A Dynamic Spatial Weight Matrix and Localized Space–Time Autoregressive Integrated Moving Average for Network Modeling

Cheng, Tao; Wang, Jiaqiu; Haworth, James; Heydecker, Benjamin; Chow, Ahf

doi:10.1111/gean.12026

Cited by 75 publications

(43 citation statements)

References 40 publications

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“…In 2014, a new space-time model, the localized STARIMA (LSTARIMA) model, was proposed by Cheng et al [13] to consider spatial heterogeneity and temporal non-stationarity. The model is described by the following form:…”

Section: Lstar Model Constructionmentioning

confidence: 99%

“…In the LSTARIMA model, a time variant weight matrix was introduced to improve traffic prediction accuracy with lower AR and MA orders. Furthermore, the speed difference was used to construct the weight matrix in the LSTARIMA model [13]. The speed of a road is an important character of traffic flow, but is not the essential one in terms of impact to surrounding roads.…”

Section: Weight Matrix Constructionmentioning

confidence: 99%

“…The first step was to build a spatial adjacency matrix based on the topological structure of the network, which appears in Figure 1. In this paper, spatial adjacency matrices of spatial orders up to three were constructed as per Reference [13]. The spatial neighborhood information can be found in Table 2 with the first, second, and third order neighbors separated.…”

Section: Construction Of a Dynamic Spatial Weight Matrixmentioning

confidence: 99%

“…In the LSTARIMA model of Cheng et al [13], the same weight matrix W was used for AR and MA components of the whole road network. We used different matrices, W and U, for AR and MA components.…”

Section: Introductionmentioning

confidence: 99%

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Localized Space-Time Autoregressive Parameters Estimation for Traffic Flow Prediction in Urban Road Networks

Chen

Zhang

et al. 2018

Applied Sciences

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Abstract:With the rapid increase of private vehicles, traffic congestion has become a worldwide problem. Various models have been proposed to undertake traffic prediction. Among them, autoregressive integrated moving average (ARIMA) models are quite popular for their good performance (simple, low complexity, etc.) in traffic prediction. Localized Space-Time ARIMA (LSTARIMA) improves ARIMA's prediction accuracy by extending the widely used STARIMA with a dynamic weight matrix. In this paper, a localized space-time autoregressive (LSTAR) model was proposed and a new parameters estimation method was formulated based on the LSTARIMA model to reduce computational complexity for real-time prediction purposes. Moreover, two theorems are given and verified for parameter estimation of our proposed LSTAR model. The simulation results showed that LSTAR provided better prediction accuracy when compared to other time series models such as Shift, autoregressive (AR), seasonal moving average (Seasonal MA), and Space-Time AR (STAR). We found that the prediction accuracy of LSTAR was a bit lower than the LSTARIMA model in the simulation results. However, the computational complexity of the LSTAR model was also lower than the LSTARIMA model. Therefore, there exists a tradeoff between the prediction accuracy and the computational complexity for the two models.

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Section: Lstar Model Constructionmentioning

confidence: 99%

Section: Weight Matrix Constructionmentioning

confidence: 99%

Section: Construction Of a Dynamic Spatial Weight Matrixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Localized Space-Time Autoregressive Parameters Estimation for Traffic Flow Prediction in Urban Road Networks

Chen

Zhang

et al. 2018

Applied Sciences

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“…In this study, a regime-switching space-time model is employed to implement real-time traffic forecasting using traffic speed data in three real networks of the USA. In order to capture dynamics of autocorrelation in road traffic networks, Cheng and Wang et al (2014) developed a Localised STARIMA model (LSTARIMA) for short-term forecasting of travel time using a dynamic spatial weight matrix. Overall, these models benefit from the accurate space-time representation that present on road traffic networks (Vlahogianni et al, 2014).…”

Section: Introductionmentioning

confidence: 99%