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原文传递 A hybrid model based method for bus travel time estimation
题名: A hybrid model based method for bus travel time estimation
正文语种: 英文
作者: B. Anil Kumar; Lelitha Vanajakshi; Shankar C. Subramanian
作者单位: Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600 036, INDIA; Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600 036, INDIA
关键词: bus travel time estimation; k-NN classifying algorithm; exponential smoothing; Kalman filtering; bus arrival information
摘要: Providing accurate information about bus arrival time to passengers can make the public transport system more attractive. Such information helps the passengers by reducing the uncertainty on waiting time and the associated frustrations. However, accurate estimation of bus travel time is still a challenging problem, especially under heterogeneous and lane-less traffic conditions. The accuracy of such information provided to passengers depends mainly on the estimation method used, which in turns depends on the input data used. Hence, developing suitable estimation methods and identifying the most significant/appropriate input data are important. The present study focused on these aspects of development of estimation methods that can accurately estimate travel time by using significant inputs. In order to identify significant inputs, a data mining technique, namely the k-NN classifying algorithm, was used. It is based on the similarity in pattern between the input and historic data. These identified inputs were then used in a hybrid model that combined exponential smoothing technique with recursive estimation scheme based on the Kalman Filtering (KF) technique. The optimal values of the smoothing parameter were dynamically estimated and were updated using the latest measurements available from the field. The performance of the proposed algorithm showed a clear improvement in estimation accuracy when compared with existing methods.
出版年: 2018
期刊名称: Journal of Intelligent Transportation Systems Technology Planning and Operations
卷: 22
期: 5
页码: 390-406
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