Bus travel time prediction using a time-space discretization approach

Kumar, B. Anil; Vanajakshi, Lelitha; Subramanian, Shankar C.

doi:10.1016/j.trc.2017.04.002

Cited by 84 publications

(33 citation statements)

References 45 publications

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“…Such theory-based approaches usually focus on recreating the traffic conditions in the future time intervals and then deriving travel times from the predicted traffic state [6]. Such theory based models can be classified as a) Macroscopic, b) Microscopic, and c) Mesoscopic, based on the level of detail it captures [7,8,9,10]. Majority of the studies under this category used the conservation of vehicle principle to predict travel time under homogenous traffic condition [11,6] and a few studies developed simulation approaches [12], dynamic traffic assignment based methods [13] for the same.…”

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

Self Cite

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“…It was found that the proposed dynamic model was feasible and applicable for bus travel time prediction and had the best prediction performance among other models in multiple bus routes. Kumar et al (2017) proposed a hybrid model that combined exponential smoothing technique based on the Kalman Filtering (KF) technique. The proposed model showed significant improvement compared with existing models in the prediction of bus travel time.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the developing countries, traffic f low is mixed, characterized with a variety of vehicles; motorized and non -motorized, share the same lane, interrupted by traffic police even at signalized intersections, and with unpredictable waiting time at bus stops (Arhin et al, 2016;Kumar et al, 2017). These traffic characteristics cause uncertainties variation on traffic parameters and variables, including travel time.…”

Section: Introductionmentioning

confidence: 99%

Comparative Assessment of Dynamic Travel Time Prediction Models in the Developing Countries Cities

2020

IJTTE

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Providing real and accurate travel time information usually, assists road users to plan their trips and choose the appropriate mode of transport. However, accurate prediction of travel time is a challenging problem, especially in developing countries where heterogeneous flow conditions exist and there are no records of information about the travel time for travelers. Most of the dynamic travel-time prediction models developed emphasize on link travel time without taking into account delay time at the intersections and waiting time at the bus stops. The objective of this study was to compare Multi-Linear Regression and Artificial Neural Network models to obtain a suitable model for developing a dynamic travel-time prediction model using waiting time at the bus stop, intersection delay time, link distance, traffic volume, link travel time, peak hours and off-peak hours as model inputs. Link travel time was modeled by a well-trained Neural Network and Kalman filtering dynamic algorithm using field survey data collected by employing public buses in Dar es Salaam city. The model was validated by using data collected in five main routes in Dar es Salaam City. The Root Mean Square Error and Mean Absolute Percent Error were used to evaluate the performance of the model by comparing it with other prediction models. Findings indicate that the integration of the Artificial Neural Network and Kalman Filter algorithm model (ANN-KF) promised to be a reasonable model for predicting dynamic travel time in Dar es Salaam city.

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“…Kalman filtering is a common method for bus arrival time prediction [1,2]. In reference [3], the Godunov scheme is used in the prediction scheme based on the Kalman filter. Support Vector Machine (SVM) [4] is widely used in this task.…”

Section: Introductionmentioning

confidence: 99%

Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network

et al. 2019

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Accurate prediction of bus arrival times is a challenging problem in the public transportation field. Previous studies have shown that to improve prediction accuracy, more heterogeneous measurements provide better results. So what other factors should be added into the prediction model? Traditional prediction methods mainly use the arrival time and the distance between stations, but do not make full use of dynamic factors such as passenger number, dwell time, bus driving efficiency, etc. We propose a novel approach that takes full advantage of dynamic factors. Our approach is based on a Recurrent Neural Network (RNN). The experimental results indicate that a variety of prediction algorithms (such as Support Vector Machine, Kalman filter, Multilayer Perceptron, and RNN) have significantly improved performance after using dynamic factors. Further, we introduce RNN with an attention mechanism to adaptively select the most relevant input factors. Experiments demonstrate that the prediction accuracy of RNN with an attention mechanism is better than RNN with no attention mechanism when there are heterogeneous input factors. The experimental results show the superior performances of our approach on the data set provided by Jinan Public Transportation Corporation.

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Bus travel time prediction using a time-space discretization approach

Cited by 84 publications

References 45 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

Comparative Assessment of Dynamic Travel Time Prediction Models in the Developing Countries Cities

Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network

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