Journal of Geo-information Science >
Modeling and Simulation Analysis of Virtual Public Transportation Environment Based on MAS
Received date: 2014-08-06
Revised date: 2014-11-01
Online published: 2015-05-10
To solve the problems about public transportation models in which the passenger behaviors and their interactions with public transportation are rarely concerned, a concept of virtual public transportation environment and the vector representation of its elements is proposed. Meanwhile, a transportation agent model and a passenger behavior agent model were built with the MAS method. Furthermore, an algorithm was proposed to simulate transportations, passengers, and the temporal and spatial variations in public transportation operation. Then, a prototype system of virtual public transport environment was implemented. In the end, a small-scale public transport environment, which was composed by7 roads, 8 bus routes, 31bus stops and 10 000 rides, was designed as the experiment environment. It produced 1428 bus runs and 30 436 passengers'swiping card data of getting on and off buses. The experimental data was analyzed while comparing with the average passenger waiting time under different departure intervals. The results show that the total travel time distribution is consistent with the input data, and the average waiting time is reduced if reducing the departure intervals of buses. It proves that the above models can effectively simulate public transportation environments. As a result, the traffic managers and researches could benefit from the prototype system and models, and it is helpful for them to make rational decisions.
LUO Laiping, ZHANG Jing, LI Yougang, HU Xinglin, MENG Tiantian . Modeling and Simulation Analysis of Virtual Public Transportation Environment Based on MAS[J]. Journal of Geo-information Science, 2015 , 17(5) : 583 -589 . DOI: 10.3724/SP.J.1047.2015.00583
[1] Rexfelt O, Schelenz T, Karlsson M, et al. Evaluating the effects of bus design on passenger flow: Is agent-based simulation a feasible approach[J]. Transportation research part C, 2014,38:16-27.
[2] Euchi J, Mraihi R. The urban bus routing problem in the Tunisian case by the hybrid artificial ant colony algorithm[J]. Swarm and evolutionary computation, 2012,2:15-24.
[3] Guihaire V, Hao J. Transit network design and scheduling: A global review[J]. Transportation research part A, 2008,M (42):1251-1273.
[4] 钟玲,孙强南,鞠彦兵.公交车运行的仿真模型及优化[J]. 北京机械工业学院学报,2005,20(2):18-21.
[5] Mandiau R, Champion A, Auberlet J, et al. Behaviour based on decision matrices for a coordination between agents in urban traffic simulation[J]. Applied intelligence, 2008,28(2):121-138.
[6] Tian L. Traffic flow simulation in scenario with signalized intersection and bus stop[J]. Journal of transportation systems engineering and information technology, 2012,12(5):90-96.
[7] Liu Y, Niu H. Simulation and analysis of traffic flow model on multi-platform harbor-style bus stop[J]. Journal of transportation systems engineering and information technology, 2012,12(5):97-102.
[8] Meignan D, Simonin O, Koukam A. Simulation and evaluation of urban bus-networks using a multiagentapproach[J]. Simulation modelling practice and theory, 2007,15: 659-671.
[9] Yu B, Yang Z, Jin P, et al. Transit route network designmaximizing direct and transfer demand density[J]. Transportation research part C, 2012, 22:58-75.
[10] 臧志刚,陆锋,李海峰,等.7 种微观交通仿真系统的性能 评价与比较研究[J].交通与计算机,2007,25(1):66-70.
[11] Fernández R. Modelling public transport stops by microscopic simulation[J]. Transportation research part C, 2010,18:856-868.
[12] Doniec A, Mandiau R, Piechowiak S, et al. A behavioral multi-agent model for road traffic simulation[J]. Engineering applications of artificial intelligence, 2008,21:1443-1454.
[13] Schelenz T, Suescun A, Wikström L. Passenger-centered design of future buses using agent-based simulation[J]. ProcediaSocial and Behavioral Sciences, 2012,48:1662-1671.
[14] Ma N, Yin G, Zuo Y. Passenger sharing of the high-speed railway from sensitivity analysis caused by price and runtime based on the multi-agent system[J]. Journal of Industrial Engineering and Management, 2013, 6(4):1210-1222.
[15] Balbo F, Pinson S. Towards a multi-agent modeling approach for urban public transportation system[J]. Engineering Societies, 1999,160-174.
[16] Dia H. An agent-based approach to modeling driver route choice behavior under the influence of real-time information[J]. Transportation Research Part C: Emerging Technologies, 2002,10C(56):331-349.
[17] Bhouri N, Balbo F, Pinson S. An agent-based computational approach for urban traffic regulation[J]. Progress in Artificial Intelligence, 2012,1(2):139-147.
[18] 隽志才,谭云龙,倪安宁.公交车辆运行微观交通仿真模 型研究[J].公路交通科技,2008,25(8):119-127.
[19] 龙瀛,张宇,崔承印.利用公交刷卡数据分析北京职住关 系和通勤出行[J].地理学报,2012,67(10):1339-1352.
[20] 石俊飞,李通,张振.公交线路选择的模型与算法[J].计算 机与数字工程,2008,36(7):47-49.
[21] Ma X, Wang Y, Chen F, et al. Transit smart card data mining for passenger origin information extraction[J]. Journal of Zhejiang University-Science C(Computers & Electronics), 2012,13(10):750-760.
[22] Kusakabe T, Iryo T, Asakura Y. Estimation method for railway passengers'train choice behavior with smart card transaction data[J]. Transportation, 2010, 37:731-749.
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