Estimation and prediction of dynamic matrix travel on a public transport corridor using historical data and real-time in
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Estimation and prediction of dynamic matrix travel on a public transport corridor using historical data and real‑time information Felipe Zúñiga1 · Juan Carlos Muñoz1,2 · Ricardo Giesen1,2 Accepted: 9 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In this paper a new methodology to estimate/update and forecast dynamic real time origin–destination travel matrices (OD) for a public transport corridor is presented. The main objective is to use available historical data, and combine it with online information regarding the entry and exit of each particular user (e.g. through the fare system, FS), to make predictions and updates for the OD matrices. The proposed methodology consists of two parts: (1) an estimation algorithm for OD matrices of public transport (EODPT), and (2) a prediction algorithm (PODPT) based on artificial neural networks (ANNs). The EODPT is based on a model that incorporates the travel time distribution between OD pairs and the modeling of the travel destination choice as a multinomial distribution, which is updated using a Bayesian approach with new available information. This approach makes it possible to correct the estimates of both the current OD interval matrices and of preceding intervals. The proposed approach was tested using actual demand data for the Metro of Valparaiso corridor in Chile (Merval), and simulated travel information in the corridor. The results are compared favorably with a static approach and can support the use of this methodology in real applications. The execution times obtained in the test cases do not exceed 10 s. Keywords OD matrices estimation · OD matrices prediction · Real-time
* Juan Carlos Muñoz [email protected] Felipe Zúñiga [email protected] Ricardo Giesen [email protected] 1
Department of Transport Engineering and Logistics, Pontifical Catholic University of Chile, Vicuña Mackenna 4860, Macul, Casilla 306, Correo 22, Santiago, Chile
2
Centro de Desarrollo Urbano Sustentable, CEDEUS, Santiago, Chile
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1 Introduction The operation of public transport systems is characterized by the variability in both travel times between successive stops and passenger demand in each of them. This variability tends to produce vehicle bunching, which ends up deteriorating waiting times, service reliability and comfort measures. In order to avoid these negative impacts on the level of service, certain control measures can be implemented in a way to regularize the time intervals between vehicles. The results reported by Delgado et al. (2009, 2012), show that schemes which combine such control measures delivered encouraging results, offering even levels of service for users and significant improvements over those obtained with each measure individually. The availability of new technologies offers extremely advantageous opportunities in the creation and implementation of systems that control and regulate the evolution of public transport systems. Many systems currently use technologies of automatic
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