As an important component of intelligent transportation-management systems, accurate traffic-parameter prediction can help traffic-management departments to conduct effective traffic management. Due to the nonlinearity, complexity, and dynamism of highway-traffic data, traffic-flow prediction is still a challenging issue. Currently, most spatial–temporal traffic-flow-prediction models adopt fixed-structure time convolutional and graph convolutional models, which lack the ability to capture the dynamic characteristics of traffic flow. To address this issue, this paper proposes a spatial–temporal prediction model that can capture the dynamic spatial–temporal characteristics of traffic flow, named the spatial–temporal self-attention graph convolutional network (STA-GCN). In terms of feature engineering, we used the time cosine decomposition and one-hot encoding methods to capture the periodicity and heterogeneity of traffic-flow changes. Additionally, in order to build the model, self-attention mechanisms were incorporated into the spatial–temporal convolution to capture the spatial–temporal dynamic characteristics of traffic flow. The experimental results indicate that the performance of the proposed model on two traffic-volume datasets is superior to those of several baseline models. In particular, in long-term prediction, the prediction error can be reduced by over 5%. Further, the interpretability and robustness of the prediction model are addressed by considering the spatial dynamic changes.
Freight bus is a new public transportation means for city logistics, and each freight bus can deliver and pick up goods at each customer/supplier location it passes. In this paper, we study the route planning problem of freight buses in an urban distribution system. Since each freight bus makes a tour visiting a set of pickup/delivery locations once at every given time interval in each day following a fixed route, the route planning problem can be considered a new variant of periodic vehicle routing problem with pickup and delivery. In order to solve the problem, a Mixed-Integer Linear Programming (MILP) model is formulated and an Adaptive Large Neighborhood Search (ALNS) algorithm is developed. The development of our algorithm takes into consideration specific characteristics of this problem, such as fixed route for each freight bus, possibly serving a demand in a later period but with a late service penalty, etc. The relevance of the mathematical model and the effectiveness of the proposed ALNS algorithm are proved by numerical experiments.
The microbial fermentation process often involves various
biological
metabolic reactions and chemical processes. The mixed bacterial culture
process of 2-keto-l-gulonic acid has strong nonlinear and
time-varying characteristics. In this study, a probabilistic Bayesian
deep learning approach is proposed to obtain a highly accurate and
robust prediction of product formation. The Bayesian optimized deep
neural network (BODNN) is utilized as basic model for prediction,
the structural parameters of which are optimized. Then, the training
datasets are classified into different categories according to the
prior evaluation of prediction error. The final forecasting is a weighted
combination of BODNN models based on the Bayesian hybrid method. The
weights can be interpreted as Bayesian posterior probabilities and
are computed recursively. The validation of 95 industrial batches
is carried out, and the average root mean square errors are 1.51 and
2.01% for 4 and 8 h ahead prediction, respectively. The results illustrate
that the proposed approach can capture the dynamics of fermentation
batches and is suitable for online process monitoring.
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