A Study of Aggregated Speed in Road Networks Using Cellular Automata

Several recent works have focused on studying the relationship between the aggregated flow and density in arterial road networks. Analogous studies involving aggregated speed appear not to have been yet undertaken, however. Here we study and compare such relations for arterial road networks controlled by different types of adaptive traffic signal systems, under various boundary conditions. To study such systems we simulate stochastic cellular automaton models. Our simulation results suggest that network speed could be used as a surrogate for density, due to a strong anticorrelation between these two network observables. Since speed estimates can be more easily obtained than density estimates, e.g. from probe vehicle data, this suggests that Macroscopic Fundamental Diagrams relating aggregated flow with speed might be a practically useful alternative to those relating flow to density

Average travel time estimations for urban routes that consider exit turning movements

This paper presents a methodology for real-time estimation of exit movement-specific average travel time on urban routes by integrating real-time cumulative plots, probe vehicles, and historic cumulative plots. Two approaches, component based and extreme based, are discussed for route travel time estimation. The methodology is tested with simulation and is validated with real data from Lucerne, Switzerland, that demonstrate its potential for accurate estimation. Both approaches provide similar results. The component-based approach is more reliable, with a greater chance of obtaining a probe vehicle in each interval, although additional data from each component is required. The extreme-based approach is simple and requires only data from upstream and downstream of the route, but the chances of obtaining a probe that traverses the entire route might be low. The performance of the methodology is also compared with a probe-only method. The proposed methodology requires only a few probes for accurate estimation; the probe-only method requires significantly more probes