Bus travel time prediction: a log-normal auto-regressive (AR) modelling approach
mode - bus, place - asia, planning - methods, technology - intelligent transport systems
Travel time prediction, time-series analysis, partial correlation, non-stationary, log-normal distribution, multi-section ahead prediction
Accurate prediction of arrival time of buses is still a challenging problem in dynamically varying traffic conditions especially under Indian traffic conditions. The present study proposes two predictive modelling methodologies using the concepts of time series analysis, namely (a) classical seasonal AR model with possible integrating non-stationary effects and (b) linear non-stationary AR approach, a novel technique exploiting the notion of partial correlation for learning from data to predict arrival time of buses efficiently. Reported existing studies did not explore the distribution of travel time data and its effects on modelling. The present study conducted a detailed analysis of the marginal distributions of the data and incorporated it into the predictive models. A multi-section ahead travel time prediction algorithm is also proposed to facilitate real time implementation. From the results, it was found that the proposed method is able to perform better than many of the existing approaches.
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Dhivya Bharathi, B., Anil Kumar, B., Achar, A., & Vanajakshi, L. (2020). Bus travel time prediction: a log-normal auto-regressive (AR) modelling approach. Transportmetrica A: Transport Science, Vol. 16, pp. 807-839.