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Abstract: The characterization of the dynamics of traffic states remains fundamental to seeking for the solutions of diverse traffic problems. To gain more insights in traffic dynamics in the temporal domain, this paper explored traffic patterns in higher-dimensional state spaces, where we attempted to map the one-dimensional traffic series into appropriate multidimensional spaces by Takens’ algorithm. After such a state space reconstruction, we then made use of the largest Lyapunov exponent to depict the rate of expansion or contraction of traffic state trajectories in the reconstructed spaces. The correlation dimension was further estimated to examine if the traffic state trajectories exhibited chaotic-like or stochastic-like motions. An empirical study using flow, speed, and occupancy time-series data as well as the speed-flow, speed-occupancy, and flow-occupancy paired data collected from dual-loop detectors on a freeway of Taiwan was conducted. The numerical results revealed that different nonlinear traffic patterns could emerge depending on the observed time-scale, history data and time-of-day. In addition, with consideration of sequential order and spatiotemporal features, more information about traffic dynamical evolution was extracted. [Copyright &y& Elsevier]

Abstract: In this work, we apply the iterative learning control approach to address the traffic density control problem in a macroscopic level freeway environment with ramp metering. The traffic density control problem is first formulated into an output tracking and disturbance rejection problem. Through rigorous analysis, it is shown that the iterative learning control method can effectively deal with this class of control problem and greatly improve the traffic response. Next, the iterative learning control is combined with error feedback in a complementary modular manner to achieve the output tracking and system robustness. The effectiveness of the new approach is further verified through case studies with intensive simulations. [Copyright &y& Elsevier]

Abstract: This paper describes and discusses the possibilities of steering the transportation planning process in the face of bounded rationality and unbounded uncertainty: (a) through the introduction of the concept of ‘systemicity’; (b) by expanding the spectrum of the existing planning paradigm currently in use; (c) by reducing complexity through the application of tests of adequacy, dependency, suitability, and adaptability; (d) through the introduction of soft systems thinking; and (e) by using ‘abductive’ in addition to deductive and inductive inferencing. It is concluded that the application of these strategies, adjustments, and tests to the existing planning procedure will hopefully enrich and strengthen our planning effort and make it more robust. [Copyright &y& Elsevier]