Bus Arrival Time Prediction: A Spatial Kalman Filter Approach

Achar, Avinash; Bharathi, Dhivya; Kumar, B. Anil; Vanajakshi, Lelitha

doi:10.1109/tits.2019.2909314

Cited by 51 publications

(27 citation statements)

References 32 publications

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“…Bus speed/arrival time prediction has been studied widely [9,12,17]. Some researchers focus on the travel time/speed of all links between two bus stops [13,23], assuming the homogeneity between each link.…”

Section: ) Bus Speed/travel Time Predictionmentioning

confidence: 99%

“…This assumption can be easily violated due to different traffic conditions, road designs, etc. Furthermore, considering the segmentation of traffic flow by traffic signals, some researchers divide road segments based on signalized intersections [6,9]. This segmentation method includes regular bus traveling, dwell at bus stops, and unexpected stoppages into the travel speed/time.…”

Section: ) Bus Speed/travel Time Predictionmentioning

confidence: 99%

“…In light of the propagation of congestion and the disturbance caused by traffic incidents, bus speeds can be both spatially and temporally correlated. The investigation of spatial and temporal effects in bus travel speed/time is gaining increasing attention recently [6,[9][10][11]. However, most existing studies address the spatial and temporal effects separately without modeling the spatiotemporal interaction (STI) effects [12].…”

Section: Introductionmentioning

confidence: 99%

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Online Bus Speed Prediction With Spatiotemporal Interaction: A Laplace Approximation-Based Bayesian Approach

Cui

Xie

Hu³

et al. 2021

IEEE Access

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This study proposes a novel Bayesian hierarchical approach for online bus speed prediction by explicitly accounting for the spatiotemporal interaction (STI) of speed observations. The use of Laplace approximation can expedite the estimation of Bayesian models and enable the implementation of online prediction. Large numbers of trials are carried out to identify significant predictors and the optimal length of the look-back time window to achieve the highest prediction accuracy. The spatiotemporal interacting patterns are also explored, and results show that the Type IV model assuming the structured spatial effect interacts with the structured temporal effect can best accommodate the bus speed data. Besides, prediction errors of the Type IV model randomly distribute over time and space. The proposed model can achieve high prediction accuracy and computational efficiency without compromising the interpretability of the contributing factors and the unobserved spatiotemporal heterogeneity. The proposed model can be used to assist public transit operation and management, such as bus scheduling, congestion warning, and the development of proactive measures to mitigate bus delays.

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Section: ) Bus Speed/travel Time Predictionmentioning

confidence: 99%

Section: ) Bus Speed/travel Time Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Online Bus Speed Prediction With Spatiotemporal Interaction: A Laplace Approximation-Based Bayesian Approach

Cui

Xie

Hu³

et al. 2021

IEEE Access

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“…However, some scholars paid more attention to the limitations of hotness diffusion path and speed, ignoring the changes of hotness itself. Their research fields mainly focus on predictions of bus arrival time and short-term traffic flow [13].…”

Section: Introductionmentioning

confidence: 99%

A Flood-Discharge-Based Spatio-Temporal Diffusion Method for Multi-Target Traffic Hotness Construction From Trajectory Data

Zhang

Qin

et al. 2020

IEEE Access

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“…Currently, there are many ways to predict bus arrival times. 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.…”

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 Arrival Time Prediction: A Spatial Kalman Filter Approach

Cited by 51 publications

References 32 publications

Online Bus Speed Prediction With Spatiotemporal Interaction: A Laplace Approximation-Based Bayesian Approach

Online Bus Speed Prediction With Spatiotemporal Interaction: A Laplace Approximation-Based Bayesian Approach

A Flood-Discharge-Based Spatio-Temporal Diffusion Method for Multi-Target Traffic Hotness Construction From Trajectory Data

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

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