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Accurate monitoring of air quality can no longer meet people's needs. People hope to predict air quality in advance and make timely warnings and defenses to minimize the threat to life. This paper proposed a new air quality spatiotemporal prediction model to predict future air quality and is based on a large number of environmental data and a long short-term memory (LSTM) neural network. In order to capture the spatial and temporal characteristics of the pollutant concentration data, the data of the five sites with the highest correlation of time-series concentration of PM2.5 (particles with aerodynamic diameter ≤2.5 mm) at the experimental site were first extracted, and the weather data and other pollutant data at the same time were merged in the next step, extracting advanced spatiotemporal features through long- and short-term memory neural networks. The model presented in this paper was compared with other baseline models on the hourly PM2.5 concentration data set collected at 35 air quality monitoring sites in Beijing from January 1, 2016, to December 31, 2017. The experimental results show that the performance of the proposed model is better than other baseline models. [ABSTRACT FROM AUTHOR]

Since the limitation of carbon emissions, China's photovoltaic (PV) industry has developed vigorously, while some traditional heavy industries have been violently hit. Therefore, the industrial production data exhibits significant nonlinear and complexity characteristics, which may affect prediction accuracy, thus hindering the corresponding department's decision-making. Consequently, a novel structure-adaptive fractional Bernoulli grey model is presented in this paper to surmount this toughie, and the core innovations can be summarized as follows. Initially, a novel time function term is utilized to depict the accumulative time effect, which can smoothly represent the dynamic variations and significantly strengthen the robustness of the new model. Besides, the fractional-order accumulation technique, which could effectively improve the predicting accuracy, is employed in the proposed model. Furthermore, the adaptability and generalizability of the proposed model can be enhanced by the self-adaptive parameters optimized by the Particle Swarm Optimization. For illustration and verification purposes, experiments on forecasting the annual output of Photovoltaic modules in China and the annual output of steel in Beijing are compared with a range of benchmarks, including the classic GM (1, 1), conventional econometric technology, and machine learning methods. And the results confirmed that the proposed model is superior to all benchmark models, which indicates that the novel model is indeed suitable for industrial production forecasting. [ABSTRACT FROM AUTHOR]

Traffic prediction is the cornerstone of intelligent transportation system. In recent years, graph neural network has become the mainstream traffic prediction method due to its excellent processing ability of unstructured data. However, the network relationship in the real world is more complex. Multiple nodes and various associations such as different types of stations and lines in rail transit always exist at the same time. In an end-to-end model, the training accuracy will suffer if the same weights are assigned to multiple views. Thus, this paper proposes a framework with multi-view and multi-layer attention, which aims to solve the problem of node prediction involving multiple relationships. Specifically, the proposed model maps multiple relationships into multiple views. A graph convolutional neural network of multiple views with multi-layer attention learns the optimal regression of nodes. Furthermore, the model uses an autoencoder module to alleviate the over-smoothing problem during the training phase. With the historical dataset of Beijing rail transit, the experiment proves that the prediction accuracy of the model is generally better than the baseline traffic prediction algorithms. [ABSTRACT FROM AUTHOR]

Emergencies have a significant impact on the passenger flow of urban rail transit. It is of great practical significance to accurately predict the urban rail transit passenger flow and carry out research on its temporal and spatial distributions under emergency conditions. Urban rail transit operating units currently use video surveillance information mainly to process emergencies and rarely use computer vision technology to analyze passenger flow information collected. Accordingly, this paper proposes a passenger flow-based temporal and spatial distribution model for urban rail transit emergencies based on the CPT. First, this paper clarifies the categories and classification of urban rail transit emergencies, analyzes the factors affecting passenger route selection, and establishes a generalized travel cost model for passengers under emergencies. Second, this paper establishes a passenger route choice behavior model for urban rail transit based on the cumulative prospect theory. Finally, taking Beijing as an example, this paper analyzes passenger travel behavior under emergencies based on multiple logistic regression models and analyzes the impact of emergencies on rail transit travel behavior. The research results show that the cumulative prospect theory can better describe the route choice behavior of rail transit passengers under emergencies than the existing models, and this model is of great significance for handling urban rail transit emergencies. The model proposed in this paper can provide a theoretical basis for the government and relevant departments to formulate traffic management measures. [ABSTRACT FROM AUTHOR]

Emergencies have a significant impact on the passenger flow of urban rail transit. It is of great practical significance to accurately predict the urban rail transit passenger flow and carry out research on its temporal and spatial distributions under emergency conditions. Urban rail transit operating units currently use video surveillance information mainly to process emergencies and rarely use computer vision technology to analyze passenger flow information collected. Accordingly, this paper proposes a passenger flow-based temporal and spatial distribution model for urban rail transit emergencies based on the CPT. First, this paper clarifies the categories and classification of urban rail transit emergencies, analyzes the factors affecting passenger route selection, and establishes a generalized travel cost model for passengers under emergencies. Second, this paper establishes a passenger route choice behavior model for urban rail transit based on the cumulative prospect theory. Finally, taking Beijing as an example, this paper analyzes passenger travel behavior under emergencies based on multiple logistic regression models and analyzes the impact of emergencies on rail transit travel behavior. The research results show that the cumulative prospect theory can better describe the route choice behavior of rail transit passengers under emergencies than the existing models, and this model is of great significance for handling urban rail transit emergencies. The model proposed in this paper can provide a theoretical basis for the government and relevant departments to formulate traffic management measures. [ABSTRACT FROM AUTHOR]

Short-term traffic prediction under corrupted or missing data for large-scale transportation networks has become an important and challenging topic in recent decades. Since the critical roads have predictive power on their adjacent roads, this paper proposes a novel hybrid short-term traffic state prediction method based on critical road selection optimization. First, the utility function of the quality of service (QoS) for the critical roads in a large-scale road network is proposed based on the coverage and the data score. Then, the critical road selection optimization model in the transportation networks is presented by selecting an appropriate set of critical roads with the maximum proportion of the total calculation resources to maximize the utility value of the QoS. Also, an innovative critical road selection method is introduced, which is considering the topological structure and the mobility of the urban road network. Subsequently, the traffic speed of the critical roads is regarded as the input of the convolutional long short-term memory neural network to predict the future traffic states of the entire network. Experiment results on the Beijing traffic network indicate that the proposed method outperforms prevailing DL approaches in the case of considering critical road sections. [ABSTRACT FROM AUTHOR]

In rainy weather, the accurate prediction of traffic status not only helps road traffic managers to formulate traffic management methods but also helps travelers design travel routes and even adjust travel time. In this paper, based on six-dimensional data (e.g., past and present spatiotemporal traffic status, road network structure, pavement type, water accumulation, and rainfall level), a fuzzy neural network (FNN) prediction system is proposed to predict traffic status. The traffic status evolution trend is related not only to the existing traffic but also to the new traffic demand. Therefore, the FNN prediction system designed includes offline and online parts using the data of the past and the day separately and avoids the forecast of new traffic demand. The fuzzy C-means clustering algorithm is applied to cluster traffic status data under similar rainy weather in the past to form an offline initial dataset, which is used to train FNN weight parameters. The online part uses real-time detection data and the parameters trained by the offline part to further predict the traffic status and returns the prediction errors to the offline part to correct the weight parameters to further improve prediction accuracy. Finally, the FNN prediction system is verified using real Beijing expressway network data. The verification results show that the prediction system can guarantee prediction accuracy and can be used to effectively identify traffic status. [ABSTRACT FROM AUTHOR]

In a multimodal public transport network, transfers are inevitable. Planning and managing an efficient transfer connection is thus important and requires an understanding of the factors that influence those transfers. Existing studies on predicting passenger transfer flows have mainly used transit assignment models based on route choice, which need extensive computation and underlying behavioral assumptions. Inspired by studies that use network properties to estimate public transport (PT) demand, this paper proposes to use the network properties of a multimodal PT system to explain transfer flows. A statistical model is estimated to identify the relationship between transfer flow and the network properties in a joint bus and metro network. Apart from transfer time, the number of stops, and bus lines, the most important network property we propose in this study is transfer accessibility. Transfer accessibility is a newly defined indicator for the geographic factors contributing to the possibility of transferring at a station, given its position in a multimodal PT network, based on an adapted gravity-based measure. It assumes that transfer accessibility at each station is proportional to the number of reachable points of interest within the network and dependent on a cost function describing the effect of distance. The R-squared of the regression model we propose is 0.69, based on the smart card data, PT network data, and Points of Interest (POIs) data from the city of Beijing, China. This suggests that the model could offer some decision support for PT planners especially when complex network assignment models are too computationally intensive to calibrate and use. [ABSTRACT FROM AUTHOR]

With the rapid growth of car ownership, traffic congestion has become one of the most serious social problems. For us, accurate real-time travel time predictions are especially important for easing traffic congestion, enabling traffic control and management, and traffic guidance. In this paper, we propose a method to predict urban road travel time by combining XGBoost and LightGBM machine learning models. In order to obtain a relatively complete data set, we mine the GPS data of Beijing and combine them with the weather feature to consider the obtained 14 features as candidate features. By processing and analyzing the data set, we discussed in detail the correlation between each feature and the travel time and the importance of each feature in the model prediction results. Finally, the 10 important features screened by the LightGBM and XGBoost models were used as key features. We use the full feature set and the key feature set as input to the model to explore the effect of different feature combinations on the prediction accuracy of the model and then compare the prediction results of the proposed fusion model with a single model. The results show that the proposed fusion model has great advantages to urban travel time prediction. [ABSTRACT FROM AUTHOR]