POOLE, ADAM,JAMES (2017) Macroscopic Traﬃc Model Validation of Large Networks and the Introduction of a Gradient Based Solver. Doctoral thesis, Durham University.
|PDF (AJP thesis) - Accepted Version|
Traﬃc models are important for the evaluation of various Intelligent Transport Systems and the development of new traﬃc infrastructure. In order for this to be done accurately and with conﬁdence the correct parameter values of the model must be identiﬁed. The focus of this thesis is the identiﬁcation and conﬁrmation of these parameters, which is model validation. Validation is performed on two diﬀerent models; the ﬁrst-order CTM and the second-order METANET model. The CTM is validated for two UK sites of 7.8 and 21.9 km and METANET for the same two sites using a variety of meta-heuristic algorithms. This is done using a newly developed method to allow for the optimisation method to determine the number of parameters to be used and the spatial extent of their application. This allows for the removal of expert engineering knowledge and ad-hoc decomposition of networks.
This thesis also develops a methodology by use of Automatic Diﬀerentiation to allow gradient based optimisation to be used. This approach successfully validated the METANET model for the 21.9 km site and also a large network surrounding the city of Manchester of 186.9 km. This proves that gradient based optimisation can be used for the macroscopic traﬃc model validation problem. In fact the performance of the developed gradient method is superior to the meta-heuristics tested for the same sites. The methodology deﬁned also allows for more data to be obtained from the model such as its Jacobian and the sensitivity of the objective function being used relative to the individual parameters. Space-Time contour plots of this newly acquired data show structures and shock waves that are not visible in the mean speed contour diagrams.
|Item Type:||Thesis (Doctoral)|
|Award:||Doctor of Philosophy|
|Keywords:||Macroscopic traffic modelling, CTM, METANET, Evolutionary Algorithms, Resilient backpropagation, particle swarm optimisation.|
|Faculty and Department:||Faculty of Science > Engineering, Department of|
|Copyright:||Copyright of this thesis is held by the author|
|Deposited On:||07 Dec 2017 08:48|