Using high-frequency spatial data in the traffic field, this paper explores the macroscopic fundamental diagram (MFD) and the traffic capacity of the regional road network in Guiyang City using the averaging method of the functional spatial autoregressive (FSAR) model. It reveals the traffic conditions and characteristics of Guiyang City’s regional road network. Initially, high-frequency traffic speed data are represented using functional principal component bases, transforming the parameter estimation of the FSAR model into a problem of estimating the coefficients of these basis functions. The maximum likelihood estimation method is subsequently employed to estimate the model parameters. Furthermore, the Mallows model averaging (MMA) approach is used to assign weights to the models, allowing for the computation of a weighted traffic density. Using the derived weighted traffic density and flow, the MFD of the road network is constructed, and the corresponding traffic capacity is estimated. The proposed averaging method of the FSAR model is validated through numerical simulations, demonstrating enhanced accuracy under limited sample conditions and varying spatial dependencies. The methodology is then applied to the regional road network of Guiyang City to assess traffic congestion. Results indicate that Guanshanhu District, located in the central urban area, exhibits the highest traffic capacity within the region.
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