Traffic peak period detection using traffic index cloud maps

Abstract Traffic peak period detection is one key issue in ITS research area, which can afford time information for traffic flow guidance. Classical methods devote themselves to detect the peak period of road segmentations and small road network areas. Namely, these methods focus on traffic peak period detection in small space scale. However, the traffic peak periods of road segmentations and small road network areas cannot present the traffic peak periods of the whole city. In fact, the traffic peak periods of the whole city are more important for the traffic administration department. To solve this problem, a new method for detecting traffic peak periods of the whole city is proposed, which is based on the traffic index cloud maps. Experimental results on the GPS data show that the proposed method can recognize the traffic peak periods in a large space scale accurately.

[1]  Bekir Karlik,et al.  Fuzzy c-means based support vector machines classifier for perfume recognition , 2016, Appl. Soft Comput..

[2]  Cheolkon Jung,et al.  Alternately Guided Depth Super-resolution Using Weighted Least Squares and Zero-order Reverse Filtering , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[3]  Lei Zhang,et al.  Learning Converged Propagations With Deep Prior Ensemble for Image Enhancement , 2018, IEEE Transactions on Image Processing.

[4]  Junping Du,et al.  Low-Light Image Enhancement via a Deep Hybrid Network , 2019, IEEE Transactions on Image Processing.

[5]  Vipin Tyagi,et al.  C4.5 Decision Tree Machine Learning Algorithm Based GIS Route Identification , 2018, 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN).

[6]  Hang Li,et al.  Traffic Peak Period Detection from an Image Processing View , 2018 .

[7]  Yuncai Liu,et al.  More robust and better: a multiple kernel support vector machine ensemble approach for traffic incident detection , 2014 .

[8]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[9]  De Xu,et al.  Online State-Based Structured SVM Combined With Incremental PCA for Robust Visual Tracking , 2015, IEEE Transactions on Cybernetics.

[10]  Yuncai Liu,et al.  Traffic Incident Detection Using Multiple-Kernel Support Vector Machine , 2012 .

[11]  M. Dijst,et al.  Rush hour commuting in the Netherlands : Gender-specific household activities and personal attitudes towards responsibility sharing. , 2016 .

[12]  Jianli Xiao,et al.  SVM and KNN ensemble learning for traffic incident detection , 2019, Physica A: Statistical Mechanics and its Applications.

[13]  Takashi Nagatani,et al.  Effect of stopover on motion of two competing elevators in peak traffic , 2016 .

[14]  Chandra R. Bhat,et al.  The Impacts of an Incentive-Based Intervention on Peak Period Traffic: Experience from the Netherlands , 2016 .

[15]  Sergei Vassilvitskii,et al.  Local Search Methods for k-Means with Outliers , 2017, Proc. VLDB Endow..

[16]  Jeffrey J. Rodríguez,et al.  Image Inpainting Using Nonlocal Texture Matching and Nonlinear Filtering , 2019, IEEE Transactions on Image Processing.