Research investigated ways to forecast traffic flow over an area covering multiple links of a roadway network. The question arose of whether to construct one large-scale model or to combine results from separate smaller-scale models. Intuition favors link-specific analyses, but econometric results show how using aggregate models can increase accuracy. It is shown how theory holds that correlations between traffic conditions on different roadway links and errors in link-specific observations of traffic conditions increase the relative accuracy of larger-scale models, whereas site-specific sensitivities suggest smaller-scale data. Empirical evidence is presented regarding the relative accuracy of various models of traffic flow rate. One form of a seasonal autoregressive integrated moving average time series model for general flow forecasting is chosen, along with methodologies for automatically selecting models based on input data. Studies based on data from the Tokyo Metropolitan Expressway show that increasing data aggregation consistently increases model accuracy. Sums of forecasts of link flows yield predictions of areawide flow roughly as accurate as predictions based on univariate analysis of areawide data. Areawide flows are easier to predict accurately than link flows. Modeling large-area traffic flow with a univariate model is considerably simpler but produces less subsequently useful results than use of separate, link-specific models.
Traffic Flow Forecasting and Spatial Data Aggregation
Transportation Research Record: Journal of the Transportation Research Board
Transportation Research Record: Journal of the Transportation Research Board ; 2260 , 1 ; 16-23
2011-01-01
Article (Journal)
Electronic Resource
English
Traffic Flow Forecasting and Spatial Data Aggregation
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