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Analysis of Bus Speed Using a Multivariate Conditional Autoregressive Model: Contributing Factors and Spatiotemporal Correlation
| 题名： | Analysis of Bus Speed Using a Multivariate Conditional Autoregressive Model: Contributing Factors and Spatiotemporal Correlation |
| 正文语种： | 英文 |
| 作者： | Haipeng Cui1; Kun Xie2; Bin Hu3; Hangfei Lin4; Rui Zhang5 |
| 作者单位： | 1Research Assistant, Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji Univ., 4800 Caoan Rd., Jiading District, Shanghai 201804, People’s Republic of China. 2Lecturer, Dept. of Civil and Natural Resources Engineering, Univ. of Canterbury, 20 Kirkwood Ave., Christchurch 8041, New Zealand (corresponding author). ORCID: https://orcid.org/0000-0002-8191-2786. Email: kun.xie@canterbury.ac.nz 3Research Assistant, Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji Univ., 4800 Caoan Rd., Jiading District, Shanghai 201804, People’s Republic of China. 4Professor, Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji Univ., 4800 Caoan Rd., Jiading District, Shanghai 201804, People’s Republic of China. 5Researcher, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, 1068 Xueyuan Ave., Shenzhen University Town, Shenzhen 518055, People’s Republic of China. |
| 关键词： | Bus speed; Spatial statistics; Multivariate conditional autoregressive model; Spatial correlation weight (SCW); Bayesian approach; Global Positioning System (GPS) data; Dedicated bus lane (DBL) |
| 摘要： | Bus speed indicates the performance of bus systems. Exploring the impact of contributing factors to bus speed can provide public transit agencies insights into developing improvement strategies. Bus speed observations can be correlated both spatially and temporally, but their dependence has generally been neglected. This paper proposes a novel multivariate conditional autoregressive (MCAR) model to jointly account for spatial and temporal correlations of bus speeds extracted from large-scale Global Positioning System data. The proposed MCAR model is compared with the univariate conditional autoregressive model, which only accounts for spatial correlation, and the linear regression model, which assumes independent speed observations. Results show that the MCAR model outperforms the other models by presenting a much lower deviance information criterion (DIC) value and smaller prediction errors. This confirms the necessity of addressing the spatiotemporal correlation when modeling bus speeds. Driveway density, number of bus routes, bus stop density, signal effect, and bus volume are found to affect bus speed significantly in the MCAR model. Furthermore, how the distance affects the spatial correlation is investigated by specifying different spatial correlation weight (SCW) matrices. It is found that the MCAR model with SCWs generated from the radial basis function (RBF) can yield better outcomes than one using inverse distance. The optimal shape parameter of the RBF is found to be within a range of 1–2 km. Specifically, if the shape parameter equals 2 km, the SCW of two road segments is approximately 0.88 when their midpoints are 1 km from each other. |
| 出版年： | 2019 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 145 |
| 期： | 4 |
| 页码： | 1-11 |
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