Dynamic route planning with uncertain information

1996 ◽  
Vol 9 (4) ◽  
pp. 223-232 ◽  
Author(s):  
Forouzan Golsahni ◽  
Enrique Cortes-Rello ◽  
Thomas H. Howell
Keyword(s):  
Route Planning ◽  
Uncertain Information ◽  
Dynamic Route

scholarly journals Hybrid Dynamic Route Planning Model for Pedestrian Microscopic Simulation at Subway Station

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Yongxin Gao ◽  
Feng Chen ◽  
Zijia Wang
Keyword(s):  
Route Planning ◽  
Local Environment ◽  
Subway Station ◽  
Microscopic Simulation ◽  
Navigation Algorithm ◽  
Environment Map ◽  
Global Route ◽  
Simulation Results ◽  
Local Path ◽  
Dynamic Route

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.

Heuristic-Based Dynamic Route Planning Method for a Homogeneous Multi-robot Team

2009 ◽  
Vol 23 (3) ◽  
pp. 269-287 ◽  
Author(s):  
Ahmet Yazici ◽  
Aydin Sipahioglu ◽  
Osman Parlaktuna
Keyword(s):  
Route Planning ◽  
Planning Method ◽  
Dynamic Route ◽  
Multi Robot

ROUTE PLANNING BASED ON UNCERTAIN INFORMATION IN TRANSPORT NETWORKS

Transport ◽  
2012 ◽  
Vol 27 (1) ◽  
pp. 79-85 ◽  
Author(s):  
Gábor Szűcs
Keyword(s):  
Traffic Congestion ◽  
Traffic Accidents ◽  
Decision Rules ◽  
Route Planning ◽  
Transport Network ◽  
Uncertain Information ◽  
End User ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
The Cost

The goal of this paper is to find a solution for route planning in a transport network where the network type can be arbitrary: a network of bus routes, a network of tram rails, a road network or any other type of a transport network. Furthermore, the costs of network elements are uncertain. The concept is based on the Dempster–Shafer theory and Dijkstra's algorithm which helps with finding the best routes. The paper focuses on conventional studies without considering traffic accidents or other exceptional circumstances. The concept is presented by an undirected graph. In order to model conventional real transport, the influencing factors of traffic congestion have been applied in the abstract model using uncertain probabilities described by probability intervals. On the basis of these intervals, the cost intervals of each road can be calculated. Taking into account the uncertain values of costs, an algorithm has been outlined for determining the best routes from one node to all other nodes comparing cost intervals and using decision rules that can be defined by the end user, and if necessary, node by node. The suggested solution can be applied for both one type of network as well as for a combination of a few of those.

scholarly journals A Dynamic Route-Planning System Based on Industry 4.0 Technology

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 308
Author(s):  
Duy Nguyen Duc ◽  
Thong Tran Huu ◽  
Narameth Nananukul
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Industry 4.0 ◽  
Big Data Analytics ◽  
Route Planning ◽  
Research Area ◽  
Automated Guided Vehicles ◽  
Planning System ◽  
Routing Problem ◽  
Dynamic Route

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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