Institute of Transportation, MOTC

Yi-San Huang, 2007.12
Institute of Traffic and Transportation National Chiao Tung University

　　The characterization of the dynamics of traffic states remains fundamental to seeking for the solutions of diverse traffic problems while short-term prediction of dynamic traffic states remains critical in the field of advanced traffic management systems (ATMS) and related areas. However, the scarcity of information provided by conventional one-dimensional traffic time-series data and the hasty prediction without deliberately taking into account the characteristics of traffic dynamics as well as affected factors may have shed light on the lack which need to be solved urgently.

　　Conventional analysis of traffic time series may play a part in the investigation of traffic patterns characterized by linear statistics. A certain number of studies working at the vehicle trajectories or their interactions within a time-space domain have significant contributions. Nevertheless, most of the results simulated by formulated models are not easy to be calibrated by real data. 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 space 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. In accordance with the above procedures, a novel filtering approach was proposed to inspect the characteristics of real-world temporal traffic flow dynamics.

　　In addition, a radial basis function neural network (RBFNN) and a real-time recurrent learning algorithm (RTRL) were proposed to learn about whether or not the dynamics of short-term traffic states characterized in different time intervals, collected in diverse time lags, dimensions and times of day have significant influence on the performance IV of the proposed model relative to the published forecasting methods. Furthermore, we also dabble in comparing pair predictability of linear method-RTRL algorithms and simple nonlinear method-RTRL algorithms individually using a first-order autoregressive stochastic time series AR(1) and a deterministic first-order differential-delay equation.

　　Finally, an empirical study and a sensitivity analysis were conducted. Wherein, 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 processed in the empirical study and the same traffic data was fulfilled in the sensitivity analysis with various time intervals, time lags and times of day. 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. On the other hand, the performances of RBFNN and RTRL algorithms in predicting short-term traffic dynamics are satisfactorily accepted. Furthermore, it is found that the dynamics of short-term traffic states characterized in different time intervals, collected in diverse time lags and times of day may have significant effects on the prediction accuracy of the proposed algorithms. The above findings may support that the proposed methods in this study can be used to develop traffic management schemes which are practically applicable in dynamic control.