SVM‐based hybrid approach for corridor‐level travel‐time estimation

Abstract

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.