In this study, the authors employ machine learning to develop a new solution method for solving a tri‐level network protection problem. In the upper‐level, the planner aims to minimise the impact of the interdictor's attempt to disrupt a road network through protection activities. At the middle‐level, however, the interdictor seeks to maximise the network's cost function, that is total travel time while the user equilibrium assignment models the road users behaviour at the lower‐level. The proposed solution algorithm combines implicit enumeration with machine learning for faster performance. In so doing, four machine learning methods are evaluated among which the artificial neural network model shows the best performance and thereby to be exploited. Principal component analysis is also employed as part of the data pre‐processing to perform dimensionality reduction. The proposed solution algorithm exhibits a reasonable level of tractability when employed to solve large problems in which a real‐world network is under investigation. Although it cannot guarantee global optimality, it is argued that this is an essential compromise for the application of the network optimisation problems on extensive real‐world networks and the large solution space that they generate.
Hybrid machine learning and optimisation method to solve a tri‐level road network protection problem
IET Intelligent Transport Systems ; 12 , 9 ; 1011-1019
2018-11-01
9 pages
Article (Journal)
Electronic Resource
English
tri‐level road network protection problem , roads , hybrid machine learning , neural nets , network cost function maximisation , behavioural sciences computing , user equilibrium assignment , traffic engineering computing , road users behaviour , principal component analysis , learning (artificial intelligence) , artificial neural network model , network optimisation problems , data preprocessing , dimensionality reduction , optimisation method , optimisation , protection activities
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