Real-time estimation of freeway travel time with recurrent congestion based on sparse detector data

Loop detectors distributed on freeways are very vulnerable and could be damaged or malfunctioned due to improper sealing, pavement deterioration. This may lead to poor travel time estimation as most of existing methodologies require detailed data collected from numerous detectors along a specified freeway route. To address this problem, this study proposes an effective and reliable methodology for real-time freeway travel time estimation with data from sparse detectors. In contrast to the existing methods, the proposed methodology requires significantly less number of detectors but maintains fairy good performance on travel time estimation. The proposed methodology utilises a self-organised mapping algorithm to cluster the detectors with similar traffic patterns. The data collected from the representative detectors within each cluster is then employed to estimate the travel time based on a support vector regression model. The case studies conducted for three selected freeway routes in Northern California over 3 weeks demonstrate that the proposed methodology accurately captures the fluctuation of travel time induced by the variations of traffic states. The estimated results are exceptionally accurate with smaller mean errors and root-mean-squared errors compared with the benchmark values obtained from the well-known performance measurement system in California.