Conflict Resolution through Negotiation in a Railway Open Access Market: a Multi-agent System Approach

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Conflict Resolution throughNegotiation in a Railway OpenAccess Market: a Multi-agentSystem ApproachChi Wai Tsang a & Tin Kin Ho aa Department of Electrical Engineering , The HongKong Polytechnic University , Hong KongPublished online: 01 Feb 2007.

To cite this article: Chi Wai Tsang & Tin Kin Ho (2006) Conflict Resolutionthrough Negotiation in a Railway Open Access Market: a Multi-agent SystemApproach, Transportation Planning and Technology, 29:3, 157-182, DOI:10.1080/03081060600810899

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ARTICLE

Conflict Resolution throughNegotiation in a Railway OpenAccess Market: a Multi-agent

System Approach

CHI WAI TSANG & TIN KIN HO

Department of Electrical Engineering, The Hong Kong Polytechnic University,Hong Kong

(Received 30 March 2005; Revised 27 January 2006; In final form 7 May 2006)

ABSTRACT Open access reforms to railway regulations allow multiple trainoperators to provide rail services on a common infrastructure. As railwayoperations are now independently managed by different stakeholders, conflicts inoperations may arise, and there have been attempts to derive an effective accesscharge regime so that these conflicts may be resolved. One approach is by directnegotiation between the infrastructure manager and the train service providers.Despite the substantial literature on the topic, few consider the benefits ofemploying computer simulation as an evaluation tool for railway operationalactivities such as access pricing. This article proposes a multi-agent system (MAS)framework for the railway open market and demonstrates its feasibility bymodelling the negotiation between an infrastructure provider and a train serviceoperator. Empirical results show that the model is capable of resolvingoperational conflicts according to market demand.

KEY WORDS: Railway; open market; track access pricing; train scheduling;multi-agent systems; agent negotiation

Correspondence Address : Chi Wai Tsang, Department of Electrical Engineering, The Hong Kong

Polytechnic University, Hung Hom, Kowloon, Hong Kong, PR China. Email: tcwden.ee@

polyu.edu.hk

ISSN 0308-1060 print: ISSN 1029-0354 online # 2006 Taylor & Francis

DOI: 10.1080/03081060600810899

Transportation Planning and Technology, June 2006

Vol. 29, No. 3, pp. 157�182

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Introduction

In recent years, railway regulatory reforms have been implemented inmany countries where the primary objective was to introduce intra-modal competition within railway markets. The successful reformprecedents from utilities such as gas, electricity and telecommunicationhave encouraged the adoption of an open access approach. It involvesdistributing the management of infrastructure facilities and trainoperations to independent stakeholders so that multiple train-serviceproviders can gain access to a common infrastructure by paying anaccess fee. A contestable railway market can therefore be achievedthrough competition for both track capacity and customers between thetrain operators.

Owing to the limited availability of track resources, the infrastructureprovider has to decide which provider (and when the provider) has therights to operate its train services. However, it is inevitable that theseindependently managed stakeholders will experience disputes overprices and service characteristics (e.g. train types and speeds) due totheir differences in operational objectives. To resolve conflicts betweenstakeholders, posted pricing, negotiation and auctioning have beenproposed as methods of associating the access charge either to theincurred usage cost or to the market expectation. Unfortunately, sincemost reforms are in their development infancy, the railway industry isleft to seek better means of access pricing and capacity allocation.

Most studies have aimed to analyze the existing market according tothe observed outcomes from these reforms, but there has been littleresearch devoted to evaluating the performance of the pricing mechan-isms through simulation and modelling. In fact, there has been a lack ofstudy examining the requirements and feasibility of adopting asimulation approach.

Although this article is not intended to compare the merits andlimitations of different charging regimes, it aims to devise a plausibleevaluation tool for the railway open market. This study identifies thekey modelling issues in reformed railways and investigates thefeasibility of modelling the railway open market by a multi-agentsystem (MAS) approach. MAS modelling is an increasingly popularmethod of solving distributed problems involving entities with highdegrees of autonomy, rationality and social capability. This field ofstudy contains models for both decision-making and negotiationactivities between trading parties. As a result, it provides a potentiallyviable approach to modelling the transaction in the open railwaymarket.

This article is organized as follows. The second section reviews accesscharging and traffic management problems that have emerged from the

158 Chi Wai Tsang & Tin Kin Ho

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railway open market. The third section then identifies the difficulties inmodelling the competitive market and proposes a MAS frameworkto develop a simulation tool. The fourth section models negotiationsbetween an infrastructure provider and a train service operator.A simulation study is performed in the fifth section to illustrate thecapability of the model to resolve conflicts dynamically by respondingto the market demand.

Railway Open Markets

Railway Competition

Railway businesses are often referred to as natural monopolies andoffer limited competition because of the lumpiness in infrastructureprovision. When a rail network is constructed or expanded, trackcapacity is often created in large incremental steps (lumps) relative tothe unit consumption by train services. In other words, the newly builtinfrastructure can support a potentially large increase in traffic volume.Since the fixed investment cost is recovered from the actual rise indemand, the average cost of transportation declines when trafficvolume increases over the range of additional capacity. Consequently,it is usually cheaper to provide train services when the entire demand iscaptured by a single operator (Figure 1).

In recent decades, there has been a change in perspective thatconsiders competition as a possibility for train operations (‘above-rail’activities) even though infrastructure provision (‘below-rail’ activities)may prove more elusive. The barrier to competition may therefore belowered by allowing multiple train service operators to gain access tothe infrastructure from a common provider. Countries such asArgentina and Japan have adopted a third-party access approach inwhich external train operators have mandated right of entry to a

Price

Transportation output

Average cost

AB

AP

BP

A = Demand captured by a single operator

B = Reduced demand subject to competition

Figure 1. Average cost curve

Conflict Resolution through Negotiation 159

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vertically integrated railway (Campos & Cantos, 1999; Bureau ofTransport and Regional Economies, 2003). In other words, aside fromthe trains operated by the infrastructure owner, other parties may alsorun trains on the same track. Alternatively, other places such as Swedenand the UK have sought open access reform, where train operations arecompletely separated from infrastructure provision (Campos & Cantos,1999; Bureau of Transport and Regional Economies, 2003). These newmarket structures allow competition between train operators, and someof which have also introduced additional competition in ancillaryservices such as rolling stock leasing and maintenance service provision.

Introducing ‘above-rail’ competition is considered to be an effectivemeans to reduce expenditure and increase revenue (Jensen, 1998). Acompetitive market can provide potentially more choices to consumers,which creates pressure for stakeholders in their spending. Stakeholdersare also under pressure to develop innovative plans and services so as toexploit new markets and maintain profits. Despite these ideal benefitsof competition, the access reform in railways has generated newchallenges in deriving the access charge and resolving conflicts intraffic management.

Access Pricing Policies

Posted pricing, direct negotiation and auctioning are three proposedmechanisms for accessing charge setting. In posted pricing, charge ratesare established in advance and published to access seekers. The tariff isoften composed of a basic charge in terms of the vehicle-kilometre orgross tonne-kilometre transported, and an uplift cost that is leviedaccording to the operating characteristics (e.g. freight/passengerservices and type of rolling stock). In direct negotiation, the infra-structure provider and the train operator take turns to make conces-sions on issues including access charges, train schedules and operatingcharacteristics until both stakeholders agree on the terms of usage. Forauctioning, capacity is pre-packaged into various sets of non-conflictingtrain paths to allow interested seekers to bid at their most preferredprices. The operator with the highest bid will obtain the train pathsunder a set of restrictions.

Posted pricing appears to provide train operators with more certaintyin managing their businesses, but the infrastructure provider may fail todiscriminate between train operators with different operating require-ments effectively. For example, trains travelling at different speeds maybe charged identically even though they have different traction energyand peak demand requirements. Conversely, services with identicalspeed specifications but scheduled for different traffic environmentsmight also be charged at the same price despite having different


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capacity consumption characteristics (see below). Furthermore, directnegotiation and auctioning have a better capability to distinguishbetween operators with respect to their willingness-to-pay for therights-of-way. Nevertheless, experiences have suggested that negotiatedpricing can sometimes require time consuming and costly transactions,while auctioning has never been employed in practice because of thedifficulty in devising train paths that simultaneously suit the require-ments of several train operators (Bureau of Transport and RegionalEconomics, 2003). These existing regimes have their merits andlimitations, and the railway regulators and stakeholders are still strivingfor better alternatives whenever possible.

Conflict Resolution in Resource Allocation

Along with determining a suitable pricing regime, the infrastructureprovider also needs to formulate a conflict-free and preferably efficientresource allocation plan for the access seekers. Since the train operatorsare independently managed, they will occasionally request overlappingtrain paths. The infrastructure provider then has the responsibility toresolve their disputes to the rights-of-way.

Efficient allocation is complicated by heterogeneous traffic condi-tions (i.e. when trains are operating with a wide range of speeds).Figure 2 illustrates the effect on capacity utilization when trafficdemand is homogeneous or otherwise. Capacity utilization is defined asthe ratio of the time taken in operating a set of trains with their

Distance

Time

Distance

TimeB

W

(B)

Distance

TimeA

W

(A)

Distance

Time

headwayMinium

Figure 2. Capacity utilization of (A) homogeneous traffic (B) heterogeneous traffic


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minimum headways (i.e. A and B) to the time taken in travellingat their actual timetables (i.e. W) (Gibson et al ., 2002). Clearly,when trains running at different speeds are scheduled together, morecapacity is needed to generate the same number of services (B /W �/

A /W).In principle, the cost of additional capacity consumption may be

recovered from the access charge. However, the predefined tariffs inposted pricing are unlikely to respond to the ongoing changes inrelative train speeds in the competitive market. On the contrary, directnegotiation is able to provide a means to compute dynamically theassociated costs of capacity utilization and traction power supply.Therefore, the access charge can be more appropriately recovered bynegotiation if a high transaction speed is available. In addition,negotiation allows the operational train speeds to be determined bythe requirements of the access seekers. If the service providers arewilling to afford a higher tariff, heterogeneous traffic may be allowed,otherwise the infrastructure provider may offer a cheaper access chargefor capacity saving services.

Track and rolling stock maintenance costs are further potentialconflicts requiring resolution. There is an indivisible relationshipbetween rails and wheels. Poor rail quality will induce an increasedrolling stock maintenance cost and vice versa. In a ‘closed’ railwaymarket, the maintenance cost of rails can be balanced with theinvestments on rolling stock quality. However, with the separation ofresponsibilities, service providers may seek to keep maintenance onvehicles to a minimum so as to reduce their operating costs. In order torecover the imposed maintenance fee, the infrastructure providerhas to decide whether to raise the access price to reflect the actualdamage to track or to restrict the use of track to better maintainedrolling stock.

Methodology

Modelling and Computer Simulation

Effective access pricing regimes are required to resolve disputesbetween railway stakeholders. Various post-evaluation studies havebeen conducted with respect to regulatory efficiency (Campos &Cantos, 1999), train planning processes (Watson, 2001) and account-ing performances of stakeholders (Crompton & Jupe, 2003). Findingsderived from these studies can be applied in future improvements to thesystem, which is followed by a new cycle of post-evaluation studies.Unfortunately, their time-consuming and costly execution often hindersthe actual implementation of these new findings.


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With the advance of fast computing technologies, computer simula-tion is a cost-effective means to evaluate a hypothetical change in asystem. For example, simulation suites have been developed to study avariety of traffic control strategies according to sophisticated models oftrain dynamics, traction systems and power systems (Goodman et al. ,1999). Simulation therefore allows pre-evaluation studies and avoidsirreversible changes to the physical system. Consequently, it isbeneficial if the open market can be modelled in a similar manner toassist future improvements in railway operation.

A simulation model is a computer-executed representation of thebehaviour of a system. Most simulation models are devised andimplemented as a single (or central) computation unit, which derivesthe expected system outputs by processing the user-specified inputswith an algorithm. However, the idea of central evaluation isinappropriate for the railway open market due to the following threecharacteristics.

Distributed self-interested entities. As a result of railway reforms, resourceplanning is now a distributed rather than a centralized problem anddifferent stakeholders will inevitably attempt to optimize their internalbenefits. In fact, each of these optimizations is likely to involve multipleattributes such as cost and travelling times, and subject to constraintsderived from business (e.g. availability of rolling stock supply),engineering (e.g. maximum line speeds) and regulations (e.g. regulatedceiling and floor prices). Some of these constraints and their businessobjectives are not revealed to other stakeholders to avoid possible lossof advantage during the business transactions.

A suitable modelling framework should therefore enable therepresentation of the stakeholders as separated entities with individualcontrol over their information, decisions and actions. The frameworkshould also allow separate local simulation models to solve thedistributed multi-dimensional constrained optimization problems.

Co-ordination via bargaining. Despite enjoying isolated control over theiractivities, stakeholders are still interdependent during the formula-tion of train timetables. In the case of negotiation, the stakeholderswill attempt to persuade their negotiating partner to align withtheir operational objectives through bargaining. To resemble thenatural process of resolving conflicts, the modelling of the co-ordination activities is of utmost importance. As a result, apart fromthe distributed framework, there are additional requirements onmodelling the interactions between the local entities, but classicalsimulation models are generally not designed to capture thesebehaviours.


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Rationality of local entities. In principle, solutions to the local optimiza-tion problem may take advantage of classical simulation models.Rational decisions may be the outputs of the local model while theoperational objectives of the stakeholder can be considered as the user-specified inputs. However, the choice of algorithm is complicated bythe additional inputs from the responses of its interacting entities,which can only be determined during runtime. These algorithmstherefore require a certain degree of flexibility so that the distributedentities may decide the best actions dynamically without humaninterference.

Apart from being responsive to the changing environment, anotherdimension of rationality is proactiveness. The local entities should beable to inspect their internal status and initiate activities (e.g.promotion of idle resources) that are consistent with their businessgoals in order to enhance the competitiveness of the stakeholders.

Multi-agent System

MAS modelling is a sub-field of distributed artificial intelligence (DAI).While classical AI focuses on mimicking the problem-solving ability ofindividual organisms in machines, DAI concentrates on more complexproblems which are inherently distributed, where knowledge andactivities are separated in space (Bond & Gasser, 1988). Each entityin the group has problem-solving capabilities but the entire problemcannot be solved by a single entity. Through coordinated interaction,the entities resolve the complex problem in a timely and efficientmanner (Durfee et al. , 1989). In particular, MAS allows both co-operating and competing interactions. Co-operative entities worktowards a common goal while competing entities possess individualones.

The entities in MAS are called agents. By definition, an agent is anencapsulated computer system, situated in some environment, and canact flexibly and autonomously in that environment to meet its designobjectives (Jennings & Bussman, 2003). Thus, an agent is a piece ofsoftware-driven hardware that can only work in some predeterminedapplication domains (i.e. they are not ‘super-agents’), but provided thatit is operating in these domains, it can handle its designated tasksrationally, and adapt to changes in a flexible manner without humaninterventions.

There are increasing applications of MAS in engineering systems suchas e-commerce (Au et al ., 2003; Liu & You, 2003), data-mining(Camacho et al ., 2003; Nagano et al ., 2003) and supply-chainmanagement (Garcıa-Flores et al ., 2000; Jennings et al ., 2000). Inaddition, there is a growing interest in applying MAS in transportation


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systems (Teodorovic, 2003). In these applications, it is difficult to studyand explain the complex system without modelling the intentions andactivities of the local individuals (e.g. people, vehicles and companies)because the ultimate system behaviour emerges from the interactionsamong these entities. The notion of a system composed of autonomousagents allows these complex systems to be studied from a ‘bottom-up’approach.

With the emergence of sophisticated agent development softwaresuch as JADE (Bellifemine et al ., 1999) and FIPA-OS (Emorphia,2005), the development time of software agents can be considerablyreduced. These software toolkits mainly provide generic agent servicesregarding communication, resource control, data access and encodings.Nevertheless, developers are still required to devise the local agentstructure and their communication scheme. These may be adopted fromagent modelling techniques (e.g. BDI model [Rao & Georgeff, 1995]),but it is also possible to apply analytical or AI models for agentreasoning.

Another important concern when designing a MAS is the size ofthe agent community. For example, a system could have only onelayer of agents to represent the companies in a supply chain, but it isequally feasible to expand two more layers for the departments withinthe companies and the staff working in those departments. Thethree-layered approach clearly requires a higher demand in modellingthe autonomy and interactions of the agents, but more detailedstudies can be performed at both department and personnel levels.Thus, the granularity of the agent society depends on the depth of studyrequired.

Modelling the Railway Open Market with MAS

Given its characteristics match the requirements in distributed self-interested entities, co-ordinated behaviours and local intelligence, MASprovides a suitable conceptual framework for the railway open market.A realization of MAS for railway resource management is illustrated inFigure 3.

This model contains one level of agents to represent the stakeholdersin the railway market. In addition to the infrastructure provider (IP)and train service providers (TSP), the model may also include ancillarycompanies, like rolling stock providers (RSP) and maintenance serviceproviders (MSP). These stakeholders assign their confidential informa-tion such as cost curves and operational tactics to their correspondingsoftware agents before they are connected to a common communica-tion platform. When an agent joins the platform, it is registered tothe directory facilitator (DF) agent whose function is to maintain


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Platformion Communicat

rsstakeholde

Railway

agents

Software

IP

1Agent 2Agent 3Agent

ARSP AMSP

mAgent nAgent

nNegotiatio

ATSP BTSP

DF

sconstraintloperationaofsAssignment

Figure 3. Multi-agent system framework for railway open market

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an updated record of the agent addresses and the services they provide.A stakeholder agent may therefore recognize the existence of otheragents by performing a query to the DF.

An agent on the platform will either be perceived as a resourceprovider or a purchaser without declaring its internal status to otheragents. The agents on the platform are not expected to share a commongoal, but they may form a temporal association to examine whether asale of resource is feasible and beneficial according to the pre-assignedcriteria of the stakeholders.

The framework may be used to conduct useful simulation studiesat several levels. First, the agent community can allow the study of theeffects from different degrees of competition by altering the numberof resource providers and/or purchasers. This may aid the railwayregulator to determine the appropriate level of competition. Second,different transaction policies (e.g. posted pricing, negotiation andauctioning) can be formulated and tested to improve the chargingregime. Further studies may also be performed to evaluate the impactsfrom any proposed changes in regulations, business objectivesand engineering operations by modifying the rational behaviour ofthe agents. For instance, constraints as a result of regulatory changescan be added locally to the relevant agents, and modification tobusiness objectives and scheduling mechanism may be achieved byadjusting the internal cost functions and implementing a propermathematical model respectively. Results from these simulations maybe used to improve capacity utilization and competitiveness of thestakeholders.

It is also valuable to note that agent modelling can also be appliedin third-party access markets. In this case, one of the TSP agents willalso be the same stakeholder as the IP agent. However, as mostregulations will prevent unfair gain of track access by the incumbentowner of the infrastructure, the above- and below-rail activities willstill be managed separately by different departments within thecompany. As a result, the two departments can still be modelled asseparate agents.

Development of MAS

Conducting the study outlined above requires extensive and detailedmodelling of the individual agents and their interactions. While it isbeyond the scope of this article to construct and analyze the MASdepicted in Figure 3, a model for the direct negotiation between IP andTSP is provided in this section to demonstrate the development ofrailway agents in the framework described.


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Communication Model

The negotiation scheme between a TSP agent and an IP agent isadopted from a fuzzy constraint-based model (Luo et al ., 2003). Thepossible agent actions and their relationships are illustrated in Figure 4.In this model, the bargaining process for a product is resolved as aprioritized fuzzy constraint satisfaction (PFCS) problem between abuyer and a seller. The buyer agent needs to obtain a productpossessing several attributes (e.g. price, dimensions, etc.), but theirexact values will depend on the availability of supply. The agenttherefore proposes a set of criteria based on these attributes, which ismodelled by a set of prioritized fuzzy constraints. During the negotia-tion, the buyer agent has to specify its requirements on these attributesby submitting a selected set of crisp constraints (inequalities) to theseller agent via a FIND message. The role of the seller agent is togenerate a feasible offer according to the set of constraints and its ownbenefits. When an offer is found, a CHECK reply is issued. Otherwise,a RELAX message is sent and the buyer is prompted to modify one ofthe submitted constraints, which corresponds to making a concession.However, even when an offer is generated, the buyer agent has therights to reject the product if the offer does not satisfy its requirementsor if it cannot comply with the restrictions attached with the product.In the former case, a FIND message enveloping a constraint on a newattribute will be forwarded to the seller agent, while in the latter case, aREFIND message will be issued and the seller will be asked to generatea new offer. The negotiation is terminated by either an acceptance of aproduct (DEAL) or a failure in generating a feasible offer over the entirenegotiation space (FAIL).

This model is suitable for the IP�TSP transaction because it providesthe TSP (buyer) with a means to deal with uncertainties associated with

FIND

FIND

RELAX

DEAL FIND

CHECK

FAILREFIND

(TSP)agent Buyer

(TSP)agent Buyer

(IP)agent Seller

Figure 4. Negotiation flow with PFCS


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the IP (seller). For example, track access charges are often related to themarket supply. If track capacity is limited, the TSP may either raise thepayment or select another schedule where capacity is available.However, this information is unavailable until the negotiation hasproceeded. By allowing a range of preferences on the attributes, itcreates the possibility of trade-offs between certain attributes, whichincreases the chance of striking an agreement.

Definition of Product (Track Access Rights)

A track access rights consists of a feasible train schedule, operablerolling stock and a flex level used in timetable reallocation (a parameterdenoting the flexibility that the IP can revise the TSP’s schedule whentrack capacity approaches to limitation). In order to obtain rights ofaccess, a TSP needs to pay a track access charge (TAC) to the IP. Theproduct under negotiation can therefore be defined by Eq. (1):

P� hc;C;v;fi (1)

where c is the access charge ($); C� hS; z;TD;TRi is thetrain schedule consisting of the set of visiting stations S�/{si j i�/

1, . . . ,ns ,}, the arrival time at the first station z (hh:mm), the set ofdwell times (min) TD �/{tDi j i�/1, . . . ,ns} and a set of inter-stationruntimes (min) TR �/{tRi j i�/1, . . . ,ns �/1}; v �V�fvi½i�1; :::; nrg isthe selected rolling stock; and f �F�ffi½i�1; :::; nfg is the selectedflex.

TSP Model

The prioritized fuzzy constraint model (Luo et al ., 2003) for the buyeragent is adopted and extended in a previous study (Tsang & Ho, 2004)in order to model the specific objective for a railway TSP. In that study,the satisfaction on the track access charge c and train schedule C arerepresented by a set of fuzzy membership functions mi (X), i�/1, . . . ,mand X �/{c ,C}, from which the crisp constraints are derived. A crispconstraint xa 5/x5/xb on an attribute x is denoted by the bounds[xa jxb]. At the beginning of the negotiation, these constraints are set atthe most preferred values {c ,C}. A TSP agent’s decision to make aconcession corresponds to reducing the lower limit xa or increasing theupper limit xb of a particular attribute. Also, since the TSP mayperceive some attributes to be more important than others, a priorityvalue ri �/ [0,1], i�/1, . . . ,m is associated with each attribute.

Given an offer P ?�/ �c ?,C?,v ?,f ?� received from the IP agent, theacceptability of the product is modelled by Eq. (2):


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a(X’)� min15i5m

1�ri

max15j5m

(rj)[mi(X’)�1]

8<:

9=;;X’ � fc’;C’g (2)

However, even if the access charge and schedule times are accep-table, the TSP may not be satisfied with the restrictions associated withrolling stock type and flex attached to the offer. Consequently, the TSPagent also maintains two sets of fuzzy values Fv�/{fvi j i�/1, . . . ,nr},fvi �/ [0,1] and Ff�/{ffi j i�/1, . . . ,nf}, ffi �/ [0,1] to indicate its degreeof obedience to the rolling stock and flex proposed. The overallobedience level with respect to P ?�/�c ?,C?,v ?,f ?� is defined by Eq. (3):

b(v’;f’)�minffv’; ff’g (3)

An offer is acceptable if it satisfies Eq. (4), where t �/[0,1] is theacceptance threshold specified by the user:

minfa(X’);b(v’;f’)g]t (4)

When t�/0, there is a better opportunity for a successful negotiationbecause the TSP agent may concede over the entire range specified bythe fuzzy membership functions. Conversely, when t�/1, the TSP agentwill only accept the most preferred schedule defined by the user.

IP Model

The task performed by the IP agent is modelled as a combinatorialoptimization problem (Papadimitriou & Steiglitz, 1998). Given thediscrete nature of the attributes in the definition of track access rightand the objective of maximizing the benefit of the infrastructureprovider, a combinatorial optimization problem is set up. The objectivefunction is to maximize the TAC received from the TSP and reduce thedemand in capacity utilization Dh as shown in Eq. (5):

max U�c�whDh (5)

where wh is the unit valuation of capacity consumption ($).The access charge is derived internally by the IP agent and is a

composite charge consisting of track usage charge (TUC), tractionenergy charge (TEC), peak demand charge (PDC) and congestioncharge (CGC) as defined by Eqs (6), (7), (8) and (9), respectively.

TUC�cv1 nvv

Xns�1

i�1

Li (6)

where cv1 is the charge rate ($/veh-km) for rolling stock v ; nvv is the

number of vehicles in the rolling stock; and Li is the length of track(km) in inter-station run i.


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TEC�c2E(v;C) (7)

where c2 is the charge rate ($/kWh) for electricity consumed; E(v ,C) isthe unit of energy consumed (kWh) when rolling stock v is running atschedule C;

PDC�c3DP(v;C) (8)

where c3 is the charge rate ($/MW) for the increase in peak demand,DP(v ,C) is the increase in peak demand (MW) when rolling stock v isrunning at schedule C;

CGC�c4df

Xns�1

i�1

Aiexp(hi) (9)

where c4 is the charge rate ($/min) for the expected delay caused in thenetwork, df is the discount factor associated with flex f ; and Ai is atrack specific constant.

The optimization problem defined in Eq. (5) is subject to theavailability of capacity. If the requested train schedule is occupied byanother train service, the schedule is considered as infeasible. Since theaim of the study is to demonstrate the capability of the modellingframework rather than to devise an efficient search algorithm,exhaustive search has been used to determine the optimal solution.

Simulation Set-up and Results

The railway agents in this simulation are implemented in JADE version3.2 (Bellifemine et al ., 1999). Three IP agents are constructed accordingto Table 1. It contains the settings on the capacity valuation weightingand the relevant charge rates. Three types of rolling stock and flex levelare available for all negotiations, and the respective train lengths anddiscount factors are also included. The agents are assumed to havecommitted three train schedules (Figure 5) prior to the negotiations.

Table 2 further defines seven TSP models, which display the mostpreferred train schedules and access charges, together with a set ofassigned priorities over the attributes. The fuzzy values on the threeavailable types of rolling stock and flex are set to indicate the agents’obedience level to the respective restrictions. In addition, all agentshave an identical acceptability threshold of 0.1 to avoid the possiblefailure in obtaining a track access rights.

These agents are then paired according to Table 3 to simulate eightbilateral IP�TSP transactions. The train path of interest consists ofthree inter-station runs serving a set of four stations, A to D (Table 4).The distance between stations B and C is relatively long to simulate therailway transportation between two distant cities.


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Study 1: Conflicts in Rights-of-Way

In this study, the two TSP agents require the same set of preferredschedule times and access charge. TSP-1 denotes a service provider witha passenger-centric operational objective having high commitment topunctual station dwell times and inter-station runtimes, whereas TSP-2aims at reducing costs with high priority on the access charge andservice start time (to reduce idle time cost of rolling stock). Both agentsnegotiate with the same infrastructure provider agent IP-1, but theirpreferred schedules are in conflict with a second train service, shown inFigure 5(a). The study therefore tests the ability of the agents to resolveconflicts over rights-of-way.

The results of the negotiation are depicted in Table 5 and theresultant train schedules illustrated in Figure 6. TSP-2 is able to acquirea schedule with no idle time (in operation at 07:50) and at a lowertariff, fulfilling its objective in cost reduction. In contrast, given theunavailability of track capacity, TSP-1 is unable to obtain the preferreddwell times and runtimes, even though it is willing to pay a higher fee.Nevertheless, the train service of TSP-1 has avoided causing passengerdissatisfaction on short alighting times at the first two stations byextending the station dwell times. The increase in journey time isneutralized by the delay in service start time (at 07:52). Thesedifferences in commencing time and dwell times result in overtakingthe conflicting train at stations B and C for TSP-2 and TSP-1,respectively.

The variations in the resulting schedules are related to the priorityassignment. TSP-1, the passenger-centric agent, relaxes its constraints

Table 1. IP models

Attribute IP-1 IP-2 IP-3

wh ($) 8000 10 000 10 000/cv11 ($/veh-km) 0.04 0.04 0.04

/cv21 ($/veh-km) 0.06 0.06 0.08

/cv31 ($/veh-km) 0.16 0.16 0.12

c2($/kWh) 0.05 0.05 0.05c3($/MW) 10.0 10.0 10.0c4($/min) 250 250 250

/nv1v 10 10 10

/nv2v 8 8 10

/nv3v 9 9 10

/df11.00 1.00 1.00

/df20.95 0.95 0.95

/df30.90 0.90 0.90

Traffic model TFF-1 TFF-2 TFF-2


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Figure 5. Committed train schedule prior to negotiation: (A) TFF-1; (B) TFF-2

Conflict

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Table 2. TSP models

Attribute TSP-1 TSP-2 TSP-3 TSP-4 TSP-5 TSP-6 TSP-7

/z(hh:mm) 07:50 07:50 07:50 07:50 07:50 07:50 07:50/TD(min) {5, 5, 3, 3} {5, 5, 3, 3} {5, 5, 3, 3} {5, 5, 3, 3} {5, 5, 3, 3} {5, 5, 3, 3} {5, 5, 3, 3}/TR(min) {8, 70, 7} {8, 70, 7} {6, 65, 5} {6, 65, 5} {8, 70, 7} {8, 70, 7} {8, 70, 7}c ($) 1600 1600 1600 1600 1600 1600 1600rd 0.5 0.9 0.9 0.5 0.5 0.5 0.5

/rTD1.0 0.1 0.1 1.0 1.0 1.0 1.0

/rT R0.9 0.5 0.5 0.9 0.9 0.9 0.9

rc 0.2 1.0 1.0 0.2 0.2 0.2 0.2/fv1

1.0 0.0 0.0 0.0 0.4 0.0 0.0/fv2

0.6 0.6 0.6 0.6 0.6 0.6 0.0/fv3

0.0 1.0 1.0 1.0 1.0 1.0 1.0/ff1

1.0 1.0 1.0 1.0 1.0 1.0 1.0/ff2

0.8 0.9 0.9 0.9 0.9 0.9 0.9/ff3

0.0 0.8 0.8 0.8 0.8 0.8 0.8t 0.1 0.1 0.1 0.1 0.1 0.1 0.1

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in the order of cost, start time, runtimes, and dwell times as specified bythe assigned priorities. Conversely, TSP-2 relaxes its constraints inthe reverse order. When the TSP agents encounter a RELAX message,TSP-2 will first broaden the feasible range on dwell times, but TSP-1will maintain the preferred times and compromise with a higher accesscharge. Eventually, the differences in relaxation have led to broaderacceptable ranges in dwell time at stations A and B for TSP-2 (4�7 min)and stricter times for TSP-1 (5�6 min). By employing the shorter dwelltimes of 4 min, the train service of TSP-2 is able to overtake theconflicting service at station B.

The schedule secured by TSP-2 also reduces the TAC mainly throughthe avoidance of the train operation close to 08:30 when the peakdemand is highest. This is reflected in the lower PDC of TSP-2 shown inTable 5. In addition, although the train service of TSP-2 consumesmore capacity, the congestion charge (CGC) is cheaper when a higherflex level is accepted.

The agents are therefore able to resolve conflicts according to theiroperating objectives. It is also important to point out that during thenegotiation, the TSP agents are aware of neither the rights-of-wayconflicts, nor the existence of the peak demand. Also, the TSP agentshave no cooperative intention to compromise with the IP on suchissues. However, by offering different schedules at different prices, theTSP agents respond indirectly to the availability of the market supply.

Table 3. Simulation cases

Study Cases IP-Model TSP-Model

1 1 IP-1 TSP-12 IP-1 TSP-2

2 3 IP-2 TSP-24 IP-2 TSP-35 IP-2 TSP-4

3 6 IP-3 TSP-57 IP-3 TSP-68 IP-3 TSP-7

Table 4. Track and station data

Track Origin Destination Length(km)

Track specificconstant

1 Station A Station B 20 1.22 Station B Station C 200 1.03 Station C Station D 15 1.1


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The IP agent also derives suitable schedules according to the will-ingness-to-pay of the TSP agents.

Study 2: Conflicts in Capacity Incentives

This study attempts to demonstrate the ability of the IP agent torespond to demands with different capacity incentives. ThreeTSP agents are set up to negotiate with IP-2, but unlike the previousstudy, the required train times are not in conflict with the scheduledservices. While the requested schedule of TSP-2 is set to be homo-geneous with the existing traffic, TSP-3 and TSP-4 prefer shorter inter-station runtimes (i.e. heterogeneous demand). In other words, there is aconflict in capacity incentives between the IP agent and the twoTSP agents. Moreover, priorities of TSP-3 and TSP-4 are cost reducingand passenger-centric, respectively, so that they differ in their opera-tional objectives. The capacity weighting of the IP agent is increasedfrom 8 000 to 10 000 units to enhance the effect due to capacityconsumption.

Table 6 shows the results of the three negotiations. All threeschedules are different with respect to travel times, but they sharecommon restrictions on rolling stock and flex. The inter-stationruntimes for TSP-2, TSP-3 and TSP-4 are {8, 70, 7}, {6, 68, 6} and{6, 67, 6}, respectively. When compared to their most preferredruntimes of {8, 70, 7}, {6, 65, 5} and {6, 65, 5}, only TSP-2 is able toobtain all of its most preferred values. TSP-4 needs to extend its service

Table 5. Simulation results for study 1

Category Attribute Case 1 Case 2

Track access z(hh:mm) 07:52 07:50right TD (min) {6, 6, 3, 3} {4, 4, 3, 3}

TR (min) {9, 72, 8} {8, 72, 8}c ($) 1783 1698v v2 v2

f f2 f3

Constraint set z(hh:mm) (07:50j07:52) (07:50j07:51)in final round TD (min) (5j6, 5j6, 3j3, 3j3) (4j7, 4j7, 3j4, 3j4)

TR (min) (8j9, 69j72, 7j8) (8j9, 69j72, 7j8)c ($) (0j1800) (0j1700)

IP utility U ($) 1651 1524TUC ($) 113 113TEC ($) 567 567PDC ($) 130 90CGC ($) 974 926Dh 0.0166 0.0215


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runtimes slightly, but TSP-3 requires considerable increase in journeytime. In spite of the preferred shorter inter-station runtimes for TSP-3and TSP-4, capacity utilization in the two cases is slightly higher thanthat of TSP-2.

Clearly, the minimum capacity consumption by TSP-2 results fromits inter-station runtimes being compatible with those of the existingservices. As mentioned, these trains may be scheduled to operate at theminimum headway so that more services may be scheduled on the track(Figure 2(a)). TSP-3 and TSP-4 do not lead to optimal allocation interm of capacity since their service runtimes deviate from the existingtraffic. This is consistent with the principles in scheduling hetero-geneous traffic.

Between the two agents requesting heterogeneous train services, IP-2also has the ability to propose different train schedules according totheir willingness-to-pay. At the end of the two negotiations, the upperaccess price limit of TSP-4 is higher than that of TSP-3 by $50. Beingable to collect a higher TAC, TSP-4 is granted with a schedule thatconsumes more capacity and leads to more favourable train operatingtimes from the TSP agent perspective. In other words, the IP agent candecide to allocate capacity ‘on-the-fly’. As long as the TSP is willing topay for its consumption on capacity usage, the request will be granted.Otherwise, the TSP will be required to compromise with the morehomogenous traffic condition. Rationality in capacity utilization istherefore captured by the IP agent.


Category Attribute Case 3 Case 4 Case 5

Track access z(hh:mm) 07:50 07:50 07:50right TD (min) {5, 5, 3, 3} {5, 7, 3, 3} {5, 5, 3, 3}

TR (min) {8, 70, 7} {6, 68, 6} {6, 67, 6}c ($) 1634 1649 1664v v2 v2 v2

f f3 f3 f3

Constraint set z(hh:mm) (07:50j07:51) (07:50j07:51) (07:50j07:51)in final round TD (min) (5j6, 5j6, 3j4, 3j4) (5j7, 5j7, 3j4, 3j4) (5j5, 5j5, 3j3, 3j3)

TR (min) (8j8, 70j71, 7j7) (6j7, 65j68, 5j6) (6j7, 65j67, 5j6)c ($) (0j1650) (0j1650) (0j1700)

IP utility U ($) 1541 1478 1487TUC ($) 113 113 113TEC ($) 576 580 588PDC ($) 80 80 90CGC ($) 865 872 873Dh 0.0093 0.0170 0.0186


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Figure 6. Committed train schedule after negotiation: (A) case 1; (B) case 2

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Study 3: Conflicts in Maintenance Incentives

Similar to the second study, this study involves three IP�TSP transac-tions. However, instead of having different preferred schedule timesand operation objectives, the TSP agents have different choices onrolling stock. TSP-5 is willing to accept any of the three availablerolling stock, whereas TSP-6 accepts either v2 or v3, and TSP-7 is themost restrictive as it accepts v3 only. These rolling stock haveincreasing track usage charge rates of $0.04/veh-km, $0.08/veh-kmand $0.12/veh-km, respectively.

The track access charges of the three schedules obtained arenegotiated as $1657, $1748 and $1845, respectively. InspectingTable 7 reveals that the cause of the difference is contributed by theTUC. Having the lowest TUC of $94, TSP-5 is able to obtain its targetschedule at the lowest charge. In contrast, the TUCs for TSP-6 andTSP-7 are two and three times higher than that of TSP-5. Such increasesin the TUCs result from the different charge rates. The IP agent istherefore able to associate the rolling stock condition with the trackusage rates. The better condition of v1 reduces the rail maintenancecost of the IP. As a result, the IP is willing to offer TSP-5 the schedule ata lower tariff. As TSP-6 and TSP-7 become more restrictive in theirchoices of rolling stock, they are required to pay a higher chargeaccordingly.

This study demonstrates the tug-of-war between a TSP and an IP onmaintenance cost. When a TSP agent is determined to use rolling stock


Category Attribute Case 6 Case 7 Case 8

Track access z(hh:mm) 07:50 07:50 07:50right TD (min) {5, 5, 3, 3} {5, 5, 3, 3} {5, 5, 3, 3}

TR (min) {8, 70, 7} {8, 71, 7} {8, 70, 7}c ($) 1657 1748 1845v v2 v2 v2

f f3 f3 f3

Constraint set z(hh:mm) (07:50j07:50) (07:50j07:51) (07:50j07:51)in final round TD (min) (5j5, 5j5, 3j3, 3j3) (5j5, 5j5, 3j3, 3j3) (5j5, 5j5, 3j3, 3j3)

TR (min) (8j8, 70j70, 7j7) (8j8, 70j71, 7j7) (8j8, 70j71, 7j7)c ($) (0j1700) (0j1750) (0j1850)

IP utility U ($) 1564 1639 1752TUC ($) 94 188 282TEC ($) 608 603 608PDC ($) 90 90 90CGC ($) 865 866 865Dh 0.0093 0.0108 0.0093


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of poorer quality, the IP agent avoids cross-subsidizing the trackmaintenance cost by raising the TAC. The higher track usage rate maytherefore use to control the track maintenance required from the IP.

Conclusion

We have presented a MAS framework to model an open railwaymarket. Based on this framework, an agent negotiation between an IPand TSP has been modelled. The simulation results have shown thatthese agents are able to settle the track access charge, schedule timesand restrictions autonomously according to their assigned operationalobjectives. By responding dynamically to the willingness-to-pay of theTSP agents, the IP agent is also capable of resolving conflicts in rights-of-way and deriving solutions for differing incentives in capacityutilization and maintenance cost.

Agent modelling can therefore be a plausible means to capture thefunctional and behavioural requirements of the stakeholders. However,it is not the intention of this article to suggest replacing the stakeholderswith software agents in real-life transactions because the actualinteractions between humans are certainly more complicated than themodel given in this study. Nevertheless, the continuation of develop-ment of the proposed framework is expected to provide a more sensibletool to conduct critical analysis prior to the physical implementation ofa regulatory or operational change.

The work presented here is a catalyst for further research onenhancing the capabilities of the agents. Agents modelled in this studyare not imbued with learning capabilities to adjust the satisfactionvalues and charge rates during negotiations. For example, the TSPs maynot intend to adopt a fixed operational strategy (e.g. passenger-centric/cost reducing) in their agents, but allow them to determine theirbehaviour according to the availability of track capacity supply. Byanalyzing the replies from the IP agent, the TSP agents may attempt todeduce whether the required track capacity has been occupied. In thiscase, a passenger-centric TSP agent will have no reason to insist on itsrequirements and may opt for reducing costs. Conversely, the IP agentcan learn from the TSP agent’s response and promote the idle resourcesproactively by lowering the relevant charge rates.

In addition, there is a substantial need for devising a faster algorithmin generating a train schedule by the IP agent. The time needed forsimulations using the exhaustive search algorithm becomes impracticalwhen the solution space is expanded by more stations, rolling stocktypes and flex levels. This is especially important because negotiation isan iterative process in which the algorithm will be used frequently.Since the optimization problem is combinatorial, branch-and-bound


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algorithm and dynamic programming are the potential deterministicmethods for further evaluations.

More complicated negotiations can also be modelled and studied.For instance, track capacity may be better utilized if the IP agent caninterleave between negotiations with different TSP agents. With moreinformation over the spectrum of demand, the IP agent can determine abetter sequence to conduct the negotiations. It is valuable to comparethe performance in complexity and efficiency of this mechanism againstauctioning of train paths where the IP agent is also handling multiple-transactions with different TSP agents simultaneously. In addition, theMAS framework can also include RSP and MSP agents to enrich thesimulations of the entire process and study the effect of different levelsof competition in the market.

Acknowledgements

This work is supported by the Department of Electrical Engineering of the Hong KongPolytechnic University.

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Documents

Conflict Resolution through Negotiation in a Railway Open Access Market: a Multi-agent System Approach