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With the development of modern cities, urban rail transit has become an indispensable part of residents' travelling mode, and accurate prediction of urban rail transit passenger flow is particularly important. However, due to the non-linearity and non-stability of passenger flow, the low quality of big data and the lack of data make it more and more difficult to predict the passenger flow of urban rail transit. In this paper, we propose a unique model structure that ingeniously integrates Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN) with fused residual network connections, and a self-attention mechanism. The design of this model is primarily to effectively handle complex and low-quality data, which is often prevalent in passenger flow prediction scenarios. To validate the effectiveness of the proposed model, we used data from the Beijing urban rail transit in 2016 for prediction. In the experiments, we performed comparison study and ablation study. The experimental results show that the model proposed in this paper has significantly improved prediction accuracy in both horizontal and vertical comparisons. This outcome substantiates that the proposed model can not only effectively handle complex and low-quality data but also extract short-term features and long-term dependency features of passenger flow well, thereby achieving more accurate predictions. • Integration of GRU, CNN with fused residual connections, and self-attention mechanism constitutes a novel model. • The model is capable of handling complex and lower-quality data, significantly improving the accuracy of predictions. • The model can simultaneously extract short-term features and long-term dependency features from time series. [ABSTRACT FROM AUTHOR]

The rapid adoption of electric vehicles (EVs) has led to dramatic increase in charging demands that poses great challenges for efficient charging infrastructure rollout and operation. It is crucial to accurately assess charging demand in urban areas to optimize the siting and sizing of charging infrastructure. This paper proposes a novel urban charging load forecasting model for private passenger EVs based on massive operating data of EVs in Beijing. First, the characteristics of travel patterns for private passenger EVs, urban road network, functional area distribution and charging infrastructure distribution within the entire Beijing area are identified. Then a charging load forecasting model that can simultaneously simulate trip chains for EVs is constructed by considering the occupancy states of public charging piles and the interactions among different EVs. Finally, the effectiveness of the proposed charging load forecasting model is verified based on comprehensive test data. Our findings imply that the number of EVs at recharge and the charging power can be reliably predicted with the accuracy of over 84.73 % and 81.92 %, respectively. It provides the foundation for optimal charging infrastructure planning and charging scheduling. [ABSTRACT FROM AUTHOR]

More accurate multivariate load forecasting is conducive to integrated energy system planning optimization and economic operation. In this paper, based on the consideration of exogenous factors such as weather, endogenous factors are added into the prediction, and a short-term prediction model for integrated energy multivariate load based on endo-exogenous uncertainty is established using fine-tuned transfer learning. The LSTM pre-training model is first initially established using genetic algorithm and exogenous factors such as weather. Then the exogenous uncertainty of the pre-training model and the knowledge of endogenous uncertainty in the target model are combined using fine-tuned transfer learning to continuously optimize the basic parameters of the prediction model to obtain the final multivariate load prediction model. The performance of the four models is comprehensively evaluated after the historical load validation of the integrated energy system in Beijing, China. Compared with the traditional LSTM multivariate load forecasting model, the MAPE of the proposed model in this paper is reduced by 21.03 %, 13.00 %, and 24.39 % for electricity, heat, and cooling load forecasting, respectively. • The coupling mechanism between multiple energy sources of the integrated energy system is analyzed. • An endogenous-exogenous uncertainty method is proposed to extract the dynamic coupling relationship between multiple loads. • An endogenous-exogenous uncertainty-based GA-LSTM multivariate load prediction model is developed. • Optimizing model parameters using fine-tuned migration learning to deepen the impact of endo-exogenous uncertainty. [ABSTRACT FROM AUTHOR]

• Accurate risk path forecasting in rail transit systems is a significant and challenging task. • A probabilistic deep learning framework that can process multisource data for risk path forecasting. • The model can effectively handle safety issues such as cascade failures. Failure-related urban rail events can disrupt transit system operations and traffic flow and lead to serious safety problems worldwide. A domino effect can occur if potential cascading events of major failures are not effectively mitigated and controlled. Therefore, accurate risk path forecasting in rail transit systems is a significant and challenging task. Because of the limitations of traditional models in terms of computational power and feature extraction capabilities, this paper proposes a probabilistic deep learning framework that can process multisource data for risk path forecasting in urban rail transit. This paper first proposes a method for constructing a large-scale risk path ground truth dataset when fault events occur. Then, the framework uses a graph-based feature mapping method to model social media, passenger flow, and station failure information. Finally, we proposed a spatiotemporal feature extractor and a dynamic difference weighting loss function to extract features and optimize parameters. We apply real-world data from 2018 to 2019 from the Beijing urban rail transit system for experimental analysis. The analyzed results demonstrate that the proposed model exceeds the baseline models by at least 2.9% in terms of F 1 value, fusing multi-source data exceeds using single-source data by at least 14% in terms of F 1 value and the proposed attention mechanism and dynamic loss function weights can effectively improve the forecasting performance of the model. Furthermore, the results of the different steps ahead demonstrate that the above results are robust. The application of the model can effectively handle safety issues such as cascade failures. [ABSTRACT FROM AUTHOR]

Persistent PM 2.5 pollution poses a serious threat to human health. Developing an accurate urban regional PM 2.5 forecasting is of practical significance for environmental protection. However, previous studies have mostly focused on individual monitoring stations, neglecting the influence of neighboring stations, which limits forecasting accuracy. Additionally, the PM 2.5 of a single monitoring station cannot reflect the overall situation of a region. Therefore, this paper develops a novel PM 2.5 spatiotemporal forecasting framework that combines graph convolutional module, temporal convolutional module, linear module. It enables the forecasting of PM 2.5 concentrations at multiple stations and multiple time steps in the future. Concretely, we utilize a mixed graph convolutional network to extract the spatial features of PM 2.5. Then, an improved temporal convolutional network, the second-order residual temporal convolutional network, is developed to capture complex temporal features. Following the classical "linear and non-linear" modeling strategy, a linear module is added to the forecasting framework. Experiments on the real air pollution dataset from Beijing demonstrate that our framework outperforms the state-of-the-art baselines. • Extracting and fusing spatiotemporal features of PM2.5 is crucial for prediction. • A novel spatiotemporal forecasting framework is proposed. • A second-order residual TCN is developed to extract complex temporal features. • Experiments on real air pollution dataset show our framework's superiority. [ABSTRACT FROM AUTHOR]

Accurate PM 2.5 forecasting provides a possibility for establishing an early warning system to notify the public and take precautionary measures to prevent negative effects on ambient air quality and public health. Considering strong seasonal variation in meteorological conditions, in this paper, a seasonal stacked autoencoder model combining seasonal analysis and deep feature learning is proposed for forecasting the hourly PM 2.5 concentration, named DL-SSAE model. The original data are firstly decomposed into four seasonal subseries according to the Chinese calendar, and then the Kendall correlation coefficient method is employed to search inherent relationships between PM 2.5 concentrations and meteorological parameters within 1-h ahead for each seasonal time series. The inherent relationships of each seasonal subseries are finally extracted, learned, and modeled by different deep neural networks (stacked autoencoders for regression), and the hourly PM 2.5 forecasts are yielded. The addressed model is tested by the dataset collected from three environmental monitoring stations in Beijing, China. The results demonstrate that the proposed model outperforms all other considered models with/without seasonality consideration in this paper. [ABSTRACT FROM AUTHOR]

Forecasting the crowd flows of some densely populated areas (functional areas) at urban level is vital for the city and traffic management and much efforts have been put into this field. However, considering the complex dependencies of crowd flows prediction, the current models generally neglected the multi-scale correlation of spatial view and weakened the influence of external features on crowd flows data. In this paper, we proposed a deep learning framework, called MV-RANet, to deal with them and further improve the prediction performance. Firstly, a double-branch residual attention network was utilized to model the spatial correlation between fine-grained regions and coarse-grained functional areas separately with different branch. And furthermore, some human mobility pattern information were incorporated in MV-RANet to capture the correlation between functional areas in a different view. And as for the impact of external factors, we combined them with crowd flows data by multi-channel mechanism, which can make the external factors and spatio-temporal crowd flows data one-to-one mapping, to refine their influence on the crowd flows. We evaluated our model with two different datasets, a large scale Call Detail Records (CDRs) data and Beijing taxicabs' trajectories data (TaxiBJ) (Zhang et al., 2017). The experimental results demonstrate that the proposed MV-RANet consistently outperforms all the baselines. [ABSTRACT FROM AUTHOR]

[Display omitted] • Propose variational mode decomposition optimized by capuchin search algorithm. • Propose a prediction model of optimized LSTM by pelican optimization algorithm. • Consider error sequence, and propose the error correction module CVMD-POA-LSTM. • Propose a new hybrid prediction model for PM2.5 concentration, named CEEMDAN-ApEn-CVMD-POA-LSTM-EC. • In this paper, all data and code links are shared on GitHub. With the acceleration of urbanization leading to a general decrease in air quality, accurate PM2.5 concentration prediction is of the utmost practical meaning for the control and prevention of air pollution in the region. Therefore, a new hybrid prediction model for PM2.5 concentration based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), approximate entropy (ApEn), variational mode decomposition optimized by capuchin search algorithm (CVMD), long short-term memory optimized by pelican optimization algorithm (POA-LSTM) and error correction (EC), named CEEMDAN-ApEn-CVMD-POA-LSTM-EC, is proposed. First, CEEMDAN is used to acquire a limited amount of intrinsic mode functions (IMFs). Second, calculate ApEn value for each IMF component, and divide each IMF component into high-complexity and low-complexity components by the size of ApEn values. Third, variational mode decomposition optimized by capuchin search algorithm (CVMD), named CVMD, is proposed. CVMD is used as a secondary decomposition method to further decompose high-complexity components adaptively into a finite number of IMFs. Fourth, long short-term memory optimized by pelican optimization algorithm, named POA-LSTM, is proposed. POA-LSTM predicts all IMF components, and the results of their predictions are combined to generate the original prediction results. Final, error sequence is decomposed and predicted again by the EC module CVMD-POA-LSTM to obtain prediction results of error sequence, and final prediction results are acquired by combining original prediction results and prediction results of error sequence. The datasets in Beijing, Shanghai, and Xi'an were selected for simulation experiments to demonstrate the superiority of the proposed model. Taking Beijing as an example, RMSE, MAE, MAPE and R2 values are 1.9947, 1.5577, 0.1157 and 0.9947, which are superior to other comparison models and have the best performance. [ABSTRACT FROM AUTHOR]

• A deep learning approach to predicting the electric vehicle (EV) charging demand. • A real-world large EV trajectory dataset was used for model training and validation. • The deep learning approach outperformed traditional time series prediction models. • Input data structures, and sample sizes were not influential to prediction accuracy. Short-term prediction of the Electric Vehicle (EV) charging demand is of great importance to the operation of EV fleets and charging stations. This paper develops a Long Short-Term Memory (LSTM) neural network to predict the EV charging demand at the station level for the next few hours (e.g., 1–5 h), using a unique trajectory dataset containing over 76,000 private EVs in Beijing in January 2018. To explore the performance of the LSTM model, we set up four scenarios by 1) comparing LSTM against two typical time series prediction models, i.e., the Auto-Regressive Moving Average model (ARIMA), and the Multiple Layer Perceptron model (MLP), 2) and investigating how different input data structures, sample sizes, and time spans and intervals would influence model accuracy. The results suggest that the LSTM model outperformed the ARIMA, and MLP models, and their MAPE1 values are 6.83 %, 21.58 %, and 18.31 %, respectively. In addition, we find that the time span and interval tend to be more influential to the LSTM model's prediction accuracy than input data structures, and sample sizes. In general, the LSTM model with a shorter time span or interval (e.g., 1 h) would perform better. [ABSTRACT FROM AUTHOR]

Building load forecasting is critical for energy saving and carbon emission reduction, as building loads account for a rising percentage of energy consumption. Existing load forecasting methods only target a single building and do not consider the spatial characteristics of different building loads in an architectural complex, making it impossible to effectively forecast loads for all buildings in an architectural complex simultaneously. In this paper, considering the spatial correlation among building loads in an architectural complex, a novel short-term load forecasting method is proposed to improve the load forecasting accuracy for the architectural complex. First, Pearson correlation analysis is carried out to determine the key influencing factors of load, which are treated as multi-information. Then, to realize the load forecasting of all buildings in an architectural complex at the same time, a multi-information fusion model combined with a graph convolutional network and a long-short term memory network is proposed, and spatio-temporal attention modules are included to extract the dynamic spatio-temporal correlations of load. The performance of the proposed model is evaluated using actual load data from a public university in Beijing. When compared to other current methods, the experimental results show that the proposed model not only achieves the best results for architectural complex load forecasting (the largest MAPE is only 6.86%, the smallest R2 and Accuracy reach 0.87 and 0.85, respectively), but also maintains forecasting stability to some extent over a longer forecast period. The proposed method is a meaningful step towards achieving fast and accurate architectural complex load forecasting. [ABSTRACT FROM AUTHOR]

Load forecasting analysis plays an important role for regional electric power project planning as well as consumption management. For improving the long-term load forecasting accuracy and usability, this paper proposes a new residual-type combined Grey Model-Least Squares Support Vector Machine forecasting model for the component loads by extracting the load characteristics. In this model, each component decomposed load is forecasted using characteristic-matched model. Meanwhile, Moran's I and α convergence are used to identify the spatial-temporal distribution characteristics and levels of electric consumption intensity from the perspective of spatial econometrics. The results show that the proposed model can improve the forecasting accuracy, and can avoid the poor usability in the application phase. According to a case study for Beijing, the electric consumption intensity of the city has a positive spatial correlation, and the low electric consumption intensity districts are mainly concentrated in the central region, which reveals a trend of spreading from the center to periphery. The overall electric consumption intensity shows a downward trend, but the internal differences increase with time. The results may help to carry out electric construction planning, and to formulate regionally energy-saving policies. • Electric load characteristics are captured with Empirical Mode Decomposition. • The component decomposed loads are forecasted by characteristic-matched models. • Building a residual-type combined Grey Model-Least Squares Support Vector Machine model for load forecasting. • Electric anomalies and substitution potential districts are identified from spatial econometrics. • Analyzing convergence trend and level of electric consumption intensity. [ABSTRACT FROM AUTHOR]

With the boom of big data, the Internet contains more and more personal behavior information, but it is difficult to extract effectively. A model involving multivariate processing capability must be constructed to deal with these time series with complex characteristics. In this paper, a novel hybrid model embedding Baidu Search Index is therefore proposed to implement multi-step ahead subway passenger flow forecasting. Firstly, we collect data from informative Baidu Search Index, reduce dimensionality, and screen out the powerful predictors by statistical analysis. Secondly, we extract matching common modes at similar time scales between the subway passenger flow and screened Baidu Search Index via multivariate mode decomposition being optimized by multi-objective algorithm. Furthermore, to eliminate pseudo statistical causality, we select the optimal combination of modal components between subway passenger flow and its corresponding Baidu Search Index at each time scale by an innovative multi–modal analysis strategy. Thirdly, we reconstruct the forecasting values of each selected optimal combination as the final results. The empirical results of Beijing, Shanghai and Guangzhou show that the proposed model can significantly outperform six benchmark models in both the level and directional accuracy. So introducing Baidu Search Index creates a sound opportunity to enhance the subway passenger flow forecasting ability. [ABSTRACT FROM AUTHOR]

• A force identification methodology based on measurement is proposed. • Wheel-rail contact force can be identified accurately by this model. • The identified force can be used to predict train-induced ground-borne vibration. Wheel-rail interaction plays an important role in the study of railway engineering. The wheel-rail contact forces (WCFs) are the key factors in the models addressing wheel-rail interaction. However, it is sometimes difficult to be measured, so an indirect identification methodology is proposed in this paper. Based on an analytical model and measurement, the vertical WCF can be determined. Using measured rail accelerations as input data, a calculation program is developed. The vertical WCF and the excitation forces transmitted from the vehicle-rail system to the roadbed can be obtained. The latter forces are used to predict the train-induced ground-borne vibrations. A case study of predicting ground-borne vibration in Beijing Metro is presented to verify the effectiveness of this method. The results show that the vertical WCF can be obtained efficiently and accurately, and the predicted ground-borne vibrations have good agreement with the measured ones. [ABSTRACT FROM AUTHOR]

Vegetable price predictions are of great significance to vegetable growers, particularly regarding production and management to ensure a balance in the regional supply and demand of vegetables. In the current paper, in order to improve the accuracy and efficiency of vegetable price forecasting, we propose an optimized neural network combined model based on the induced ordered weighted averaging operator. Our frameworks integrate the fruit fly algorithm (FOA) with the induced ordered weighted averaging (IWOA) operator for an enhanced performance. In particular, the FOA is employed for the parameter optimization of the generalized regression neural network (GRNN) and radial basis function neural network (RBFNN), reducing the adverse influence of man-induced factors in the model construction process and improving the learning ability of both GRNN and RBFNN. The IWOA operator calculates the weights of the single GRNN and RBFNN to address the problem of fixed weights in combination forecasting models. Monthly vegetable price data in Beijing was used to compare our method with nine single forecasting models, revealing that the optimization of the GRNN and RBFNN parameters by the FOA, the prediction accuracy of the FOA-GRNN model and FOA-RBFNN model surpass those of GRNN and RBFNN, respectively. Furthermore, results from four evaluation indexes reveal that the IOWA-based optimized neural network model exhibited a stronger predictive ability than the other nine prediction models. Results demonstrate the effectiveness of our framework for the prediction of vegetable price series, with potential applications in agricultural products of similar characteristics. • The parameter optimization of neural networks by the fruit fly algorithm. • Solving a problem of fixed weights by induced ordered weighted averaging operator. • Our model exhibiting a stronger predictive for the prediction of vegetable price series. • The vegetable price with a clear growth approaching the end of 2020. [ABSTRACT FROM AUTHOR]

• A 3-level fragment division method is proposed for actual vehicle data processing. • The influencing factors of electric vehicle energy consumption are discussed. • A novel data-driven energy consumption prediction framework is proposed. • The driving conditions prediction is integrated in the proposed framework. With increasing mass-adoption of electric vehicles, the energy consumption has become a key performance index to electric vehicle drivers, automakers and policy-makers. Accurate and real-time energy consumption prediction under real-world driving conditions is essential for alleviating the 'range anxiety' and can provide support for optimal battery sizing, energy-efficient route planning and charging infrastructures operation. In this paper, real-world driving data collected from fifty-five electric taxis in Beijing city are obtained and divided into three-level driving fragments. The influencing factors of energy consumption, including vehicle-, environment-, and driver-related factors, are extracted and studied. With the extracted key influencing factors, a novel machine learning-based energy consumption prediction framework integrated with driving condition prediction is proposed and used in actual energy consumption prediction. The real-world trip test results show that a root mean squared error of 0.159kWh (RMSE) and a mean absolute percentage error 12.68% (MAPE) are reached, the RMSE and the MAPE are respectively reduced by 32.05% and by 30.14% compared to the conventional method. [ABSTRACT FROM AUTHOR]

Air pollution, such as PM 2.5 (particulate matter with an aerodynamic equivalent diameter of less than 2.5 μm), PM 10 (particulate matter with an aerodynamic equivalent diameter of less than 10 μm), NO x , and SO x , is a global concern because it may cause many chronic and fatal diseases, especially in developing countries. To better address air pollution problems, an important step is the timely and accurate prediction of air quality. Traditional methods are mainly based on meteorological data, regression model data, remote sensing data and different retrieval methods. Numerous studies on deep learning methods have suggested that these approaches may be able to perform accurate predictions for complex systems. In this paper, a long short-term memory (LSTM) approach for predicting air quality is proposed; moreover, meteorological data are used and Chinese social media is investigated as a proxy for public perceptions and responses for air quality prediction. We gathered daily air quality data, meteorological data and Weibo check-in data for Beijing, China from January 1, 2015 to December 31, 2016. The average sentiment of the related Weibo posts was selected as the public response proxy. The performance of our proposed model is evaluated based on real data. The root-mean-square error (RMSE) and the mean absolute error (MAE) indicated that our method presented better prediction results than traditional methods in terms of the PM 2.5 , PM 10 , O 3 , NO 2 , SO 2 and CO concentrations. We focused on the prediction performance during the 2015 China Victory Day Parade period, during which social and political factors played an important role in air quality predictions. The results indicated that the proposed method, which incorporates public response data, was especially suitable for predicting the air quality in extreme short-term social events and provides a timely social measurement and feedback for environmental problems. • Social media were selected as a proxy for public perceptions for air quality. • LSTM model was proposed for air quality prediction. • Weibo data was suitable for air quality prediction during short-term social events. [ABSTRACT FROM AUTHOR]

• Traffic accident and flow data obtained simultaneously from crowdsourcing approach. • Entire traffic accidents post-impact from occurrence to recovery can be predicted. • Machine learning models were applied and good performance was achieved. • Results improved when updated information added into the model sequentially. Traffic accident management is a critical issue for advanced intelligent traffic management. The increasingly abundant crowdsourcing data and floating car data provide new support for improving traffic accident management. This paper investigates the methods to predict the complicated behavior of traffic flow evolution after traffic accidents using crowdsourcing data. Based on the available data source, the traffic condition is divided into four levels by congestion delay index: severely congested, congested, slow moving and uncongested. Four types of accidents are consequently defined based on the occurrence of each level. A hierarchical scheme is designed for identifying the most congested level and sequentially predicting duration of each level. The proposed model is validated using traffic accident data in 2017 from an anonymous source in Beijing, China by embedding three machine learning algorithms, random forest (RF), support vector machine (SVM) and neural network (NN), in the scheme. The results show NN outperforms the other two models when the assessment is conducted in absolute differences. Meanwhile, RF has a slightly better performance than SVM, especially when predicting the short-period congestion of severely congested level at the first time. By continuously updating the traffic condition information, significant improvement in accuracy can be acquired regardless of the exact model used. This study shows that emerging crowdsourcing data can be used in a real-time analysis of traffic accidents and the proposed model is effective to analyze such data. [ABSTRACT FROM AUTHOR]

When new metro tunnels undercrossing existing tunnels, deformation is inevitably caused in the stratum and the existing structures. However, the calculation results by conventional prediction methods often bring large errors compared to measured or simulated data because the complex influences of the stiffness of existing structures is always neglected. According to the equivalent layered method, an existing tunnel can be considered as an equivalent rock stratum instead. Based on the Peck formula, a theoretical model for predicting the deformations caused by construction of a new tunnel undercrossing an existing tunnel is established. This paper mainly picks the Metro Line 10 undercrossing the Metro Line 1 of Beijing subway as an engineering case study. The comparison shows that results by the prediction model fits the measured data well. The proposed model are widely applicable and provides a new prediction method to study the deformations of existing tunnels and strata caused by the undercrossing new constructions. [ABSTRACT FROM AUTHOR]