the installation error effectively. The measurement result errors are confined

Stress and depression detection on social media aim at the analysis of stress and identification of depression tendency from social media posts, which provide assistance for the early detection of mental health conditions. Existing methods mainly model the mental states of the post speaker implicitly. They also lack the ability to mentalise for complex mental state reasoning. Besides, they are not designed to explicitly capture class-specific features. To resolve the above issues, we propose a mental state Knowledge–aware and Contrastive Network (KC-Net). In detail, we first extract mental state knowledge from a commonsense knowledge base COMET, and infuse the knowledge using Gated Recurrent Units (GRUs) to explicitly model the mental states of the speaker. Then we propose a knowledge–aware mentalisation module based on dot-product attention to accordingly attend to the most relevant knowledge aspects. A supervised contrastive learning module is also utilised to fully leverage label information for capturing class-specific features. We test the proposed methods on a depression detection dataset Depression_Mixed with 3165 Reddit and blog posts, a stress detection dataset Dreaddit with 3553 Reddit posts, and a stress factors recognition dataset SAD with 6850 SMS-like messages. The experimental results show that our method achieves new state-of-the-art results on all datasets: 95.4% of F1 scores on Depression_Mixed, 83.5% on Dreaddit and 77.8% on SAD, with 2.07% average improvement. Factor-specific analysis and ablation study prove the effectiveness of all proposed modules, while UMAP analysis and case study visualise their mechanisms. We believe our work facilitates detection and analysis of depression and stress on social media data, and shows potential for applications on other mental health conditions.

Through the finite element analysis of the vertical axial load test of 9 self bearing and self insulation precast walls, the effects of the thickness of section steel, the strength of section steel, the strength of foamed ceramsite concrete, the layout of section steel and the thickness of foamed ceramsite plate on the failure mode, bearing capacity and overall stress of the wall plate are studied. The research shows that the strength of foamed ceramsite concrete is the main factor affecting the wall stress-strain curve, the layout form of section steel and wall thickness are important factors, and the change of section steel thickness and section steel strength has little effect on the stress-strain curve. The strength of foamed ceramsite concrete decreases by 30%, the wall bearing capacity decreases by 15%, and the wall deformation increases by three times. Therefore, quality management should be strengthened in the production process of foamed ceramsite concrete to prevent the strength decline of foamed ceramsite concrete due to production reasons; Foamed ceramsite concrete and section steel frame are constrained to form a stressed whole, and the ribbed section steel frame bears the main load, which provides a basis for subsequent experimental research and engineering design.

Grasping objects in a large-scale area has been investigated extensively for mobile robots. But for unmanned aerial manipulator(UAM), this is still a challenging task. In order to solve the problem of accurate grasping in a large-scale area, we propose a novel detection and control framework for UAM, which consists of two stages: preliminary localization and precise localization. At the preliminary localization stage, the RGB-D sensor mounted on the end-effector of the manipulator scans to obtain the point cloud surrounding the UAM. By bringing the known target shape and processed point cloud parameters into our proposed loss function, we select the highest priority area as the best potential region proposal, which can help the UAM to screen out the target for precise localization from the obtained point cloud. At precise localization stage, after UAM reaches the best potential region, the RGB-D sensor mounted on the drone uses the deep object pose estimation(DOPE) to estimate the 6D pose of the target. Through independently compensating for the disturbance of the UAV and manipulator, the UAM can accurately grasp the target with the estimated 6D pose. To evaluate the performance of the UAM, we conducted experiments in four different scenes. Experimental results demonstrate that the UAM can grasp the target with an average success rate of 83.4% for the large-scale scene. The above results prove the feasibility and robustness of the framework. The code is available at https://github.com/skywoodsz/CatchIt

Learning effective feature interactions behind user behavior is challenging in credit scoring. Existing machine learning methods seem to have a strong bias towards low-order or high-order interactions, or require expertise feature engineering. In this paper, we present a novel neural network approach AttentionFM, which incorporates Factorization Machines and Attention mechanism for credit scoring. The proposed model focuses more on critical features and emphasizes both low- and high-order feature interactions, with no need of manually feature engineering on raw data representation. Experimental results demonstrate that our proposed model significantly outperforms the baselines based on two public datasets.

An estimated 24 million people worldwide have dementia, the majority of whom are thought to have Alzheimer's disease(AD). Nowadays, Alzheimer's disease represents a significant public health concern and has been identified as a research priority. Most unfortunately, there is little chance of a cure for Alzheimer's disease, and the disease is difficult to detect before the dominant characteristics such as memory loss are manifested. Therefore, the diagnosis of Alzheimer's disease has become an urgent problem today. Studies have shown that mild cognitive impairment(MCI) is a state between Alzheimer's disease and normal, and the chance of it turning into Alzheimer's disease is high. Therefore, if machines can automatically learn the characteristics of three kinds of human brain magnetic resonance(MR) images through deep learning, and help doctors to diagnose patients with mild cognitive impairment or Alzheimer's disease accurately, it will be beneficial for the early diagnosis of Alzheimer's disease. In this paper, we improve the Inception(V3) neural network and further test the effectiveness of the enhanced network based on the international Alzheimer's disease data set, which consists of brain magnetic resonance images. The results show that the average accuracy of our approach can reach 85.7%.

Neural Networks have achieved great success in many computer vision tasks, especially in image recognition. However, as neural networks grow deeper and deeper, to some extend, we've found them becoming difficult to train, and requiring samples in large scale dramatically, even with the help of Dropout and Dropconnect methods, which do improve the accuracy a bit but burdens the training process as a sacrifice. To overcome this, we propose a novel neuron connection model to generate dynamic graphs of computation. As synapses have two kinds: excitatory and inhibitory ones, our model also has two kinds of connections for neurons. In addition, we propose a training algorithm that deals with non-differentiable because the equations of the connections and activation function of neurons in our model are not really differentiable. To evaluate the effectiveness the proposed method, we apply it to the image recognition task, and the results show that our proposed model achieves state-of-the-art performance on three public datasets: MNIST, CIFAR-10, and CIFAR-100.

Suicide is one of the leading causes of death worldwide. At the same time, the widespread use of social media has led to an increase in people posting their suicide notes online. Therefore, designing a learning model that can aid the detection of suicide notes online is of great importance. However, current methods cannot capture both local and global semantic features. In this paper, we propose a transformer-based model named TransformerRNN, which can effectively extract contextual and long-term dependency information by using a transformer encoder and a Bi-directional Long Short-Term Memory (BiLSTM) structure. We evaluate our model with baseline approaches on a dataset collected from online sources (including 659 suicide notes, 431 last statements, and 2000 neutral posts). Our proposed TransformerRNN achieves 95.0%, 94.9% and 94.9% performance in P, R and F1-score metrics respectively and therefore outperforms comparable machine learning and state-of-the-art deep learning models. The proposed model is effective for classifying suicide notes, which in turn, may help to develop suicide prevention technologies for social media., Highlights • Early suicide note identification may help to develop suicide prevention technologies for social media. • TranformerRNN was proposed, using the transformer encoder and BiLSTM. • TranformerRNN can extract contextual information and latent features to identify suicide notes. • Our TranformerRNN method outperforms comparable machine learning and state-of-the-art deep learning models.

Novel copper (II) ion imprinted polymers (Cu(II)-IIPs) were designed and synthesized as solid phase absorbents for the selective deep removal of Cu 2 + from concentrated zinc sulfate solution. Salicylaldoximes, used as chelating ligands, were anchored onto the polymer networks through quaternary ammonium cation spacers. The effect of pH, temperature, contact time, and swelling on the adsorption selectivity and the copper removal efficiency of Cu(II)-IIPs were investigated in a laboratory-scale cell. The results showed that the adsorption of Cu 2 + increased significantly with increasing pH from 1 to 7, and increased with decreasing operation temperature. The equilibrium Cu 2 + adsorption capacity of Cu(II)-IIPs from the zinc sulfate aqueous solution was 96 mg·g − 1 at 25 °C in pH 5.0 for 10 min. Under optimized processing conditions, the copper removal efficiency reached 99.9%, and the concentration of the residual copper (II) ions in the purified ZnSO 4 aqueous solution is 35 μg·L − 1 , significant lower than the maximum acceptable value as 100 μg·L − 1 in the hydrometallurgical zinc process.

In order to achieve simultaneous enhancements of hydrophilicity, chlorine resistance, and anti-biofouling properties, quaternary ammonium cations and salicylaldimines were densely grafted onto the aromatic polyamide (APA) thin-film composite (TFC) membrane surface by two chemical reactions. The first one is an amidation reaction of the nascent APA-TFC membrane with polyamine, followed by a condensation reaction with N,N -dialkyl- N -benzyl- N -(3-formyl-4-hydroxylbenzyl) ammonium chloride. The modified membranes were characterized through attenuated total reflectance-Fourier transform infrared spectroscopy analysis, field emission scanning electron microscopy, atomic force microscopy, zeta-potential analysis, and contact angle measurement. Experimental results showed that the modified membrane surfaces became highly hydrophilic with dense positive charges. The contact angles decreased from 65±2.5° to 32.4–20.5°. Water fluxes of the modified membranes significantly increased and salt rejections slightly decreased with increasing density of quaternary ammonium cations and salicylaldimines on the modified membrane surfaces. Chlorination tests also showed that the modified membranes tolerated 700,000 ppm h free chlorine. Despite of the increased roughness of the membrane surface after modification, very low biofouling was observed on the modified membrane surfaces due to the highly hydrophilic surface and the synergetic bacterial killing.

The molecular aggregation and exciton-polaron interaction of the host-guest system were successfully restricted by efficient molecular encapsulation. The solution-processed blue and green TADF OLEDs have been realized with external quantum efficiencies above 23% by employing the encapsulated TADF host and guest as emission layers.

Convolutional neural networks have been shown successful in extracting features from images and texts. However, it is difficult to apply convolutional neural networks directly on ubiquitous graph data since the graph data lies in an irregular structure. A significant number of researchers engrossed themselves in studying graph convolutional networks transformed from Euclidean domain. Previous graph convolutional networks overviews mainly focus on reviewing recent methods in a comprehensive ways. In this survey, we review the convolutional networks from the perspective of receptive fields. Roughly, the convolutional networks fall into three main categories: spectral based methods, sampling based methods and attention based methods. We analysis the differences of these methods and propose three potential directions for future research of graph convolutional networks.

Traffic congestion seriously affect citizens’ life quality. Many researchers have paid much attention to the task of short-term traffic congestion forecasting. However, the performance of the traditional traffic congestion forecasting approaches is not satisfactory. Moreover, most neural network models cannot capture the features at different moments effectively. In this paper, we propose an Attention-based long short-term memory (LSTM) recurrent neural network. We evaluate the prediction architecture on a real-time traffic data from Gray-Chicago-Milwaukee (GCM) Transportation Corridor in Chicagoland. The experimental results demonstrate that our method outperforms the baselines for the task of congestion prediction.

Road passage time is an important measure of urban traffic. Accurate estimation of road passage time contributes to the route programming and the urban traffic planning. Currently, the estimation of road passage time for a particular road is usually based on its historical data which is simple to express the general law of road traffic. However, with the increase of the number of roads in the urban area, the connection between roads becomes more complex. The existing methods fail to make use of the connection between different roads and the road passage time, merely based on its own historical data. In this paper, we propose a road passage time estimating model, called “CA-RPT”, which utilizes the contextual information between road connections as well as the date and time period. We evaluate our method based on a real geolocation information data set collected by mobile APP anonymously. The results demonstrate that our method is more accurate than the state-of-the-art methods.