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1. Introduction

havioring de), urbaoffma

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time to destination. The availability of performance measures inconcert with visualization will provide a powerful evaluation toolfor both designers and planners.

When planning pedestrian environments it is necessary thatdesigners take into consideration how pedestrians will respond to

them. Examples from the simulation are provided to illustratethese behaviors as they occur during the pedestrian simulation.As well, a human study of a pedestrian corridor was performedto provide another basis for validation. The results of this studyand its simulation are presented.

2. Background

When a pedestrian enters an environment, such as an airport,they have an overall purpose in mind. One possible purpose may

q This manuscript was processed by Area Editor Paul Savory.* Corresponding author. Tel.: +1 662 325 7624; fax: +1 662 325 7618.

E-mail addresses: usher@ise.msstate.edu (J.M. Usher), strawderman@ise.

Computers & Industrial Engineering 59 (2010) 736747

Contents lists availab

Computers & Indus

.emsstate.edu (L. Strawderman).published texts on architectural design and our conversation witharchitects, there appears to be no real science associated with thedesign of such facilities. It appears that design decisions are madebased on experiential knowledge concerning operations withinsuch facilities. It is believed that the availability of a simulationsystem that realistically models the behavior of pedestrians andcrowds can be used as a tool to evaluate designs of new or existingintermodal facilities, in terms of their impact on pedestrian trafcperformance in terms of such measures as level of comfort and

capability in a virtual environment is no easy task. This paper re-ports on operational behaviors of pedestrian trafc that have beenreported in the literature and then describes the implementation ofthese observed behaviors within the simulation system. The sys-tem is referred to as the Intermodal Simulator for the Analysis ofPedestrian Trafc (ISAPT). The discussion focuses on the develop-ment aimed at simulating behavioral traits exhibited by pedestri-ans as they navigate their environment. These reported behaviorsare described in concert with the strategy used for simulatingCapturing realistic pedestrian befor evaluation and planning in buildHoogendoorn, 2001; Okazaki, 1979land use (Parker, Manson, Janssen, Hmarketing (Borgers & Timmermans(Zhang, Han, & Li, 2008) and trafc o& Voellmy, 2002). The goal of our rethat can be used as an aid for designation and operation of intermodal f0360-8352/$ - see front matter 2010 Elsevier Ltd. Adoi:10.1016/j.cie.2010.07.030in simulation is usefulsign (Daamen, Bovy, &n design (Jiang, 1999),nn, & Deadman, 2003),, passenger movementns (Cetin, Nagel, Raney,is to develop a systemplanners in the evalu-

s. Based on a review of

their environment as they navigate in order to complete their indi-vidual and sometimes joint missions. It is a unique problem giventhat you have a crowd of persons traveling to unique destinationsfor various purposes. Some may be in a hurry to get to one specicdestination, while others may be visiting intermediate destinationsfor the express purpose of wasting time prior to reaching their in-tended destination. As well, as crowd density increases, the patha person takes to reach their destination becomes convoluted asthey make numerous variations in their journey while navigatingthe crowds. Overall, the environment is one that is highly dynamic.

Navigation is an innate ability for humans, but simulating thisers and planners in the design and evaluation of intermodal facilities. 2010 Elsevier Ltd. All rights reserved.Simulating operational behaviors of pede

John M. Usher *, Lesley StrawdermanDepartment of Industrial and Systems Engineering, Mississippi State University, P.O. Box

a r t i c l e i n f o

Article history:Received 10 March 2009Received in revised form 19 July 2010Accepted 28 July 2010Available online 3 August 2010

Keywords:Pedestrian simulationPedestrian navigation behaviorMicro-simulation

a b s t r a c t

Navigation is an innate abeasy task and has been of iof ISAPT, an individual-basopment is based on the othe strategies employed bypassing strategies, and distdescribed in the paper alomicro-level simulation oflevel pedestrian behavior,pedestrian corridor. Such f

journal homepage: wwwll rights reserved.rian navigationq

2, MS 39762, USA

for humans, but simulating this capability in a virtual environment is noest to researchers for over a decade. This paper describes the developmentIntermodal Simulator for the Analysis of Pedestrian Trafc. ISAPTs devel-ved behaviors of pedestrians reported from the literature and simulatesestrians for collision avoidance, including changes in speed and trajectory,e between objects. The implementation of these behaviors and strategies iswith the results from a validation study. These results illustrate that thevidual pedestrians gives ISAPT the ability to reproduce identied macro-ell as the capability to reproduce the operational statistics of an observedtionality is necessary to support the use of simulation as a tool for design-

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rates a height map whereby each cell has a specied height valueindicating the elevation of the plane represented by that cell. If the

Indbe to ensure they reach their gate in a timely manner for the sched-uled departure of a ight. This overall purpose will be carried outtaking into account one or more sub-objectives that are required,or desired, in order to satisfy the overall goal. These objectivesmight include such tasks as the requirement that they check theirluggage and/or get something to eat prior to arriving at their gate.Therefore, navigation may entail the visitation of one or moreintermediate stops prior to arriving at their nal destination. Whilenavigating a selected path, the pedestrian will alter their routebased on such factors as crowd density and constraints imposedby the architecture itself (e.g., walls, columns, etc.) and its contents(e.g., furniture, planters, etc.). Given the static nature of theseitems, a pedestrian is able to plan ahead, making slight modica-tions to their direction as they move along a selected route (Bier-laire, Antonini, & Weber, 2003). While the pedestrian is followingtheir selected path, they will need to make modications to theirmovement based on interactions with unforeseen moving obsta-cles that will likely cross their path as they travel. These obstaclesprincipally represent other pedestrians but may include such mo-bile items as courtesy vehicles.

In order to safely traverse their path, real-time reactionary deci-sions are made that steer the pedestrian to avoid collisions. There-fore, collision detection and avoidance represents a criticalcomponent of navigation. The concept of reactive navigation doesnot necessarily use predened paths. A pedestrian navigates basedon its reaction to items within the environment. Such methods in-clude the use of social force elds, rule based methods, and XZTspace methods. Even though these methods permit a pedestrianto navigate, the pedestrian still requires some overall goal or direc-tion that motivates it to move. Therefore, it is not uncommon tosee these reactive methods used in combination with a dened tar-get that a pedestrian is trying to reach, or a path that it is following.

The social force eld approach of reactive navigation involvessteering a pedestrian by the application of a combination of forcesthat arise from the pedestrians interaction with the environment.This approach is sometimes referred to by the name, particle sys-tems, in that the pedestrians in the system are each representedas a particle of a given mass moving in a specied direction at a de-ned velocity as a result of the combined effect of the surroundingforces (Braun, Musse, de Oliveira, & Bodmann, 2003). For example,a target position can be represented as an attractive force andobstacles as repulsive forces. Such combined forces that dynami-cally change as the pedestrian moves through the environment re-sult in a random path arising as they traverse the environment.Helbing and Molnar (1995), Helbing and Molnar (1997) were oneof the rst to propose such an approach for pedestrian modeling.Other examples of social force eld approaches include Lamarcheand Donikian (2004), that of Metoyer and Hodgins (2003) to sup-port visualization of pedestrian movement as an enhancement tothe presentation of architectural and urban designs, and Heigeas,Luciani, Thollot, and Castagne (2003) for visual rendering ofcrowds in the ancient Greek agora of Argos.

A second approach involves the use of rule-based systems. Suchsystems have been described as a fast approach (Soteris & Yiorgos,2006), while at the same time touted as inappropriate for use withlarge crowds (Heigeas et al., 2003). However, Loscos, Marchal, andMeyer (2003) developed a rule-based system that is able to simu-late large crowds up to 10,000 pedestrians using a 2D grid to rep-resent the environment where each cell is either empty oroccupied by a pedestrian or obstacle (i.e., building). Pedestriannavigation involves deciding which one of eight possible locationsto move to next. In this framework, collision detection and avoid-ance is implemented by each pedestrian exploring the grid up to 5

J.M. Usher, L. Strawderman / Computers &tiles ahead to identify potential collisions. A problem with this ap-proach is that angular changes in direction are limited to 45 and 90angles. This leads to unrealistic movement of pedestrians an affor-change in elevation is not within the capabilities of the pedestrianthen this cell is treated as an obstacle around which they must nav-igate. As well, Tecchia et al. (2002) divide the behavioral rulesacross four layers. The rst two layers focus on rules related to col-lision detection with obstacles (Layer 1) and other pedestrians(Layer 2). The third layer encodes behavioral rules that may beassociated with a cell, and the fourth layer provides support forthe environment to react with a pedestrian (e.g., bus responds toperson at a stop). A novel aspect of the another rule-based ap-proach of presented by Niederberger and Gross (2003) is that theydistinguish between the possible actions (e.g., movements to fol-low a path) and reactions (e.g., collision avoidance) of an agent pro-viding top priority for reactions when the system selects from aqueue the next action to perform.

Soteris and Yiorgos (2006) also employ a 2D grid for navigation,but extend the typical rule-based approach for navigation with theuse of a ow grid as a perception mechanism to measure overallcrowd density. The idea is that a pedestrian will choose to movethrough areas of low density as they travel to their destination.The purpose of this global vision mechanism is to read the collec-tive behavior of the crowd; thereby, eliminating the need for eachpedestrian to have to consider each neighbor when making a deci-sion. This perception mechanismworks in simulation but one mustask in reality how well a person is able to perceive crowd densitiesat some distance from their current location.

The third method, the XZT (or XYT) approach, considers howpedestrian ow in a 2D (XZ) coordinate system will change overtime (T). This detailed extrapolation of each pedestrians trajectorythrough space permits the determination of future collisionsamong the pedestrians. Feurtey (2000) used this approach todetermine whether a pedestrian should either slow down, makedirection adjustments, or speed up in order to avoid collision. A no-vel factor in his approach is the ability to consider multiple pedes-trians in the analysis using a cost function to arrive at the bestresponse.

Sakuma, Mukai, and Kuriyama (2005) extend the concept of col-lision avoidance by working to include human limits as dened byhistorical psychological experimentation, specically memorycapacity. However, they do not provide any details on how thismemory inuences the rules used for collision avoidance. Unlikeother studies, Sakuma et al. (2005) does model crowd densitysinuence on pedestrian speed.

The ISAPT system presented in this paper makes use of anagent-based, rule-based approach to navigation, but not with therestrictions afforded by a 2D grid based environment. The systememploys a 3D spatially continuous domain to describe the position,movement, and velocity of each pedestrian. As well, the character-istics of each pedestrian (e.g. age, size, mobility, etc.) are allowed toinuence their behavior on an individual level better representingthe diverse pedestrian crowds that are commonly found withinintermodal facilities.

3. Reported pedestrian behavior

Behavioral studies found in the literature dene a number ofstrategies used by pedestrians as they navigate through a crowd,dance Loscos is willing to take in order to be able reduce computa-tional complexity.

Expanding on the typical rule-based approach used by 2D grid-based systems, Tecchia, Loscos, and Chrysanthou (2002) incorpo-

ustrial Engineering 59 (2010) 736747 737as summarized in Table 1. Related to collision avoidance, pedestri-ans tend to either change their trajectory or change their speed.They also have a number of strategies that are employed when

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Indpassing other pedestrians. These collision avoidance patterns areimpacted not only by the individuals, but also the density of thecrowd (Bierlaire et al., 2003).

When a pedestrian realizes that a collision with another pedes-trian is imminent, they generally decide a course of action. The twomost common choices are to change their trajectory or to changetheir speed. Behavioral studies have shown that pedestrians preferchanges in speed (Blue et al., 1997). The reason behind this is likelydue to the fact that it takes more physical effort to change onestrajectory. It is much easier, both physically and mentally, for pe-destrian to make a speed change. Each pedestrian has a preferred,maximum, and current walking speed. These speeds are used tomake decisions about acceleration or deceleration as they relateto collision avoidance (Haklay et al., 2001). A pedestrian will puta high emphasis on attaining and maintaining their preferredspeed. It should be noted, however, that pedestrians will alter fromtheir preferred speed to accomplish their goal (reaching their des-tination). These acceleration and decelerations are made not onlyto avoid collisions, but also to achieve time-dependent sub-goals.The decision to change speeds is dependent upon the pedestrians

Table 1Observed pedestrian behaviors from literature.

Category Behavior

Changes in speed A Each pedestrian has a current, maximum, and preferr

B Pedestrian speed is constant unless conditions for a cC Speed change is the preferred method of collision avoD Speed decreases with an increase in crowd density

E Decisions regarding speed change are dependent upo

Changes in direction F Preference is given to maintaining their current direcG Once a trajectory change has been made, pedestrians t

of their original pathH Pedestrians choose paths that minimize the need forI Pedestrians prefer a smooth non-linear trajectory to

Passing strategies J Pedestrians tend to pass on the right in opposite-direK Pedestrians pass on both the left and right with equaL If a head-on collision is imminent, both pedestrians t

Distance betweenobjects

M Pedestrians keep minimum distance from others in cN Distance depends on type of pedestrian and type of o

738 J.M. Usher, L. Strawderman / Computers &goals, urgency, and situation awareness. The speed of the pedes-trian will remain constant unless conditions for a change of speedare presented (Willis et al., 2002). A pedestrian will keep their cur-rent speed unless an external factor (another pedestrian, obstacle,or time) presents a cause for change.

A pedestrians speed is impacted not only by their goals and ur-gency but also the surrounding crowd. With an increase in crowddensity, a pedestrians average speed is decreased. This decreasein speed is not caused simply by a lack of physical space. The speeddecrease is likely compounded by the fact that pedestrians will befaced with many more decision points when navigating through adense crowd. They will have to be very situation-aware, taking in ahigh amount of information, to navigate the crowd. The time to ob-serve the surroundings and make corresponding decisions will of-ten slow pedestrians movement.

Daamen and Hoogendoorn (2003b) studied pedestrian speed attwo densities: high density of 0.6 pedestrians/m2 and low densityof 0.15 pedestrians/m2. The speed for each density was 1.47 m/sand 1.57 m/s, respectively. Similarly, Fruin (1971) found a decreasein speed with increasing density. They reported an approximatespeed of 1.14 m/s at a density of 0.6 pedestrians/m2 and a speedof 1.40 m/s at a density of 0.21 pedestrians/m2. Though the dataare not replicated between these two studies, the emergent trendis apparent.Though pedestrians prefer changes in speed, they often areforced to change their trajectory. Pedestrians have a tendency tochoose paths to their destination that minimize the need for angu-lar displacements (Turner & Penn, 2002). Additionally, Bierlaireet al. (2003) have demonstrated that pedestrians prefer a smoothnon-linear path as opposed to a linear acute path. That is, they willnot make their direction changes on a dime. The changes in trajec-tory will be more gradual and smooth. Pedestrians have a strongpreference of keeping their current direction as they move towardtheir goal destination (Antonini et al., 2006).

A simulation model created by Blue et al. (1997) calculated that18% of steps in a pedestrians pathway included a trajectory adjust-ment. Though no indication was provided regarding the extent ofthe trajectory change (e.g. 10 or 90), the nding is critical. The de-sire for a pedestrian to remain on a straight path is circumventedby an overriding need to change the trajectory in 18% of their steps.It is important to note that although pedestrians may change theirtrajectory, they tend to move back to their original pathway oncethe collision or obstruction has been passed (Goffman, 1971).

When pedestrians are faced with collision avoidance, they often

Source

peed Haklay, Osullivan, and ThurstainGoodwin (2001)

ge exist Willis, Kukla, Kerridge, and Hine (2002)nce Blue, Embrechts, and Adler (1997)

Daamen and Hoogendoorn (2003b) andFruin (1971)

individuals goals Helbing and Molnar (1997)

Antonini, Bierlaire, and Weber (2006)to return to moving in the direction Goffman (1971)

ular displacements Turner and Penn (2002)cute linear trajectory. Bierlaire et al. (2003)

n passing Goffman (1971)obability in uni-directional ow Daamen and Hoogendoorn (2003b)to make a side-step Helbing, Molnar, Farkas, and Bolay (2001)

ds (territorial effect) Bierlaire et al. (2003)uction Willis et al. (2002)

ustrial Engineering 59 (2010) 736747make a decision to pass another pedestrian. While this may seemsimilar to the passing of automobiles on a roadway, there are somedistinct differences. No rules exist for appropriate pedestrian pass-ing behavior. For instance, there is nothing prohibiting a pedestrianfrom crossing over into opposite owing trafc in order to passsomeone. However, in the United States, pedestrians do have a ten-dency to pass on the right when completing an opposite-directionpass (Goffman, 1971). When a pass needs to be made in uni-direc-tional ow, pedestrians tend to pass on both the left and the rightwith equal probability (Daamen & Hoogendoorn, 2003b). Helbing,Farkas and Bolay (2001) observed that when a head-on collision isimminent, both pedestrians tend to make a side-step in order toavoid collision.

Another important facet of pedestrian behavior is that of spac-ing. Individual pedestrians tend to keep a minimum distance fromothers in the crowd. This is known as the territorial effect (Bierlaireet al., 2003). Willis et al. (2002) have found that the actual distancebetween people or objects depends both on the type of pedestrianand the type of obstruction. Pedestrians take into account theirfamiliarity with the surrounding pedestrians, uncertainty of theothers actions, and prioritization of trajectories when maintainingdistance to other pedestrians. The distance kept from other obsta-cles is likely dependent on the maneuvering capabilities of theindividual pedestrian.

Ind4. Simulation system

The ISAPT system is an OpenGL-based application written in theC++ programming language with an aim at supporting cross-plat-form use. The simulation was developed using the OpenSteer tool-kit (Reynolds, 1999) that was originally created to aid in thedevelopment of autonomous characters in animation and gamingapplications. It provides a foundation for the development of anindividual behavior-based simulation making use of an existinggraphical architecture. The application provides a basic frameworkin which behaviors can be developed for 2D and 3D applications.

A pedestrian is represented in the simulation as a point massapproximation providing it with the capability for linear momen-tum, but no rotational momentum. A pedestrian is dened in termsof their position, mass, velocity, direction, and limitations (i.e.,maximum force and speed). Since we are simulating humans, a pe-destrians velocity arises from forces that are self-generated andmodied by changes in these forces (referred to as steering forces).At this time, no consideration is given to forces that may ariseexternally due to collisions with other pedestrians or obstacles.The simulation employs a xed time advance mechanism witheach pedestrians position and velocity being updated in each iter-ation (time step).

The movement of each pedestrian within the simulation is dic-tated by a steering force vector that is the compilation of a set ofindividual steering forces derived from individual behaviors pro-grammed into the system. This steering force vector is resolvedinto a position and velocity for the pedestrian. Unlike the 2Dgrid-based approaches reviewed earlier that permit only discretepositioning and incremental linear and angular movement of a pe-destrian in the environment, the ISAPT system utilizes a three-dimensional (3D) spatially continuous domain to describe the po-sition and movement of a pedestrian. Each pedestrians position(x(t)) is dened in terms of a 3D point in space, and vectors are em-ployed to represent their velocity (v(t)) and acceleration (a(t)) alldened with respect to some point in time (t) within the simula-tion. The Euler equations are used to simulate the physics of real-istic movement of a pedestrian. Therefore, the velocity andposition of a pedestrian at each iteration is determined using theequations:

~vt ~vt 1 ~at Dt 1xt xt 1 ~vt Dt 2The acceleration of the pedestrian is computed taking into accountthe overall steering force vector (F(t)) generated from the simulatedbehaviors of the system and the pedestrian mass (m(t)):

~at ~Ft=mt 3The mass of the pedestrian is depicted as a function of time giventhe need to represent such tasks as baggage check or claim wherea pedestrian either reduces or increases their mass based on theactivity performed. (At this time, changes in geometry of the pedes-trian area ignored.) Using these equations, the movement of everypedestrian in the simulation is updated in response to a change intheir steering force vector prior to incrementing the simulationclock.

The basic behaviors, currently employed within ISAPT, that con-tribute to the overall steering force include moving forward (in aspecied direction), path following, target seeking, braking, as wellas collision detection and avoidance of both stationary and movingobstacles (e.g., pedestrians, benches, etc.). In this paper, the discus-sion will focus on the implementation of the steering force behav-

J.M. Usher, L. Strawderman / Computers &iors associated with collision detection and avoidance ignoring, forthe sake of brevity, the topic of dealing with stationary obstaclessince this represents a much easier task than avoiding movingobstacles and utilizes much of the same logic. The other behaviorslisted have not been modied from those offered within the Open-Steer framework and are described in Reynolds (1999).

5. Pedestrian behavior and simulation

The individual basic behaviors mentioned earlier represent thebasic building blocks ISAPT uses as a means for simulating morecomplex macro-level behavior (e.g., formation of lanes in pedes-trian trafc). However, a problem arises in determining how bestto combine the steering force contribution of each of these behav-iors to determine the overall steering force vector applied by thepedestrian. Depending on the situation, you may want only onebehavior to execute (e.g., brake at destination) or for several tobe applied in parallel (e.g., follow the path while avoiding obsta-cles). To execute just one behavior means that in a single iterationonly that one behavior would determine the overall steering force.For times when it is desired that behaviors be applied in parallel,the system would sum their individual steering force contri-butions.

It is possible that a situation may arise where the effects of par-allel behaviors may cancel out resulting in no overall change. Thismay result in an inability to avoid a collision. Therefore, somemechanism is needed to determine how best to combine thebehaviors considered. After experimenting with various possiblemethods for combining behaviors (weighted sums, lters, priori-ties, etc.), Reynolds (1999) found that the technique of prioritizeddithering was most useful. This involves prioritizing the behaviors,and then if a behaviors consideration results in no action, then thenext most important behavior is considered. This continues untileither a response is determined or all behaviors have been consid-ered. At this time, this mechanism has been implemented using thelogic shown below.

if (near Target)applyBrakesteeringForce = 0

else if (need to avoid pedestrians)steeringForce += Avoid Moving Collision

else if (need to avoid obstacles)steeringForce += Avoid Stationary Collision

elsesteeringForce += Follow PathsteeringForce += Seek Target

end if

This logic is not perfect and can still result in situations where a per-son may collide with another person or obstacle. However, watch-ing any crowd of persons in actions reveals this is not an unusualbehavior in reality. The best strategy for combining these behaviorsis not settled at this point and represents a continuing research ef-fort that will be formally explored in the near future. At this time,this paper focuses on the specic mechanisms and strategies imple-mented in ISAPT for behavioral simulation at the operational level.Examples are provided to illustrate the systems capability to cor-rectly simulate the reported observed pedestrian behaviors summa-rized in Table 1.

The overall approach ISAPT uses involves each pedestrian eval-uating their best move taking into account the future position ofthe other pedestrians in their immediate vicinity. The system doesnot try to predict changes that other pedestrians may make, but

ustrial Engineering 59 (2010) 736747 739works to create a response that is the least disruptive and willallow other pedestrians to continue in their current paths. Usingthe anisotrophy property (like Hoogendoorn & Bovy, 2001) the

ments in their speeds to account for loss in mobility. Behavior Eis not currently considered by the system at this point in its devel-opment, but will come into play as the system capabilities expandto include consideration of each pedestrians purpose and agenda,as mentioned earlier.

5.2. Changes in direction

Behavior F has a pedestrian giving preference to maintainingtheir current direction. As mentioned earlier, the basic drive of a

740 J.M. Usher, L. Strawderman / Computers & Industrial Engineering 59 (2010) 736747pedestrian only considers those pedestrians in front of them forcollision avoidance.

5.1. Changes in speed

As stated previously, the basic drive of a pedestrian within theISAPT system is to move forward from its origin to its destination.In the system, each pedestrian is dened by a set of attributes thatincludes a mean (desired) and maximum speed. These values varyfrom one pedestrian to the next according to a normal distributionand are inuenced by the pedestrians age and gender in satisfac-tion of behavior A.1 A pedestrians current speed will be the sameas their mean speed and remain constant, as per behavior B, unlessthe environmental conditions warrant a need to make a change.Such conditions prompting a change by the pedestrian may be assimple as an impending collision with another pedestrian, or as com-plex as the presence of a crowd out of which a pedestrian is unableto navigate requiring that they adjust their speed (s(t)) to move withthe ow of the other persons in their immediate vicinity. This ad-justed speed is calculated as:

st st 1 t1t2

4

where t1, time until the pedestrian will collide with their closestthreat; t2, lead time, the time period into the future within whichpotential collisions are considered; v(t) = s(t) (forward directionvector).

In ISAPT, the collision detection mechanism explores the imme-diate vicinity of each pedestrian looking for other pedestrians thatrepresent potential future threats, the system extrapolates the fu-ture paths of the pedestrians in the vicinity exploring successivetime periods out in the future. For example, the position of eachpedestrian in the vicinity Dt time periods out in the future wouldbe determined by:

xt Dt xt vt Dt 5A collision is then tagged for those cases where the distance be-

tween these two pedestrians is less than the sum of their radii plusa set personal space attribute of each pedestrian. This evaluationassumes that each pedestrian is represented in the 2D plane by acircle, but could be modied to consider other spatial geometries.The system performs this evaluation for each pedestrian in thevicinity, at each successive time period out in the future, up tothe specic lead time of the pedestrian under evaluation(Dt = t2). After examining the collision potential of all other pedes-trians in the immediate area, the system will return the time, t1,representing the time until collision with the most imminent pe-destrian. Given the two values, t1 and t2, their ratio provides a con-venient factor for adjusting the speed to deal with an impendingthreat. This capability of dealing with collision avoidance bychanging speed matches that of behavior C. The decision to changespeed as opposed to changing direction (a behavior discussed later)in response to a potential collision is handled using probabilisticsettings with speed changes occurring x% of the time and directionchanges (1 x)% of the time with x > (1 x). A value of 80% is usedfor x in the current implementation, but future studies are neededto determine an appropriate value for this setting.

When a pedestrian is in an area consisting of several potentialthreats, the same algorithm mentioned above results in a naturalreduction in speed to account for the crowded conditions illustrat-ing the observed behavior D. Therefore, as crowd density increasesthe pedestrians trailing behind others will begin to make adjust-1 References to the individual behaviors listed in Table 1 will be indicated by theuse of italics (e.g., behavior A).pedestrian is to move forward to its destination by moving in thedirection of a chosen path. The pedestrian will maintain the givendirection of their path until an impending threat arises from othermoving or unforeseen stationary obstacles. The path they are onwill already have considered visible stationary obstacles when itwas generated as a part of path planning, but sometimes a modi-cation to this path may be needed if an unexpected stationaryobstacle is encountered (i.e., a piece of luggage left by someonein the middle of the oor). In accordance with behavior G, if a direc-tion change is made due to a stationary or moving object, the pe-destrian will return to a trajectory in the direction of theiroriginal path. This is illustrated in Fig. 1a where as two pedestriansare approaching one another in opposing ow, pedestrian #2makes a direction change to avoid pedestrian #1. The overall his-torical path movement is visible in Fig. 1b indicating that pedes-trian #2 has returned to their original direction and will slowlybegin to migrate (as indicated by the acceleration vector pointingdown and to the left) back to the original path as they continueon toward their destination. This return is guided by the contribu-tion of two steering behaviors, one that seeks to move along thepath and the other that contributes to seeking the target destina-tion. Each of these is weighted to impact their contribution.

In ISAPT, the avoidance behavior of the pedestrian depends onthe distance between the pedestrians. In a manner similar to(Sakuma et al., 2005) and Heigeas et al. (2003), ISAPT recognizestwo boundaries and like the psychophysical volume of Heigeaset al. (2003) the size of these volumes is variable. The outer bound-ary, termed the sensory boundary, is dened by the lead time of thepedestrian (dened earlier as t2). Instead of using a set distance fora boundary as in (Sakuma et al., 2005), each pedestrian has a per-sonal lead time, and if another pedestrian poses a future threat inthat a collision will take place within that lead time period, then anavoidance maneuver is prescribed by that pedestrian. This use oftime is more appropriate that a set distance since it takes into ac-count the speed of both pedestrians; thereby, allowing a pedes-trian to compensate for how quickly the threat is approachinggiving them sufcient time to react. This lead time is adjusted inresponse to changes in crowd density as prescribed by Fruin(1971). The inner boundary, termed the personal boundary, is pro-vided for those cases where an adjustment is made (by the pedes-trian themselves or perhaps someone else) that results in a suddenencounter of another pedestrian collision threat deep inside theFig. 1. Changes in trajectory.

threat from behind, then passing left or right is chosen with equalprobability (as per behavior K).

5.4. Distance between objects

Behavior M involves keeping a minimum distance from otherpedestrians. This is controlled by that inner boundary that wasmentioned earlier. When the inner boundary of a pedestrian ispenetrated, then using the logic discussed in Section 5, they willslow down, stop, or move aside depending on the position of obsta-cles in its immediate area. This will continue until something intheir immediate environment changes (e.g., another pedestrianmoves out of the way or changes their speed). As per behavior N,each pedestrian has their own value for this inner boundary repre-senting their personal comfort zone, which is currently set to val-ues on the order of 00.5 m. However, at this time, this distance isnot a function of the obstruction type.

6. Validation

The validity of ISAPT will be demonstrated in two ways. First,emergent crowd behaviors reported in the literature will be com-pared to those demonstrated with ISAPT. Due to the fact that thesimulation is modeled at an individual pedestrian level, any emer-gent collective behavior that has been previously veried serves tovalidate the model. Additionally, specic relationships between

Indsensory boundary. Such an encounter requires a quick drasticchange (e.g., stopping, or side-stepping) to avoid a collision.

In the scenario of Fig. 1, pedestrian #2 has a longer lead time(larger sensory distance) than #1 (3 s versus 2.5 s); therefore, it de-tects and reacts to pedestrian #1 before pedestrian #1 ever reacts.This is the reason why the path of pedestrian #1 is unaltered. Ifthey have the same lead time, then both pedestrians will noticeeach other and both make subsequent adjustments to their path.

Behaviors H and I are a function of the method used to deter-mine what overall paths a pedestrian will follow as they navigateto their destination. The idea is that they will choose a path thatwill result in a streamlined route from origin to destination. Thisis the idea of smoothing a path of sharp edges. This behavior isnot embodied at the collision detection and avoidance level, butmanifested at the strategic and tactical planning levels wherepaths are determined and re-evaluated on a periodic basis. At theoperational level, this is supported by the implementation of thephysics of pedestrian movement in that sharp turns at normalwalking speed are not really possible, non-linear trajectories re-quire that the pedestrian rst slow or stop, and then change direc-tion prior to moving again.

5.3. Passing strategies

The behavioral strategies for passing (behaviors J through L) areimplemented within the steering system of ISAPT with observanceof behaviors J and L as discussed above. As mentioned, when a pe-destrian within the sensory boundary represents a collision threatthen a temporary modication of trajectory is considered. Whendeciding on how a pedestrian should avoid a potential collision,most systems only consider the position and direction of the onepedestrian threat they are facing ignoring any other pedestriansin the immediate area. Aside from Feurteys (2000) use of a com-plex analytical approach that does take into account multiplepedestrians in determining a path, only some of the 2D grid-basedapproaches will make limited consideration of others in their rule-based approach, but even in these cases the extent of considerationis not robust and limited by the number of rules employed. Osaragi(2004) employs an equation to calculate a measure of collisionthat is used to determine if a pedestrian should move left or rightto avoid a collision. This measure divides the forward eld of anagent in two and computes a measure for each side taking into ac-count those pedestrians on a side that are within a dened region.The measure is computed as the sum of the product of the time anddistance separating the pedestrian in question from each other pe-destrian. The pedestrian will them move to the side with a lowervalue of the measure. Hoogendoorn and Bovy (2001), employ amethod that takes into account density, likelihood of interaction,destination, and preference in the computation of a utility valuefor deciding which side to choose.

In a similar manner, the ISAPT system has the capability to con-sider all pedestrians in the immediate area of the threat. The sys-tem rst looks ahead to the identied pedestrian threat anddivides the eld of view (FOV) into two halves (left and right). Itthen computes a density measure for each area that takes into ac-count not only the distance, but also the relative direction of eachpedestrian in each half of the FOV. The equation is:

Density measure Xi

directionidistancei

6

Pedestrians moving in the same direction are favored over thosemoving in the opposite direction. The further away a pedestrian is

J.M. Usher, L. Strawderman / Computers &the less impact it has on the measure. Following consideration ofall other pedestrians in the area, the pedestrians avoidance maneu-ver will be to the side with the smaller density measure. The densityconsiderations of individual pedestrians are what contribute to laneformation in pedestrian trafc. This behavior is illustrated in Fig. 2where one can see that the pedestrian has favored (as it should) theside with those pedestrians moving in their same direction. Eventhough passing to the left of the threat (pedestrian #5) is possible,this would place the pedestrian in a ow counter to their own. Inthe situation where the density values are equal, then if the threatis approaching the pedestrian from the front, a right side pass is fa-vored (as per behavior J) and if the pedestrian is approaching the

Fig. 2. Passing strategy employing density measure.ustrial Engineering 59 (2010) 736747 741pedestrian metrics (e.g. density, ow) will be demonstrated. Onceagain, these relationships have been reported in the literature andwill be used to test the validity of ISAPT. The last test will involve

comparing the results of a human study of a pedestrian sidewalkwith a simulation of the same system.

6.1. Emergent collective behaviors

A variety of crowd behaviors have been documented in the lit-erature, based on not only simulation studies but empirical data aswell. The most widely referenced collective behaviors include laneformation, intersection striping, bottleneck negotiation, aisleusage, and the impact of density on pedestrian speed.

For the system runs covered in this section, ISAPT was set up tosimulate pedestrian trafc moving either uni- or bidirectionally in

to see if the micro-simulation of pedestrian trafc would repro-duce this behavior. The number of pedestrians was kept constant

6.1.2. Lane formationWhen pedestrian density exceeds a critical value, dynamic lanes

emerge. The pedestrian lanes consist of pedestrians that share thesame intended direction and approximately the same velocity. Thenumber of lanes formed depends on the density of pedestrian traf-c and the width of the walkway. The formation of lanes is a resultof self-organized pedestrian behavior. Pedestrians prefer to walkbehind another pedestrian rather than making their own path. Byjoining a travel lane, a pedestrian is able to minimize interactionsthat require avoidance maneuvers. This leads to more efcient tra-vel for pedestrians (Daamen & Hoogendoorn, 2003a; Daamen &Hoogendoorn, 2003b; Goffman, 1971; Helbing, Buzna, Johansson,& Werner, 2005; Helbing & Molnar, 1997; Helbing et al., 2001;Weng, Shen, Yuan, & Fan, 2007).

When two lanes are formed, the division generally occurs in thecenter of the walkway. Using the same hallway setup as was simu-lated above, experiments were run to determine if the micro-levelsimulation of pedestrian behavior would result in the formation oflanes. The previous setup was modied such that the number ofpedestrians would be equally divided between those traveling ineach of the two directions in the hallway. The initial conditionswere

Multiple simulation runs were made with different numbers of

742 J.M. Usher, L. Strawderman / Computers & Industrial Engineering 59 (2010) 736747in each simulation run and ranged from 20 to 220 pedestrians. Gi-ven that the desired speed (free ow) of each pedestrian varied, thecycle time of each pedestrian was logged after an initial warm-upperiod. The average cycle time of each pedestrian was then com-puted and expressed as a fraction of their free-ow speed.

The results of these runs are shown in Fig. 3 where the averagefraction of free-ow speed for all pedestrians is plotted versus theavailable area per pedestrian in the system. These results illustratethat ISAPT correctly simulated the trend whereby pedestrian trafcslows as trafc density increases. An early published report by Fru-in (1971) illustrating the impact trafc density has on speed for pe-destrian in a walkway is shown in the same gure for comparison.The graph represents the results tting a curve to the observationsreported by Fruin. Note the similarity in the shape of the curveswith the greatest difference being 10% at high density (1 m2/s).a hallway that is 8 m wide. The region of interest for observationwas a 26 m long hallway containing no other entry or exit points.The pedestrians were initially randomly distributed throughoutthe hallway in both the horizontal and vertical direction. The de-sired velocity of each pedestrian was generated from a normal dis-tribution with a mean of 1.2 m/s and a standard deviation of 0.2 m/s. The number of pedestrians in the system was kept constant ineach simulation run. When a pedestrian exits the region of interest,the system would reintroduce that pedestrian back at the start ofthe hallway with their initial position across the width of the hall-way (y-value) at the entry point being determined using Eq. (7)where the current position is altered by a normally distributed va-lue (mean zero and variance of 1) whose value is constrained to0.5 m.

NewY-Position CurrentY-Position ClipN0;1;0:5;0:5 7

6.1.1. Speed as a function of densityGiven the many reports on the fact that pedestrian walking

speed is most signicantly impacted by trafc density, we desiredFig. 3. Speed as a function of trafc density.pedestrians in the system to illustrate the various lane formationbehaviors that would result. Table 2 shows the results from runningISAPT for various crowd densities. This table lists the direction ofeach lane, its averagewidth, and the number of pedestrians occupy-ing the lane. Three lanes form in most cases, but the number oflanes and the direction of the lanes vary. Fig. 5 illustrates the casewhere two lane form as a result of the interaction of the 80 pedes-trians within the system, while Fig. 6 shows three lanes for the caseof 120 pedestrians. Lane formation occurs quite quickly with theinitial semblance of lanes appearing within 30 s and clear lane dis-tinctions with almost no deviation at around 70100 s into the run.

6.1.3. Speed distribution across a hallwayPedestrians who have a faster speed than the average lane tra-

vel tend towards the outer edges of the walkways (Helbing et al.,

Fig. 4. Initial conditions for bidirectional ow of 80 pedestrians.

Table 2Lane formation results.

Number of pedestrians No. lanes Lane: ow direction/width (m)/occupancy

Top lane Middle lane Bottom lane

40 3 Right/2/12 Left/4.5/20 Right/1.5/860 3 Right/2.5/21 Left/4.5/30 Right/1/980 3 Right/2/17 Left/4/40 Right/2/2380 3 Left/1/8 Right/4.5/40 Left/2.5/3280 2 Right/4/40 Left/4/40

100 3 Left/2/26 Right/4/50 Left/2/24such that the pedestrians were randomly placed on their side of thehallway resulting in an initial mass chaos of pedestrian movementas they strive to navigate to the opposite end. Fig. 4 illustrates theinitial conditions for a run with 80 total pedestrians in the system.120 3 Right/2.5/31 Left/3.5/60 Right/2/29140 3 Right/2/33 Left/4/70 Right/2/37200 2 Right/4/100 Left/4/100

2001). This was tested using the same hallway setup for the case of80 pedestrians moving in two lanes of bidirectional ow. Speeddata of each pedestrian was collected as they passed through a0.1 m wide vertical zone halfway down the hallway. This zonewas further subdivided into 0.2 m wide blocks and the speed andposition of each pedestrian whose center passed through this blockwas averaged. Graphs of these results are shown in Fig. 7 and illus-

ior at these intersections depends on the number of walking direc-tions present. When only two trafc directions are present at anintersection, striping formations emerge. If the two directions areexactly opposite, the striping becomes lane formation. If, however,the two directions are not exactly opposite, pedestrians formstripes to proceed through the intersection. This is most noticeablewhen two wide streams of pedestrians intersect. According toHelbing,

Stripes are a segregation phenomenon, but not a stationaryone. The stripes are density waves moving in the direction ofthe sum of the directional vectors of both intersecting ows.Stripes extend sidewards into the direction that is perpendicu-lar to their direction of motion. Therefore, pedestrians move for-ward with the stripes and sidewards within the stripes(Helbing et al., 2005).

The emergence of stripes allows pedestrians to move throughthe intersection without the need to stop. Similar to lane forma-tion, striping at intersections maximizes travel efciency by limit-

Fig. 5. Lane formation for 80 pedestrians.

Fig. 6. Lane formation for 120 pedestrians.

J.M. Usher, L. Strawderman / Computers & Industrial Engineering 59 (2010) 736747 743trate the existence of a speed gradient supporting the observedbehavior.

6.1.4. Intersection stripingAt intersections, pedestrians are forced to interact with other

pedestrians traveling in different directions. The emergent behav-Fig. 7. Speed variation across hallway.ing obstructions and increasing average speed. To test thisbehavior, an intersection between two hallways was modeled inISAPT with the method of pedestrian generation being identicalto that used in previous experiments and the initial position ofeach pedestrian of the 140 pedestrians randomly generated. Tobest illustrate this behavior we rst removed the variability inthe walking speed of the pedestrians. This would mean that oncethe crowd reached steady state it would be easier to see the strip-ing phenomena if it occurred. Fig. 8 shows the results indicatingvery clear vertical stripes in the crowds motion. Fig. 9 illustratesthe results when variability in walking speed is permitted. Onecan see that the simulated crowd still exhibits the striping behav-ior although not as clearly as before.

6.2. Pedestrian sidewalk study

An additional method for validation was replicating empiricaldata with the simulation of a pedestrian corridor. To achieve this,video footage of an outdoor sidewalk was captured. The sidewalksarea of interest measured 11.3 m long by 3 m wide. There were noobstructions on either side of the sidewalk. The location of thesidewalk was in front of a campus building and observations wererecorded during a break between classes. Therefore, the majorityof the 51 pedestrians in the footage were students. Of the 51Fig. 8. Stripe formation with no variability.

The desired (mean) and max speeds for each agent are set tothe observed value for each individual pedestrian theyrepresent.

Case 2: The time between arrivals of agents is the same as inCase 1. The desired speeds of the agents are normally distrib-uted with those walking in each specic direction taken from

trian corridor. Errors on the overall mean time in the system ran-

744 J.M. Usher, L. Strawderman / Computers & Industrial Engineering 59 (2010) 736747pedestrians, 18 entered the sidewalk on the left (moving right) and33 entered on the right (moving left). The footage also captureddata for one bicyclist.

Video footage was captured using a high denition digital videocamera and saved as image stacks. This resulted in a compilation of901 images. The micro-level behavior of each pedestrian was cap-tured by tracking pedestrian coordinates frame by frame resultingin over 4000 points used in the analysis. These coordinates werethen used to calculate pedestrians paths, speeds, and interactions.Additional details on the empirical data collection and analysismethods employed can be found in (Lee, Strawderman, & Usher,2008).

When comparing the empirical results to the simulation results,we will use the term pedestrian to refer to the observed pedes-trian in the system (empirical data), and the term agent to repre-sent the simulated pedestrians (simulation data). The goal of thisvalidation phase is to show that the simulation accurately depictsthe sidewalk system. This is shown by comparing the simulationdata to the empirical data. The characteristics of the pedestriansspeed are given in Table 3.

6.2.1. Simulation setup and experimentation

Fig. 9. Stripe formation with variability.A sidewalk of the same dimensions as mentioned above wasconstructed in the simulation making use of penetrable barriersto dene the edges of the sidewalk. These types of barriers providea boundary for the agent that acts as a guide but which can be pen-etrated if desired for emergency purposes (i.e., overcrowding).ISAPT has the capability to make use of varying levels of detail withrespect to the assigned walking speeds for agents, their interarrivaltimes, as well as origin and destination positions. Three differentexperiments (cases) were setup to test the simulation varying thelevel of detail provided to the simulation. The performance statisticof interest is the mean time in the system for the pedestrians.

Case 1: The time between arrivals of each agent to the system,from either direction, is taken directly from the observed data.

Table 3Pedestrian statistics.

Walking direction Pedestrian speed (m/s) M

Mean Variance M

Left 1.30 0.0083 2Right 1.43 0.0285 3Both 1.34 0.0189 2ged from 0% to 1.71% and errors on the variance range from 2%to 20% depending on the level of detail of the input data used inthe simulation. It is interesting to see that relaxing the datarequirements between Cases 2 and 3, did not have much impactthe accuracy of the results. The last column of data does a pair wisecomparison, looking at the absolute value of the difference be-tween each of the pedestrians observed in the study and that oftheir corresponding simulated agent. For Case 1, the mean of thisdifference is in the vicinity of just a 1/20 of a second with the re-sults of Cases 2 and 3 not being much different from each otherwith mean differences of approximately of 1 s. At worst, this repre-sents an 813% error when looking at the time in the system forthe individual pedestrians within the simulation.

The time in the system for the observed pedestrians was nor-mally distributed as were the results of the replicated experimentsacross each pedestrian. Given this, a paired t-test was performedcomparing the mean time across the 10 replications for each ofthe 51 agents with their pedestrian counterpart to get an idea ifa signicant difference existed. The results shown in Table 5 andindicate additional support of ISAPTs ability to model the macro-level system behavior of the pedestrian trafc.

ax speed (m/s) Max speed/mean speed

ean Variance Mean Variance

.75 0.115 2.12 0.049a different distribution whose parameters match those givenin Table 3. The max speeds of each agent are calculated by mul-tiplying their desired speed by the ratio value from Table 3 fortheir particular direction (e.g., the max speed of an agent walk-ing left will be 2.12 desired speed).

Case 3: Time between arrivals of all agents follows an exponen-tial distribution with a mean value equivalent to the meanobserved value (1.59 s), and each agents desired and maxspeeds are dened using the same procedure as in Case 2.

Case 1 represents simulating a pedestrian area where there is anabundance of pedestrian data available. Given that this is not oftenthe situation, Case 2 relaxes the data requirements by making useof a selected distribution to characterize the desired and maxspeed of each pedestrian that enters the system. Case 3, then rep-resents the most popular scenario for the use of such a tool, wheredistributions are commonly selected to represent both the interar-rival times and speed of the pedestrians. In each experiment, thesame number of agents (51 pedestrians and one bicyclist) weregenerated and the simulation was run until all agents exited the re-gion of interest.

6.2.2. Simulation resultsTen replications were run for each of the three cases and the re-

sults are shown in Tables 4 and 5. The results in Table 4 report thestatistics on the time in the system from both the experimentaland simulation studies. Overall, the simulation did an excellentjob of predicting the time in the system behavior for this pedes-.18 0.236 2.23 0.047

.90 0.197 2.16 0.050

adjustment for pedestrian 13 long before the encounter. FromFig. 10b we can see that the change by pedestrian 9 begins atapproximately 16.5 s into the study period, while pedestrian 13 be-

Table 4Simulation results.

Case Walking direction Number of pedestrians SimulationTime in system (s)

nce

J.M. Usher, L. Strawderman / Computers & IndTo provide a more complete picture, the length of time requiredfor all pedestrians to exit the system was also considered. Thislength of time is inuenced by the interarrival time characteristicsalong with the mean and max speed of the pedestrians and theirinteraction during their journey through the pedestrian corridor.Table 6 reports these results for the three cases indicating errorsin the range from 0% to 1.4%.

In addition to the macro-level behavior, there is also interest inthe ability of ISAPT to capture the micro-level behavior of a pedes-trian as they walk from origin to destination. Given the data col-lected in the sidewalk study, it is possible to map out the pathtaken by a pedestrian for direct comparison with their correspond-ing simulated agent. The position of each pedestrian was extractedfrom the video data and a corresponding position for each agentwas recorded during each replication of the simulation. The x-axis

Mean Varia

1 Right 18 8.08 1.22Left 33 8.78 0.52Both 51 8.53 0.86

2 Right 18 8.02 1.03Left 33 8.79 0.49Both 51 8.52 0.82

3 Right 18 8.17 0.98Left 33 8.92 0.46Both 51 8.65 0.78

Table 5Results of paired t-test.

Case t value 95% CI P-value

1 0.56 (0.030, 0.053) 0.5752 0.02 (0.262, 0.129) 0.9813 1.08 (0.114, 0.381) 0.285represents a pedestrians position along the length of the sidewalkand the y-axis represents their position relative to the width of thesidewalk (with the point (0, 0) at the top left of the frame). In thesimulation, the x and y positions of each agent were averagedacross the 10 replications of the simulation at each instance in time(Dt = 0.1 s) and compared with the positions of their correspondingpedestrian from the video study at that same point in time. Foreach agent, the mean of the difference in both the x and y positionvalues between each pedestrian and agent for their entire pathwere computed. Table 7 lists the statistics on this data for the re-sults of Case 1 and Case 3. (Given the similarity between Cases 2and 3, no analysis was performed on the Case 2 data.) These resultsillustrate howwell the simulation is able to represent the micro-le-vel navigational behavior of the pedestrians. These results couldprobably be improved further if the experimental data is smoothedto account for any possible error on the part of the operator wheninterpreting the video position of each pedestrian.

Looking at some of the specic pedestrian interactions gives usan idea of the navigational capability of ISAPT. In the video footage

Table 6Time last pedestrian exits the system.

Case Simulation Observation Error (%)

1 90.10 s 90.10 s 0.002 89.69 90.10 0.463 88.85 90.10 1.39it is easy to discern that both pedestrians 9 and 13 alter their pathsto avoid a potential head-on collision with each other. Fig. 10ashows a graph of their x and y positions for the paths of both thepedestrian and agent, while Fig. 10b and c shows their respectivex and y positions over time. Given the orientation of Fig. 10a, pe-destrian 9 is traveling from left to right and pedestrian 13 walksfrom right to left. In this gure you can see the adjustments madeto their paths in response to a potential collision. At the beginningof their journey you can see that pedestrian 9 begins to make an

Observation Error (%) Per agentTime in system (s) |TSim TObs|Mean Variance Mean Variance Mean Variance

8.06 1.01 0.25 20.79 0.063 0.0418.77 0.52 0.11 0.00 0.030 0.0128.52 0.80 0.12 7.50 0.042 0.022

8.06 1.01 0.50 1.98 1.108 2.2828.77 0.52 0.23 5.77 0.722 0.8758.52 0.80 0.00 2.50 0.865 1.359

8.06 1.01 1.36 2.97 1.119 2.1238.77 0.52 1.71 11.54 0.718 0.8768.52 0.80 1.53 2.50 0.870 1.302

Table 7Deviation in travel path for Cases 1 and 3.

Case 1 Case 3

|Dx| |Dy| |Dx| |Dy|

Mean 0.0937 0.0949 0.4070 0.0878Variance 0.00546 0.00422 0.0725 0.00284Max 0.487 0.357 1.3460 0.2639Min 0.0310 0.0257 0.0757 0.0224

ustrial Engineering 59 (2010) 736747 745gins their change at 19.6 s. Fig. 10c shows that they will actuallypass each other at approximately 21.3 s into the observation peri-od. Using Fig. 10c and extrapolating the x-position of pedestrian 13at 16.5 s, it was determined that pedestrian 9 begins their pathchange while 7.6 m away from pedestrian 13, whereas pedestrian13 begins their change while only 5.5 m separates them from im-pact. This illustrates the variability in each pedestrians responsetime. In ISAPT this parameter can be individually set and alteredfor each pedestrian. This is an important capability given the vari-ability of this value across cultures and the fact that it is a functionof crowd density.

Given that the time of collision would have been at 21.3 s, youcan see in Fig. 10b that at this point in time, the y-axis separationbetween the two pedestrians is 0.93 m what that between theagents is 0.68 m. The path followed by pedestrian 13 shows twoadjustments. The rst at x = 11 m is in response to pedestrian 9and another that begins at approximately 5 m (24 s) for a bicyclistthat enters the observation area at time 29.8 s. The paths taken bythe agents exhibits similar behavior to the pedestrians. Examiningthe agents paths we see that agent 13 responds to agent 9 withtheir initial path closely following that of their pedestrian counter-part. However, an overcorrection results that is a combination ofthe approaching bicyclist (mentioned above) and their goal ofreaching their target position at the exit from the system. (Eachagent is given a destination when they enter the system that isbased on the actual exit point of their pedestrian counterpart. Thisdestination contributes to the overall steering behavior of the

Ind(b)

(a)

746 J.M. Usher, L. Strawderman / Computers &pedestrian.) Looking at the path of agent 9 in Fig. 10a, a slightcorrection that occurs at approximately 7 m along the x-axis is anavigational correction made by the agent to ensure that theynot walk off the sidewalk. Overall, the maximum value of thex and y-axis deviations of agent 9 are 0.25 and 0.28 m, while thex and y-axis deviations for agent 13 are 0.36 and 0.68 m.

As opposed to a head-on collision, another popular dynamic be-tween pedestrians is when two pedestrians are traveling in thesame direction but at different speeds such that the faster pedes-trian will maneuver to pass the slower one. This behavior was ob-served in the interaction between pedestrians 12 and 15 as theywalked from right to left (high value on x-axis to low value). Pedes-trian 12 was traveling at a mean rate of 1.01 m/s, while pedestrian15 walked at the mean rate of 1.36 m/s. Fig. 11a shows that thepoint at which they would pass one another (intersect on the x-axis) falls outside of the observation region, occurring at anapproximately x-axis value of 2 (at around 32 s into the study).From Fig. 11b we can extrapolate that the distance separating theircenters when they pass will be approximately 0.65 m, indicating aclose proximity when passing occurs. In this gure you can see thatthe passing behavior of the pedestrian is accurately simulated bythe agent along with the actual travel path as seen by the overlap

(c)

Fig. 10. Travel paths of head-on passing pedestrians/agents 9 and 13.in the plots of each pedestrian and its corresponding agent. Overall,the maximum value of the x and y-axis deviations of agent 12 are0.10 and 0.13 m, while the x and y-axis deviations for agent 15 are0.25 and 0.15 m.(a)

(b)

Fig. 11. Travel paths of from-behind passing pedestrians/agents 12 and 15.

ustrial Engineering 59 (2010) 7367477. Conclusions

ISAPT provides a system with the capability to simulate pedes-trian trafc within intermodal facilities. Some of the unique char-acteristics of ISAPT include the fact that as opposed to thediscrete 2D grid-based systems that currently exist, ISAPT providesa 3D spatially continuous domain to represent the position andmovement of pedestrians. The attributes of each pedestrian canbe set independently taking into account variations in their weight,age, size, and mobility. Pedestrian speeds are allowed to vary on acontinuous scale following observed distributions characterized interms their preferred and maximum speeds. As opposed to analyt-ical methods that are grounded in force theory alone, ISAPT has theadvantage in that the individual steering behaviors were devel-oped based on the micro-level pedestrian behaviors that have beenreported in the literature. The animation of pedestrian trafc takesplace in real time permitting simultaneous display of the trafcow while the simulation is running.

The results of the pedestrian validation study indicate ISAPTsability to model pedestrian trafc at both the macro and micro-le-vel. From the individual behaviors of the simulated agents emer-gent collective behaviors arose that were validated by publishedstudies of pedestrian behavior. These behaviors including lane for-mation, intersection striping, distribution of speeds across a corri-dor, and the reproduction of the speed versus density graphs fromthe literature. As well, based on a simulation of an observed pedes-trian corridor, the simulation was able to predict the macro-levelperformance of the system while also exhibiting observed micro-level behavior of the individual pedestrians within the system.The analytical results demonstrate ISAPTs ability to generate accu-rate trafc performance parameters (mean time in system, trafc

ow, etc.) that are commonly used in the evaluation of intermodalfacility designs and layouts.

As can be expected in pedestrian trafc, the video from the pe-destrian study showed several of the pedestrians walking togetherin groups of two. The simulation, at this time, is not able to directlysimulate this behavior in providing a group type of response to

Feurtey, F. (2000). Simulating the collision avoidance of pedestrians. Dept. ofElectronic Engineering, University of Tokyo, Tokyo, Masters.

Fruin, J. (1971). Pedestrian planning and design. Metropolitan Association of UrbanDesigners and Environmental Planners, Elevator World.

Goffman, E. (1971). Relations in public: Microstudies of the public order. New York:Basic Books.

Haklay, M., OSullivan, D., & ThurstainGoodwin, M. (2001). So go down town:Simulating pedestrian movement in town centres. Environment and Planning B:

J.M. Usher, L. Strawderman / Computers & Industrial Engineering 59 (2010) 736747 747external stimuli. However, in Case 1 of the sidewalk validationstudy, since the mean and max speeds of the agents match thoseof the observed pedestrians, then in the simulation this groupingbehavior is seen visually, but the behavior deviates if the groupis required to respond to a collision threat since a group responseis not currently programmed into the system.

Another limitation of the current system is that if the origin anddestination of a pedestrian are separated across a large distanceand the architecture/layout of static obstacles between them issomewhat complex, then reactive navigation can sometimes resultin an unusual path for the pedestrian. This problem requires thatthe user dene paths composed of intermediate points betweenthe origin and destination to overcome the complexity of the build-ings architecture.

Future work includes conducting additional human studies tovalidate the simulation model on a larger scale. Work is in progresson the simulation of an airport lobby area involving a host of ser-vices to travelers. To handle this complex system, ISAPT will be ex-panded to include the concept of pedestrian agendas incorporatingmore advanced pedestrian maneuvers and planning tactics to han-dle activity scheduling and route planning within more complexbuilding architectures.

Acknowledgement

This project is sponsored by the US Department of Transporta-tion (Grant No. DTOS59-06-G-00041).

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Simulating operational behaviors of pedestrian navigationIntroductionBackgroundReported pedestrian behaviorSimulation systemPedestrian behavior and simulationChanges in speedChanges in directionPassing strategiesDistance between objects

ValidationEmergent collective behaviorsSpeed as a function of densityLane formationSpeed distribution across a hallwayIntersection striping

Pedestrian sidewalk studySimulation setup and experimentationSimulation results

ConclusionsAcknowledgementReferences