Detection and Optimization of Traffic Networks Based on Voronoi Diagram

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...

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Bibliographic Details
Published in:Discrete dynamics in nature and society 2021, Vol.2021, p.1-19
Main Authors: Tao, Rui, Liu, Jian, Song, Yuqing, Peng, Rui, Zhang, Dali, Qiao, Jiangang
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Chromatography
Decision-making
Genetic algorithms
Methods
Network management systems
Noise
Optimization
Prominences
Smoothing
Time series
Traffic assignment
Traffic congestion
Traffic control
Traffic flow
Traffic information
Transportation networks
Transportation systems
Voronoi graphs
Wavelet transforms
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Summary: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.
ISSN:1026-0226
1607-887X
DOI:10.1155/2021/5550315