A Bayesian Spatiotemporal Approach for Bus Speed Modeling

Hu, Bin; Xie, Kun; Cui, Haipeng; Lin, Hetong

doi:10.1109/itsc.2019.8917523

Cited by 4 publications

(5 citation statements)

References 20 publications

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“…Lastly, this paper focuses on the potentials of the proposed BSTI model in online prediction by proposing an online prediction framework and conducting a large number of experiments regarding the major hyperparameters. All these aspects are not mentioned in Hu, et al [53].…”

Section: B Gaps and Contributionsmentioning

confidence: 96%

“…In addition, this paper presents three major advancements comparing to our previous work in Hu, et al [53]. Firstly, we analyze all the four types of Bayesian spatiotemporal interaction (BSTI) patterns thoroughly by proposing new BSTI models, which are not studied in Hu, et al [53]. Secondly, this study utilizes a novel computation method for Bayesian model estimation, which is the INLA method.…”

Section: B Gaps and Contributionsmentioning

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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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: B Gaps and Contributionsmentioning

confidence: 96%

Section: B Gaps and Contributionsmentioning

confidence: 99%

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

Cui

Xie

Hu³

et al. 2021

IEEE Access

Self Cite

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“…Any prediction approach fulfilling these requirements is applicable. In this paper, we use a Bayesian prediction framework, as it fulfills those requirements and is state-ofthe-art [43], [45], [47].…”

Section: A Prediction Frameworkmentioning

confidence: 99%

What Do We Know When? Modeling Predictability of Transit Operations

Büchel

Corman

2022

IEEE Trans. Intell. Transport. Syst.

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Predictions of transit delays are crucial to passengers and operators. Passengers utilize the predictions to decide on departure time, route choice, and mode choice, whereas operators decide on schedules, timetables, rolling stock allocation, and control actions. We introduce the concept of predictability of transit travel times as the study of the reduction of the predicted variability with the temporal approaching of a predicted event.We evaluate predictability on a real-life test case in Zurich, Switzerland, spanning multiple transit lines over one year of operations. The concept is shown based on predictions obtained by a state-of-the-art Bayesian network approach, where we show how predictability (in general) can be modeled as an exponential decay phenomenon. The study of predictability of transit operations leads to additional insights for control actions and system analysis compared to other complementary concepts such as punctuality or regularity, for instance, concerning bunching, identification of bottlenecks, and passenger routing.

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“…Different from traditional traffic speed prediction, these models rarely used linear models (such as ARIMA [6,7]) to predict the bus speed because the bus speed is not a standard problem, and the prediction results are not better. For analyzing the nonlinear features within bus speed data, many nonlinear models are exploited by researchers, such as the Bayesian network [8], Support Vector Machine (SVM) [9], radial basis function neural networks (RBFNN) [10], Kalman filter [11], back propagation neural network (BPNN) [12], and extreme learning machine (ELM). e core idea of nonlinear models is to capture the nonlinear relationship within speed data and mine more potential information without prior knowledge [13].…”

Section: Related Workmentioning

confidence: 99%

“…Sun et al [2] proposed a hybrid prediction model based on the LSTM and attention mechanism, it can capture the spatial and temporal features, but it is weak in capturing the longterm temporal dependency. Hu et al [8] proposed a novel Bayesian model to characterize space-time interaction patterns and to construct a bus speed prediction model further. Gu et al [19] proposed a fusion model based on LSTM, EGRA, and the gated recurrent unit (GRU), and a double-layer structure is constructed based on the LSTM and GRU in order to resolve the problem that the singlelayer LSTM or GRU cannot capture the short-term temporal dependency.…”

Section: Related Workmentioning

confidence: 99%

An Improved Deep Spatial-Temporal Hybrid Model for Bus Speed Prediction

Zhai

Cui

Zhang

et al. 2020

Mathematical Problems in Engineering

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For resolving or alleviating the transportation problems, it is necessary to efficiently manage the public transportation and provide public transport services with high quality and advocate green travel, which rely on accurate traffic data. In order to obtain more accurate bus speed in the future, this paper proposed a novel dynamic hierarchical spatial-temporal network model based on Grey Relation Analysis (EGRA), the convolutional neural network (CNN), and the gated recurrent unit (GRU). The proposed model is named the DHSTN; it exploited EGRA to analyze and choose the suitable candidate line sections with high impacts on the target section and, then, construct a multilayer structure based on the CNN, GRU, and attention mechanism to analyze and capture the spatial and temporal dependency, and finally, the extreme learning machine (ELM) is exploited for the fusion of the long-term and short-term dependency to predict the bus speed variation in the next time interval. Comparative experiments indicate that the DHSTN has better performances, the mean absolute error is around 2.6, and it meets the real requirements.

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A Bayesian Spatiotemporal Approach for Bus Speed Modeling

Cited by 4 publications

References 20 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

What Do We Know When? Modeling Predictability of Transit Operations

An Improved Deep Spatial-Temporal Hybrid Model for Bus Speed Prediction

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