A Statistical Simulation Model for the Analysis of the Traffic Flow Reliability and the Probabilistic Assessment of the Circulation Quality on a Freeway Segment

Measuring the traffic quality and congestion level is fundamental in highway engineering, and several decades of studies and research have pursued this specific objective, especially for freeways. Nowadays, smart technologies on personal devices and information shared by users have made available various online information platforms that provide dynamic representations of the use of the road network. If, on the one hand, these tools provide a simple and direct representation of the quality of circulation, on the other hand, their aggregated information is only partial for those dealing with traffic and highway engineering. This branch of engineering relies on multidimensional knowledge of traffic flow phenomena, and only through their in-depth knowledge, we can assess traffic quality and congestion risk. After identifying the different approaches for analyzing in quantitative terms the traffic quality on the freeway, the paper deepens the reliability approach. From this point of view, the paper aims to unite the two perspectives in the literature, namely, the probabilistic analysis of traffic instability with the characterization of speed random processes and the analysis of breakdowns with the survival analysis. For this purpose, the work outlines a procedure based on the estimation and simulation of ARIMA models for speed random processes in a freeway section, particularly on the leftmost lane, to assess the traffic reliability function. Applying the Product Limit Method to the Monte Carlo simulation results makes it possible to obtain probabilistic assessments of congestion, considering the Level of Service density limits defined in the Highway Capacity Manual. Its application to a case study makes it possible to illustrate the application of the method, which can be easily applied to historical and near-real-time data using a continuous flow of information.