Predicting Bus Real-time Travel Time Basing on both GPS and RFID Data

Song, Xinghao; Teng, Jing; Chen, Guojun; Shu, Qichong

doi:10.1016/j.sbspro.2013.08.258

Cited by 27 publications

(12 citation statements)

References 5 publications

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“…Literature has also seen time series appraoches for travel time prediction by modelling several other related variables such as delays [64], headways [65], dwell time [66], running speed [67], etc. Only a few studies modelled the travel time observations directly for a BATP problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Bus travel time prediction: a log-normal auto-regressive (AR) modelling approach

Bharathi

Kumar

Achar³

et al. 2020

Transportmetrica A: Transport Science

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Providing real-time arrival time information of the transit buses has become inevitable in urban areas to improve the efficiency of the public transportation system. However, accurate prediction of arrival time of buses is still a challenging problem in dynamically varying traffic conditions especially under heterogeneous traffic condition without lane discipline. One broad approach researchers have adopted over the years is to divide the entire bus route into sections and model the correlations of section travel times either spatially or temporally. The proposed study adopts this approach of working with section travel times and developed two predictive modelling methodologies namely (a) classical time-series approach employing a seasonal AR model with possible integrating non-stationary effects and (b) linear non-stationary AR approach, a novel technique to exploit the notion of partial correlation for learning from data to exploit the temporal correlations in the bus travel time data. Many of the reported studies did not explore the distribution of travel time data and incorporated their effects into the modelling process while implementing time series approach. The present study conducted a detailed analysis of the marginal distributions of the data from Indian conditions (that we use for testing in this paper). This revealed a predominantly log-normal behaviour which was incorporated into the above proposed predictive models. Towards a complete solution, the study also proposes a multi-section ahead travel time prediction algorithm based on the above proposed classes of temporal models learnt at each section to facilitate real time implementation. Finally, the predicted travel time values were corroborated with the actual travel time values. From the results, it was found that the proposed method was able to perform better than historical average, exponential smoothing, ARIMA, and ANN methods and the methods that considered either temporal or spatial variations alone.

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Section: Literature Reviewmentioning

confidence: 99%

Bus travel time prediction: a log-normal auto-regressive (AR) modelling approach

Bharathi

Kumar

Achar³

et al. 2020

Transportmetrica A: Transport Science

View full text Add to dashboard Cite

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“…The data used for the prediction are the GPS data obtained manually over 10 days because the buses did not have AVL systems. Song et al [14] proposes a prediction model in which the TT between two consecutive stops on a route depends on the speed of the vehicle, which varies as it accelerates and decelerates, and by the time the vehicle is stationary because of traffic signals. This TT behaviour is predicted by an exponential smoothing function.…”

Section: Related Workmentioning

confidence: 99%

Bus Travel Time Prediction Model Based on Profile Similarity

Cristóbal

Padrón

Quesada-Arencibia

et al. 2019

Sensors

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In road-based mass transit systems, travel time is a key factor in providing quality of service. This article proposes a method of predicting travel time for this type of transport system. This method estimates travel time by taking into account its historical behaviour, represented by historical profiles, and the current behaviour recorded on the public transport vehicle for which the prediction is to be made. The model uses the k-medoids clustering algorithm to obtain historical travel time profiles. A relevant feature of the model is that it does not require recent travel time data from other vehicles. For this reason, the proposed model may be used in intercity transport contexts in which service planning is carried out according to timetables. The proposed model has been tested with two real cases of intercity public transport routes and from the results obtained we may conclude that, in general, the average error of the predictions is around 13% compared to the observed travel time values.

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“…There are also articles focused on information systems providing on-line information about position of vehicles. Articles [16], [17] and [18] can be mentioned as examples.…”

Section: Fig 1 Illustration Of Problem With Nttij or Nttjimentioning

confidence: 99%

Timetable synchronisation: Urban public transport in busy hubs of long-distance transport

Bulíček

2018

MATEC Web Conf.

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The article is focused on timetable synchronization problem. Interconnection of urban public transport and long-distance transport in busy hubs is researched. Intension is to find adequate time positions of urban public transport arrivals and departures with an effort to minimize passengers’ time loss related to this interchange. Timetable of long distance transport is considered as constant (given). Timetable of urban public transport is result of optimization (and presupposed to be periodic). Nonlinear integer mathematical programming model is formulated. Model is implemented in Microsoft Excel Solver. Parallel way based on exhaustive-search algorithm is introduced as well. This algorithm provides extended set of output data able to be used in transport planning. Two different criterions are proposed. Each interchange (pair of vehicles) has the same weight in the case of uniform approach. Intensity approach considers interchanges as weighted by numbers of interchanging passengers. Some additional modifications of proposed model are mentioned for possibility to regard some important technological aspects and to reach more individualized solution. Model is illustrated by the case study located to Pardubice main station as a hub of passenger railway transport. Interface with urban public transport is mentioned as an illustrational example of synchronization.

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Predicting Bus Real-time Travel Time Basing on both GPS and RFID Data

Cited by 27 publications

References 5 publications

Bus travel time prediction: a log-normal auto-regressive (AR) modelling approach

Bus travel time prediction: a log-normal auto-regressive (AR) modelling approach

Bus Travel Time Prediction Model Based on Profile Similarity

Timetable synchronisation: Urban public transport in busy hubs of long-distance transport

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