Abstract
Traffic peak is an important parameter of modern transport systems. It can be used to calculate the indices of road congestion, which has become a common problem worldwide. With accurate information about traffic peaks, transportation administrators can make better decisions to optimize the traffic networks and therefore enhance the performance of transportation systems. We present a traffic peak detection method, which constructs the Voronoi diagram of the input traffic flow data and computes the prominence of candidate peak points using the diagram. Salient peaks are selected based on the prominence. The algorithm takes O(n log n) time and linear space, where n is the size of the input time series. As compared with the existing algorithms, our approach works directly on noisy data and detects salient peaks without a smoothing prestep and thus avoids the dilemma in choosing an appropriate smoothing scale and prevents the occurrence of removing/degrading real peaks during smoothing step. The prominence of candidate peaks offers the subsequent analysis the flexibility to choose peaks at any scale. Experiments illustrated that the proposed method outperforms the existing smoothing-based methods in sensitivity, positive predictivity, and accuracy.
Highlights
Accurate information about traffic peaks is important in planning, maintenance, and control of modern transport systems
Automatic traffic event detection is important in intelligent transportation systems (ITSs). e detection can be performed with streaming data clustering [8], marking points clustering [9], and deep learning methods [10]
Traffic network optimization improves a traffic network, maximizing its performance. e problem is formulated as an NP-hard integer program, and there exists no exact polynomial-time algorithm; heuristic methods such as genetic algorithms (GAs) are common ways to solve the problem [22]
Summary
Accurate information about traffic peaks is important in planning, maintenance, and control of modern transport systems. (ii) e goal of smoothing is to eliminate or reduce the fluctuation and noise to avoid weak and false peaks, and it is crucial to select an appropriate smoothing scale Choosing such a scale for a given traffic data requires prior knowledge about the data, which is often not available before the data analysis is finished. Most fluctuation and noise have smaller amplitude than the peaks we are interested in Based on this property, we introduce a Voronoi diagram-based traffic peak detection method. E new method has the following advantages It works on noisy data directly and avoids the difficulty in choosing an appropriate smoothing scale. It computes the prominence of candidate peak points and offers the subsequent analysis step the flexibility to choose peaks at any scale.
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