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doi: 10.3934/dcdss.2020378

Traffic congestion pricing via network congestion game approach

1. 

School of Mathematics, Southeast University, Jiangsu Provincial Key Laboratory of Networked Collective Intelligence, Nanjing 210096, China

2. 

Jiangsu Provincial Key Laboratory of Networked Collective Intelligence, School of Mathematics, Southeast University, Nanjing 210096, China

3. 

Intelligent Transportation System Research Center, Southeast University, Nanjing 210096, China

4. 

Beijing Urban Construction Design and Development Group Co., Ltd, Beijing 100000, China

* Corresponding author: Jianquan Lu

Received  January 2020 Revised  March 2020 Published  May 2020

Fund Project: This work was supported by the National Natural Science Foundation of China under Grant No. 61973078, the Natural Science Foundation of Jiangsu Province of China under Grant no. BK20170019, "333 Engineering" Foundation of Jiangsu Province of China under Grant BRA2019260, and Jiangsu Provincial Key Laboratory of Networked Collective Intelligence under Grant No. BM2017002

This paper investigates the optimization of traffic congestion systems via network congestion game approach. Firstly, using the semi-tensor product(STP) of matrices, the matrix expression of network congestion game is obtained. Secondly, a necessary and sufficient condition is proposed to guarantee that the traffic systems can be transformed into network congestion game with given performance criterion as its weighted potential function. Then an algorithm is provided to design the traffic congestion price in the case that conversion can be established. Thirdly, by designing proper learning rule, the optimization of traffic systems can be achieved when individuals optimize their own utility function. Moreover, two special cases which make our results more accord with reality and rich. Finally, an example is exploited to demonstrate the effectiveness of our obtained results.

Citation: Jing Zhang, Jianquan Lu, Jinde Cao, Wei Huang, Jianhua Guo, Yun Wei. Traffic congestion pricing via network congestion game approach. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020378
References:
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O. CandoganA. Ozdaglar and P. A. Parrilo, Dynamics in near potential games, Games and Economic Behavior, 82 (2013), 66-90.  doi: 10.1016/j.geb.2013.07.001.  Google Scholar

[2]

D. Cheng, H. Qi and Y. Zhao, An Introduction to Semi-Tensor Product of Matrices and Its Applications Networks, World Scientific Singapore, 2012. Google Scholar

[3]

D. ChengF. HeH. Qi and T. Xu, Modeling, analysis and control of networked evolutionary games, IEEE Transactions on Automatic Control, 60 (2015), 2402-2415.  doi: 10.1109/TAC.2015.2404471.  Google Scholar

[4]

D. Cheng, H. Qi and Z. Li, Analysis and control of Boolean Networks - A Semi-tensor Product Approach, London: Springer, 2011. Google Scholar

[5]

D. Cheng, On finite potential games, Automatic, 50) (2014), 1793-1801.  doi: 10.1016/j.automatica.2014.05.005.  Google Scholar

[6]

X. DingH. Li and Q. Yang, Stochastic stability and stabilization of n-person random evolutionary Boolean games, Applied Mathematics and Computation, 306 (2017), 1-12.  doi: 10.1016/j.amc.2017.02.020.  Google Scholar

[7]

P. Dubey, Inefficiency of nash equilibria, Mathematics of Operations Research, 11 (1986), 1-8.  doi: 10.1287/moor.11.1.1.  Google Scholar

[8]

J. EliassonL. HultkrantzL. Nerhagen and L. S. Rosqvist, The stockholm congestion-charging trial 2006: Overview of effects, Transportation Research Part A: Policy and Practice, 43 (2009), 240-250.  doi: 10.1016/j.tra.2008.09.007.  Google Scholar

[9]

J. FuglestvedtT. BerntsenG. MyhreK. Rypdal and R. B. Skeie, Climate forcing from the transport sectors, Proceedings of the National Academy of Sciences of the United States of America, 105 (2008), 454-458.  doi: 10.1073/pnas.0702958104.  Google Scholar

[10]

R. Gopalakrishnan, J. R. Marden and A. Wierman, An architectural view of game theoretic control, ACM Sigmetrics Performance Evaluation Review, 38 (2011), 31. Google Scholar

[11]

Y. HaoS. PanY. Qiao and D. Cheng, Cooperative control via congestion game approach, IEEE Transactions on Automatic Control, 63 (2018), 4361-4366.  doi: 10.1109/TAC.2018.2824978.  Google Scholar

[12]

C. Huang, J. Lu, G. Zhai, J. Cao, G. Lu and M. Perc, Stability and stabilization in probability of probabilistic boolean networks, IEEE Transactions on Neural Networks and Learning Systems, (2020), 1–11. doi: 10.1109/TNNLS.2020.2978345.  Google Scholar

[13]

K. Hymel, Does traffic congestion reduce employment growth?, Journal of Urban Economics, 65 (2009), 127-135.  doi: 10.1016/j.jue.2008.11.002.  Google Scholar

[14]

T. LiuJ. Wang and D. Cheng, Game theoretic control of multi-agent systems, SIAM J. Control Optimization, 57 (2019), 1691-1709.  doi: 10.1137/18M1177615.  Google Scholar

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C. Li, F. He, H. Qi and D. Cheng, Potential games design using local information, 2018 IEEE Conference on Decision and Control (CDC), 2018, arXiv: 1807.05779v1. doi: 10.1109/CDC.2018.8619561.  Google Scholar

[16]

S. LeY. Wu and X. Sun, Congestion games with player-specific utility functions and its application to NFV networks, IEEE Transactions on Automation Science and Engineering, 16 (2019), 1870-1881.  doi: 10.1109/TASE.2019.2899504.  Google Scholar

[17]

B. LiY. LiuK.I. Kou and L. Yu, Event-triggered control for the disturbance decoupling problem of Boolean control networks, IEEE Transactions on Cybernetics, 48 (2018), 2764-2769.  doi: 10.1109/TCYB.2017.2746102.  Google Scholar

[18]

H. LiX. DingA. Alsaedi and F. E. Alsaadi, Stochastic set stabilization of n-person random evolutionary Boolean games and its applications, IET Control Theory Application, 11 (2017), 2152-2160.  doi: 10.1049/iet-cta.2017.0047.  Google Scholar

[19]

L. LinJ. Cao and L. Rutkowski, Robust event-triggered control invariance of probabilistic Boolean control networks, IEEE Transactions on Neural Networks and Learning Systems, 31 (2020), 1060-1065.  doi: 10.1109/TNNLS.2019.2917753.  Google Scholar

[20]

J. LuL. SunY. LiuD. W. C. Ho and J. Cao, Stabilization of Boolean control networks under aperiodic sampled-data control, SIAM Journal on Control and Optimization, 56 (2018), 4385-4404.   Google Scholar

[21]

J. LuM. LiT. HuangY. Liu and J. Cao, The transformation between the Galois NLFSRs and the Fibonacci NLFSRs via semi-tensor product of matrices, Automatica, 96 (2018), 393-397.  doi: 10.1016/j.automatica.2018.07.011.  Google Scholar

[22]

S. Le, Y. Wu and X. Sun, A Generalization of weighted congestion game and its nash equilibrium seeking, Proceeding of the 37th Chinese Control Conference, 2018. doi: 10.23919/ChiCC.2018.8483432.  Google Scholar

[23]

I. Milchtaich, Representation of finite games as network congestion games, Int J Game Theory, 42 (2013), 1085-1096.  doi: 10.1007/s00182-012-0363-5.  Google Scholar

[24]

D. Monderer and L. S. Shapley, Potential games, Games and Economic behavior, 14 (1996), 124-143.  doi: 10.1006/game.1996.0044.  Google Scholar

[25]

H. QiY. WangT. Liu and D. Cheng, Vector space structure of finite evolutionary games and its application to strategy profile convergence, Journal of Systems Science & Complexity, 29 (2016), 602-628.  doi: 10.1007/s11424-016-4192-7.  Google Scholar

[26]

R. W. Rosenthal, A class of games possessing pure-strategy nash equilibria, International Journal of Game Theory, 2 (1973), 65-67.  doi: 10.1007/BF01737559.  Google Scholar

[27]

R. W. Rosenthal, The network equilibrium problem in integers, Networks, 3 (1973), 53-59.  doi: 10.1002/net.3230030104.  Google Scholar

[28]

L. Wang, M. Fang, Z. Wu and J. Lu, Necessary and sufficient condition on pinning stabilization for stochastic Boolean networks, IEEE Transactions on Cybernetics, 2019, 1–10. doi: 10.1109/TCYB.2019.2931051.  Google Scholar

[29]

Y. Wu and T. Shen, Policy iteration algorithm for optimal control of stochastic logical dynamical systems, IEEE Transactions on Neural Networks and Learning Systems, 29 (2018), 2031-2036.  doi: 10.1109/TNNLS.2017.2661863.  Google Scholar

[30]

M. Xu, Y. Liu, J. Lou, Z. Wu and J. Zhong, Set stabilization of probabilistic Boolean control networks: A sampled-data control approach, IEEE Transactions on cybernetics, 2019, 1–8. doi: 10.1109/TCYB.2019.2940654.  Google Scholar

[31]

M. B. Yildirim and D. W. Hearn, A first best toll pricing framework for variable demand traffic assignment problems, Transportation Research Part B, 39 (2005), 659-678.  doi: 10.1016/j.trb.2004.08.001.  Google Scholar

[32]

H. Yang and X. Wang, Managing network mobility with tradable credits, Transportation Research Part B, 45 (2011), 580-594.  doi: 10.1016/j.trb.2010.10.002.  Google Scholar

[33]

H. Yang and X. Zhang, Multi-class network toll design problem with social and spatial equity constraints, Journal of Transportation Engineering, 128 (2002), 420-428.   Google Scholar

[34]

H. Yang and X. Zhang, Existence of anonymous link tolls for system optimum on networks with mixed equilibrium behaviors, Transportation Research Part B, 42 (2010), 99-112.   Google Scholar

[35]

X. ZhangH. Yang and H. Huang, Multi-class multicriteria mixed equilibrium on networks and uniform link tolls for system optimum, European Journal of Operational Research, 189 (2008), 146-158.  doi: 10.1016/j.ejor.2007.05.004.  Google Scholar

[36]

J. ZhongB. LiY. Liu and W. Gui, Output feedback stabilizer design of Boolean networks based on network structure, Frontiers of Information Technology Electronic Engineering, 21 (2020), 247-259.  doi: 10.1631/FITEE.1900229.  Google Scholar

[37]

J. ZhongD. W. C. Ho and J. Lu, Pinning controllers for activation output tracking of Boolean network under one-bit perturbation, IEEE Transactions on Cybernetics, 49 (2019), 3398-3408.  doi: 10.1109/TCYB.2018.2842819.  Google Scholar

[38]

S. ZhuJ. Lu and Y. Liu, Asymptotical stability of probabilistic Boolean networks with state delays, IEEE Transactions on Automatic Control, 65 (2020), 1779-1784.  doi: 10.1109/TAC.2019.2934532.  Google Scholar

[39]

S. ZhuJ. LuY. LiuT. Huang and J. Kurths, Output tracking of probabilistic Boolean networks by output feedback control, Information Sciences, 483 (2019), 96-105.  doi: 10.1016/j.ins.2018.12.087.  Google Scholar

show all references

References:
[1]

O. CandoganA. Ozdaglar and P. A. Parrilo, Dynamics in near potential games, Games and Economic Behavior, 82 (2013), 66-90.  doi: 10.1016/j.geb.2013.07.001.  Google Scholar

[2]

D. Cheng, H. Qi and Y. Zhao, An Introduction to Semi-Tensor Product of Matrices and Its Applications Networks, World Scientific Singapore, 2012. Google Scholar

[3]

D. ChengF. HeH. Qi and T. Xu, Modeling, analysis and control of networked evolutionary games, IEEE Transactions on Automatic Control, 60 (2015), 2402-2415.  doi: 10.1109/TAC.2015.2404471.  Google Scholar

[4]

D. Cheng, H. Qi and Z. Li, Analysis and control of Boolean Networks - A Semi-tensor Product Approach, London: Springer, 2011. Google Scholar

[5]

D. Cheng, On finite potential games, Automatic, 50) (2014), 1793-1801.  doi: 10.1016/j.automatica.2014.05.005.  Google Scholar

[6]

X. DingH. Li and Q. Yang, Stochastic stability and stabilization of n-person random evolutionary Boolean games, Applied Mathematics and Computation, 306 (2017), 1-12.  doi: 10.1016/j.amc.2017.02.020.  Google Scholar

[7]

P. Dubey, Inefficiency of nash equilibria, Mathematics of Operations Research, 11 (1986), 1-8.  doi: 10.1287/moor.11.1.1.  Google Scholar

[8]

J. EliassonL. HultkrantzL. Nerhagen and L. S. Rosqvist, The stockholm congestion-charging trial 2006: Overview of effects, Transportation Research Part A: Policy and Practice, 43 (2009), 240-250.  doi: 10.1016/j.tra.2008.09.007.  Google Scholar

[9]

J. FuglestvedtT. BerntsenG. MyhreK. Rypdal and R. B. Skeie, Climate forcing from the transport sectors, Proceedings of the National Academy of Sciences of the United States of America, 105 (2008), 454-458.  doi: 10.1073/pnas.0702958104.  Google Scholar

[10]

R. Gopalakrishnan, J. R. Marden and A. Wierman, An architectural view of game theoretic control, ACM Sigmetrics Performance Evaluation Review, 38 (2011), 31. Google Scholar

[11]

Y. HaoS. PanY. Qiao and D. Cheng, Cooperative control via congestion game approach, IEEE Transactions on Automatic Control, 63 (2018), 4361-4366.  doi: 10.1109/TAC.2018.2824978.  Google Scholar

[12]

C. Huang, J. Lu, G. Zhai, J. Cao, G. Lu and M. Perc, Stability and stabilization in probability of probabilistic boolean networks, IEEE Transactions on Neural Networks and Learning Systems, (2020), 1–11. doi: 10.1109/TNNLS.2020.2978345.  Google Scholar

[13]

K. Hymel, Does traffic congestion reduce employment growth?, Journal of Urban Economics, 65 (2009), 127-135.  doi: 10.1016/j.jue.2008.11.002.  Google Scholar

[14]

T. LiuJ. Wang and D. Cheng, Game theoretic control of multi-agent systems, SIAM J. Control Optimization, 57 (2019), 1691-1709.  doi: 10.1137/18M1177615.  Google Scholar

[15]

C. Li, F. He, H. Qi and D. Cheng, Potential games design using local information, 2018 IEEE Conference on Decision and Control (CDC), 2018, arXiv: 1807.05779v1. doi: 10.1109/CDC.2018.8619561.  Google Scholar

[16]

S. LeY. Wu and X. Sun, Congestion games with player-specific utility functions and its application to NFV networks, IEEE Transactions on Automation Science and Engineering, 16 (2019), 1870-1881.  doi: 10.1109/TASE.2019.2899504.  Google Scholar

[17]

B. LiY. LiuK.I. Kou and L. Yu, Event-triggered control for the disturbance decoupling problem of Boolean control networks, IEEE Transactions on Cybernetics, 48 (2018), 2764-2769.  doi: 10.1109/TCYB.2017.2746102.  Google Scholar

[18]

H. LiX. DingA. Alsaedi and F. E. Alsaadi, Stochastic set stabilization of n-person random evolutionary Boolean games and its applications, IET Control Theory Application, 11 (2017), 2152-2160.  doi: 10.1049/iet-cta.2017.0047.  Google Scholar

[19]

L. LinJ. Cao and L. Rutkowski, Robust event-triggered control invariance of probabilistic Boolean control networks, IEEE Transactions on Neural Networks and Learning Systems, 31 (2020), 1060-1065.  doi: 10.1109/TNNLS.2019.2917753.  Google Scholar

[20]

J. LuL. SunY. LiuD. W. C. Ho and J. Cao, Stabilization of Boolean control networks under aperiodic sampled-data control, SIAM Journal on Control and Optimization, 56 (2018), 4385-4404.   Google Scholar

[21]

J. LuM. LiT. HuangY. Liu and J. Cao, The transformation between the Galois NLFSRs and the Fibonacci NLFSRs via semi-tensor product of matrices, Automatica, 96 (2018), 393-397.  doi: 10.1016/j.automatica.2018.07.011.  Google Scholar

[22]

S. Le, Y. Wu and X. Sun, A Generalization of weighted congestion game and its nash equilibrium seeking, Proceeding of the 37th Chinese Control Conference, 2018. doi: 10.23919/ChiCC.2018.8483432.  Google Scholar

[23]

I. Milchtaich, Representation of finite games as network congestion games, Int J Game Theory, 42 (2013), 1085-1096.  doi: 10.1007/s00182-012-0363-5.  Google Scholar

[24]

D. Monderer and L. S. Shapley, Potential games, Games and Economic behavior, 14 (1996), 124-143.  doi: 10.1006/game.1996.0044.  Google Scholar

[25]

H. QiY. WangT. Liu and D. Cheng, Vector space structure of finite evolutionary games and its application to strategy profile convergence, Journal of Systems Science & Complexity, 29 (2016), 602-628.  doi: 10.1007/s11424-016-4192-7.  Google Scholar

[26]

R. W. Rosenthal, A class of games possessing pure-strategy nash equilibria, International Journal of Game Theory, 2 (1973), 65-67.  doi: 10.1007/BF01737559.  Google Scholar

[27]

R. W. Rosenthal, The network equilibrium problem in integers, Networks, 3 (1973), 53-59.  doi: 10.1002/net.3230030104.  Google Scholar

[28]

L. Wang, M. Fang, Z. Wu and J. Lu, Necessary and sufficient condition on pinning stabilization for stochastic Boolean networks, IEEE Transactions on Cybernetics, 2019, 1–10. doi: 10.1109/TCYB.2019.2931051.  Google Scholar

[29]

Y. Wu and T. Shen, Policy iteration algorithm for optimal control of stochastic logical dynamical systems, IEEE Transactions on Neural Networks and Learning Systems, 29 (2018), 2031-2036.  doi: 10.1109/TNNLS.2017.2661863.  Google Scholar

[30]

M. Xu, Y. Liu, J. Lou, Z. Wu and J. Zhong, Set stabilization of probabilistic Boolean control networks: A sampled-data control approach, IEEE Transactions on cybernetics, 2019, 1–8. doi: 10.1109/TCYB.2019.2940654.  Google Scholar

[31]

M. B. Yildirim and D. W. Hearn, A first best toll pricing framework for variable demand traffic assignment problems, Transportation Research Part B, 39 (2005), 659-678.  doi: 10.1016/j.trb.2004.08.001.  Google Scholar

[32]

H. Yang and X. Wang, Managing network mobility with tradable credits, Transportation Research Part B, 45 (2011), 580-594.  doi: 10.1016/j.trb.2010.10.002.  Google Scholar

[33]

H. Yang and X. Zhang, Multi-class network toll design problem with social and spatial equity constraints, Journal of Transportation Engineering, 128 (2002), 420-428.   Google Scholar

[34]

H. Yang and X. Zhang, Existence of anonymous link tolls for system optimum on networks with mixed equilibrium behaviors, Transportation Research Part B, 42 (2010), 99-112.   Google Scholar

[35]

X. ZhangH. Yang and H. Huang, Multi-class multicriteria mixed equilibrium on networks and uniform link tolls for system optimum, European Journal of Operational Research, 189 (2008), 146-158.  doi: 10.1016/j.ejor.2007.05.004.  Google Scholar

[36]

J. ZhongB. LiY. Liu and W. Gui, Output feedback stabilizer design of Boolean networks based on network structure, Frontiers of Information Technology Electronic Engineering, 21 (2020), 247-259.  doi: 10.1631/FITEE.1900229.  Google Scholar

[37]

J. ZhongD. W. C. Ho and J. Lu, Pinning controllers for activation output tracking of Boolean network under one-bit perturbation, IEEE Transactions on Cybernetics, 49 (2019), 3398-3408.  doi: 10.1109/TCYB.2018.2842819.  Google Scholar

[38]

S. ZhuJ. Lu and Y. Liu, Asymptotical stability of probabilistic Boolean networks with state delays, IEEE Transactions on Automatic Control, 65 (2020), 1779-1784.  doi: 10.1109/TAC.2019.2934532.  Google Scholar

[39]

S. ZhuJ. LuY. LiuT. Huang and J. Kurths, Output tracking of probabilistic Boolean networks by output feedback control, Information Sciences, 483 (2019), 96-105.  doi: 10.1016/j.ins.2018.12.087.  Google Scholar

Figure 1.  The approach of network congestion game
Figure 2.  The traffic network graph
Table 1.  system objective function $ P(a) $
a 111 112 121 122 211 212 221 222
$ P(a) $ 63 70 72 71 72 71 72 62
a 111 112 121 122 211 212 221 222
$ P(a) $ 63 70 72 71 72 71 72 62
Table 2.  Utility matrix of network congestion game
u\s 111 112 121 122 211 212 221 222
$ u_1 $ 8 18 20 26 26 20 20 8
$ u_2 $ 8 18 26 20 20 26 20 8
$ u_3 $ 41 48 46 45 46 45 49 40
u\s 111 112 121 122 211 212 221 222
$ u_1 $ 8 18 20 26 26 20 20 8
$ u_2 $ 8 18 26 20 20 26 20 8
$ u_3 $ 41 48 46 45 46 45 49 40
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