In the travel process of urban residents, travelers will take a series of activities such as imitation and exclusion by observing other people’s travel modes, which affects their following trips. This process can be seen as a repeated game between members of the travelers. Based on the analysis of this game and its evolution trend, a multi-dimensional game model of low-carbon travel for residents is established. The two dimensional game strategies include whether to accept the low-carbon concept and whether to choose low-carbon travel. Combined with evolutionary game theory, the low-carbon travel choices of residents in different cities are simulated, and the evolutionary stability strategies are obtained. Finally, the influences of the main parameters of the model on the evolution process and stability strategies are discussed. The results show that travelers would develop towards two trends. Cities with more developed public traffic system have a higher proportion of receiving low-carbon concept and choosing low-carbon travel. Cities with underdeveloped public transport system could increase this proportion by some measures such as encouraging residents to choose slow transport and increasing the propaganda of low-carbon travel, but the positive effects of the measures like propaganda have a limited impact on the proportion.
在考虑低碳因素的城市居民出行过程中, 出行者会通过观察其他人的出行方式, 产生模仿、排 斥等一系列活动, 从而影响到后续的出行, 由此过程可以看作出行群体成员之间的反复博弈. 本文在 对低碳背景下出行群体的这种博弈及其演化趋势分析的基础上, 把交通方式的选择和是否接收低碳理 念作为策略的两个维度, 建立了居民低碳出行多维博弈模型, 并结合演化博弈理论对不同城市居民的 低碳出行选择进行仿真, 得到选择的演化稳定策略, 进而讨论了模型主要参数变化对演化过程及稳定 策略的影响. 结果表明, 城市出行者将向着演化稳定策略的趋势发展. 公共交通越发达的城市, 接受 低碳理念并选择低碳出行的比例越高. 公共交通不够发达的城市可以通过鼓励个人慢行交通, 加大对 低碳出行的宣传等措施提高这个比例, 但宣传等做法带来的积极效果较为有限.
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LIU Zhao, LI Ling, ZHANG Yue-jun. Investigating the CO2emission differences among China’s transport sectors and their influencing factors [J]. Natural Hazards, 2015, 77(2): 1323–1343. DOI:https://doi.org/10.1007/s11069-015-1657-2.
LIU Di-yi, DU Hui-bin, SOUTHWORTH F, MA Shou-feng. The influence of social-psychological factors on the intention to choose low-carbon travel modes in Tianjin, China [J]. Transportation Research Part A: Policy and Practice, 2017, 105: 42–53. DOI: https://doi.org/10.1016/j.tra.2017.08.004.
SCHWANEN T, BANISTER D, ANABLE J. Rethinking habits and their role in behaviour change: The case of low-carbon mobility [J]. Journal of Transport Geography, 2012, 24: 522–532. DOI: https://doi.org/10.1016/j.jtrangeo.2012.06.003.
JING Peng, ZHAO Meng-yuan, HE Mei-ling, CHEN Long. Travel mode and travel route choice behavior based on random regret minimization: A systematic review [J]. Sustainability, 2018, 10(4): 1185. DOI: https://doi.org/10.3390/su10041185.
JIA Ning, LI Li-ying, LING Shuai, MA Shou-feng, YAO Wang. Influence of attitudinal and low-carbon factors on behavioral intention of commuting mode choice–A cross-city study in China [J]. Transportation Research Part A: Policy and Practice, 2018, 111: 108–118. DOI: https://doi.org/10.1016/j.tra.2018.03.010.
MARSDEN G, MULLEN C, BACHE I, BARTLE I, FLINDERS M. Carbon reduction and travel behaviour: Discourses, disputes and contradictions. in governance [J]. Transport Policy, 2014, 35: 71–78. DOI: https://doi.org/10.1016/j.tranpol.2014.05.012.
GENG Ji-chao, LONG Ru-yin, CHEN Hong, LI Qian-wen. Urban residents’ response to and evaluation of low-carbon travel policies: Evidence from a survey of five eastern cities in China [J]. Journal of Environmental Management, 2018, 217: 47–55. DOI: https://doi.org/10.1016/j.jenvman.2018.03.091.
NEUMANN J V. Zur theorie der gesellschaftsspiele [J]. Mathematische annalen, 1928, 100(1): 295–320. DOI: https://doi.org/10.1007/BF01448847.(in German)
NASH J F. Equilibrium points in N-person games [J]. Proceedings of the National Academy of Sciences of the United States of America, 1950, 36(1): 48–49. DOI: https://doi.org/10.2307/88031.
HARSANYI J C. Games with randomly disturbed payoffs: A new rationale for mixed-strategy equilibrium points [J]. International journal of game theory, 1973, 2(1): 1–23. DOI: https://doi.org/10.1007/BF01737554.
SELTEN R, STOECKER R. End behavior in sequences of finite Prisoner’s Dilemma supergames: A learning theory approach [J]. Journal of Economic Behavior & Organization, 1986, 7(1): 47–70. DOI: https://doi.org/10.1016/0167-2681(86)90021-1.
YAO Wang, JIA Ning, ZHONG Shi-quan, LI Li-ying. Best response game of traffic on road network of non-signalized intersections [J]. Physica A: Statistical Mechanics and its Applications, 2018, 490: 386–401. DOI: https://doi.org/10.1016/j.physa.2017.08.032.
FAN Hong-qiang, JIA Bin, TIAN Jun-fang, YUN Li-fen. Characteristics of traffic flow at a non-signalized intersection in the framework of game theory [J]. Physica A: Statistical Mechanics and Its Applications, 2014, 415: 172–180. DOI: https://doi.org/10.1016/j.physa.2014.07.031.
LIU Miao-miao, CHEN Yong-sheng, LU Guang-quan, WANG Yun-peng. Modeling crossing behavior of drivers at unsignalized intersections with consideration of risk perception [J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2017, 45: 14–26. DOI: https://doi.org/10.1016/j.trf.2016.11.012.
BJ0RNSKAU T. The zebra crossing game-Using game theory to explain a discrepancy between road user behaviour and traffic rules [J]. Safety Science, 2017, 92: 298–301. DOI: https://doi.org/10.1016/j.ssci.2015.10.007.
ALVAREZ I, POZNYAK A, MALO A. Urban traffic control problem: A game theory approach [C]// IEEE Decis Contr P. Seoul, Korea, 2008: 2168–2172. DOI: https://doi.org/10.3182/20080706-5-KR-1001.01213.
BUI K N, JUNG J J. Cooperative game-theoretic approach to traffic flow optimization for multiple intersections [J]. Computers & Electrical Engineering, 2017, 5: 1–13. DOI: https://doi.org/10.1016/j.compeleceng.2017.10.016.
ELHENAWY M, ELBERY A A, HASSAN A A, RAKHA H A. An intersection game-theory-based traffic control algorithm in a connected vehicle environment [C]// 2015 IEEE 18th International Conference on Intelligent Transportation Systems. IEEE, 2015: 343–347. DOI: https://doi.org/10.1109/ITSC.2015.65.
NAOHIRO U, EIICHI T. A study of dispatcher’s route choice model based on evolutionary game theory [J]. Procedia-Social and Behavioral Sciences, 2012, 39: 495–509. DOI: https://doi.org/10.1016/j.sbspro.2012.03.125.
LIN Kai, LI Chen-si, GIANCARLO F, RODRIGUES J. Vehicle route selection based on game evolution in social internet of vehicles [J]. IEEE Internet of Things Journal, 2018, 5(4): 2423–2430. DOI: https://doi.org/10.1109/JIOT.2018.2844215.
PORTILLA C, VALENCIA F. Non-linear model predictive control based on game theory for traffic control on highways [J]. IFAC Proceedings Volumes, 2012, 45(17): 436–441. DOI: https://doi.org/10.3182/20120823-5-NL-3013.00046.
LIN Cheng, LOU Xiao-ming, ZHOU Jing, MA Jie. A mixed stochastic user equilibrium model considering influence of advanced traveler information systems in degradable transport network [J]. Journal of Central South University, 2018, 25(5): 1182–1194. DOI: https://doi.org/10.1007/s11771-018-3817-5.
ADRIANO F, SIMONE G A. Mean field game approach for multi-lane traffic management [J]. IFAC Papersonline, 2018, 51(32): 793–798. DOI: https://doi.org/10.1016/j.ifacol.2018.11.448.
YAN Fei, YAN Gao-wei, REN Mi-feng, TIAN Jian-yan, SHI Zhong-ke. A novel control strategy for balancing traffic flow in urban traffic network based on iterative learning control [J]. Physica A Statistical Mechanics & Its Applications, 2018, 508: 519–531. DOI: https://doi.org/10.1016/j.physa.2018.05.134.
SU Bei-bei, CHANG Hong, CHEN Yong-zhou, HE Da-ren. A game theory model of urban public traffic networks [J]. Physica A: Statistical Mechanics and its Applications, 2007, 379(1): 291–297. DOI: https://doi.org/10.1016/j.physa.2006.12.049.
CHANG Hui, XU Xiu-lian, HU Chin-kun, FU Chun-hua, FENG Ai-xia, HE Da-ren. A manipulator game model of urban public traffic network [J]. Physica A: Statistical Mechanics and Its Applications, 2014, 416: 378–385. DOI: https://doi.org/10.1016/j.physa.2014.09.015.
ŠKRINJAR J P, ABRAMOVIĆ B, BRNJAC N. The use of game theory in urban transport planning [J]. Tehnicki Vjesnik-Technical Gazette, 2015, 22(6): 1617–1621. DOI: https://doi.org/10.17559/TV-20140108101820.
OHTSUKI H, HAUERT C, LIEBERMAN E, NOWAK M A. A simple rule for the evolution of cooperation on graphs and social networks [J]. Nature, 2006, 441(7092): 502–505. DOI: https://doi.org/10.1038/nature04605.
CHEN Xing-guang, ZHOU Jing, ZHU Zhen-tao. Evolutionary game analysis of the travel mode choice for urban travelers [J]. Journal of Industrial Engineering Engineering Management, 2009, 23(2): 130, 140–142. DOI:https://doi.org/10.1109/CLEOE-EQEC.2009.5194697.(in Chinese)
XIAO Hai-yan, DU Wei. Repeated games analysis of trip model choice behavior [J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(2): 24–28, 35. DOI: https://doi.org/10.16097/j.cnki.1009-6744.2015.02.004.(in Chinese)
CALASTRI C, BORGHESI S, FAGIOLO G. How do people choose their commuting mode? An evolutionary approach to travel choices [J]. Economia Politica, 2018: 1–26. DOI: https://doi.org/10.1007/s40888-018-0099-1.
CHEN Yue-xia, CHEN Long, ZHA Qi-fen. Carbon emissions measurement of urban traffic individual in Zhenjiang [J]. Journal of Jiangsu University: Natural Science Edition, 2015, 36(6): 645–649. DOI: https://doi.org/10.3969/j.issn.1671-7775.2015.06.005.(in Chinese)
CHATTERJEE K, VELNER Y. Hyperplane separation technique for multidimensional mean-payoff games [J]. Journal of Computer and System Sciences, 2017, 88: 500–515. DOI: https://doi.org/10.1016/j.jcss.2017.04.005.
Foundation item: Project(BK20160512) supported by the Natural Science Foundation of Jiangsu Province, China; Project(16YJCZH027) supported by the Humanity and Social Science Youth Foundation of Ministry of Education of China; Project(15GLC004) supported by the Social Science Foundation of Jiangsu Province, China
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Wu, X., He, M., Cao, S. et al. Evolution trend analysis of urban residents’ low-carbon travel development based on multidimensional game theory. J. Cent. South Univ. 26, 3388–3396 (2019) doi:10.1007/s11771-019-4261-x
- low-carbon travel
- evolution trend
- multidimensional game
- travel modes