Traffic Network Modeling and Extended Max-Pressure Traffic Control Strategy Based on Granular Computing Theory

Reasonable traffic network model and flexible traffic control strategy play important roles in improving the urban traffic control efficiency. Introducing granular computing theory into traffic network modeling and traffic control is a useful attempt, since granular computing is closer to the human thinking in solving problems. In this paper, the traffic elements are depicted using S-rough set to achieve the granulation partition of traffic network. Four layers are partitioned in the proposed hierarchical multigranularity traffic network model, such as vehicle layer, platoon layer, segment and intersection layer, and subregion layer. Each traffic granule is represented in rough representation form, and the dynamic characteristics are described using the elementary transfer operations based on S-rough set theory. As an application on the proposed traffic network model, an extended max-pressure traffic control strategy is applied on the platoon and segment and intersection layer. Simulation results illustrate that the proposed traffic network model and traffic control strategy achieve better performance.

[1]  Long Chen,et al.  Analysis of Roadway Traffic Accidents Based on Rough Sets and Bayesian Networks , 2018 .

[2]  Licai Yang,et al.  Data-driven car-following model based on rough set theory , 2018 .

[3]  Peter G Furth,et al.  Self-Organizing Traffic Signals Using Secondary Extension and Dynamic Coordination Rules , 2014 .

[4]  Alan Wee-Chung Liew,et al.  Missing Value Imputation for the Analysis of Incomplete Traffic Accident Data , 2014, ICMLC.

[5]  Yong Qin,et al.  Understanding structure of urban traffic network based on spatial-temporal correlation analysis , 2017 .

[6]  Qi Yang,et al.  Traffic Congestion Identification and Analysis of Urban Road Network Based on Granular Computing , 2014 .

[7]  Xu Yang,et al.  City traffic flow breakdown prediction based on fuzzy rough set , 2017 .

[8]  He Fu-gu BGrR:Large-scale Network Routing Speedup Techniques Based on Granular Computing , 2014 .

[9]  Jun Ding,et al.  Multi-modal traffic signal control with priority, signal actuation and coordination , 2014 .

[10]  Pravin Varaiya,et al.  Max pressure control of a network of signalized intersections , 2013 .

[11]  Lorenzo Livi,et al.  Granular computing, computational intelligence, and the analysis of non-geometric input spaces , 2016 .

[12]  Preeti Bajaj,et al.  Performance Improvement of Traffic Flow Prediction Model using Combination of Support Vector Machine and Rough Set , 2017 .

[13]  Danwei Wang,et al.  Distributed traffic signal control for maximum network throughput , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[14]  Theresa Beaubouef,et al.  Rough Sets , 2019, Lecture Notes in Computer Science.

[15]  Yiyu Yao,et al.  Granular Computing: basic issues and possible solutions , 2007 .

[16]  Haiqing Hu,et al.  S-Rough Recognition of Knowledge and General Threshold Encryption Authentication Scheme of Recognition Conclusion , 2006, Sixth International Conference on Intelligent Systems Design and Applications.

[17]  Han Liu,et al.  Granular computing-based approach for classification towards reduction of bias in ensemble learning , 2017, GRC 2017.

[18]  Yiyu Yao,et al.  Interpreting Concept Learning in Cognitive Informatics and Granular Computing , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[19]  Geert Wets,et al.  A New Weighting Approach Based on Rough Set Theory and Granular Computing for Road Safety Indicator Analysis , 2016, Comput. Intell..

[20]  Wu Qi-gang Traffic Management and Decision-Making Support Based on Granular Computing , 2008 .

[21]  Cheng Xinhong,et al.  Granular Computing in Intelligent Transportation: An Exploratory Study , 2015 .

[22]  Kai-Quan Shi S-rough sets and its applications in diagnosis-recognition for disease , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[23]  Witold Pedrycz,et al.  Granular Computing: Perspectives and Challenges , 2013, IEEE Transactions on Cybernetics.

[24]  Emilio Frazzoli,et al.  Back-pressure traffic signal control with unknown routing rates , 2013, ArXiv.