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The research reported in this paper develops a heuristic automated tool (SPḎCAL) for calibrating steady-state traffic stream and car-following models using loop detector data. The performance of the automated procedure is then compared to off-the-shelf optimization software parameter estimates, including the MINOSand Branch and Reduce Optimization Navigator (BARON) solvers. The model structure and optimization procedure is shown to fit data from different roadway types and traffic regimes (uncongested and congested conditions) with a high quality of fit (within 1% of the optimum objective function). Furthermore, the selected functional form is consistent with multiregime models, without the need to deal with the complexities associated with the selection of regime breakpoints. The heuristic SPḎCAL solver, which is available for free, is demonstrated to perform better than the MINOSand BARON solvers in terms of execution time (at least 10 times faster), computational efficiency (better match to field data), and algorithm robustness (always produces a valid and reasonable solution). [ABSTRACT FROM AUTHOR]

We address the controversial issue of traffic flow modeling, whether first-order, second-order, or other traffic models are best supported by empirical facts and theoretical considerations. This is done by a critical discussion of the pros and cons of the different theoretical approaches and by the analysis of a large set of empirical data with new evaluation techniques. Specifically, we investigate characteristic properties of the congested traffic states on a 30-km-long stretch of the German freeway A5 near Frankfurt/Main. Among the approximately 245 breakdowns of traffic flow at several different bottlenecks in 165 days, we have identified five different kinds of spatiotemporal congestion patterns and their combinations. Based on an "adaptive smoothing method" for the visualization of detector data, we also discuss particular features of breakdowns, such as signs of unstable traffic flow and the "boomerang effect," which often seems to be caused by overtaking maneuvers of trucks. Controversial issues such as "synchronized flow" or stop-and-go waves are addressed as well. Our empirical results are compared with the implications of different theoretical concepts such as first-order traffic models and the phase diagram of congested traffic states predicted by some second-order models and the nonlocal, gas-kinetic based traffic model (GKT model). For a correct understanding of empirical observations such as the "general pattern," it is important to consider particularities such as the fact that off-ramps can act as bottlenecks, when activated by downstream on-ramp bottlenecks. As sequences of off- and on-ramps generate different congestion patterns than single on-ramps, they must be treated as interconnected bottlenecks. Furthermore, our empirical results question Kerner's three-phase theory. [ABSTRACT FROM AUTHOR]