In this paper, we propose a Distributed Architecture for Railway Traffic Control using Multi-Agent Systems called DARTCMAS. It has a hierarchical organizational model that consists of three main layers of diverse agents: “Train Agents” at the bottom, “Station Agents” in the middle and “Center of Traffic Control Agent” in the top layer. Any train in the railway traffic system is considered as an intelligent agent that has reactive, proactive and interactive properties, simultaneously. DARTCMAS considers local traffic control centers that are embedded in some selected stations as “Station Agents”. Lastly, “Center of Traffic Control Agent” acts as a comprehensive monitoring and supporting reference. We define an internal architecture for each of the agents. Furthermore, two types of interactions among agents are developed: first, inter-layer interactions that represent communications between two adjacent layers in the organizational model of DARTCMAS; and second, intra-layer interactions that represent relations between the same agents type in a specific layer. Simulations have been performed using MATLAB and the “Java Agent DEvelopment framework” whose results show the effectiveness of the model.
LetiaTHuleaMMironR. Distributed scheduling for real-time railway traffic control. The international multiconference on computer science and information technology, Piscataway, NJ: IEEE Press, 2008. IMCSIT 2008. Wisla, Poland, 20–22 October 2008, pp.679–685.
2.
U.S. Congress. Office of Technology Assessment. Automatic Train Control in Rail Rapid Transit. Washington, D.C.: Government Printing Office, 1976.
3.
Corman F. Real-time railway traffic management: dispatching in complex, large and busy railway networks. Report no. T2010/14, 2010. Netherlands: TRAIL Research School.
4.
Baskar LD, De Schutter B and Hellendoorn H. Decentralized traffic control and management with intelligent vehicles. In: The ninth TRAIL congress, Delft, Rotterdam, Netherlands, November 2006.
5.
XuCXuRChengW. Research of the CTC system of the Jiaoji Trunk Line. J China Railw Soc2007; 1: 7–15.
6.
AmbegodaADe SilvaWHemachandraK. Centralized traffic controlling system for Sri Lanka Railways. The fourth international conference on information and automation for sustainability. Sri Lanka, 12–14 December 2008, pp.145–149Piscataway, NJ: IEEE Press.
7.
VernazzaGZuninoR. A distributed intelligence methodology for railway traffic control. IEEE Trans Veh Technol1990; 39(3): 263–270.
8.
BerezinskiMHolubiecJPetriczekG. Multilevel models for railway traffic control. The international conference on control, Coventry, UK, January 1994, pp.54–56Stevenage, UK: IEE Press.
9.
JiaLMZhangXD. Distributed intelligent railway traffic control based on fuzzy decisionmaking. Fuzzy Sets Syst1994; 62(3): 255–265.
10.
MissikoffM. An object-oriented approach to an information and decision support system for railway traffic control. Engng Appl Artif Intel1998; 11(1): 25–40.
11.
ŞahinI. Railway traffic control and train scheduling based on inter-train conflict management. Transport Res, Part B: Methodol1999; 33(7): 511–534.
12.
KitaharaFKeraKBekkiK. Autonomous decentralized traffic management system. The international workshop on autonomous decentralized systems. Chengdu, China, 21–23 September 2000, pp.87–91Piscataway, NJ: IEEE Press.
13.
BlumJEskandarianA. Enhancing intelligent agent collaboration for flow optimization of railroad traffic. Transp Res, Part A: Policy Pract2002; 36(10): 919–930.
14.
Proença H and Oliveira E. MARCS: multi-agent railway control system. In: The conference on advances in artificial intelligence. Puebla, Mexico, 22–26 November 2004, pp.12–21.
15.
TsangCWHoTK. Optimal track access rights allocation for agent negotiation in an open railway market. IEEE Trans Intell Transp Syst2008; 9(1): 68–82.
16.
SiahvashiAMoaveniB. Automatic train control based on the multi-agent control of cooperative systems. J Math Comput Sci2010; 1(4): 247–257.
17.
Deng P, Yingping Z and Chuansheng Z. On intelligent automatic train control of railway moving automatic block systems based on multi-agent systems. In: The 29th Chinese control conference. Beijing, China, 29–31 July, pp.4471–4476.
18.
NingBTangTDongH. An introduction to parallel control and management for high-speed railway systems. IEEE Trans Intell Transp Syst2011; 12(4): 1473–1483.
19.
TaoTCaiB. A train control system for low-density lines based on intelligent autonomous decentralized system (IADS). The sixth international symposium on autonomous decentralized systems, Pisa, Italy, 9–11 April 2003, pp.203–207Piscataway, NJ: IEEE Press.
20.
ArayaSSoneS. Traffic dynamics of automated transit systems with pre-established schedules. IEEE Trans Syst Man Cybern1984; 14(4): 677–687.
21.
BreusegemVVCampionGBastinG. Traffic modelling and state feedback control for metro lines. IEEE Trans Autom Control1991; 36(7): 770–784.
22.
GoodmanCJMurataS. Metro traffic regulation from the passenger perspective. Proc IMechE, Part F: J Rail Rapid Transit2001; 215(2): 137–147.
23.
KrasemannTJr. Greedy algorithm for railway traffic re-scheduling during disturbances: a Swedish case. IET Intell Transp Syst2010; 4(4): 375–386.
24.
Quanye H, Xiaoming W and Jianwu D. Cooperative multi-agent systems in traffic control. In: The 27th Chinese control conference. China, 16–18 July 2008, pp.577–580.
25.
BöckerJLindJZirklerB. Using a multi-agent approach to optimise the train coupling and sharing system. Eur J Oper Res2001; 131(2): 242–252.
26.
WangFY. Agent-based control for networked traffic management systems. IEEE Trans Intell Syst2005; 20(5): 92–96.
27.
ZhaoDDaiYZhangZ. Computational intelligence in urban traffic signal control: a survey. IEEE Trans Syst Man Cybern, Part C: Appl Rev2012; 42(4): 485–494.
28.
BoCChengHH. A review of the applications of agent technology in traffic and transportation systems. IEEE Trans Intell Transp Syst2010; 11(2): 485–497.
29.
RussellSJNorvigPCannyJF. Artificial intelligence: a modern approach, Englewood Cliffs, NJ: Prentice Hall, 1995.
30.
WeissG. Multiagent systems: a modern approach to distributed artificial intelligence, Cambridge, MA: MIT Press, 2000.
D'ArianoA. Innovative decision support system for railway traffic control. IEEE Intell Transp Syst Mag2009; 1(4): 8–16.
33.
HorlingBLesserV. A survey of multi-agent organizational paradigms. Knowl Engng Rev2004; 19(4): 281–316.
34.
Wooldridge M. An introduction to multiagent systems. Second ed. Chichester, West Sussex, UK: John Wiley, 2009.
35.
Anders E, Theeg G and Vlasenko SV. Railway signalling & interlocking: international compendium. Eurailpress, Germany, 2009.
36.
PachlJ. Railway operation and control, Second ed. Mountlake Terrace, WA: VTD Rail Publishing, 2009.
37.
IEEE 1474.1: 1999. IEEE standard for communications-based train control (CBTC). Performance and functional requirements.
38.
Fabio B, Giovanni C and Dominic G. Developing multi-agent systems with JADE. Chichester, West Sussex, UK: Wiley, 2007.
39.
Rossetti RJF, Ferreira PAF, Braga RAM and Oliveira EC. Towards an artificial traffic control system. In: The 11th international conference on intelligent transportation systems. Beijing, China, 12–15 October 2008, pp.14–19. Piscataway, NJ: IEEE Press.
40.
Bavafa-ToosiYBlendingerCMehrmannV. Modeling, analysis, synthesis, and simulation of time-optimal train traffic in large networks. IEEE Trans Autom Sci Engng2008; 5(1): 43–52.
41.
DeSalvoJS. A process function for rail linehaul operations. J Transp Econom Policy1969; 3(1): 3–27.
