###### 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. LSTAR; STARIMA; parameters estimation; traffic flow prediction; urban road network 2018 1 2018 1 2018 1 2018 1 2018 1 2018 1 2018 1 2018 1 2018 1 2018 1 2018 1 2018 1 2018 1 2018 1 2018 1 2018 1 2018 1 2018 1 2018 1 2018 2018 2018 2  
The earliest traffic prediction method was based on the macroscopic traffic simulation model proposed by [PERSON] [4] and [PERSON] [5], known as the Lighthill-Whitham-Richards (LWR) model. In this model, vehicles in the highway are treated as \"traffic flow\" and their dynamics can be analyzed with the continuous fluid conservation equation in fluid mechanics. The microscopic traffic modeling method of cellular automaton (CA) [6] simulates the traffic flow dynamics by analysis of the interaction of individual vehicles. Traffic simulation models focus on traffic flow dynamics using only current traffic information; no historic information is needed. The weakness of the traffic simulation model is that it needs the origination-destination (OD) matrix of all vehicles to simulate the traffic dynamics, which is normally hard to collect. The autoregressive moving average (ARMA) model or autoregressive integrated moving average (ARIMA) model [7], also called the Box-Jenkins model, is an important prediction model in economics and other areas. Furthermore, it is considered as the standard of time series prediction. ARIMA and its variations as seasonal ARIMA (SARIMA) [8], vector ARMA (VARMA) models [9], and so on have been widely used for traffic prediction. The space-time ARIMA (STARIMA) [10] model has a long historical background which is based on the ARMA with exogenous inputs (ARMAX) model. Since the 1980s, STARIMA has been applied to different areas such as river flow, spread of disease, spatial econometrics, and so on. In 2005 [11], the STARIMA methodology was first proposed for the spatiotemporal behavior of traffic flow. In the STARIMA model, traffic flow data is in the form of a spatial time series which is collected at specific locations at constant intervals of time to be used for the short-term forecasting of space-time stationary traffic-flow processes. Furthermore, the model can be used for assessing the impact of traffic-flow changes on other parts of the network through the use of weight matrices estimated on the basis of the distances among the various locations. Unlike the VARMA model, which is a generic model without any known information, the number of parameters to be estimated for STARIMA is much less, as the road network topology is considered.