An Expressway ETC Missing Data Restoration Model Considering Multi-Attribute Features.

Electronic toll collection (ETC) data mining has become one of the hotspots in the research of intelligent expressway extension applications. Ensuring the integrity of ETC data stands as a critical measure in upholding data quality. ETC data are typical structured data, and although deep learning holds great potential in the ETC data restoration field, its applications in structured data are still in the early stages. To address these issues, we propose an expressway ETC missing transaction data restoration model considering multi-attribute features (MAF). Initially, we employ an entity embedding neural network (EENN) to automatically learn the representation of categorical features in multi-dimensional space, subsequently obtaining embedding vectors from networks that have been adequately trained. Then, we use long short-term memory (LSTM) neural networks to extract the changing patterns of vehicle speeds across several continuous sections. Ultimately, we merge the processed features with other features as input, using a three-layer multilayer perceptron (MLP) to complete the ETC data restoration. To validate the effectiveness of the proposed method, we conducted extensive tests using real ETC datasets and compared it with methods commonly used for structured data restoration. The experimental results demonstrate that the proposed method significantly outperforms others in restoration accuracy on two different datasets. Specifically, our sample data size reached around 400,000 entries. Compared to the currently best method, our method improved the restoration accuracy by 19.06% on non-holiday ETC datasets. The MAE and RMSE values reached optimal levels of 12.394 and 23.815, respectively. The fitting degree of the model to the dataset also reached its peak (R2 = 0.993). Meanwhile, the restoration stability of our method on holiday datasets increased by 5.82%. An ablation experiment showed that the EENN and LSTM modules contributed 7.60% and 9% to the restoration accuracy, as well as 4.68% and 7.29% to the restoration stability. This study indicates that the proposed method not only significantly improves the quality of ETC data but also meets the timeliness requirements of big data mining analysis.

Nocardiopsis eucommiae sp. nov., a novel endophytic actinomycete isolated from leaves of Eucommia ulmoides Oliv.

A novel Gram-stain-positive, endophytic actinobacterium, designated strain HDS5T, was isolated from leaves of Eucommia ulmoides Oliv. collected from Changde City, Hunan Province, PR China. Strain HDS5T produced yellowish oil green substrate mycelia on Gause's synthetic medium, which also carried yellowish oil green aerial hyphae, fragmenting into rod-shaped elements with smooth surfaces. Strain HDS5T grew at pH 5.0-11.0 (optimum, pH 7), at 20-40 °C (optimum, 28 °C) and in the presence of 0-8.0% NaCl (w/v; optimum, 0-1.0 %). Whole-cell hydrolysates contained meso-diaminopimelic acid as the diagnostic amino acid and no diagnostic sugars. The predominant fatty acids were iso-C16:0 and C18 : 1 ω9c. The menaquinones were MK-10(H2), MK-10(H4) and MK-10(H6). Strain HDS5T showed high 16S rRNA gene sequence similarity to Nocardiopsis prasina DSM 43845T (99.72 %), Nocardiopsis ganjiahuensis DSM 45031T (99.31 %), Nocardiopsis exhalans JCM 11759T (99.17 %), Nocardiopsis alba DSM 43377T (99.11 %), Nocardiopsis metallicus KBS6T (99.11 %), Nocardiopsis valliformis DSM 45023T (99.04 %), Nocardiopsis listeri NBRC 13360T (98.97 %), Nocardiopsis lucentensis DSM 44048T (98.83 %), Nocardiopsis terrae YIM 90022T (98.83 %) and <98.7 % similarities to other type strains. Phylogenetic analysis of 16S rRNA gene sequences and whole-genome sequences showed that strain HDS5T was closely related to N. prasina DSM 43845T. However, the average nucleotide identity based on blast and digital DNA-DNA hybridization values between them were determined to be 90.1 and 40.9 %, respectively, below the threshold of 95-96 and 70 % for the delineation of prokaryotic genomic species, suggesting that strain HDS5T represents a novel Nocardiopsis species. Furthermore, the morphological and physio-biochemical characteristics were sufficient to distinguish strain HDS5T from N. prasina DSM 43845T. Consequently, based on phenotypic and genotypic characteristics, strain HDS5T represents a new Nocardiopsis species, for which the name Nocardiopsis eucommiae sp. nov. is proposed. The type strain is HDS5T (=MCCC 1K06172T=JCM 34707T).

Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data.

To scientifically and effectively evaluate the service capacity of expressway service areas (ESAs) and improve the management level of ESAs, we propose a method for the recognition of vehicles entering ESAs (VeESAs) and estimation of vehicle dwell times using electronic toll collection (ETC) data. First, the ETC data and their advantages are described in detail, and then the cleaning rules are designed according to the characteristics of the ETC data. Second, we established feature engineering according to the characteristics of VeESA and proposed the XGBoost-based VeESA recognition (VR-XGBoost) model. Studied the driving rules in depth, we constructed a kinematics-based vehicle dwell time estimation (K-VDTE) model. The field validation in Part A/B of Yangli ESA using real ETC transaction data demonstrates that the effectiveness of our proposal outperforms the current state-of-the-art. Specifically, in Part A and Part B, the recognition accuracies of VR-XGBoost are 95.9% and 97.4%, respectively, the mean absolute errors (MAEs) of dwell time are 52 and 14 s, respectively, and the root mean square errors (RMSEs) are 69 and 22 s, respectively. In addition, the confidence level of controlling the MAE of dwell time within 2 min is more than 97%. This work can effectively recognize the VeESA and accurately estimate the dwell time, which can provide a reference idea and theoretical basis for the service capacity evaluation and layout optimization of the ESA.

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.

Flight capacity of adult Culex pipiens pallens (Diptera: Culicidae) in relation to gender and day-age.

Culex pipiens pallens (L.) is the most common mosquito in houses of central and northern China. It is the primary vector of lymphatic filariasis and Japanese encephalitis. The flight range of mosquitoes is an important factor predicting the risk area of transmission of mosquito-borne pathogens to vertebrate hosts. The flight performance of Cx. pipiens pallens was measured with a 26-channel computer-monitored flight-mill system. We found that females had longer flight capability than males for total flight distance (TFD) and total flight duration (TFDr), and females flew faster than males based on mean flight velocity. No significant difference in flight capability was found between different age-groups in males. However, certain age-groups of females showed significant differences in TFDr and TFD. Specifically, TFD and TFDr tended to be shortest for 5- and 6-d-old females. These significant differences in flight capability between ages and genders provide insights to determine the size of operational area to achieve effective control of Cx. pipiens pallens and minimize the risk of the related mosquito-borne epidemic diseases of lymphatic filariasis and Japanese encephalitis.

Research and design of simulation and verification system of intelligent expressway based on ETC big data.

Electronic toll collection (ETC) system records a large number of travel trajectories of vehicles on expressways, and it has a great potential application value. However, the current simulation system mainly focuses on simulating the characteristics of traffic flow while ignoring the real-time flow conditions of the road is difficult to calculate and display quantitatively, and the overall optimization cost is also notably substantial. Currently, there is a lack of a simulation system tailored for the ETC environment, which addresses the challenge of real-time traffic flow computation and holistic optimization, fulfilling the requisites of pertinent research. According to the topological structure inherent to an actual provincial road network on expressways, this paper devises a framework for a simulation system that conforms to the current ETC environment. We solved the critical problem of generating simulation data in the simulation system by establishing a Feature Extraction Algorithm for spatio-temporal features derived from ETC transaction data (Edata). Then we put forward Traffic Control Strategy Algorithm in ETC simulation system, which can provide decision indicators for simulating the control of traffic flow of the expressway. At the same time, we optimized the improved Multi-Task Scheduling Algorithm (ETC_MTS) based on the application scenario of real-time parallelism of multi-task on expressways, which provides better execution performance compared with the current mainstream algorithms such as Shortest Job First Scheduling Algorithm (SJFS), Priority Scheduling Algorithm (Priority), First Come First Serve Scheduling Algorithm (FCFS) and Round Robin Scheduling Algorithm (RR).

An over-the-horizon potential safety threat vehicle identification method based on ETC big data.

Smart cars rely on sensors like LIDAR and high-precision map-based perception for driving environment sensing. However, they can't detect low-speed vehicles beyond visual range, affecting safety and comfort. Manual vehicles face similar challenges. Low-speed driving contributes to expressway accidents due to limited visibility, road design, and equipment performance. To enhance safety, an over-the-horizon potential safety threat vehicle identification method using ETC big data is proposed. It consists of three layers. The first layer is the vehicle section travel speed sensing layer based on the wlp-XGBoost algorithm. The second layer is the in-transit vehicle position estimation layer based on the DR-HMM algorithm. The third layer is the Multi-information fusion of potential safety threat vehicle identification layer. Dynamic real-time detection and identification of potential safety threats in expressway sections were achieved, and simulations were conducted using real-time ETC data from Quanxia section on an ETC platform. Results show accurate prediction of vehicle speed and position in different road sections and traffic situations, with over 95% accuracy and recall in identifying potential safety threat vehicles. It perceives changes in the traffic conditions of road sections in real-time based on the changing trend of potential safety threat vehicle numbers, providing a vital reference for speed planning and risk avoidance.

An expressway traffic congestion measurement under the influence of service areas.

Identifying traffic congestion accurately is crucial for improving the expressway service level. Because the distributions of microscopic traffic quantities are highly sensitive to slight changes, the traffic congestion measurement is affected by many factors. As an essential part of the expressway, service areas should be considered when measuring the traffic state. Although existing studies pay increasing attention to service areas, the impact caused by service areas is hard to measure for evaluating traffic congestion events. By merging ETC transaction datasets and service area entrance data, this work proposes a traffic congestion measurement with the influence of expressway service areas. In this model, the traffic congestion with the influence of service areas is corrected by three modules: 1) the pause rate prediction module; 2) the fitting module for the relationship between effect and pause rate; 3) the measurement module with correction terms. Extensive experiments were conducted on the real dataset of the Fujian Expressway, and the results show that the proposed method can be applied to measure the effect caused by service areas in the absence of service area entry data. The model can also provide references for other traffic indicator measurements under the effect of the service area.

CSFPre: Expressway key sections based on CEEMDAN-STSGCN-FCM during the holidays for traffic flow prediction.

The implementation of the toll free during holidays makes a large number of traffic jams on the expressway. Real-time and accurate holiday traffic flow forecasts can assist the traffic management department to guide the diversion and reduce the expressway's congestion. However, most of the current prediction methods focus on predicting traffic flow on ordinary working days or weekends. There are fewer studies for festivals and holidays traffic flow prediction, it is challenging to predict holiday traffic flow accurately because of its sudden and irregular characteristics. Therefore, we put forward a data-driven expressway traffic flow prediction model based on holidays. Firstly, Electronic Toll Collection (ETC) gantry data and toll data are preprocessed to realize data integrity and accuracy. Secondly, after Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) processing, the preprocessed traffic flow is sorted into trend terms and random terms, and the spatial-temporal correlation and heterogeneity of each component are captured simultaneously using the Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN) model. Finally, the fluctuating traffic flow of holidays is predicted using Fluctuation Coefficient Method (FCM). Through experiments of real ETC gantry data and toll data in Fujian Province, this method is superior to all baseline methods and has achieved good results. It can provide reference for future public travel choices and further road network operation.

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.

Key technology development needs and applicability analysis of renewable energy hybrid technologies in off-grid areas for the Rwanda power sector.

Until recently, the Rwanda power sector increased rapidly to double the 2010 installed capacity. The energy consumption in Rwanda experienced a steady rise correspondingly with the population and modern socio-economic life. Consequently, Rwanda household access to electricity increased to 53% by September 2019. Not only does 47% of Rwanda's population lack electricity access, there are persistent power failures and the grid is also unstable. Using renewable energy hybrid technologies in off-grid areas might be a solution to this problem. However, the high cost of renewable energy hybrid systems has led to its slow adoption in many developing countries. Hence, it is important to find the most appropriate hybrid combinations that reduce energy cost and access electricity generation that maximizes the available renewable energy resources. This paper examines some new technology development needs related to the power sector in Rwanda. Secondly, four different 100% renewable energy hybrid systems were designed and simulated to support rural and remote areas considering an average load demand of 158.1 kWh/day with a peak load of 18 kW. The hybrid systems simulation and optimization were obtained using HOMER (hybrid optimization model for electric renewables) software. The input data were obtained from National Aeronautics and Space Administration (NASA) for solar and wind resources, and hydro resources were from real-time field data for selected study site. The simulation results indicate hydro/solar/battery hybrid is the most cost-effective and environmentally viable alternative for off-grid rural electrification because of low net present cost (NPC) and least greenhouse gas emissions. The proposed hybrid combination could apply to other rural areas in the region and elsewhere in the world especially where climate conditions are similar.

A Digital Compressed Sensing-Based Energy-Efficient Single-Spot Bluetooth ECG Node.

Energy efficiency is still the obstacle for long-term real-time wireless ECG monitoring. In this paper, a digital compressed sensing- (CS-) based single-spot Bluetooth ECG node is proposed to deal with the challenge in wireless ECG application. A periodic sleep/wake-up scheme and a CS-based compression algorithm are implemented in a node, which consists of ultra-low-power analog front-end, microcontroller, Bluetooth 4.0 communication module, and so forth. The efficiency improvement and the node's specifics are evidenced by the experiments using the ECG signals sampled by the proposed node under daily activities of lay, sit, stand, walk, and run. Under using sparse binary matrix (SBM), block sparse Bayesian learning (BSBL) method, and discrete cosine transform (DCT) basis, all ECG signals were essentially undistorted recovered with root-mean-square differences (PRDs) which are less than 6%. The proposed sleep/wake-up scheme and data compression can reduce the airtime over energy-hungry wireless links, the energy consumption of proposed node is 6.53 mJ, and the energy consumption of radio decreases 77.37%. Moreover, the energy consumption increase caused by CS code execution is negligible, which is 1.3% of the total energy consumption.

Directional virtual backbone based data aggregation scheme for Wireless Visual Sensor Networks.

Data gathering is a fundamental task in Wireless Visual Sensor Networks (WVSNs). Features of directional antennas and the visual data make WVSNs more complex than the conventional Wireless Sensor Network (WSN). The virtual backbone is a technique, which is capable of constructing clusters. The version associating with the aggregation operation is also referred to as the virtual backbone tree. In most of the existing literature, the main focus is on the efficiency brought by the construction of clusters that the existing methods neglect local-balance problems in general. To fill up this gap, Directional Virtual Backbone based Data Aggregation Scheme (DVBDAS) for the WVSNs is proposed in this paper. In addition, a measurement called the energy consumption density is proposed for evaluating the adequacy of results in the cluster-based construction problems. Moreover, the directional virtual backbone construction scheme is proposed by considering the local-balanced factor. Furthermore, the associated network coding mechanism is utilized to construct DVBDAS. Finally, both the theoretical analysis of the proposed DVBDAS and the simulations are given for evaluating the performance. The experimental results prove that the proposed DVBDAS achieves higher performance in terms of both the energy preservation and the network lifetime extension than the existing methods.