This article presents an overview of the agent-based modeling and simulation approach and its recent developments in transport fields, with the purpose of discovering the advantages and gaps and encouraging more valuable investigations and applications of agent-based models. We clarify the agent-based model from agents, the background of development, and the basic structure applied in transport systems. Then, the agent-based transport modeling toolkits are discussed. The applications of agent-based models in transport systems are reviewed in three time scale models followed by an additional discussion of hybrid modeling approaches. The extensive modeling of the beliefs, desires, learning, and adaptability of individuals and the optimization problems using agent-based models are explored. Besides, we point out some limitations in terms of calibration and validation procedure, agents’ behavior modeling, and computing efficiency. In conclusion, some recommendations are given and suggest potential and insightful directions such as Big Data and Digital Twin for future research.
Foggy weather can cause such problems as blurred image information and the loss of image details, which may pose great challenges to road traffic target detection based on images and videos. In this study, we propose a domain-adaptive road vehicle target detection method to implement domain adaptation for the real foggy scene. We firstly constructed a highway vehicle detection dataset with foggy images (HVFD), which contains normal weather images and foggy images and provides a complete data support for vehicle detection based on computer vision. Secondly, by improving CycleGAN we designed an improved generative confrontation network (CPGAN), which realised the style transfer between foggy images and normal weather images. Finally, we formulated a YOLOv4 target detection framework according to the domain adaptation based on the pre-trained YOLOv4 fog vehicle detection model. The experimental results show that the method we put forward can effectively improve vehicle detection performance and reduce the work of manually labelling a large number of foggy image tags, which has a strong generalisation ability for computer vision-based applications in low-visibility weather.
In order to prevent smoke from flowing backward and penetrating into adjacent tunnel through cross channels during evacuation in case of fire accidents in highway tunnels, it is extremely necessary to implement the linkage control scheme for double-hole tunnel ventilation system. Therefore, six linkage control schemes for ventilation system are proposed in this article. Firstly, these six schemes were numerically simulated through Fire Dynamics Simulator (FDS) to obtain the flow velocity, flow direction, and smoke distribution under different schemes when the peak power of fire accidents is 30 MW. And two better schemes were selected for further investigation based on the evacuation conditions in the upper stream of fire source. Secondly, a 1 : 10 scale model experiment was conducted to test these two schemes, and the results from numerical simulation and model experiment were compared and verified. Finally, the location of fire accident, the time to develop stable opposite airflow, and the number of open cross channels were analyzed to confirm the applicability of the recommended scheme. The results show that the optimal scheme is to switch on the fans in the upstream of the fire accident and the fans on both sides of the cross channel in the non-fire tunnel to form positive pressure to control the smoke flow. This scheme ensures a stable and uniform smoke flow in the cross channel and personnel’s safety during evacuation.
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