Identification and Prediction of Urban Traffic Congestion via Cyber-Physical Link Optimization

In this paper, to accurately predict and evaluate the traffic states in local urban areas, an effective as well as efficient algorithm combined with leakage integral echo state network (LiESN) and Pearson correlation analysis is presented. First, aiming at the parameter optimization of LiESN, the differential evolution algorithm is used to calibrate the key parameters. Second, the road weight allocation is carried out by Pearson correlation analysis considering the specific topology characteristics of local road nets. On this basis, the corresponding flow-frameworks are designed. Finally, the congestion delay data of local road nets in Chongqing Nan’an district, acquired from AutoNavi, are used to verify the validity and rationality of our method. Experimental results indicate that the proposed identification and prediction mechanism relates the congestion indicators to actual traffic operating mechanism in a more reasonable way.

[1]  Herbert Jaeger,et al.  Optimization and applications of echo state networks with leaky- integrator neurons , 2007, Neural Networks.

[2]  P. Groenen,et al.  Nonlinear Forecasting with Many Predictors Using Kernel Ridge Regression , 2011 .

[3]  Song Jun.  Fractal Characteristics of Mountain Cities' Traffic Flow with Sub-health State , 2012 .

[4]  Weili Wang,et al.  Differential evolution algorithm with strategy adaptation and knowledge-based control parameters , 2017, Artificial Intelligence Review.

[5]  Pattreeya Tanisaro,et al.  Time Series Classification Using Time Warping Invariant Echo State Networks , 2016, 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA).

[6]  Sergio Gomez Colmenarejo,et al.  Hybrid computing using a neural network with dynamic external memory , 2016, Nature.

[7]  Xiangdong Xu,et al.  Elastic demand with weibit stochastic user equilibrium flows and application in a motorised and non-motorised network , 2015 .

[8]  D. Alaigba,et al.  Heterogeneity Index for the Assessment of Relationship Between Land Use Pattern and Road Traffic Congestion in Apapa-Oworoshoki Express way, Lagos Metropolis , 2017 .

[9]  Lu Shu-xian,et al.  Parameter Optimization of Leaky Integrator Echo State Network with Internal-point Penalty Function Method , 2017 .

[10]  Darong Huang,et al.  A New Synergistic Forecasting Method for Short-Term Traffic Flow with Event-Triggered Strong Fluctuation , 2018, J. Control. Sci. Eng..

[11]  Ning Xiong,et al.  Adapting Differential Evolution Algorithms For Continuous Optimization Via Greedy Adjustment Of Control Parameters , 2016, J. Artif. Intell. Soft Comput. Res..

[12]  Jing Xu,et al.  Household-Level Global Positioning System Travel Data to Measure Regional Traffic Congestion , 2012 .

[13]  Alfredo Milani,et al.  Algebraic Differential Evolution Algorithm for the Permutation Flowshop Scheduling Problem With Total Flowtime Criterion , 2016, IEEE Transactions on Evolutionary Computation.

[14]  Alfredo Milani,et al.  A Differential Evolution Algorithm for the Permutation Flowshop Scheduling Problem with Total Flow Time Criterion , 2014, PPSN.

[15]  Huang Da-rong,et al.  Traffic Congestion Status Identification Method for Road Network with Multi-source Uncertain Information , 2018 .

[16]  Dan Wang,et al.  Adaptive Pinning Synchronization of Complex Networks with Negative Weights and Its Application in Traffic Road Network , 2018 .

[17]  Min-Yuan Cheng,et al.  An efficient hybrid differential evolution based serial method for multimode resource-constrained project scheduling , 2015, KSCE Journal of Civil Engineering.

[18]  Huang Da-ron.  Control technology review of traffic congestion in urban road network under networked dynamic scheduling and control , 2013 .

[19]  Michael Hagmann,et al.  Asymmetric properties of the Pearson correlation coefficient: Correlation as the negative association between linear regression residuals , 2016 .

[20]  Jouni Lampinen,et al.  A Fuzzy Adaptive Differential Evolution Algorithm , 2005, Soft Comput..

[21]  Shu-Xian Lun,et al.  A novel model of leaky integrator echo state network for time-series prediction , 2015, Neurocomputing.

[22]  Harald Haas,et al.  Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.

[23]  Junfei Qiao,et al.  Growing Echo-State Network With Multiple Subreservoirs , 2017, IEEE Transactions on Neural Networks and Learning Systems.