| Summary: | 碩士 === 國立交通大學 === 運輸科技與管理學系 === 98 === The interests and applications of short-term traffic forecasting have been growing in the recent years. Many of the applications in Advance Traveler Information System (ATIS) and Advance Traffic Management Systems (ATMS) , which aim at providing useful information to travelers and improving the overall efficiency of road network, require an estimation and forecasting of the traffic conditions of the network. With a historical database of past traffic data from various types of vehicle detectors, real-time traffic information is collected which will be used to estimate the current traffic conditions and predict the condition in near future. Whereas most of the literature focused on the traffic flow prediction on the freeways, modeling traffic flow in urban arterials is more challenging as there are disturbances such as motorcycles and traffic signals in urban area.
In this study, traffic flow forecasting models for urban arterials are proposed. Seasonal autoregressive integrated moving average (ARIMA) and space-time autoregressive moving average (STARMA) model, which incorporates the spatial correlations of the time series, are investigated. A case study using the traffic data from 24 vehicle detectors in Taipei city, Taiwan are performed. The forecasting performance of STARMA model are also examined by static, 1-step ahead rolling and 2-step ahead rolling strategies when real-time information can be obtained.
The findings of this thesis are as follows. The estimated results reveal that both ARIMA and STARMA model are suitable for traffic flows forecasting in urban area. However, in the ARIMA model, there are up to five parameters for each detector, whereas there are only 6 parameters in the STARMA model. With a large number of detector locations in the system to be forecasted, the STARMA model shows a relative simple structure as compared to the ARIMA model which is univariate in nature. Traffic flows of urban area are not an isolated system and will be influenced by the flows from other adjacent locations, consequently, STARMA model considering the spatial relationship between each time series can improve the forecasting accuracy. Finally, the results of forecasting performance tests prove that using real-time information to forecast is better than merely using historical information to forecast.
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