Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan.

Non-recurrent congestion disrupts normal traffic operations and lowers travel time (TT) reliability, which leads to many negative consequences such as difficulties in trip planning, missed appointments, loss in productivity, and driver frustration. Traffic incidents are one of the six causes of non-recurrent congestion. Early and accurate detection helps reduce incident duration, but it remains a challenge due to the limitation of current sensor technologies. In this paper, we employ a recurrence-based technique, the Quadrant Scan, to analyse time series traffic volume data for incident detection. The data is recorded by multiple sensors along a section of urban highway. The results show that the proposed method can detect incidents better by integrating data from the multiple sensors in each direction, compared to using them individually. It can also distinguish non-recurrent traffic congestion caused by incidents from recurrent congestion. The results show that the Quadrant Scan is a promising algorithm for real-time traffic incident detection with a short delay. It could also be extended to other non-recurrent congestion types.

On a topological criterion to select a recurrence threshold.

In this work, a topological criterion is proposed for selecting a recurrence threshold for constructing a recurrence plot of a time series. It is based on a metric structure of the set of the recurrence plots that is defined by the recurrence plot deviation distance among recurrence plots, introduced in a previous paper by the authors. In this process for a range of threshold values, the corresponding recurrence plots are constructed. Then, a value of the threshold is considered to be optimal when the image of its recurrence plot remains close to images of the other recurrence plots constructed for close values of thresholds based on the topological criterion introduced in the present work. The results are applied both to time series emanating from the Lorenz dynamical system and to Molecular Dynamic simulations.

On a topological classification of recurrence plots: Application to noise perturbed molecular dynamics time series.

In the present paper, a topological classification of recurrence plots of time series that are constructed with equal embedding dimension and delay time is proposed by defining a metric structure in the set of those recurrence plots. To achieve this, the Recurrence Plot deviation distance and the Recurrence deviation plot are introduced along with qualitative and quantitative indices, which allows for a detailed comparison of the recurrence plots of two time series and their corresponding dynamical systems. In the range of values studied, the application of additive noise on the thermostat results in a less deterministic system behavior, quite close to that of the system with the unperturbed thermostat. On the other hand, multiplicative noise of small strength results in a less deterministic system quite close to the unperturbed system behavior. For a larger strength, the system presents a behavior which is significantly different from that of the unperturbed system. The new methodology introduced here provides detailed quantitative and graphical insights for the way that noise, additive and multiplicative, influences on the morphology of recurrence plots of the unperturbed and noise perturbed time series. The results are encouraging for potential future applications on the analysis of complex dynamical systems.

Nonlinear dynamics in Divisia monetary aggregates: an application of recurrence quantification analysis.

We construct recurrence plots (RPs) and conduct recurrence quantification analysis (RQA) to investigate the dynamic properties of the new Center for Financial Stability (CFS) Divisia monetary aggregates for the United States. In this study, we use the latest vintage of Divisia aggregates, maintained within CFS. We use monthly data, from January 1967 to December 2020, which is a sample period that includes the extreme economic events of the 2007-2009 global financial crisis. We then make comparisons between narrow and broad Divisia money measures and find evidence of a nonlinear but reserved possible chaotic explanation of their origin. The application of RPs to broad Divisia monetary aggregates encompasses an additional drift structure around the global financial crisis in 2008. Applying the moving window RQA to the growth rates of narrow and broad Divisia monetary aggregates, we identify periods of changes in data-generating processes and associate such changes to monetary policy regimes and financial innovations that occurred during those times.