06_a Knowledge Based Problem Solving Method in Gis Application

8/6/2019 06_a Knowledge Based Problem Solving Method in Gis Application

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Knowledge-Based Systems 24 (2011) 542-553

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Knowledge-Based SystemsELSEVIER journal homepage: www.elsevier.com/locate/knosys

A knowledge-based problem solving method in GISapplicationHui Wei ". Qing-xin Xu, Xue-song TangCognitiveModel and Algorithm Laboratory. Department of Computer Science.Fudan University. Shanghai 200433. PRChina

ARTICLE INFO ABSTRACTModel design for theme analysis is one of the biggest challenges in GIS.Many real applications in GISrequire functioning not only in data management and visualization, but also in analysis and decision-making. Confronted with an application of planning a new metro line in a city, a typical GISis unableto accomplish the task in the absence ofhuman experts or artificial intelligence technologies. Apart frombeing models for analyzing in different themes, some applications are also instances ofproblem solving inAI.Therefore, in order to strengthen its ability in automatic analysis, many theories and technologiesfrom AI can be embedded in the GIS.In this paper, a state space is defined to formalize the metro lineplanning problem. Byutilizing the defined state evaluation function, knowledge-based rules and strate-gies, a heuristic searching method is developed to optimize the solutions iteratively. Experiments areimplemented to illuminate the validity of this AI-enhanced automatic analysis model of GIS.

2011 Elsevier B.V.All rights reserved.

Article history:Received 11 May 2009Received in revised form 10 January 2011Accepted 18 January 2011Available online 25 January 2011Keywords:Problem solvingHeuristic searchingKnowledge-based systemMetro planningGIS

1. IntroductionAn important utilization of geographical information systems

(GIS) is to solve distribution and optimization problems effectively.Examples include: How to allocate planting areas of different cropson limited cultivated land, so as to make good use of the cultivatedland and get the desired economic benefit? How should a retailcompany decide the locations and scales of the stores in a cityfor maximum profit? How should a tourist effectively plan a travelschedule in terms of destination, weather condition and budget?How should the transportation department build a metro systemin a city and achieve the optimal design? These are all concreteapplications and typical problem solving in artificial intelligence(AI) that have two characteristics in common: all contain a greatdeal of geographical or spatial information, and the use of knowl-edge in some professional domains.

Enhanced GIS, which refers to a combination of GIS and expertsystem, decision support system or other methods in AI, is usuallycalled Intelligent GIS. This type of hybrid Information System isaimed at dealing with complicated applications of GIS and iswidely used in many areas, such as agriculture, forestry, ecosys-tem, traffic, transportation, environmental protection and publichealth. However, as the geographically relevant data is one of theessential parts in GIS, sophisticated reasoning mechanisms are re-quired for processing dynamic information and the knowledge ofthe real world. Thus, to what extent the knowledge-based intelli-gent methods are embedded in GIS is of great importance. In terms

* Corresponding author.E-mail address:[email protected] (H.Wei).0950-7051/$ - see front matter 2011 Elsevier B.V.Allrights reserved.doi: 10.1 016/j.knosys.2011.01.007

of the methodology of problem solving, an intelligent system is de-signed to solve specific knowledge-required problems. So whetherthe solution is made by human or by computer is a significant dif-ference that decides whether a GIS-based system is a managementtool or an intelligent decision-maker.

Metro lines exist in most metropolises as a fast, punctual, envi-ronmentally friendly and high-capacity public mode oftransporta-tion. Their design, construction and operation require extensiveprofessional knowledge and experience. Hence, an expert systemcan be developed to assist the decision-making for better perfor-mance and cost efficiency. A considerable amount of good researchhas been done during the past 30 years. Table 1 succinctly reviewsthe expert systems, knowledge-based systems and some othermetro-concerned AI applications that were directly designed foror can be easily transplanted into the metro line system. In general,those AI-concerned applications cover every aspect of public trans-portation, such as design, construction, operation and service pro-vision, with quite mature technologies. Nevertheless, there existsome common problems. First, the systems rely on human-computer interaction and need human support to a great extent,or many manual interventions, as they cannot solve problemsindependently. This reflects the fact that the knowledge is not suf-ficient, and GIS mainly plays the role of a database. Second, theywork in limited fields with limited functionalities, and are notcapable of reasoning using multi-domain knowledge. Unsurpris-ingly, the design and operation of a public transportation systemsuch as a metro system requires exactly the capability of usingmulti-domain knowledge. Third, the positioning of metro-linesand stations needs the consideration of multiple factors and thesatisfaction of multiple restrictions. Furthermore, a real intelligent
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H. Wei et ol.] Knowledge-Based Systems 24 (2011) 542-553

Table 1The previous ES or KBS applications in the metro domain or closely related to it.

Phase Existing Main technologyworks

Design Road network [1 [ GIS + Graph[2[ CSP[3[ GIS + Fuzzy-logic

Land attribute [4[ GIS[5[ Case-based[6[ GIS + ES

Site selection [7[ ES, DSS[S[ ES[9[ KBS+MP[10[ GIS + DSS[11 [ Rule-based

Cost estimation [12[ KBSEnvironment [13[ Fuzzy setevaluation

Construction Geological monitor [14[ Data miningEcological effect [15[ GIS + DSS

[16,17[ GIS + State-transition

No violating guideline [lS[ KBSOperation Control [19[ ES

[20[ Fuzzy logicMaintenance [21[ Rule-based

[22[ Case-basedIncident management [2,23[ GIS + ESPlan & Schedule [24[ ANN

[25[ GA[26[ CSP

Data collection [27[ RFID + ESOptimum [2S[ GIS + MAStransportationSecurity [29-31[ PR+CV

Service Shortest route [32[ GPS + SearchPersonalized planning [33[ Ontology

[34[ GIS + ES

system should be required to provide candidate strategies anddecisions automatically.

This paper is divided into six sections. The first part is the intro-duction of AI-based GIS applications and an explanation of whyproblems in these applications can be solved by AI technology.The second section presents a conceptual model of problem solvingunder a GISbackground, and a knowledge-based searching methodis designed to solve the planning problems. The following section isabout applying a GIS platform to build a virtual and detailed envi-ronment, on which many complicated applications will be concep-tualized and formalized. The fourth section, which is the core ofthis paper, elaborately explains the whole philosophy of usingthe heuristic searching algorithm for problem-solving in metro-line automatic planning, and presents the algorithms for definingstate spaces, defining the state evaluation function and formalizingknowledge. The fifth section displays the experimental results ofusing this method to plan a metro-line automatically. The last sec-tion is a discussion about deep integration of the problem solvingtechnology of AI and GIS in actual applications.

2. System design of an enhanced GISmodelIn order to enable a GIS to process knowledge-required tasks

intelligently, the system must be imbedded with problem solvingfunctions. A conceptual AI-strengthened framework is presentedin Fig. 1, which is composed by two main parts: one part (green 11 For interpretation of color in Fig. 1, the reader is referred to the web version of

this article.

543

modules) is the GIS infrastructure and the other (red modules) con-sists of modules of knowledge-based management, the state eval-uation function and the heuristic searching with regard to problemsolving. In Fig. 1, the GIS module is responsible for the manage-ment and visualization of spatial information and other attributeinformation, including the distribution of hotels, roads network,lakes and city boundaries. All of these are classical functions ofGIS software.

There are three main functional modules used for problem solv-ing. The first part is the knowledge base, which includes two typesof knowledge, fact and rule. All of the knowledge is used for cityplanning. The second part is the state evaluation function, whichis the key to searching in state space. The above two parts are openand can be updated according to a concrete application. Thoughthe searching algorithm for problem solving is general in AI, therules, state space, heuristic strategy and state evaluation functionare highly task-specific. They need to be defined elaborately, espe-cially when the task is multi-domain and uncertain reasoning isconcerned. That is to say a great challenge exists in the implemen-tation of means-ends analysis. The third part is the state space andheuristic searching function. The details of these are presented inSection 4.

3. Representing facts in GISplatformGIS is a powerful platform to store, visualize and fuse data. It

integrates a great deal of trivial data to construct sets of itemsfor visibility and discriminability, or in other words, to organizethose tremendous amounts of data. Typically GIS organizes datathrough many theme layers, and positions are used as referencepoints. Whereas only a small part of the data is visible, and manyfacts, such as the attributes of a recently built metro station in atraffic network, are invisible, these hidden data might play animportant role in the problem solving. So it is crucial to establisha form of data representation and implement it for the currentapplication.

Besides the geographical data of objects in the real world, anintelligent GIS is also expected to have a knowledge base for pos-sessing as many knowledge items as possible, in order to providea sufficient base for inference. What kind of facts should beadopted by the GIS platform depends on the specific problem. Asfor urban road network planning, it is plausible that the position-related facts that are desired for a GIS platform are current trafficnetworks, including roads, highways, metro lines and stations,the population distribution in the city at different periods, andthe distribution of features such as residential areas, business dis-tricts, public facilities, infrastructure of the city and natural sights.However, as to how many attributes of each of them should be re-stored in the database, it is difficult to make a decision in advance,so a scalable design is necessary to enable possible knowledgeexpansion. As far as the road network planning is concerned, theGIS platform can be established by obtaining the useful data forthe planning from maps and the real world. The position-relatedinformation and attributes of the geographic entities are storedin the tables of a relational database.

4. Knowledge-based heuristic searchingSearching is a powerful tool for problem solving. It consists of a

state space, a set of rules of state transformation, a state evaluationfunction, and a heuristic strategy. Suppose that a new metro linebetween two positions needs to be designed in a city, how can thistask be done by the aforementioned enhanced GIS? It must beemphasized that this kind of task is highly knowledge dependent.


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544 H. Wei et ol.] Knowledge-Based Systems 24 (2011) 542-553:------------------------I1 Problem solving by heurist ic searching11111

1 11 1i:::::::::>I1 11 11 11 1 State space of searching1 1

_ _ . . I : : _ L .. ._ _ __ - T I - - - - - - - - - - - - - - _ _ _ _ _ .I n - - - -_ _ _ _ _ _ _ _____ 1_ - - - - - - - - - 1

Database Theme layersmanagement management

Data maintenance, Querying, and Visualization

State or solutionevaluation function:Status(S,)= F,( l+

F,( l+F 3( l + . . ..

~ - - - - - - - - - - - - - 1 l - - - - - - - - - - - - - - 1 - - - - - - 1 1 - - - - - -r------ --------. 1----- ------~: Spatial information update 1 1 Specialty of Task :1 1_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ L I

Strategy of searching

1111

~ R2: ..R3: ..

Knowledgebase (rules):Rl:......

111111-----1"f----

r - - - - - -~- - - - --I

Fig. 1. A conceptual structure of problem solving in GISapplication.

Knowledge acquisitionL .1

For instance, the decision to use an existing tunnel is better than todig a new one to cross a river.4.1. D efinition of state space

Designing a metro line is a typical problem-solving task in GISapplications, where use of knowledge is absolutely necessary forseeking the solution.

If the two terminal stations, middle stations and direction con-trol points of the metro line can be determined, the problem issolved. Fig. 2 illustrates how the solution evolves. The left part isan initial solution {Po, pd, or in other words, it is an initial stateof a solution, which simply connects two terminals. But usuallythis coarse solution is far from the expected one. Next, a new pointnear one of the terminal stations can be chosen and inserted intothe sequence {Po, PI}' This point can be a middle station or a direc-tion control point of the metro line. Thus an improved solution {Po,PI, P2} comes into being. Similarly other points near the currentpoints can be chosen and inserted into the former solution, whichgenerates a new solution. It is plausible that the latter solution isbetter than the former one. This process iterates until a sequence{Po, Pl,"" Pn} is satisfactory to be the final solution. And it is not

. . . . . . ~

Initial solution

a one-step process, but an iterative process to optimize and ap-proach gradually, which is shown in the middle and right partsof Fig. 2. Any two direction control points of the metro line canbe supposed to be connected by a straight line, so a solution canbe represented by a point sequence, for example L={po , Pl,""P n} represents a metro line, where Po and Pn are the two terminalstations, p;( 1 ,::;;i,::;;- 1, iEN) is a middle station or direction con-trol point of the metro line. If a middle solution finds more thanone candidate point that is proper, multiple offspring solutionscan be obtained from their parent solutions. All those possiblepoint sequences make up the state space, where every state is acandidate solution. One state can be derived from another byinserting new points. Expanding solutions in this way establishesa tree of solutions, which is the state space. Then searching isneeded to find the optimal point sequence that meets the require-ments of passengers' capacity and the constraints of construction.Here a refined method is used to find a desired solution. The origi-nal problem starts from an initial solution {Po, PI}, which usually isquite coarse. All kinds of knowledge, such as the city's layout, thedistribution of the city's infrastructure, geological and ecologicalsituation, environment, population distribution, the status of trafficflow and other knowledge, needs to be considered to evolve the

. . . . . . ~

An optimized solutionFig. 2. Process of solution refining.

StartFinal solution


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H. Wei et ol.] Knowledge-Based Systems 24 (2011) 542-553 545

Too expensive to accept

. .,,,. .Final solution {start, p'], P'2,"', P'l6, end}

Fig. 3. State space in a tree structure.

solution, and ultimately achieve a relatively optimal solution thatmeets all the requirements.

How to get an offspring solution from its parent solution, i.e.how to transform from one state to the next state? There are twokinds of situations, whereby one is connecting to a new middle sta-tion and the other is inserting a new direction control point. In theformer, abundant knowledge is helpful to find a preferred positionfor a station in a band-like area, and the axis of this linear area isthe current solution. Usually more than one candidate point existsfor consideration. For example, suppose a middle station of thecurrent state is Si, and there are three candidate stations P 1 , P2and P3 near Si, which are a museum, a park and a railway station,respectively. Considering that a railway station always has manypassengers, it is more reasonable to choose P3 as the neighboringstation (i.e. Si+1) of s; In the latter, a metro line sometimes needssome points to control its direction. For example, suppose thereis a historic site between the stations Si and Si+1. Ifa straight railwayis constructed between s, and Si+1, the historic site might be dam-aged. So a direction control point Cil that is away from that historicsite can be placed between s, and Si+1 to avoid it. This also producesa new state. Fig. 3 shows a state space as a tree structure.

4 .2 . D efin itio n o f s ta te ev alu atio n fu nc tio nIn the searching process, how does the enhanced GIS evaluate

whether a new state is better than the current one? And how doesit judge whether it is necessary to expand the current state to anew one? In order to measure the quality of a state, a state evalu-ation function needs to be defined. A state is a complex point se-quence, and every point in it is decided by many factors. So it isrational to use different functions to evaluate different factors,respectively. These factors should be related to the alleviation oftraffic pressure, optimization of the public traffic network, betterdistribution of urban functionalities, balance of population in dif-ferent districts, difficulty in construction and cost, degree of envi-ronmental and ecological damage, and so on. Generally, thefactors can be divided into two categories, positive ones and nega-tive ones. The former holds the advantages of constructing such ametro line, such as alleviating traffic pressure and promotingurbanization, while the latter reflects the cost of constructing ametro line, such as construction expense and destruction of valu-able relics. The evaluation function is quite crucial to the quality

Table 2Possible venues with dense population nea r a station.

judgment of a state. There are several principles that need to be fol-lowed in defining an evaluation function. They are:(a) Considering as many factors as possible and quantifying

them.(b) A factor with positive effect is assigned by a positive value

and a factor with negative effect is assigned by a negativevalue.

(c) Different factors influence metro line planning with differentweights.

These factors can be divided into six classes: transportation effi-ciency, efficiency of traffic network optimization, efficiency in thefuture, expense of construction, cultural cost and ecological cost.A state evaluation function is defined as a vector F = if1 , f2 ' h,f4'fs , MEach fi in it refers to a fore-mentioned factor. The first threeelements refer to the positive effect factors and the later three cor-respond to the negative effect factors. Suppose L = { P O ,P 1 , " . ,P n} tobe a state. The following is the detailed calculation of each factor J; .Transportation efficiency if1(L)) means the number of peoplethat can be transported by the metro line. Because stations playthe roles of assembling and diffusing passengers, their positionsmust affect the value of transportation efficiency markedly. Nowdefine

1 5 1I 1 ( L ) = . .: .passengerJoad(si)i~O

where passenger_load(si) is the passenger stream that may beundertaken by station Si,where 1 5 1 is the number of stations in solu-tion L. The function is defined as below

I T a b . 2 1passengerJoad(si) =2 . . : . c on tn (a re a( si, r ), aj)j~l

X ( ~ j 1 0 W u P ) x PUj (j10WUj ( t) , t) )where Table 2 is the collection of all possible venues and I T ab . 2 1represents the number of venues in Table 2. More specifically, ajis a venue listed Table 2 and its existing scope is defined in (area(-si ,r)), which is centered at Si with a radius r. contn(a j, a rea ( si ,r ) ) re-turns a Boolean value illustrating whether aj can be found inarea(si,r), and f lowa j ( t) gives the passenger flow at time t, and finally

Railway station Gymnasium Art museumark Long-distance bus s tation Airport Historical sights Theatre Music hall


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546 H. Wei et ol.] Knowledge-Based Systems 24 (2011) 542-553

A railway station A gymnasium An amusement p arkFig. 4. Probability distribution of passengers in some venues.

Paj (x , t) is the probability that the volume of passenger flow is x attime t in aj. Then the total is as follows:

Fig. 4 is the distributions of probabilities of the number of pas-sengers that emerge at some venues (such as a railway station, agymnasium, or a plaza) at different moments. These data can be ac-quired by daily statistics. Because the stream of passengers fromthe residential area, working area and commercial area is compar-ative steady, they will be discussed later.

E ffic ien cy o f tr affic n etw or k o ptim iz atio n ( fz (L )) is defined as thesum of the following parts: the numbers of bus lines, metro linesand light railways that L connects, the number of new candidateroute that links two positions by L, and the number of traffic hingesand functional centers of the city that can be connected by L. As-sume that busline(A) represents a set of bus routes crossing areaA , and busstop(bl;) represents all stops of bus route bl;. A Booleanfunction

Vx E 51 , v v E 52, Ilx - y l1 2 ~ 500 m,51 = us..stop(bI1),52 = us..stop(bb)otherwise

is defined to decide whether two bus routes bh and bb share thesame or almost the same bus stops, which reflects the feasibilityof transferring from one to the other. If the distance between twostops is longer 500 m, it is believable that they cannot be easilyreached from each other. Three other Boolean functions are asfollows:

{1, there is no bus route running

indir(A1,A 2 ) = between area A1 and A20, otherwise

and

h . { 1 ,nge(A) = 0, area A is a traffic hingeotherwiseand

{ 1 area A is a functional centre of citylocalcenter(A) = '0, otherwiseThen it has

1 5 1 1 5 1 ( )h(L) ~ W21 xL L L L ninset(busstop(ld,busstop(lj))i=O j=i 4E bus ii n e (a r e a (S j ,r ) ) i /: : :b u s ii n e (a r e a (s j ,r ) )1 5 1 1 5 1 1 5 1+ W22 L Lindir(area(Si ,r) ,area(Sj, r)) + W23 X L hinge(area(Si' r))

i= O j=i i= O1 5 1+ W24 X Llocalcenteriareais., r))

i= O

where W2; (1 ~ i~, EN) are the weight factors, and the first ad-dend indicates the number of adjacent bus stops along the specificsolution.

A new metro line must prompt the development level in anarea. E fficie nc y in fu tu re ( h( L) ) is defined as a sum of weighted dif-ferences, including the building difference, service facility differ-ence and road density difference between the station area andthose in the whole city. Assume that denB(A) represents the build-ing density in area A , d en R (A ) represents the road density in area A ,denF(A) represents the density of service facilities in area A , andCITY represents the whole area of the city in which a new metroline will be constructed. Define RDD(L) as

1 5 1 d dRDD(L) ='" I enR (C IT Y) - enR (ar ea(s;, r))1tt denR(CITY)Here define BFD(L) as

1 5 1 ( IdenB(C/TY)-denB(area(Si, r ) ) I )BFD(L) ='" MAX denB(c/TY ) .~ IdenF(c/TY )-denF(area(s i,rlll,~O denF(c/TY)Then we defineh (L ) =0.6 x RDD(L) + 0.4 x BFD(L) .

E xpense of constr uction (U L) ) is defined as the negative of thesum of all expenses, including digging tunnels, laying the railwayline and constructing stations. It is defined as

(1 5 1 )f4(L) =-1x dig(L) +pav(L) +~ con(s;)

where dig(L) is the expenses of digging the tunnel. Suppose that thewhole tunnel has m segments, and their geological situations mightbe different. Even in the same segment, the ground situation overthe tunnel could be variable.

If a segment is divided into several shorter parts in terms oftheir particular situations, a combination


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the searching along this direction will be stopped. Two strategiesare proposed to accelerate the searching process, and their trigger-ing conditions are adjustable according to the demand.(a) Control the number of middle stations.The distance between two neighboring stations should be no

less than a constant MIN_D and no greater than MAX_D. In orderto ensure the newly produced candidate points converge to the ter-minal station s.; an assigned area A is shown in Fig. 5 as the scopeof searching for the middle station Si-1, and Si_1sn.lQR. It can beproved that the newly added station will incline towards the ter-minal station gradually.

As there are so many points on the map, it is neither necessarynor possible to check all of them. Since each station has an effi-ciency radius, as long as the station is located within this area,the position will not affect its transportation function significantly.So the program takes a small area as the basic searching unit, in-stead of a pixel in the map. In Fig. 5 the searching area is dividedinto many small sectors, and p is the representative of them. Cut-ting the map into many small pieces can augment the granularityof searching and facilitate the searching speed.

Suppose that stations Si-1and s, are the two stations. A new sta-tion p is obtained by searching from station Si-1. Afterwards, itneeds to be considered whether it is necessary to add a directioncontrol point between station Si-1 and p. Iff4 decreases, the controlpoint should be added. The necessary direction control points canbe added in accordance with rules R_A20 and R_A32. The railwayshould, to the greatest extent, avoid curving with great curvature,thus the selection of desired points is direction limited. The rangeis set within an arc of 90. If direction points Si-1 and Siare alreadyselected, the next one Si+1can only be selected in the area B shownin Fig. 6, where Si_1Si.lSR, LSOQ = 45,LQOT = 90, LROT = 45.(b) Terminate the improper states in time.If every value of a solution's evaluation function is better than

the expected value, the solution stops expanding. For example, ifin one solution it requires a station to be built under a deep river,where the expense of building the station is greater than the bud-get, the solution is not valid. The following is a list of the conditionsthat stop searching immediately:(1) lS I >MAX_S;(2) IPn-1Pnl < 2*MIN_D;(3) UL) > MAX_MONEY_COST;(4 ) ri > MAX_CONS_DTY;(5 ) fs(L) > MAX_CUL_COST;

Fig. 5. Searching area for stations.

Fig. 6. Available area for avoiding a sharp turn.

(6) hE {HISTORICSITE} /\ hE area(si,100m) /\ s, E L;(7) /6(L) > MAX_ENV_COST.These conditions are divided into two types. The first type in-

cludes the first two conditions, any solution satisfying anyone ofthem is called a "good solution", which can be accepted as the finalsolution. All the other conditions belong to the second type, and asolution under one of those conditions is called a "bad solution",which is out of consideration. When more than one candidate stateexists, the best one has the priority to be expanded. One state cangenerate many subsequent states. In this case, the states are sortedby their values. The sorting order is f1,f4, fz, h. fS,f6.

Based on the heuristic searching algorithm, the famous A* algo-rithm in AI is employed to generate a state tree for the metro-lineplanning.5. Experiments and results analysis

Shanghai metro system currently has established 12 metrolines, with 11 lines currently in service. The system comprises267 stations with over 410 km of railway It operates trains to carry4.5 million customers per day on average and the peak number hasexceeded 7 million. A further proposal to extend the system hasbeen published on the official website, which claims that the futuresystem will achieve 21 lines and 600 km by 2020 [35]. Faced withsuch a tremendous railway network, it is necessary to conduct thesimulation and optimization based on knowledge in advance. Herea software system was developed to test the above-mentionedideas. All experiments are based on the actual data of Shanghaiin China. To achieve a clear map, only a small part of the data thatis most concerned is displayed in the maps.5.1. Experiments of designing a single metro line

Using our knowledge-based designing system, many experi-ments were done. The first one was designing a new metro-line be-tween residential area AI and industrial area A2. Fig. 7 is one ofthebest results. This metro-line originates from A 1 and passes throughindustrial area A3 and commercial area A4. It intersects Metro-lines1,2 and 3 at stations sl, s2 and s3 respectively. Stations s4 and s5are located in the center of the city. They can greatly alleviate thetransportation pressure. The stations s5 and s6 are neighboring sta-tions, which are separated by Huangpu River, and tl and t2 aredirection control points. If the railway is desired to connect s5


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Fig. 7. Experimental result of planning a new metro-line.and s6 in a straight line, a new tunnel needs to be built. But nowthe railway goes through an existing tunnel tIt2, which greatly re-duces construction difficulty and expense. For simplicity, this solu-tion is considered as 'an Optimal Solution'.

Fig. 8 is an example of a part of the search tree. The red nodesand red lines represent those states and routes that are pruned.The reason is that those states failed to satisfy conditions of strat-egies 1 or 2 in the previous sections. The green nodes and lines arethe part of process to seek possible solutions, and the leaf nodesare the final solutions, such as s81O, s84 and s85. The black nodesare nodes that have not been expanded yet. For the sake of compu-tational efficiency, only a limited number of nodes have a chance tobe expanded.

The next three experiments, Fig. 9(a)-( c), were done to comparethe results for applying different strategies of optimization. A ma-jor difference between the solutions in Fig. 9(a) and Fig. 7 is thatthe former has seven stations fewer. Except that the join betweens1 and s2 is slightly curved, the rest of the railway line is almoststraight. The reason is that it did not take into account the maximi-zation of the transportation efficiency and state evaluation func-tion f4 performs as a dominant index. So a solution with fewerstations and shorter length requires lower construction expense,

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Fig. 10. Experimental result of planning metro-line 4.In order to evaluate the knowledge-based search algorithm's

performance, some statistical data were collected with respect tothe number of nodes, pruned nodes, average branches of non-leaf

nodes and time cost. These data were based on 15 experimentswith a condition of 20 stations but with different terminals. After-wards, one new station was added and another 15 experimentswere conducted.

From the observation of the experimental results, the followingstatistics are acquired. A large proportion of the nodes in the searchtree were pruned for accelerating the searching speed. The ratio re-mains stable, with an average of 0.71. The total number of nodes ina search tree is directly proportional to its depth, and the averagevalue is 16.46. During the searching for new stations each time, thenumber of candidate stations is between 8 and 10, and 8.97 onaverage. This illustrates that the candidates are sufficient for beingselected. Supposing a non-leaf node has 6 offspring on average,then 5 of them can be pruned, and only one may be left to be ex-panded. The average computational time of 150 experiments is lessthan 2 min and even in the worst situation is no more than 3 min.Based on these statistics, our algorithm can be considered to beefficient.

5 .2 . E xp er im e nts o n m e tr o lin e n etw o rk sApart from doing experiments for a single metro line, the

authors of this paper have also simulated and experimented onall the existing metro lines in Shanghai. The aim is to verify the

lSusStop. N,~[fcliR Station l Em ,po ri um .P I = HospItal,Po;tOifl~ Hetel & 1 >0 0 1. U h rO J) ' S"~ht T r i ~ 1=~~~~laIZ\'lJteIndus t ra~ Zc .e R 2 '; .i d .; .r w f ii il Z a r. e

S l !5 S t o p, I t .. tr o l; " " S t a t i O J > E ll Ip or l"m, P la za H os pi ta l Po5tOff iee Ho te l S d li oo l, library S ig 'h l

Tun ne l.IGreenhelt CO l lJ i ;f f ie r c; ' I. ;. '! ZoJ t~Irl 'Ii 'l IStriiilZI{lr.'e R es 'id .e li tG 1 Z o ne

\ A

(a) Section A-B and Section E-F match well with theexisted Line 1. Section C-D locates in an area withsuperior commercial prosperity, highly crowdedpopulation and dense road network, This area is thetraditional center of Shanghai, and has consistentdemand for transportat ion, so it is reasonable to placemultiple stations here.

(b) Section A-B and Section D-E coincide well ,approximately 50%, with the existed Line l.Section B-C t raverses the center of Shanghai,where exists a great deal of working andresident population. This makes the metro lineto be desired owing to its great transportationefficiency. However, section C-D crosses anarea that was a traditional area of heavy

1!l;~5Slp..4':tlOli,,, Stolt"", I Em ;p o r~ l 1cm , P .m a Hbspit;I,IPostOfflio: H ote 'l S c IIOO I. I.Ibrary S jg ih t1lu:r.~l

G r= -I "i .b =' ~ C om m src E'1 Z o n . - eIOOll5trall Zon:e I < s


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H. Wei et ol.] Knowledge-Based Systems 24 (2011) 542-553 55 1

Linel Line2 Line3 Line4 Line5 Line6 Line? LinelEKl

Line3EKl

LineS Line9 Line! 0 Line!! Line!3 LinelEKt

IOF10F2.F30F4 .1'5.P6.F70F8 D F 9 1Fig. 12. The required constraint knowledge categories and priorities when the self-design error is less than 20%for each metro line (a) the relative density of Shanghai'swhole metro-network.

1 2 0. 0 0 %1 0 0. 0 0 %80. 0 0 %6 0 . 0 0%4 0 . 0 0%20. 0 0 %

O . 00 %

When L ine 10 - - - -e s ig n ed - - - Whe n L in e 2 e xt en si onWhen line 4 _ _ _ . -When line 1 ue~~~~(a) The relative density of Shanghai's wholemetro-network

(b) Line I 's design error changing

( d) L i ne I O' s d es ig n e rr or c h an gi ng

( e) L i ne 4 's d es ig n e rr or c h an g in g

Fig. 13. The influence of knowledge for design accuracy in different metro network density.( e) L in e 2 E X T's d esi gn e rro r c h an gin g


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Referencesnfluence of the multi-domain knowledge in the heuristic searchand the diversity between the theoretical calculation and reality.The design of the heuristic search considers the following factors:(1) Environment and ecosystem (F1), e.g. to avoid the wetland

in the urban area;(2) Population density (F2);(3) Interchange amongst metro lines (F3);(4) Interchange between metro lines and other modes of trans-

portation (F4), for instance, the railway station and airportare of utmost significance as connection targets;

(5) Route optimization (F5), e.g. avoid sharp turns and overlap-ping with the major traffic streams on the surface;

(6) Connectivity of important nodes (F6), e.g. parks, culturalfacilities and the commercial center;

(7) Construction (F7), for instance, utilization of existingbridges, tunnels and railways;

(8) Urbanization (F8), for instance, facilitating the urbanizationof the country and dispersing population to the suburbs;

(9) Coverage to areas without metro line access (F9).The experiments were implemented according to the order of

the establishment of the metro lines in Shanghai. The origin anddestination are configured in prior and one or more of the aboverestrictive factors were selectively applied. The algorithm choosesand joins the middle stations and eventually the errors in diverseconditions are calculated. If the locations of the real metro stationsare considered as the target nodes, the mean square error betweenthe experiments and the reality is the fitting error. Fig. 12 presentsthat the similarity between the simulation and the reality is above80%. Considering the extensions of Line 1, Line 4, Line 10 and Line2, in order to achieve better design accuracy, Fig. 13 illustrates thephilosophy of using the restrictive rules, the corresponding orderand the consequent influence in different network densities.According to the experimental results, a random search can barelyachieve the desired results. In the initial phase of construction, dueto the low network density, it requires enormous constraintknowledge to acquire a result close to the human design. Whenthe network is very dense, it requires relatively fewer knowledgecategories; hence the computational speed is enhanced.

6. ConclusionArtificial intelligence technology can be used comprehensively

in GIS applications. By taking advantage of accurate position-related information processing, based on the GIS platform, andthe sophisticated knowledge representation and reasoningmethods in AI, the enhanced intelligence GIS is capable of solvingmore complicated problems compared to common GIS applica-tions. The programming method is adept at calculating precisemathematical formulations, so a mathematical model can beestablished with knowledge integrated into the GIS database andproblem solving can be achieved through a mathematicalprogramming method. Besides mathematical programming, it isconceivable that many techniques and methods in AI can beintegrated into GIS for better adaptability and greater depth ofembedding to achieve better performance, which is worthy ofstudy in future work.

AcknowledgmentsThis work was supported by the 973 Program (Project No.

201OCB327900).

[1] j.B.Mena, Automatic vectorization ofsegmented road networks by geometricaland topological analysis of high resolution binary images, Knowledge-BasedSystems 19 (2006) 704-718.[2] jia-song Wang, Bao-qing Zhao, Chun Ye, De-qing Yang, Zhen Huang,

Optimizing layout of urban street canyon using numerical simulationcoupling with mathematical optimization, journal of Hydrodynamics 18 (3)(2006) 345-351.

[3] josef Benedikt, Sebastian Reinberg, Leopold Riedl,A GISapplication to enhancecell-based information modeling, Information Sciences 142 (2002) 151-160.[4] Frank Witlox, Expert systems in land-use planning: an overview, ExpertSystems with Applications 29 (2005) 437-445.[5 ] Amjad Waheed, Hojjat Adeli,Case-based reasoning in steel bridge engineering,Knowledge-Based Systems 18 (1) (2005) 37-46.[6] S.D.Kirkby, Integrating a GISwith an expert system to identify and managedryland salinization, Applied Geography 16 (4) (1996) 289-303.[7 ] Yao-Min Fang, Li-Yu Lin, Chua-Huang Huang, Tien-Yin Chou, Anintegrated information system for real estate agency-based on service-oriented architecture, Expert Systems with Applications 36 (2009)

11039-11044.[8] Wann-Ming We, An integrated expert system/operations research approachfor the optimization of waste incinerator sitting problems, Knowledge-BasedSystems 18 (6) (2005) 267-278.[9] Wei Hui, Xu Qing-xin, Bai Yu, Lai Loi Lei, A knowledge-based creation of

mathematical programming for GISproblem solving, Geographic InformationSciences 11 (2) (2005) 97-112.[10] E.A. Ellis, P.K.R.Nair, P.E. Linehan, H.W. Beck, C.A Blanche, A GIS-baseddatabase management application for agroforestry planning and treeselection, Computers and Electronics in Agriculture 27 (2000) 41-55.[11] john D. Saint , j . Desachy, A cartographic problem solving support system ingeographical information system, in: International Symposium on Geoscienceand Remote Sensing, IGARSS93, 'Better Understanding of Earth Environment',vol. 3, 1993, pp. 1547-1549.[12] Abdulrezak Mohamed, Tahir Celik, Knowledge based-system for alternativedesign, cost estimating and scheduling, Knowledge-Based Systems 15 (3)

(2002) 177-188.[13] P. Klungboonkrong, M.A.P. Taylor, A microcomputer-based system formulticriteria environmental impacts evaluation of urban road networks,Computer, Environment and Urban Systems 22 (5) (1998) 425-446.[14] Shiuan Wan, Tsu Chiang Lei,A knowledge-based decision support system toanalyze the debris-flow problems at Chen-Yu-Lan River, Taiwan, Knowledge-Based Systems 22 (2009) 580-588.[15] Ift ikhar U. Sikder, Knowledge-based spatial decision support systems: anassessment of environmental adaptabili ty of crops, Expert Systems withApplications 36 (2009) 5341-5347.[16] Mare P. Vayssieres, Mel R.Georage, Linda Bernherm, julie Young, Richard E.Plant, An intel ligent GIS for rangeland impact assessment. Proceedings ofFourth Annual Conference on AI,Simulation, and Planning in High AutonomySystems, 'Integrating Virtual Reality and Model-Based Environments', 1993,pp. 109-115.[17] Richard E. Plant, Marc P. Vayssieres, Combining expert system and GIStechnology to implement a state-transition model of Oak Woodlands,Computers and Electronics in Agriculture 27 (2000) 71-93.[18] AM. Buis, R.A.Vingerhoed, Knowledge-based systems in the design of a newparceling, Knowledge-Based Systems 9 (5) (1996) 307-314.[19] W. Wen, A dynamic and automatic traffic light control expert system forsolving the road congestion problem, Expert Systems with Applications 34 (4)

(2008) 2370-2381.[20] E.Bailly, S.Hayat, D.jolly, et aI.,Command and control of automated subwayin mode of disrupted march using fuzzy logic, journal of Intelligent & FuzzySystems 6 (3) (1998) 329-343.[21] A Chun, H.Wai, D.Yeung,W. Ming, Rule-based approach to the validation ofsubway engineering work allocation plans, in: Proceedings of InternationalConference on Computing, Communications and Control Technologies, vol. 5,

2004, pp. 275-280.[22] Daniel Charlebois, David G.Goodenough, AS. (Pal) Bhogal, Stan Matwin, Case-based Reasoning and Software Agents for Intel ligent Forest InformationManagement[C]. in: International Geoscience and Remote SensingSymposium, IGARSS'96, 'Remote Sensing for a Sustainable Future ', vol. 4,

1996, pp. 27-31.[23] Sam Subrammaniam, AG. Hobeika, Dan Schierer, A new hybrid expert-GIS forwide-area incident management, IEEEInternational Conference on Systems,Man, and Cybernetics, 'Humans, Information and Technology', vol. 2, 1994, pp.

1710-1715.[24] Mehdi Fallah-Taft, The application of artificial neural networks to anticipatethe average journey time of traffic in the vicinity of merges, Knowledge-BasedSystems 14 (3-4) (2001) 203-211.[25] R. Elizondo, V. Parada, L Pradenas, et aI., An evolutionary and constructiveapproach to a crew scheduling problem in underground passenger transport,journal of Heuristics 16 (4) (2010) 575-591.[26] L Ingolott i, P. Tormos, A Lova, Domain-dependent distributed models forrailway scheduling, Knowledge-Based Systems 20 (2) (2007) 186-194.[27] W. Wen, An intelligent traffic management expert system with RFIDtechnology, Expert Systems with Applications 37 (4) (2010) 3024-3035.


12/12


[28[ Semiye Demircan, Musa Aydin, S.Savas Durduran, Finding optimum route ofelectrical energy transmission line using 3 multi-criter ia with Q-Iearning,Expert Systems with Applications 38 (4) (2011) 3477-3482.[29[ B. Krausz, R. Herpers, MetroSurv: detecting events in subway stat ions,Multimedia Tools and Applications 50 (1) (2010) 123-147.[30[ D. Aubert, F. Guichard, S. Bouchafa, Time-scale change detection applied toreal-time abnormal stationary monitoring, Real-time Imaging 10 (1) (2004)

9-22.[31 [ LY. Li, W.M. Huang, Y.H. Gui, et aI., Statistical modeling of complexbackgrounds for foreground object detection, IEEETransactions on ImageProcessing 13 (11) (2004) 1459-1472.

553

[32[ N.Kim, H.S.Lee,K.J.Oh, et aI., Context-aware mobile service for routing thefastest subway path, Expert Systems with Applications 36 (2) (2009) 3319-3326.[33[ Abolghasem Sadeghi Niaraki, Kyehyun Kim, Ontology based personalizedroute planning system using a multi-criteria decision making approach, ExpertSystems with Applications 36 (2009) 2250-2259.

[34[ Keechoo Choi, Tschangho John Kim,A hybrid travel demand model with GISAnd expert systems, Computer, Environment and Urban Systems 20 (4!5)(1996) 247-259.

[35[ The Official Website of Shanghai Metro. Available at: (last accessed at 31.12.2010).

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