This article addresses the problem of the accuracy of short-term traffic flow forecasting in the complex case of urban signalized arterial networks. A new, artificial intelligence (AI)-based approach is suggested for improving the accuracy of traffic predictions through suitably combining the forecasts derived from a set of individual predictors. This approach employs a fuzzy rulebased system (FRBS), which is augmented with an appropriate metaheuristic (direct search) technique to automate the tuning of the system parameters within an online adaptive rolling horizon framework. The proposed hybrid FRBS is used to nonlinearly combine traffic flow forecasts resulting from an online adaptive Kalman filter (KF) and an artificial neural network (ANN) model. The empirical results obtained from the model implementation into a real-world urban signalized arterial demonstrate the ability of the proposed approach to considerably overperform the given individual traffic predictors.
Keywords: City size Land use Transport network Traffic congestion dynamics Macroscopic fundamental diagram a b s t r a c t This paper presents an alternative approach for analyzing the relationship between land use and traffic congestion by employing the Macroscopic Fundamental Diagram (MFD). The MFD is an empirically observed relationship between traffic flow and traffic density at the level of an urban region, including hypercongestion, where flow decreases as density increases. This approach is consistent with the physics of traffic and allows the parsimonious modeling of intra-day traffic dynamics and their connection with city size, land use and network characteristics. The MFD can accurately measure the inefficiency of land and network resource allocation due to hypercongestion, in contrast with existing models of congestion. The findings reinforce the 'compact city' hypothesis, by favoring a larger mixed-use core area with greater zone width, block density and number of lanes, compared to the peripheral area. They also suggest a new set of policies, including the optimization of perimeter controls and the fraction of land for transport, which constitute robust second-best optimal strategies that can further reduce congestion externalities.
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