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Based on the characteristics of expressway driving behavior, a punishment avoidance variable is introduced in this study to modify the theory of planned behavior (TPB), and the analysis model of expressway speeding behavior is improved and verified through survey data. The mechanism of the effects of attitude to behavior, subjective norm, perceived behavioral control, and punishment avoidance on expressway speeding behavior is analyzed. The results show that drivers lack a correct understanding of expressway speeding behavior and that punishment avoidance has a significant effect on expressway speeding behavior. Younger drivers (25-34), men, High income earners, and those who received more penalty points are considered prone to speeding. The study provides valuable contributions to the development of the Chinese version of the expressway speeding analysis model.

The Ordinary Kriging method is a common spatial interpolation algorithm in geostatistics. Because the semivariogram required for kriging interpolation greatly influences this process, optimal fitting of the semivariogram is of major significance for improving the theoretical accuracy of spatial interpolation. A deep neural network is a machine learning algorithm that can, in principle, be applied to any function, including a semivariogram. Accordingly, a novel spatial interpolation method based on a deep neural network and Ordinary Kriging was proposed in this research, and elevation data were used as a case study. Compared with the semivariogram fitted by the traditional exponential model, spherical model, and Gaussian model, the kriging variance in the proposed method is smaller, which means that the interpolation results are closer to the theoretical results of Ordinary Kriging interpolation. At the same time, this research can simplify processes for a variety of semivariogram analyses.

We demonstrate an all-fiber, high-sensitivity, dual-parameter sensor for humidity and temperature. The sensor consists of a symmetrical, non-adiabatic, tapered, single-mode optical fiber, operating at the wavelength near the dispersion turning point, and a cascaded fiber Bragg grating (FBG) for temperature compensation. At one end of the fiber’s tapered region, part of the fundamental mode is coupled to a higher-order mode, and vice versa at the other end. Under the circumstances that the two modes have the same group index, the transmission spectrum would show an interference fringe with uneven dips. In the tapered region of the sensor, some of the light transmits to the air, so it is sensitive to changes in the refractive index caused by the ambient humidity. In the absence of moisture-sensitive materials, the humidity sensitivity of our sensor sample can reach −286 pm/%RH. In order to address the temperature and humidity crosstalk and achieve a dual-parameter measurement, we cascaded a humidity-insensitive FBG. In addition, the sensor has a good humidity stability and a response time of 0.26 s, which shows its potential in fields such as medical respiratory dynamic monitoring.

Accurately forecasting the demand of urban online car-hailing is of great significance to improving operation efficiency, reducing traffic congestion and energy consumption. This paper takes 265-day order data from the Hefei urban online car-hailing platform from 2019 to 2021 as an example, and divides each day into 48 time units (30 min per unit) to form a data set. Taking the minimum average absolute error as the optimization objective, the historical data sets are classified, and the values of the state vector T and the parameter K of the K-nearest neighbor model are optimized, which solves the problem of prediction error caused by fixed values of T or K in traditional model. The conclusion shows that the forecasting accuracy of the K-nearest neighbor model can reach 93.62%, which is much higher than the exponential smoothing model (81.65%), KNN1 model (84.02%) and is similar to LSTM model (91.04%), meaning that it can adapt to the urban online car-hailing system and be valuable in terms of its potential application.

In response to the common issue of traffic spillover at short-distance intersections, which tends to be overlooked due to its potential for self-dissipation, this paper conducts a study on predicting traffic spillover dissipation at short-distance intersections using the machine learning models. First, conditions for identifying traffic spillover and determining its dissipation at short-distance intersections are proposed based on traffic wave theory, and the traffic operation and data collection scenario for a short-distance intersection is built using traffic simulation software, VISSIM 11. Next, data sets required as inputs for the prediction model are collected and generated, based on queue length of stranded vehicles and the dissipation state of traffic spillover, to improve model interpretability. Finally, a two-stage prediction model for traffic spillover dissipation is constructed using a Bi-LSTM model. The first stage of the model predicts the queue length of stranded vehicles on the road segment, which is then used as feature data for the second stage to predict the spillover dissipation state between short-distance intersections. The results show that the model’s prediction of the queue length of stranded vehicles in the first stage outperforms the DT, CNN, RF, LSTM, and GRU models, with a prediction accuracy of 93.4%, which verifies the feasibility of selecting the Bi-LSTM model in this paper. The model’s prediction of traffic spillover dissipation state in the second stage outperforms single-stage prediction models, with the model achieving an accuracy of 92.88% in traffic spillover identification and 90.72% in traffic spillover dissipation state prediction. This validates the effectiveness of the two-stage prediction method proposed in this paper and is conducive to further improving the model’s prediction accuracy. The proposed method can accurately predict traffic spillover and their dissipation at short-distance intersections, enabling the targeted selection of signal control strategies to address traffic spillover issues, thereby effectively improving the capacity of short-distance intersections.

In response to the issue of young truck drivers’ weaker perception of potential risks, which makes them more prone to engaging in risky driving behaviors, the direct influence of risk perception on behavior was innovatively considered. An improved theory of planned behavior (TPB) model was developed and a study on risky driving behavior among young truck drivers was conducted. Valid questionnaire data from 330 young truck drivers in China were collected, and the improved TPB model was validated and analyzed through structural equation modeling. The results indicate that the improved TPB model can effectively explain the risky driving behavior among young truck drivers. Specifically, attitudes toward behavior, subjective norms, and perceived behavioral control have significant positive effects on behavioral intention, while behavioral intention and perceived behavioral control have significant positive effects on behavior. In addition, risk perception has a significant negative effect on behavioral intention and behavior. Furthermore, a comparison with the traditional TPB model reveals that the improved TPB model performs better in terms of fit and explanatory power. Fit indices CMIN/DF, RMSEA, and AGFI were optimized by 16%, 18%, and 1.5%, respectively, and there was a 5% increase in explanatory power for behavior variance, validating the rationality and effectiveness of the improved TPB model. This provides decision support for the development of intervention measures for risky driving behavior among young truck drivers in the future.

Deubiquitinating modification of proteins is involved in the pathogenesis of diseases. Here, we investigated the role and regulating mechanism of a deubiquitinating enzyme (DUB), ovarian tumor domain-containing protein 1 (OTUD1), in diabetic cardiomyopathy (DCM). We find a significantly increased OTUD1 expression in diabetic mouse hearts, and single-cell RNA sequencing shows OTUD1 mainly distributing in cardiomyocytes. Cardiomyocyte-specific OTUD1 knockout prevents cardiac hypertrophy and dysfunction in both type 2 and type 1 diabetic male mice. OTUD1 deficiency restores cardiac AMPK activity and mitochondrial function in diabetic hearts and cardiomyocytes. Mechanistically, OTUD1 binds to AMPKα2 subunit, deubiquitinates AMPKα2 at K60/K379 sites, and then inhibits AMPK T172 phosphorylation through impeding the interaction of AMPKα2 and its upstream kinase CAMKK2. Finally, silencing AMPKα2 in cardiomyocytes abolishes the cardioprotective effects of OTUD1 deficiency in diabetic mice. In conclusion, this work identifies a direct regulatory DUB of AMPK and presents a OTUD1-AMPK axis in cardiomyocytes for driving DCM. Diabetic cardiomyopathy involves dysregulated protein deubiquitination. Here the authors show that deubiquitinating enzymes OTUD1 knockout prevents cardiac hypertrophy by deubiquitinating AMPKα2 at K60/K379, disrupting CAMKK2-mediated phosphorylation.

The lithium-ion battery (LIB) has become the primary power source for new-energy electric vehicles, and accurately predicting the state-of-health (SOH) of LIBs is of crucial significance for ensuring the stable operation of electric vehicles and the sustainable development of green transportation. We collected multiple sets of charge–discharge cycle experimental data for LiFePO4 LIB and employed several traditional machine learning models to predict the SOH of LIBs. It was found that the RF model yielded relatively superior predictive results, confirming the feasibility of applying the RF model to SOH prediction for the electric vehicle LIB. Building upon this foundation, further research was conducted on the RF improved model for LIB SOH prediction. The PSO algorithm was employed to adaptively optimize five major parameters of the RF model: max_depth, n_estimators, max_features, min_samples_split, and min_samples_leaf. This adaptation addresses the issue of prediction errors that stem from human experience to optimize parameters in the RF model. The results indicate that the RF improved model proposed in this paper can further improve the prediction accuracy of LIB SOH. Its model evaluation index also outperform others, demonstrating the effectiveness of this approach in the management of LIB SOH for new-energy electric vehicles. This contributes significantly to urban environmental protection and the development of green transportation.

Young pedestrians have a high rate of traffic violations and are vulnerable. In this study, theory of planned behavior (TPB) questionnaires were collected from a sample of 395 young pedestrians. Reliability analysis demonstrated that the TPB questionnaire was effective and credible. An analysis model was established based on the TPB. The motivations for traffic violation behaviors among young pedestrians on intersections were studied from the perspective of social psychology. The results revealed that the most common violation behavior of young pedestrians was crossing on yellow light (29.7%). Male young pedestrians reported the higher intention of violating regulations. Behavioral attitude (0.14), subjective norm (0.17), and perceived behavioral control (0.12) all affected young pedestrians’ behavioral intentions. Relatives and friends played a positive role in mitigating young pedestrians’ intentions to commit violations at intersections. Perceived behavior control had the weakest influence on young pedestrians’ intentions to violate regulations. Behavioral intention (0.31) was the most direct and significant predictor of behavior. The results of the study are valuable for the identification of the causes of traffic violations among young pedestrians, and they can serve as a reference for the implementation of effective interventions.

The growth of ride-hailing services has made people’s daily commutes more convenient but has increased traffic on the road. However, the data needed to verify the impact of ride-hailing services on the urban road traffic network are lacking. This study matches data on the trajectories of different kinds of vehicles in Xuancheng city in the urban road network by using vehicle information data, ride-hailing information data, and license plate data recorded by the traffic bayonet system from December 26, 2018, to January 25, 2019. We used two indices, the detecting intensity and the detecting rate, to analyze the characteristics of travel based on ride-hailing services in Xuancheng. The results show that the ride-hailing vehicles have obvious travel characteristics of morning peak and evening peak, and in central urban areas and through the proposed indices of the travel time occupation rate and the travel space occupation rate to further quantitatively analyze the spatial and temporal characteristics of travel of different kinds of vehicles. Following this, we calculated the average ratios of different kinds of vehicles on congested sections of the road network and used simple regression to analyze the relationship between this and the average speed on these sections to quantitatively analyze the impact of ride-hailing on traffic congestion. The work here can provide effective decision-making support to the government for managing travel based on ride-hailing services.