Time delay estimation of traffic congestion based on statistical causality

Abstract

Obtaining accurate time delay estimates is important in traffic congestion analysis because they can be used to address fundamental questions regarding the origin and propagation of traffic congestion. However, estimating the exact time delay during congestion is a challenge owing to the complex propagation process between roads and the high uncertainty regarding the future behavior of the process. To aid in accurate time delay estimation during congestion, we propose a novel time delay estimation method for the propagation of traffic congestion due to traffic accidents using lag-specific transfer entropy (TE). In the proposed method, nonlinear normalization with a sliding window is used to effectively reveal the causal relationship between the source and target time series in calculating the TE. Moreover, Markov bootstrap techniques are adopted to quantify uncertainty in the time delay estimator. To the best of our knowledge, our proposed method is the first to estimate the time delay based on the causal relationship between adjacent roads. We validated its efficacy using simulated data and real user trajectory data obtained from a major GPS navigation system applied in South Korea.