3D Geo-Network for Agent-based Building Evacuation Simulation

Transcription

1 Chapter 18 3D Geo-Network for Agent-based Buldng Evacuaton Smulaton Jnmu Cho and Jyeong Lee Abstract. Ths paper dscusses 3D geometrc network extracton for buldng evacuaton smulaton wth an agent-based model. 3D geometrc network represents the nternal structure of a buldng, whch provdes agent-based models wth the shortest path for evacuaton. 3D geometrc network of a buldng can be bult from computer-aded desgn (CAD) fle (vector) and scanned blueprnt (raster) through wall extracton and 3D topology constructon. We test two wall extracton methods: vector-based medal-axs transformaton (MAT) and rasterbased thnnng. For vector-based MAT, straght MAT s used to extract wall structure from wall polygon generated from CAD data. For raster-based thnnng, boundary peelng thnnng s used to extract wall structure from scanned blueprnt. The extracted 3D geometrc network s then used n an agent-based model for buldng evacuaton smulaton. In the evacuaton smulaton, human bengs are consdered to be the only movng agents. To model human behavor, we adopt a socal force model to consder human-to-human and human-to-wall nteractons durng evacuaton. We test smple evacuaton scenaro n a stuaton of jam by enforcng dfferent numbers of people n three rooms. The results show that the average velocty ncreases contnuously before jams, decreases durng jams at doorways and outer exts, and eventually ncreases agan as ndvduals escape the jams Introducton As geographc nformaton system (GIS) contnues to mature, three-dmensonal (3D) modelng has become a tool for GIS analyss wth varyng levels of success [34]. There s ncreasng demand for 3D geometrc network model n varous dscplnes ncludng emergency servces and cadastre management [2, 6, 16, 33, 34]. If there s Department of Geoscences, Msssspp State Unversty jc778@msstate.edu Unversty of Seoul, Department of Geonformatcs, 13 Srpdae-gl, Dongdaemun-gu Seoul , Korea, jlee@uos.ac.kr 283

2 284 Jnmu Cho and Jyeong Lee an emergency stuaton n a buldng, 3D geometrc network can provde fast way to egress the buldng. Emergency servces rely on both macro- and mcro-level GIS [16]. The ultmate emergency management would functon on macro scales such as urban areas and on a mcro scale such as ndvdual buldngs. On the macro-level, an emergency response GIS n a large cty mght route responders to a buldng, and then, on the mcro-level, route them to the emergency room through the shortest path once they arrve at the buldng s entrance [20]. Therefore, emergency response on mcro-level features such as buldngs depends upon 3D geometrc network regardng the nternal structure of a buldng. Most current commercal GIS, however, do not provde tools to model 3D geometrc network representng the nternal structure of a buldng. They only nclude surface-based 3D representaton methods. ESRI 3D Analyst provdes tools for surface generaton, volume calculaton, and vewshed analyss. ESRI ArcScene emphaszes vsualzaton through texture mappng and fly-through. ERDAS Imagne VrtualGIS and Intergraph GeoMeda also provde 3D fly-through tools. Whle these commercal GIS systems are prmarly concerned wth 3D vsualzaton, they do not provde any tool for 3D geometrc network representng and analyzng the nternal structure of buldngs [35]. On the other hand, smulaton on the emergency stuaton requres a model that s a smplfed representaton of realty [21]. A model can be dynamc f the output represents a later pont n tme and represents tme steps n the operaton of a dynamc process [10, 22]. Dynamc models are used to assess dfferent scenaros by attemptng to project quantfable mpacts nto the future [4]. The possble results of smulaton can be vsualzed and help decson-makers avod dsastrous stuatons. As a dynamc modelng tool, agent-based model has become one of the key computatonal approaches to smulate collectve outcomes of complex geographc phenomena based on ndvdual agents states and behavors. In partcular, human behavor wth respect to the evacuaton stuaton can be smulated on the 3D geometrc network n a buldng. The purpose of ths paper s to provde a method to buld 3D geometrc network data and to use the 3D network data to smulate a buldng evacuaton. 3D geometrc network data can be produced from computer-aded desgn (CAD) fles and scanned blueprnts. Human behavor n the evacuaton process s then smulated usng agentbased model on the 3D network data. In Secton 18.2, we descrbe the framework of buldng evacuaton smulaton system that utlzes both agent-based model and 3D geometrc network n GIS envronment. Secton 18.3 and 18.4 detal the procedure to extract 3D geometrc network from blueprnts that are ether CAD fle and scanned paper desgns. In Secton 18.3, 3D geometrc network data are derved from CAD fle usng straght medal-axs transformaton that extracts lne wall structure from polygon wall. In Secton 18.4, scanned blue prnt data are converted to 3D geometrc network. In Secton 18.5, buldng evacuaton s smulated usng agent-based model based on the nternal structure of buldng and 3D geometrc network nformaton. Key deas are summarzed n the concluson.

3 18. 3D Geo-Network for Agent-based Buldng Evacuaton Smulaton Framework of Buldng Evacuaton Smulaton System An ntegraton of agent-based modelng and GIS offers mprovements to understand complex spatal phenomena. GIS provdes agent-based model wth spatotemporal GIS data to smulate the dstrbuted nature of actons and reactons at the agent (ndvdual) level. Agent-based model provdes GIS wth the representaton of feature dynamcs such as temporal changes n spatal patterns. Therefore, n ths study, a buldng evacuaton system s desgned n GIS envronment (Fg. 18.1). For evacuaton smulaton, we utlze Agent Analyst 1 as agent-based modelng tool and 3D GeoNet for nput data generaton. Fg Buldng Evacuaton Smulaton System Archtecture For agent movement, we adopt and mplement Helbng et al. s socal force model [13] (see detals n secton ) n Agent Analyst. Agent Analyst takes buldng structure data (Fg. 18.1d) and runs a socal force model that ncorporates a mxture of soco-psychologcal and physcal forces of human-to-human and human-to-wall nteractons for pedestran movement. The smulaton s teratve and the results n each step are vsualzed dynamcally n ArcMap (Fg. 18.1e). So, we can explore agents evacuaton process n a buldng. Current commercal software for pedestran evacuaton smulaton such as ASERI 2 and Legon 3 utlzes CAD fle for the physcal envronment of a movng agent n a buldng. To buld shortest paths from CAD fle, users should manually model agents 1 Agent Analyst s an extenson of ArcGIS, software and materals avalable at 2 ASERI ( Advance Smulaton of Evacuaton of Real Indvduals) s avalable at 3 Legon s avalable at

4 286 Jnmu Cho and Jyeong Lee routes from each room to destnaton, whch s labor ntensve process. Instead of manual modelng, we have developed 3D GeoNet that automatcally extracts the nternal structure of a buldng such as rooms, walls, and doors (and exts) and 3D geometrc network from buldng blueprnt data. The nternal structures of a buldng from 3D GeoNet provdes agent-base model wth geometrc confguraton (Fg. 18.1b) for agents egress movement and the 3D geometrc network provdes wth connectvty and adjacency of rooms for agents egress routes (Fg c). These data are used as nput of Agent Analyst though ArcMap. Two common data used as blueprnt of a buldng (Fg a) are CAD fles and paper desgns that we can utlze n order to extract the nternal structure of a buldng. Followng two sectons descrbes how to buld 3D geometrc network data from both CAD and scanned blueprnt data D Geometrc Network Extracton from CAD Data CAD data are commonly used as dgtal blueprnt of a buldng [24]. The extracton of 3D geometrc network of a buldng from CAD data requres the converson of a room to a node and a shared wall to a lnk based on dual graph theory [18]. The process starts by extractng wall structure of a buldng snce walls n CAD data are stored as closed double lnes that need to be converted to sngle lnes. From the wall structure, 3D geometrc network can be generated through 3D topologcal structure. Detals on ndvdual steps are explaned n the followng sub sectons Straght Medal-Axs Transformaton Walls n CAD blueprnt of a buldng are represented as closed double lnes. To buld 3D network data, the double lne walls need to be converted to sngle lnes after the converson of the closed double lnes to polygons usng ArcInfo s Buld command. Then, lnes are extracted from the wall polygons. There are several approaches to medal-axs transformaton (MAT) for extractng a lne from a polygon. A skeleton of a polygon can be extracted by the Delaunay trangulaton based transformaton [25]. One or two edges of the Delaunay trangles are nsde the nput polygon. Connectng the mdponts of the nsde edges of the trangles produces the skeleton of the polygon (Fg a). The skeleton approxmates a medal-axs of the polygon. Ths method has been mplemented to extract morphologcal structures of natural features such as the human body [25]. A true medal-axs can be derved from the Vorono-Dagram based MAT [5, 17]. Lee [17] has developed an O(n log n) algorthm for polygons wth concave corners. Hs algorthm s based on the dvde-and-conquer method, whch requres four steps. Frst, the Vorono edges for each vertex of a polygon are generated. Second, polygon edges are grouped nto chans at the concave vertexes. Thrd, Vorono edges are generated for each chan and merged wth each successve chan. Fnally, the medal-axs s created by mergng the fnal two chans and by removng the Vorono edges ncdent at the vertexes of the polygon (Fg b). In Chn et al. s algorthm [5], the medal-axs s the connecton of all centers of nner crcles of a polygon. The nner

5 18. 3D Geo-Network for Agent-based Buldng Evacuaton Smulaton 287 crcles touch the polygon n two or more ponts. Both MAT methods create second order lnes at the T ntersectons of skeleton lnes. It s more dffcult to calculate the nteracton between walls and humans usng a second order lne rather than a straght lne. a. Skeleton based on Delaunay Trangulaton [25] b. Medal-Axs Transformaton [17] c. Straght Medal-Axs Transformaton [7] Fg Medal-Axs Transformaton Algorthms The straght MAT s another method to extract a medal-axs [7]. The straght medal-axs s constructed by a shrnkng process n whch the edges of a polygon move nward wth the same speed (Fg c). All edges are reduced to three event ponts eventually: edge, splt, and vertex events. An edge event occurs when an edge collapses down to a pont (Fg a). If neghborng edges of the edge event stll have nonzero length, they become adjacent. A splt event occurs when a reflex vertex colldes wth and splts an edge (Fg b). A splt event dvdes a component of the shrnkng polygon nto two smaller components. A vertex event occurs when two or more reflex vertces are collapsed to the same pont (Fg c). A vertex event can ntroduce a new reflex vertex nto the shrnkng polygon, whch eventually create another splt event. Once the component of the shrnkng polygon becomes a trangle, the straght medal-axs s completed by connectng three vertces of the trangle to ts center. Connectng these reduced ponts forms a straghtened skeleton. The algorthm s thus appled to construct a roof on a gven ground plan [12]. To extract wall structure from wall polygon data n ths paper, Eppsten and Erckson s straght MAT algorthm [7] s used because the algorthm can avod the second order lnes n order to calculate the nteracton between walls and humans n a buldng evacuaton model. The straght medal-axs transformaton can also avod the extracton of Y shape medal axs from T polygon. a. Edge Events b. Splt Event c. Vertex Event Fg Event Ponts of Straght Medal-Axs Transformaton [7]

6 288 Jnmu Cho and Jyeong Lee Topologcal Data Structure and 3D Geometrc Network Geometrc network for 3D nternal structure of buldngs can be created through 3D topologcal data extracton. Boundary-based representatons (B-Rep) consttute the most popular model to store 3D topologcal data. The Urban Data Model (UDM) [6] s based on the B-Rep model and uses boundares: faces, edges, and nodes. UDM allows us to query topologcal relatonshps between rooms. However, topologcal relatonshps n UDM are mplct, whch requres extra processng power for nterpretaton. Bllen and Zlatanova [3] have proposed the Dmensonal Model (DM) n whch objects are composed of dmensonal elements. DM allows for topographcal relatonshps between any combnaton of one, two, and three-dmensonal objects. However, DM would also be resource ntensve when appled to an entre buldng. Snce B-Rep models requre a large volume of data and consderable power to process, Lee and Kwan [20] have proposed a smple 3D topologcal data structure called Combnatoral Data Model (CDM). CDM conssts of node-relaton structure (NRS) and herarchcal network structure (HNS) to represent topologcal relatonshps between objects. In NRS, a node represents a room and an edge represents a wall shared by two rooms. HNS, a subset of NRS, stores edges that connect two nodes through a door, an elevator, or a star. Therefore, NRS represents adjacency nformaton and HNS connectvty. Ths paper adopts CDM to buld 3D topologcal structure because t allows for fast processng of 3D analyss wth the concse 3D topologcal data structure. Based on the wall structure extracted from CAD data usng the straght MAT, the NRS algorthm bulds a 3D topologcal structure, n whch a node represents a room and an edge represents a topologcal relatonshp (adjacency and connectvty) (Fg ). Fg D Topologcal Data Constructon from Wall Structure The algorthm to generate NRS data needs four steps. Frst, on the wall structure, doorways of ndvdual rooms are easly extracted by extendng drecton vector of each end node to the nearest end node (Fg a). The doorway data are stored separately for later use to buld connectvty. Second, walls and exts are then used to

7 18. 3D Geo-Network for Agent-based Buldng Evacuaton Smulaton 289 buld room polygons usng ArcGIS Buld operator that splt a lne segment shared by three rooms (Fg b). Thrd, nodes for NRS are dentfed for ndvdual rooms (Fg c). Fnally, shared walls are converted to lnks that represent adjacency and doorways are converted to lnks that represent connectvty n 3D topologcal structure (Fg d). All edges represent connectvty s a subset of the edges that represent adjacency (Fg ). Once horzontal NRS data s generated for each floor, HNS data lnks NRS of each floor by vertcal connectvty through an elevator or a starway [20, 30]. Each node (a room) s vertcally adjacent to above and below nodes (rooms). S2 R201 R202 R205 R204 H2 R203 S1 R101 H1 R102 S2 R201 R202 H2 R205 R204 R203 R105 S1 R101 H1 R104 R103 R102 R104 A. Adjacency B. Connectvty Fg D Topologcal Data based on Combnatoral Data Model [30] Three dmensonal topologcal data should be converted to 3D geometrc network structure n order to model human behavor n buldng evacuaton smulaton. Hallways are represented by nodes n 3D topologcal data but they should be recognzed as edges that connect rooms for navgaton. The process to buld 3D geometrc network from 3D topologcal data requres two steps [19] (Fg ). Frst, nodes that represent hallways are converted to edges. Hallway nodes are removed and hallway polygons are converted to edges usng the MAT algorthm. Second, all edges from the other nodes need to be adjusted perpendcularly to the hallway edges n order to mantan shortest connectvty n 3D topologcal data. R105 R205 R105 S2 S1 R204 U R201 R101 H1 R104 H2 R203 R103 R202 R102 R103

8 290 Jnmu Cho and Jyeong Lee Fg D Geometrc Network from 3D Topologcal Data Structure [19] 18.4 Converson from Scanned Blueprnt to 3D Geometrc Network The wall structure of a buldng can be extracted from scanned blueprnt usng three fundamental algorthms: Vorono dagram-based thnnng, mathematcal operatorbased thnnng, and the boundary peelng method [1] (Fg. 18.7). The Vorono dagram-based thnnng algorthm s the vector-based method, whch starts by collectng sample ponts of a regon s boundary from raster data [29]. The, the ncremental algorthm [11] computes Delaunay trangulatons of the sample ponts. A Vorono edge s a part of the skeleton f ts correspondng dual Delaunay edge les completely wthn the regon s boundary (Fg. 18.7a). If a skeleton has parastc branches, the skeleton can be smplfed by retractng the leaf nodes of the skeleton to ther parent nodes [9]. Fnally, resultng vector skeleton s converted to raster skeleton. Whle the Vorono dagram-based thnnng algorthm may preserve the morphologcal shape of complex regon, t does not extract straght lne even from smple rectangular area wthout an extreme number of sample ponts (Fg. 18.7a).

9 18. 3D Geo-Network for Agent-based Buldng Evacuaton Smulaton 291 Fg Skeleton Extracton usng Thnnng Algorthm for Scanned Blueprnt Data The mathematcal operator extracts skeleton of a regon usng two steps: thnnng and prunng [28] (Fg. 18.7b). The thnnng derves a general skeleton of an area feature and prunng removes parastc branches of the skeleton [31]. Thnnng process requres predefned sequental wndow flters (3x3 wndows) that preserve eght drectons from the center of the wndow. These sequental flters are appled one by one to thn an area feature teratvely untl no changes occur. The thnnng process produces the parastc branches mostly at the end part of the skeleton (left mage n Fg. 18.7b), whch are removed by prunng. The prunng process utlzes some of the eght drectonal flters of whch drecton s correspondng to the drectons of the parastc branches [31]. Prunng s also teratve process untl no further changes occur and produces the fnal skeleton of a regon (rght mage n Fg. 18.7b). Whle the mathematcal operator better approxmates medal-axs of a regon than the Vorono dagram-based thnnng method, t requres nteractve selecton of drectonal flters for prunng. Snce the result skeleton depends on the selecton of prunng flters, the mathematcal operator method s not robust. The boundary peelng algorthm [27] does not need the sequental flters and the prunng process of the mathematcal operator method. The boundary peelng algorthm starts by ntalzng the regon to ON and the background to OFF. Every ON pxel s nspected by a 3x3 wndow, n whch the center pxels are erased (set to OFF) f the pxels are not requred for preservng connectvty, mantanng end lnes, and preventng nward eroson. In the thnnng process, ON-valued center pxels are erased (ERASED) f three condtons are satsfed. Frst, f the connectvty, defned as the number of chans of connected ON pxels n the neghborhood, s equal to 1, the center ON pxel can be erased (Fg. 18.8a). The erasure of the center pxel wll not destroy connectvty wthn any ON chans n the neghborhood. If the connectvty s

10 292 Jnmu Cho and Jyeong Lee 2 or more, the connectvty wll be broken. Second, f the maxmum length of a chan of the connected ON pxels n the neghborhood s greater than 1, the center ON pxel can be erased (Fg. 18.8b). If the maxmum length s 1, the center ON pxel s the end locaton of the end lne that should be mantaned. Thrd, f the maxmum length of a chan of the connected OFF pxels n the neghborhood s greater than 1 and less than 7, the center ON pxel can be erased (Fg. 18.8c). If the maxmum length s 1, the erasure of the center pxel can ntrude further nto ON regons. Fnally, f all these three condtons are satsfed, the center ON pxel s set to ERASED. The ERASED-value pxels are treated as f they were ON values to prevent uneven eroson on a sngle teraton. At the end of ndvdual teratons the ERASED pxels are set to OFF pxels. Iteratons stop when pxels are no longer erased. The results are the skeleton of nput area (see Fg. 18.7c). Fg Condtons to Erase Center ON-Value Pxel n Boundary Peelng Process Skeletons from the boundary peelng method n raster doman can approxmate the medal axs of a regon better than those produced by the Vorono dagram-based method. Further, the boundary peelng algorthm s robust than the mathematcal operator-based method snce t does not requre prunng that uses nteractve drectonal flters. Therefore, boundary peelng algorthm s mplemented n ths study and used to extract the skeleton of wall structure n order to buld 3D geometrc network data. For experment, vector polygon walls (Fg. 18.9a) are converted to raster data (Fg. 18.9b) and used as nput for the boundary peelng process. The output of the boundary peelng process s the skeleton of wall structure (Fg. 18.9c). Fg Wall Skeleton Extracton usng Boundary Peelng Algorthm Although skeletons may not be medal-axs, they are enough to represent walls to dentfy rooms n a buldng. The result wall skeleton needs to be converted to vector lne segments n order to buld 3D geometrc network through 3D topologcal structure.

11 18. 3D Geo-Network for Agent-based Buldng Evacuaton Smulaton 293 The process to buld 3D geometrc network from wall skeleton s already explaned n the prevous secton (see Secton ). In the next secton, 3D geometrc network nformaton s used to generate nput data for buldng evacuaton smulaton. The movng agents (human) n the evacuaton smulaton utlze the shortest path nformaton from 3D geometrc network to fnd the nearest outer ext from ther respectve locatons Buldng Evacuaton: Agent-Based Smulaton The key element of an agent-based model s agent. Agents are autonomous ndvduals who can make ndependent decson and nfluence on a smulaton [8, 23, 32]. Agents have ther own data and behavors. Agent data are current states that nclude the agent s nternal state and relatonshp wth other agents. Agent behavors sense ther surroundng to solve complex problems, to communcate, to move, and to adapt ther altered state [4]. The other elements of an agent-based model are relatonshps and envronments. A relatonshp between agents s specfed by lnkng agents wthn a system. Envronments defne the space n whch agents nteract wth the envronment and other agents Internal Structure of a Buldng and Agent s Data Buldng evacuaton smulaton requres four nput data: humans, rooms, walls, and doors (or exts). 3D geometrc network s used to store connectvty n doors data and adjacency n rooms. Among these nput data, humans and rooms are agents. Humans are movng agents that navgate to ext the buldng. Humans are located randomly n several rooms. Human agents have fve data: LOCATION, SIZE, ROOMID, SITUATION, and SPEED. LOCATION stores current locaton of an agent for hs movement. SIZE stores each agent s physcal sze for calculatng the nteracton wth other agents. ROOMID stores agent s current room. SITUATION stores the awareness of an emergency stuaton n a buldng. SPEED s agent s desred speed. The drecton of agent s movement s decded by the agent s locaton and the nteracton wth obstacles such as other agents and walls. Rooms are modeled as statc agents that update ther own states n case a room has an emergency stuaton such as a fre. Rooms data are generated by ArcGIS Clean operator durng 3D NRS constructon process (see Fg. 18.4b and Secton ). Room agents have ther own data: ROOMID, STATE, and NEIGHBOR. ROOMID s a room number. STATE stores the stuaton of the room. NEIGHBOR stores the adjacency nformaton of each room, whch s bult from 3D geometrc network. Walls and doors are envronments that provde humans wth locatonal nformaton to assess ext strategy n a buldng. Walls are extracted by straght MAT (see Secton ) and doors are generated by drectonal vector durng 3D NRS constructon process (see Fg. 18.4a and Secton ). Especally, geometrc network data are used to buld evacuaton routes from each room to outer ext based on the connectvty of rooms. Doors data stores the connectvty of two rooms for human agent s movng drecton at each door.

12 294 Jnmu Cho and Jyeong Lee Human Behavor Behavors rule an agent s actons that change agents locaton and update agents status. In a buldng evacuaton, the only movng agents are humans. The behavors of human can be derved from the paradgms of pedestran movements [14, 15, 26]. To smulate the crowd dynamcs n a buldng, Helbng et al. [13] provde a socal force model based on Newton s acceleraton equaton (Equaton 1). The frst term n Equaton 1 represents soco-psychologcal force. The second and thrd terms represent human nteracton (f ) (see Equaton 2) and human to wall nteracton (f w ) (see Equaton 3) forces. f = m a = m dv dt = m 0 0 v ( t) e ( t) v ( t) (1) f τ + f + j w w Here, each pedestran () of weght (m ) lkes to move wth a certan desred speed (v 0 ) n a certan desred drecton ) through tme (t), and therefore tends to correspondngly adapt hs or her actual velocty (v (e0 ) wth a certan reacton tme ( τ ). f = { A exp[( r d ) / B ] + kg( r d )} n + κg( r d ) Δv t (2) t j In Equaton 2, the frst term f = A exp[( r d ) / B ] n s a repulsve nteracton force of two agents and j to stay away from each other. A and B are constant that can reproduce the dstance kept at normal desred veloctes. Hgher A produces greater repulsve forces wth B range overlap between two agents. d denotes the dstance between center of two agents (H and H j ). r s sum of rad r and r j of two agents (H and H j ). n = (H -H j )/ d s the normalzed vector pontng from agent j to. If two agents touch each other (r > d ), body compresson kg ( r d ) n and relatve t tangental moton κg( r d ) Δv jt are consdered. If they are apart from each other (r < d ), g(x) s zero. k and κ determne the obstructon effect n cases of physcal nteractons. f w = { A exp[( r d ) / B ] + kg( r d )} n + κg( r d )( v t ) t (3) w w w w w w Equaton 3 captures the nteracton between humans and walls. d w means the dstance to wall W. n w denotes the drecton perpendcular to W. t w s the drecton tangental to W. Other terms are dentcal to those n Equaton 2. To mplement Equaton 1 for human movement n buldng evacuaton smulaton, sx behavors of human agents are used: MYSTATE, MYROOM, MYEXIT, MAINFORCE, P2PFORCE, P2WFORCE, MOVE, and UPDATE. The MYSTATE acton checks an agent s states. MYROOM checks current room stuaton wth room ID, whch decdes the agent s desred speed (v 0 ). MYEXIT checks current doorway

13 18. 3D Geo-Network for Agent-based Buldng Evacuaton Smulaton 295 to decde the desred drecton (e 0 ) of the agent. MAINFORCE calculates the socopsychologcal force (frst term) n Equaton 1. P2PFORCE calculates the human nteracton force usng Equaton 2. P2WFORCE calculates the human to wall nteracton force usng Equaton 3. MOVE changes the agent s locaton based on the result velocty of Equaton 1 that s sum of MAINFORCE, P2PFORCE and P2WFORCE. Fnally, UPDATE updates the agent s locaton and states Buldng Evacuaton Smulaton usng Agent-Based Model Among varous agent-based modelng tools such as Swarm, MASON, Repast, and so on, Agent Analyst that s based on Repast 4 s used n ths study snce t s tghtly coupled wth ArcGIS for data management and vsualzaton. There are two types of agents (vector and generc agents) n the Agent Analyst. A generc agent s a nonspatal agent such as emergency announcements. A vector agent s a spatal agent that s stored as features n a shapefle. In our evacuaton smulaton, all nput data ncludng humans, rooms, walls, and doorways are modeled as vector agents. In buldng evacuaton model, the only movng agent s human and therefore the smulaton s based on the human behavor model (see Equaton 1) and the characterstcs of the crowd dynamcs. We take frst fve characterstcs from Helbng et al. s [13] nne characterstcs of crowd dynamcs n order to smplfy the stuaton: (1) people move or try to move consderably faster than normal; (2) ndvduals start pushng each other, and nteractons among people become physcal n nature; (3) movng and, n partcular, passng through a bottleneck becomes uncoordnated; (4) at exts, archng and cloggng are observed; (5) jams buld up. We assume that all people n the buldng know ts structure so that they know where the nearest ext s and how to get to t. Door data stores room connectvty from 3D geometrc network, whch provdes the shortest path nformaton. To run the socal force model (see Equaton 1), we have specfed the parameters as follows: a mass of m s average 60kg. Intal desred speed (v 0 ) s 5m/s; ntal desred drecton (e 0 ) s toward ext; dstrbuted pedestran dameters 2r n the nterval [0.5m, 0.8m] consderng shoulder wdths; the constant A s 5000 and B s 0.08m; the parameter k s 750kg/s -2 and κ s 3000kg/ms. In the smulaton, we consder 15 people (N) n three rooms to generate evacuaton wth and wthout a jam stuaton for each room. The smulaton shows the evacuaton process through tme (Fg ). People follow the shortest path to the outer ext through the door of ther room. Fgs. 10a to 18.10f show the evacuaton stuaton n each room n the buldng. The number of movng people and the average speed are shown n Fg g. When people start to jam n a room (Fg b and Fg c), the number of movements and the average speeds start to decrease (Fg g). As people who are nearest to the door ext the room, the movements and the average velocty ncrease agan. However, at the outer ext of the hallway, there s another jam because people out of dfferent rooms meet at the ext, whch decrease the average speed agan (Fg d and Fg g). Therefore, f there are many people n a buldng and they want to evacuate 4 Repast s an agent-based modelng tool and can download source codes and materals at repast.sourceforge.net.

14 296 Jnmu Cho and Jyeong Lee at the same tme, jams can occur at several places such as doorways of each room, starways, and outer exts of the buldng. The jams make more dstant people take even longer to evacuate and may result n many casualtes n an emergency stuaton such as a fre. Fg Buldng Evacuaton Smulaton usng Agent-based Model In ths study, we smulate egress movement smply n one story buldng n 2D dsplay envronment. We can easly extend t to mult-story buldng by the connecton of multple floors n 2D envronment or may be drectly vsualzed n 3D envronment such as VRML. For evacuaton modelng n mult-story buldng, we need to consder two aspects: vertcal connecton and agents speed n vertcal movement. For vertcal connecton of floors, 3D geometrc network already dentfes starwells, elevators, and escalators and stores vertcal connectvty nformaton. For vertcal egress movement, the agents desred speed should be adjusted based on the type of space for vertcal movement. In case of emergency, only starwells may be consdered for vertcal movement and the desred speed should be reduced snce agents could not move freely n starwells wth the same speed as n hallways Concluson An ntegraton of agent-based modelng and GIS offers mprovements to understand complex spatal phenomena. Wth agent-based models, GIS can represent feature dynamcs such as temporal changes n spatal patterns for further tme-seres spatal analyss. However, current GIS are stll nascent n the representaton of 3D nternal structure of buldngs for mcro-level spatal modelng such as buldng evacuaton smulaton. In ths study, we have dscussed buldng evacuaton smulaton n GIS envronment by ntegratng 3D geometrc network constructon procedure (3D GeoNet) wth agent-based model (Agent Analyst). These tools are tghtly coupled wth ArcMap that s used for vsualzaton. 3D GeoNet generates the nternal structure data of a buldng

15 18. 3D Geo-Network for Agent-based Buldng Evacuaton Smulaton 297 and 3D geometrc network data and provdes nput data for Agent Analyst. Agent Analyst produces sequental results of agents egress movement. The sequental results of smulaton are vsualzed n ArcMap. Especally, the automated generaton of 3D geometrc network data s mportant for buldng evacuaton n order to provde pedestrans routng nformaton wth room connectvty. In ths study, 3D geometrc network has been created from buldng blueprnt data through wall extracton and 3D topology constructon. We tested two wall extracton methods: vector-based MAT and raster-based thnnng. For vectorbased MAT, we utlzed straght MAT to extract wall structure from wall polygon from CAD data. For raster-based thnnng, we tested boundary peelng algorthm to extract wall structure from scanned blueprnt. Wth wall structure extracted usng both extracton methods, doorways and rooms were dentfed. By 3D dualty, rooms were converted to nodes and the walls shared by rooms were converted to lnks n 3D topologcal structure. 3D geometrc network was bult by convertng nodes n 3D topologcal structure to lnks f the nodes represent hallways. In partcular, the topologcal connectvty and adjacency nformaton n 3D geometrc network were stored doorway and room data, respectvely, whch were used n buldng evacuaton smulaton. Wth spatal data ncludng rooms, walls, doors, and humans, a buldng evacuaton was smulated n GIS envronment usng Agent Analyst. Especally, human agents behavor n buldng evacuaton was modeled by adoptng Helbng et al. s socal force model [13]. We tested smple evacuaton wth a jam stuaton by enforcng dfferent numbers of people n three rooms. As expected, the results showed that the average velocty ncreased contnuously before jams, decreased durng jams at doorways and outer exts, and eventually ncreased agan as ndvduals escaped from jams. As we emphaszed n the paper, 3D geometrc network provdes detaled sub-unt structure of a feature such as a buldng for observng and understandng mcro-level navgaton. Especally, the ntegraton of 3D geometrc network and an agent-based model provdes current GIS wth new methodology that allows a smulaton for dynamc spatal process n a mcro-level envronment such as a buldng. Future buldng evacuaton smulaton wll nvolve enhanced human behavor model and more complex buldng structure under dsastrous stuaton such as fres Acknowledgments Ths research was supported by a grant from Seoul R&BD Program (10592) funded by the Cty of Seoul, South Korea References 1. Asante, K., Madment, D.: Creatng a Rver Network from the Arcs n the Dgtal Chart of the World. Avalable at (1999) 2. Benhamu, M., Doytsher, Y.: Toward a spatal 3D cadastre n Israel. Computers, Envronments and Urban Systems 27: (2003)

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