The objective of this study is to develop an accurate model for corridor‐level travel‐time estimation. Different approaches, such as k‐nearest neighbour (k‐NN), gradient boosting decision tree (GBDT) and support vector machines (SVMs), were used in this study. Further, this study also developed a hybrid model combining a data‐driven approach (SVM) and a model‐based approach [particle filter (PF)] for corridor‐level travel‐time estimation. Both static and dynamic parameters, such as road geometry, intersection length, location information from Global Positioning System devices, dwell time etc. were used as influential factors for modelling. The proposed algorithm was tested on a study corridor of length 59.48 km, in the arterials of Mumbai, India. The data was collected using a probe‐vehicle technique for five days during the morning peak period (from 8.00 am to 11.00 am) for two modes (car and bus). The mean absolute percentage error values obtained for the hybrid model for the two modes were: 9.96 (car) and 11.24 (bus). The performance of the proposed hybrid (SVM‐PF) algorithm showed a clear improvement in accuracy in comparison to existing standard methods such as k‐NN, GBDT and SVM.
SVM‐based hybrid approach for corridor‐level travel‐time estimation
IET Intelligent Transport Systems ; 13 , 9 ; 1429-1439
2019-09-01
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
India , decision trees , SVM‐based hybrid approach , particle filter , hybrid model , corridor‐level travel‐time estimation , model‐based approach , k‐nearest neighbour , nearest neighbour methods , Mumbai , support vector machines , gradient methods , particle filtering (numerical methods) , k‐NN , gradient boosting decision tree , data‐driven approach , GBDT , traffic information systems , study corridor
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