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The purpose of this paper is to construct a task assignment model for U-shaped production lines with collaborative task, which is optimized by minimizing the number of workers and balancing the workload of the operators. The ultimate goal is to increase productivity by increasing the U-line balance and balancing the load on the operators.

First, task selection and update mechanism are analyzed and the task selection mechanism suitable for collaborative task is proposed. Second, M-COMOSAL is obtained by improving the original COMOSAL. Finally, The M-COMOSAL algorithm and the COMAOSAL algorithm are used to perform job assignment on the double-acting clutch U-shaped assembly line.

According to the allocation scheme obtained by M-COMSOAL, the beat can be adjusted according to the change of order demand. The final allocation scheme is superior to the COMSOAL algorithm in terms of number of workers, working time, production tempo and balance rate. In particular, compared with the old scheme, the new scheme showed a decrease of 16.7% in the number of employees and a 18.8% increase in the production line balance rate. Thus, the method is helpful to reduce the number of operators and balance the workload.

The new algorithm proposed in this paper for the assignment of collaborative task can minimize the number of workers and balance the load of operators, which is of great significance for improving the balance rate of U-shaped production lines and the utilization of personnel or equipment.

Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps.

This paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing.

Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction.

The incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations.

The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications.

This study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning.

In order to unravel the evolution of microstructure characteristics and the change of mechanical properties of bituminous mixture in the freezing and thawing environment in cold region, this study starts from macroscopic experiments and analyzes the changes in mechanical properties of asphalt mixtures before and after freezing and thawing in detail. On this basis, the displacement of key particles in the structure of asphalt mixture under the action of external forces (before and after freezing and thawing) is simulated through the combination macroscopic and microscopic methods.

The climate in China exhibits high complexity and diversity, divided into five zones based on the temperature difference from south to north. Considering that the significant effect of geography and natural climate on the design, construction and maintenance of asphalt pavement, the criterion for the road construction at different areas should be highly different.

The results show that the mechanical properties of asphalt mixture greatly decrease due to the influence of freeze-thaw, and the displacement of key particles in the structure of asphalt mixture (several representative particle sizes were selected through experiments) is obviously observed because of the action of external force. By analyzing the variation of several key particle sizes after freezing-thawing cycle, the gradation standard of asphalt mixture aggregate suitable for cold area was obtained. The research results have certain theoretical and practical value for the design and application of asphalt mixture in cold area.

The results show that the mechanical properties of asphalt mixture greatly decrease due to the influence of freeze-thaw, and the displacement of key particles in the structure of asphalt mixture (several representative particle sizes were selected through experiments) is obviously observed because of the action of external force. By analyzing the variation of several key particle sizes after freezing-thawing cycle, the gradation standard of asphalt mixture aggregate suitable for cold area was obtained. The research results have certain theoretical and practical value for the design and application of asphalt mixture in cold area.