Providing accurate bus travel time information is very important to help passengers plan their trips and reduce waiting times. Due to the uncertainty of the bus travel time, the traditional prediction value of the travel time point cannot accurately describe the reliability of the prediction result, which is not conducive to passengers waiting for the bus according to the prediction result. At the same time, due to the large differences in the individual driving styles of the bus drivers, the travel time data fluctuate greatly, and the accuracy and reliability of the point prediction results are further reduced. To address this issue, this study develops a personalized bus travel time prediction intervals model for different drivers based on the bootstrap method. Personalized travel time prediction intervals were constructed for drivers with different driving styles. To further improve the quality of travel time prediction intervals, this study optimizes training data sets considering driving style factors. Then, this paper integrates hierarchical clustering, an artificial neural network, and the bootstrap method to construct another prediction intervals model for bus travel time based on driver driving style clustering and the bootstrap method. The real−world driving data sets of the No. 239 bus in Shenyang, China, were used for experimental verification. The results showed that the two models constructed in this paper can effectively quantify the uncertainty of the point prediction results, the PICP of each interval exceeding the confidence level set (80%). It was also found that the quality of the prediction intervals constructed by clustering the driving style data is better (MPIW values decreased by 23.33%, 54.24%, and 28.61 respectively, and the corresponding NMPIW values also decreased by 18.93%, 10.39%, and 14.19%, respectively), which can provide passengers with more reasonable suggestions for waiting time.

Providing accurate and real-time bus travel time information is crucial for both passengers and public transportation managers. However, in the traditional bus travel time prediction model, due to the lack of consideration of the influence of different bus drivers’ driving styles on the bus travel time, the prediction result is not ideal. In the traditional bus travel time prediction model, the historical travel data of all drivers in the entire bus line are usually used for training and prediction. Due to great differences in individual driving styles, the eigenvalues of drivers’ driving parameters are widely distributed. Therefore, the prediction accuracy of the model trained by this dataset is low. At the same time, the training time of the model is too long due to the large sample size, making it difficult to provide a timely prediction in practical applications. However, if only the historical dataset of a single driver is used for training and prediction, the amount of training data is too small, and it is also difficult to accurately predict travel time. To solve these problems, this paper proposes a method to predict bus travel times based on the similarity of drivers’ driving styles. Firstly, the historical travel time data of different drivers are clustered, and then the corresponding types of drivers’ historical data are used to predict the travel time, so as to improve the accuracy and speed of the travel time prediction. We evaluated our approach using a real-world bus trajectory dataset collected in Shenyang, China. The experimental results show that the accuracy of the proposed method is 13.4% higher than that of the traditional method.

The research and development of new accident-tolerant fuel cladding materials has emerged as a critical focus in international academic and engineering fields following the Fukushima nuclear accident. Due to the outstanding resistances in corrosion and radiation as well as high-temperature creep properties, oxide dispersion-strengthened (ODS) FeCrAl alloys have been studied extensively during the past decade. Current review articles in this field have primarily focused on the effects of chemical composition on the anti-corrosion performance and species of nano-oxide. However, several key issues have not been given adequate attention, including processing methods and parameters, high-temperature stability mechanisms, post-deformation microstructural evolution and high-temperature mechanical properties. This paper reviews the progress of basic research on ODS FeCrAl alloys, including preparation methods, the effects of preparation parameters, the thermal stability and irradiation stability of oxides, the microstructural deformation, and the mechanical properties at elevated temperatures. The aspects mentioned above not only provide valuable references for understanding the effects of preparation parameters on the microstructure and properties of ODS FeCrAl alloys but also offer a comprehensive framework for the subsequent optimization of ODS FeCrAl alloys for nuclear reactor applications.

Providing accurate information about bus travel times can help passengers plan their itinerary and reduce waiting time. However, due to various uncertainty factors and the sparsity of single-route data, traditional travel time predictions cannot accurately describe the credibility of the prediction results, which is not conducive to passengers waiting based on the predicted results. To address the above issues, this paper proposes a bus travel time prediction intervals model based on shared road segments, multiple routes’ driving style similarity, and the bootstrap method. The model first divides the predicted route into segments, dividing adjacent stations shared by multiple routes into one section. Then, the hierarchical clustering algorithm is used to group all drivers in multiple bus routes in this section according to their driving styles. Finally, the bootstrap method is used to construct a bus travel time prediction interval for different categories of drivers. The travel time data sets of Shenyang 239, 134, and New Area Line 1 were selected for experimental verification. The experimental results indicate that the quality of the prediction interval constructed using a data set fused with multiple routes is better than that constructed using a single-route data set. In the two cases studied, the MPIW of the three time periods decreased by 101.04 s, 151.72 s, 33.87 s, and 126.58 s, 127.47 s, 17.06 s, respectively.