Traffic Aware Many-Objective Dynamic Route Planning

Author(s):  
Ying Ying Liu ◽  
Fatemeh Enayatollahi ◽  
Parimala Thulasiraman
Keyword(s):  
1996 ◽  
Vol 9 (4) ◽  
pp. 223-232 ◽  
Author(s):  
Forouzan Golsahni ◽  
Enrique Cortes-Rello ◽  
Thomas H. Howell

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Yongxin Gao ◽  
Feng Chen ◽  
Zijia Wang

To make agents’ route decision-making behaviours as real as possible, this paper proposes a layered navigation algorithm, emphasizing the coordinating of the global route planning at strategic level and the local route planning at tactical level. Specifically, by an improved visibility graph method, the global route is firstly generated based on static environment map. Then, a new local route planning (LRP) based on dynamic local environment is activated for multipath selection to allow pedestrian to respond changes at a real-time sense. In particular, the LRP model is developed on the basis of a passenger’s psychological motivation. The pedestrians’ individual preferences and the uncertainties existing in the process of evaluation and choice are fully considered. The suitable local path can be generated according to an estimated passing time. The LRP model is applied to the choice of ticket gates at a subway station, and the behaviours of gate choosing and rechoosing are investigated. By utilizing C++, the layered navigation algorithm is implemented. The simulation results exhibit agents’ tendency to avoid congestion, which is often observed in real crowds.


2009 ◽  
Vol 23 (3) ◽  
pp. 269-287 ◽  
Author(s):  
Ahmet Yazici ◽  
Aydin Sipahioglu ◽  
Osman Parlaktuna

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 308
Author(s):  
Duy Nguyen Duc ◽  
Thong Tran Huu ◽  
Narameth Nananukul

Due to the availability of Industry 4.0 technology, the application of big data analytics to automated systems is possible. The distribution of products between warehouses or within a warehouse is an area that can benefit from automation based on Industry 4.0 technology. In this paper, the focus was on developing a dynamic route-planning system for automated guided vehicles within a warehouse. A dynamic routing problem with real-time obstacles was considered in this research. A key problem in this research area is the lack of a real-time route-planning algorithm that is suitable for the implementation on automated guided vehicles with limited computing resources. An optimization model, as well as machine learning methodologies for determining an operational route for the problem, is proposed. An internal layout of the warehouse of a large consumer product distributor was used to test the performance of the methodologies. A simulation environment based on Gazebo was developed and used for testing the implementation of the route-planning system. Computational results show that the proposed machine learning methodologies were able to generate routes with testing accuracy of up to 98% for a practical internal layout of a warehouse with 18 storage racks and 67 path segments. Managerial insights into how the machine learning configuration affects the prediction accuracy are also provided.


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