Bus arrival time prediction with real-time and historic data

Xu, Haitao; Jing, Ying

doi:10.1007/s10586-017-1006-1

Cited by 43 publications

(26 citation statements)

References 21 publications

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“…Hence, we believe that LSTM algorithm is suitable for dealing with problems related to time series. To enhance the prediction accuracy, [25] present a road segment issue and predict bus position according to path-section graph. Moreover, [26] present a revised road segment method, which considers both road section and bus stop.…”

Section: B Bat Predictionmentioning

confidence: 99%

A Bus Arrival Time Prediction Method Based on Position Calibration and LSTM

et al. 2020

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Bus arrival time prediction not only provides convenience for passengers, but also helps to improve the efficiency of intelligent transportation system. Unfortunately, the low precision of bus-mounted GPS system, lack of real-time traffic information and poor performance of prediction model lead to low estimation accuracy-greatly influence bus service performance. Hence, in this paper, a GPS calibration method is put forward, while projection rules of specific road shapes are discussed. Moreover, two traffic factors, travel factor and dwelling factor, are defined to express real-time traffic state. Then, considering both historic data and real-time traffic condition, a hybrid dynamic BAT prediction factor, which achieves accuracy enhancement by taking into account traffic flow evaluation results and GPS position calibration, is defined. A LSTM training model is construct to realize BAT prediction. Experiment results demonstrate that our technique can provide a higher level of accuracy compared to methods based on traditional time-ofarrival techniques, especially in the accuracy of multi-stops BAT prediction. INDEX TERMS Bus arrival time prediction, LSTM model, GPS data calibration.

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Section: B Bat Predictionmentioning

confidence: 99%

A Bus Arrival Time Prediction Method Based on Position Calibration and LSTM

et al. 2020

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“…There are three parameters in the DA-RNN: the number of road segments input each time T, the size of hidden states for the encoder m, and the size of hidden states for the decoder p. The optimization method is grid search. The search range of T is (5,10,15,20,25). We set m = p for simplicity.…”

Section: Parameter Settings and Evaluation Metricsmentioning

confidence: 99%

“…In the proposed methods, SVM is combined with a Genetic Algorithm [5], Kalman filter [6], and artificial neural network (ANN) [7], respectively. In addition to Kalman filtering and SVM, there are other time series prediction methods, such as road segment average travel time [8], the Relevance Vector Machine Regression [9], clustering [10], Queueing Theory combined with Machine Learning [11], and Random Forests [12]. Artificial neural networks have been widely used in various research fields in recent years [13][14][15].…”

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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“…However, the requirement of a large sample size imposes a restriction on the use of this method in real time. In [6], a clustering algorithm was used to determine the distribution of the travel time of the road segment.…”

Section: Related Workmentioning

confidence: 99%

Performance comparison of machine learning methods in the bus arrival time prediction problem

Agafonov

Yumaganov

2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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The problem of predicting the movement of public transport is one of the most popular problems in the field of transport planning due to its practical significance. Various parametric and non-parametric models are used to solve this problem. In this paper, heterogeneous information affecting the prediction value is used to predict the arrival time of public transport, and a comparison of the main machine learning algorithms for the public transport arrival time forecasting is given: neural networks, support vector regression. An experimental analysis of the algorithms was carried out on real traffic information about bus routes in Samara, Russia.

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Bus arrival time prediction with real-time and historic data

Cited by 43 publications

References 21 publications

A Bus Arrival Time Prediction Method Based on Position Calibration and LSTM

A Bus Arrival Time Prediction Method Based on Position Calibration and LSTM

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

Performance comparison of machine learning methods in the bus arrival time prediction problem

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