Forecasting the evolution of fast-changing transportation networks using machine learning

Intelligent traffic congestion forecasting using BiLSTM and adaptive secretary bird optimizer for sustainable urban transportation

Traffic congestion forecasting is one of the major elements of the Intelligent Transportation Systems (ITS). Traffic congestion in urban road networks significantly influences sustainability by increasing air pollution levels. Efficient congestion management enables drivers to bypass heavily trafficked areas and reducing pollutant emissions. However, properly forecasting congestion spread remains challenging due to complex, dynamic, and non-linear nature of traffic patterns. The advent of Internet of Things (IoT) devices has introduced valuable datasets that can support the development of intelligent and sustainable transportation for modern cities. This work presents a Deep Learning (DL) approach of Reinforcement Learning (RL) based Bidirectional Long Short-Term Memory (BiLSTM) with Adaptive Secretary Bird Optimizer (ASBO) for traffic congestion prediction. The experimentation is evaluated on Traffic Prediction Dataset and achieved better Mean Square Error (MSE) and Mean Absolute Error (MAE) with results of 0.015 and 0.133 respectively. Compared to the existing algorithms like RL, Deep Q Learning (DQL), LSTM and BiLSTM, the RL - BiLSTM with ASBO outperformed with the parameters MSE, RMSE, R2, MAE and MAPE with 37%, 27.44%, 26%, 33.52% and 35.8% respectively. The better performance demonstrates that RL- BiLSTM with ASBO is well-suited to predict congestion patterns in road networks.

Regional mobility and COVID-19 vaccine hesitancy: Evidence from China

China's Zero-COVID Policy imposed stringent restrictions on citizens' mobility to curb the spread of COVID-19. While effective in reducing viral transmission, these measures may have inadvertently delayed or deterred vaccine uptake by fostering a heightened sense of security. This study examines the relationships between intra- and inter-regional travel mobility and individual hesitancy towards COVID-19 vaccines (HCV), leveraging the Baidu Mobility Index and data from a cross-sectional survey of 12,000 participants. Our descriptive analysis reveals that (a) individual attitudes toward COVID-19 vaccines are more polarized across regions with different mobility levels than toward vaccines in general and (b) regions with higher population mobility exhibit lower levels of hesitancy toward COVID-19 vaccines. Our OLS and IV results further demonstrate that a one-standard-deviation increase in inter-provincial travel rates is associated with a decrease of 0.0112-0.0195 standard deviations in HCV, whereas intra-provincial mobility is not correlated. Overall, this paper suggests prioritizing the roll-out of COVID-19 vaccines or similar initiatives in areas with higher mobility levels, where residents perceive greater risks and exhibit a higher likelihood of seeking vaccination.

Artificial Intelligence-Guided Gut-Microenvironment-Triggered Imaging Sensor Reveals Potential Indicators of Parkinson's Disease

The gut-brain axis has recently emerged as a crucial link in the development and progression of Parkinson's disease (PD). Dysregulation of the gut microbiota has been implicated in the pathogenesis of this disease, sparking growing interest in the quest for non-invasive biomarkers derived from the gut for early PD diagnosis. Herein, an artificial intelligence-guided gut-microenvironment-triggered imaging sensor (Eu-MOF@Au-Aptmer) to achieve non-invasive, accurate screening for various stages of PD is presented. The sensor works by analyzing α-Syn in the gut using deep learning algorithms. By monitoring changes in α-Syn, the sensor can predict the onset of PD with high accuracy. This work has the potential to revolutionize the diagnosis and treatment of PD by allowing for early intervention and personalized treatment plans. Moreover, it exemplifies the promising prospects of integrating artificial intelligence (AI) and advanced sensors in the monitoring and prediction of a broad spectrum of diseases and health conditions.

Artificial intelligence-based prediction model for the elemental occurrence form of tailings and mine wastes

With the advent of the second industrial revolution, mining and metallurgical processes generate large volumes of tailings and mine wastes (TMW), which worsens global environmental pollution. Studying the occurrence of metal and metalloid elements in TMW is an effective approach to evaluating pollution linked to TMW. However, traditional laboratory-based measurements are complicated and time-consuming; thus, an empirical method is urgently needed that can rapidly and accurately determine elemental occurrence forms. In this study, a model combining Bayesian optimization and random forest (RF) approaches was proposed to predict TMW occurrence forms. To build the RF model, a dataset of 2376 samples was obtained, with mineral composition, elemental properties, and total concentration composition used as inputs and the percentage of occurrence forms as the model output. The correlation coefficient (R), coefficient of determination, mean absolute error, root mean squared error, and root mean squared logarithmic error metrics were used for model evaluation. After Bayesian optimization, the optimal RF model achieved accurate predictive performance, with R values of 0.99 and 0.965 on the training and test sets, respectively. The feature significance was analyzed using feature importance and Shapley additive explanatory values, which revealed that the electronegativity and total concentration of the elements were the two features with the greatest influence on the model output. As the electronegativity of an element increases, its corresponding residual fraction content gradually decreases. This is because the solubility typically increases with the solvent's polarity and electronegativity. Overall, this study proposes an RF model based on the nature of TMW that can rapidly and accurately predict the percentage values of metal and metalloid element occurrence forms in TMW. This method can minimize testing time requirements and help to assess TMW pollution risks, as well as further promote safe TMW management and recycling.

Spatiotemporal dynamics of traffic bottlenecks yields an early signal of heavy congestions

Heavy traffic jams are difficult to predict due to the complexity of traffic dynamics. Understanding the network dynamics of traffic bottlenecks can help avoid critical large traffic jams and improve overall traffic conditions. Here, we develop a method to forecast heavy congestions based on their early propagation stage. Our framework follows the network propagation and dissipation of the traffic jams originated from a bottleneck emergence, growth, and its recovery and disappearance. Based on large-scale urban traffic-speed data, we find that dissipation duration of jams follows approximately power-law distributions, and typically, traffic jams dissolve nearly twice slower than their growth. Importantly, we find that the growth speed, even at the first 15 minutes of a jam, is highly correlated with the maximal size of the jam. Our methodology can be applied in urban traffic control systems to forecast heavy traffic bottlenecks and prevent them before they propagate to large network congestions.