Analysis of Bus Speed Using a Multivariate Conditional Autoregressive Model: Contributing Factors and Spatiotemporal Correlation

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

doi:10.1061/jtepbs.0000226

Cited by 6 publications

(9 citation statements)

References 27 publications

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“…As such, the Bayesian spatiotemporal models have superior performance in terms of both results interpretation and model estimation. Researchers in the transportation domain have applied this framework to various fields [11,51,52].…”

Section: ) Bayesian Spatiotemporal Approachesmentioning

confidence: 99%

“…In the first level, for each road segment, bus speeds can be modeled using a Gaussian distribution as follows [11,54] 2 ( , )…”

Section: A Basic Modelsmentioning

confidence: 99%

“…The abovementioned data were obtained from field surveys and the Baidu Street Map. For further details, please refer to Cui, et al [11].…”

Section: B Variable Extractionmentioning

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%

“…The Bayesian methods are widely used in the statistical domain for their great interpretability of the results [17]. The Markov Chain Monte Carlo (MCMC) simulation is commonly adopted to estimate the Bayesian models and takes a long time to converge [11,22], especially for complex model specifications. Therefore, MCMC-based Bayesian models fail to meet the computation efficiency criterion of online prediction.…”

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: ) Bayesian Spatiotemporal Approachesmentioning

confidence: 99%

“…In the first level, for each road segment, bus speeds can be modeled using a Gaussian distribution as follows [11,54] 2 ( , )…”

Section: A Basic Modelsmentioning

confidence: 99%

“…The abovementioned data were obtained from field surveys and the Baidu Street Map. For further details, please refer to Cui, et al [11].…”

Section: B Variable Extractionmentioning

confidence: 99%