Human movement behaviour in urban spaces

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Human movement behaviour in urban spaces: Implications for the design and modelling of effective pedestrian environments Article in Environment and Planning B Planning and Design · November 2004 DOI: 10.1068/b3060 · Source: RePEc

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Environment and Planning B: Planning and Design 2004, volume 31, pages 805 ^ 828

DOI:10.1068/b3060

Human movement behaviour in urban spaces: implications for the design and modelling of effective pedestrian environments Alexandra Willisô

School of Psychology and Sociology, Napier University, South Craig, Craighouse Road, Edinburgh EH10 5LG, Scotland; e-mail: [email protected]

Nathalia Gjersoe, Catriona Havard, Jon Kerridge}, Robert Kukla}

Transport Research Institute, Redwood House, 66 Spylaw Road, Edinburgh E10 5BR, Scotland; e-mail: [email protected], [email protected] Received 9 August 2003; in revised form 20 January 2004

Abstract. Despite a burgeoning research effort directed at the design and modelling of effective urban spaces for pedestrians, remarkably little is known about how pedestrians actually negotiate urban spaces. This paper reports the results of a video-based observational study aimed at exploring: (1) individuals' movement preferences within uncluttered environments, in particular: (a) desired walking speed, (b) microscopic position preferences, and (c) interpersonal distances between companions while walking; and (2) the ways in which these variables might be influenced by the various personal, situational, and environmental factors that characterise the context in which pedestrians move. The microscopic movement trajectories of 2613 participants were investigated in a covert, video-based observational study of three mixed-use (residential/retail) urban environments close to the city centres of Edinburgh and York, United Kingdom. Age, gender, level of mobility, group size, time of day, and location were found to have significant effects on movement preferences across the range of locations studied. We concluded that a number of influential factors affect how humans negotiate urban spaces, and suggested how these factors may be taken into account in attempts to design and model effective urban spaces for pedestrians.

1 Introduction Predicting individuals' movement patterns through space is becoming an increasingly important goal of urban and transport planners interested in designing effective urban spaces for pedestrians (see Batty, 1997; 2001). Such predictions, however, are not simpleönot least because a large number of variables, relating both to pedestrians themselves and to the situations and environments in which they find themselves, must be taken into account. Simulation models offer a potential means by which planners can predict the movement patterns of large numbers of pedestrians as they negotiate various urban spaces. Of these, models that operate at the level of individual pedestrians currently dominate the literature, no doubt reflecting their greater potential to mimic pedestrian behaviour in a realistic and flexible way [see, however, Al-Gadhi and Mahmassani (1991), and Blue and Adler (1998), for examples that focus on movement behaviour at the aggregate level of pedestrian crowds]. Such `microscopic' models can be differentiated according to the principles and rules that underpin the movement decisions of model pedestrians as they pass through virtual space. Space-syntax models, for example, are based on the principle of `direct perception' (see Gibson, 1979): pedestrians in such models act by evaluating the visual properties of footway surfaces around them and making a movement decision based on which route offers them the greatest affordance (Hillier et al, 1993; Turner and Penn, 2002). Most other models, however, are based on the principle that each pedestrian is ô Corresponding author. Also at the Transport Research Institute. } Also at the School of Computing.

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A Willis, N Gjersoe, C Havard, J Kerridge, R Kukla

motivated by a desire to reach a particular goal location, and will choose the optimum path (normally the most direct) in order to get there. How this path is chosen may be determined by simple `social force' rules that describe individuals' interactions with other pedestrians in a range of crowded contexts (for example, Helbing et al, 2001), rules of utility maximisation that describe a rational choice made by pedestrians in weighing up the various advantages and trade-offs in choosing a particular route (for example, Hoogendoorn and Bovy, 2003), or rules of queueing or helping that describe people's behaviour in evacuation scenarios (for example, Galea and Galparsoro, 1994; Lovas, 1994). STREETS, an agent-based simulation model, seeks to mimic route-choice behaviour at both microscopic and mesoscopic spatial scales, using a combination of deterministic way-finding and simple interaction rules (for example, Haklay et al, 2001). Our own agent-based model of pedestrian movement, PEDFLOW, uses a contextmediated approach to select which movement decision is the most appropriate from a range of options associated with the prevailing conditions (Kerridge et al, 2001; Kukla et al, 2001; 2003). In order to be of value as an evaluative tool, any microscopic model of pedestrian flow must yield a meaningful output that can be readily used by planners to evaluate the `walkability' of pedestrian areas. Such output may take the form of an aggregate trail of individuals' trajectories through space, indicating regions of high and low pedestrian use, or, more commonly, estimates of average delay, based on comparisons of ideal versus actual average walking speeds and distances experienced by agents in the simulation. More importantly, perhaps, in order to be of value as a predictive tool, any microscopic model must be able to simulate realistic pedestrian behaviour. However, our understanding of how pedestrians behave at a microscopic level, and how their behaviour is shaped by the various personal, situational, and environmental factors that characterise their interactions with urban spaces, remains extremely poor. Although some researchers have attempted to validate their models using purposely collected observational data relating to a specific behaviour and case-study location (see, for example, Helbing et al, 2001), there is widespread agreement that a more comprehensive appreciation of how pedestrians negotiate space is required to enhance the validity and reliability of models that seek to simulate movement behaviour at a microscopic level (see Hoogendoorn et al, 2003; Kerridge et al, 2001; Turner and Penn, 2002). The empirical literature does provide some insights relating to various elements of human movement behaviour in urban spaces. Of these, the most fundamental include walking speed (a parameter central to most microscopic simulation models) and the various distances people choose to maintain between themselves and other entities around them (such as obstacles, building edges, kerbs, and other pedestrians) while walking. The reported mean walking speed of pedestrians in urban environments, for example, varies between approximately 1.0 ^ 1.1 m sÿ1 (for example, Polus et al, 1983; Virkler, 1998) and 1.5 m sÿ1 (Knoblauch et al, 1996). A number of factors may explain this variation in recorded speeds. Personal factors such as age and gender certainly appear to contribute: there is a tendency, for example, for males to walk faster than females (for example, Boles, 1981; Knoblauch et al, 1996), though this is not always the case (see Fugger et al, 2000), and for walking speed to decline with increasing age during adulthood (Bowman and Vecellio, 1994; Coffin and Morrall, 1995). Situational factors (in other words, those that characterise the particular context in which a pedestrian finds himself or herself, but which are not `fixed' from one outing to the next) may also help explain differences in recorded walking speeds between studies. It is well known, for example, that the prevailing density of other pedestrians has a significant effect on individuals' walking speeds: indeed, the speed ^ flow relationship of pedestrian movement patterns is well documented (for example, Fruin, 1971; Henderson, 1971).

Human movement behaviour in urban spaces

807

Other situational factors, such as group size and level of mobility may also play a role, although these have not received as much attention in the literature (see, however, Boles, 1981; Knoblauch et al, 1996). Finally, a number of environmental factors may also influence spontaneous walking speeds. We know, for example, that prevailing temperature affects how fast pedestrians move (Rotton et al, 1990), and that walking speeds vary according to the type of facility studied: people seem to move more quickly when crossing roads, for example, than when negotiating a footway (see, for example, Lam et al, 1995), and when the volume of prevailing traffic is high (Fugger et al, 2000; Knoblauch et al, 1996). It is also likely that differences in the overall function of a pedestrian area (for example, shopping, leisure, business, transport interchange, `route to school') will also prove significant, presumably because of the differing priorities and goals of the pedestrians who populate them. Studies exploring the space preferences of pedestrians in urban areas have mainly focused on establishing various levels-of-service criteria relating to pedestrian traffic in crowded or potentially crowded areas (for example, Fruin, 1971; Pushkarev and Zupan, 1975). Early reports suggested that people prefer to maintain a buffer zone of around 0.45 m between themselves and the edges of buildings (for example, Ciolek, 1978; Fruin, 1971), a smaller distance (approximately 0.1 m) to stationary items of street furniture (Habicht and Braaksma, 1984) and a larger distance (around 0.8 to 0.9 m) between themselves and other pedestrians (Dabbs and Stokes, 1975); one report also suggested that people like to maintain a distance of around 0.75 m between themselves and their companion(s) when walking (Burgess, 1983). Many of these preliminary findings, however, have remained largely uncorroborated (see, for example, Kwon et al, 1998), and the effects of various personal and environmental characteristics on these spacing behaviours remain unexplored. Although useful for shaping broad guidelines in the design of high-volume pedestrian facilities, such studies are of limited use to researchers interested in modelling the microscopic movement behaviour of individual pedestrians. First, interstudy differences in the methodology (not least the method of data collection and the criteria used for measurement), type of environment, and the nature and density of the prevailing pedestrian population make valid comparisons between studies almost impossible: this essentially precludes a comprehensive understanding of how the many confounding factors affect pedestrians' movement behaviour. Second, many agent-based models calculate differences between ideal and actual movement patterns experienced by modelled pedestrians in determining the output of a simulation (for example, in estimating average delay); however, few empirical studies have attempted to explore the fundamental aspects of movement behaviour in ìdeal' circumstances, concentrating instead on pedestrian activity in a variety of constrained or congested contexts. In the present paper we attempt to address these shortcomings through the systematic study of naturalistic human movement behaviour in a range of uncongested, urban spaces. Specifically, our study aimed to characterise the elements of behaviour fundamental to all pedestrian models (namely, desired walking speeds and spacing behaviours), and how they vary according to various personal, situational, and environmental characteristics, within environments that offer the freedom for pedestrians to negotiate space as they wish: our overall aim, then, was to enhance our understanding of how our walking behaviour is shaped by the context in which we move. The study formed part of a larger research project concerned with developing an agent-based model of pedestrian movement (PEDFLOW) according to empirical analyses of how pedestrians actually behave (see, for example, Kukla et al, 2001; Willis et al, 2000; 2002). The findings, we hope, should also prove useful to any researcher interested in designing more effective pedestrian spaces and in modelling pedestrian behaviour at a microscopic level.

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2 Methods Microscopic movement patterns of individual pedestrians were investigated in a series of covert, video-based observational studies conducted at three case-study locations in York and Edinburgh, United Kingdom. We focused on environments that are typical of many medium-sized European cities: namely, those comprising pavements bounding a single-carriageway road (of variable traffic flow), and within which space for competing users is limited. Video cameras were mounted discreetly within each site of interest in order to avoid the well-known phenomenon (the òbserver effect') that individuals change their behaviour when they know they are being watched. Movement trajectories of all pedestrians who met our inclusion criteria were analysed by using a combination of commercially available motion-tracking software and custom measurement tools. Data relating to a range of personal, situational, and environmental factors were recorded alongside various measured and derived variables relating to walking behaviour (such as desired walking speed and spacing behaviour) and subjected to multiple statistical analyses. All work was conducted in accordance with the British Psychological Society (BPS)'s ethical code of conduct and the UK Data Protection Act (1998). 2.1 Background

The current study represents the first attempt at a systematic survey of microscopic movement behaviours in urban environments using covert, video-based techniques. Two main drawbacks with this type of approach may explain why no such survey has been attempted to date. First, the problem of calibrating a foreshortened video image, which arises when the camera is not positioned 908 laterally and horizontally with respect to the ground, has typically limited video surveys to locations where bulky scaffolds can be erected to hold the camera directly over the observation area (for example, Pushkarev and Zupan, 1975): such methods are expensive, may encourage atypical behaviours, and consequently restrict the range of environments that can be usefully surveyed. Second, tracking the fine-grained trajectories of pedestrians as they negotiate space has so far proven technologically challenging, and immensely laborious. Recent developments in digital image capture and processing at least provide the potential for more accurate, automated methods of trajectory tracing (for example, Hoogendoorn et al, 2003; Makris and Ellis, 2002; Teknomo et al, 2001): however, no techniques are yet available that allow the automatic tracking of pedestrians outside the controlled environment of the laboratory with an acceptable degree of reliability. For this reason, we developed a semiautomatic protocol which is flexible enough to permit the study of a range of urban locations without the need for highly conspicuous equipment, and which operates at a scale appropriate for the fine-grained analysis of individual movement trajectories through space. This protocol incorporates a set of standardised procedures that maximises accuracy in tracking pedestrians' movements and minimises the time taken for analysis (see Willis et al, 2000; 2002). 2.2 Sample

A total of 2613 pedestrians took part, unwittingly, in the survey. Because we were interested in exploring movement patterns under conditions that most closely approximated ìdeal' circumstances (that is, unencumbered by obstacles or other pedestrians), only pedestrians deemed freely able to select their position and walking speed were eligible for inclusion. Based on a combination of estimates of pedestrian level of service A suggested by Fruin (1971) and the Highway Capacity Manual (TRB, 2000), this translated to a total of no more than two other people (excluding companions) within an area 6 m directly ahead of the pedestrian under analysis and 0.75 m either side as he or she entered the sampling area. As well as meeting this criterion, eligible


809

participants had to move across the entire sampling frame without stopping or retracing their steps, and be readily identifiable according to each of the four principal personal and situational characteristics of interest in the present study (age group, gender, level of mobility, and group size). Only those walking alone, or with one or two companions, were eligible for inclusion. 2.3 Procedure

Video footage was collected from each of three case-study locations using (a) a digital camcorder mounted on a tripod, (b) closed-circuit television cameras operated by collaborators within local city councils, or (c) VHS video cameras installed by private survey companies. Footage was then digitised by using a standard video capture card housed in a PC. The spatial resolution of digitised video images varied between 3526288 pixels for VHS video footage and 7686576 pixels for digital video camera footage. The frame rate was 25 frames per second in all cases. Three case-study locations were studied at various intervals between April 2000 and March 2001 (see figure 1, over). All were mixed-use (residential/retail) areas within 1 mile of their respective city centres. The first two sites were selected for their position in relation to the city of York's footstreets district. This area comprises sixteen streets at the heart of the city centre designated by the council for priority pedestrian use: within this area, all vehicular traffic (save emergency vehicles) is prohibited during most of the day, typically between 10.30 and 17.00. The three case-study locations were: (a) High Petergate, York: a narrow, mixed-use street just outside, but adjacent to the city's footstreets district. The road was approximately 3.9 m wide and the pavements either side approximately 1.3 m in width. This street was selected for study because a daytime traffic restriction was scheduled for implementation during the lifetime of the survey, allowing behaviour to be studied both before and after the scheme was implemented. (b) Stonegate, York: another narrow, mixed-use street similar in dimensions and architectural style to High Petergate, but at the centre of the established footstreets precinct of York city centre. Pavement and road dimensions here are almost identical to those in High Petergate (pavements 1.35 m wide, road 4.35 m). (c) Polwarth Gardens, Edinburgh: a more typical urban environment, comprising pavements (1.75 m width) bordering a bidirectional, single-carriageway street (approx. 11.5 m). Daytime traffic flow through this area (approximately 248 ^ 1100 vehicles per hour) is typically light to moderate. Between one and two hours' footage was typically collected during any one session, although a twelve-hour period were surveyed at one case-study location (Polwarth Gardens, Edinburgh) in order to investigate the effects of time of day. Filming took place on weekdays between Monday and Thursday. The weather on all days was overcast or cloudy with sunny periods (never raining) and the temperature moderate, ranging from approximately 7 to 158 C. Footage of all pedestrians who met our inclusion criteria were clipped from digitised master recordings and imported into commercially available motion analysis software for analysis (WinAnalyseÕ, Mikromak, Berlin). The software included several useful routines which could be employed, first, to calibrate the foreshortened video image using real-world coordinates of visible features on the ground (such as road markings), then to track the changing position of the pedestrian across each frame of the video clip. Custom software, developed by the authors in JAVA (see Kukla et al, 2001), was used to transform these data into a two-dimensional plot of each pedestrian's trajectory through the filming area, within an environmental context defined by

810


(a)

(b)

(c) Figure 1. Still images captured from digitised video footage of the three case study locations. (a) High Petergate, York (hand-held digital video camera: resolution 7686576 pixels). (b) Stonegate, York (VHS closed-circuit television cameras controlled by the City of York Council: resolution 3846288 pixels). (c) Polwarth Gardens, Edinburgh (VHS video cameras commissioned by traffic survey company: resolution 3526288 pixels).


811

Pedestrians' trajectories

Pedestrians' positions at time t

Pavement boundaries

Figure 2. A bird's eye visualisation of pedestrian trajectories through one of the case-study locations (High Petergate), as depicted by our custom measurement software. This software collects data about the frame-by-frame position of each pedestrian under analysis, plots his or her x=y trajectory in relation to salient features of the environment (for example, pavement boundaries, companions, etc), and provides a range of tools for the systematic measurement of various speeds and distances that characterise human movement behaviour in urban spaces.

the user (see figure 2). In this way, a bird's-eye visualisation of each pedestrian's journey could be generated, and a range of measurements made relating to various aspects of his or her microscopic movement. As well as key measurements describing how people moved through the survey areas, trained observers noted a range of personal characteristics about the individual(s) under surveillance (for example, age group and gender), situational characteristics that were specific to, but not necessarily fixed attributes of the individual (such as group size, time of day, and level of mobility), and environmental features (notably, the location) that described the overall context in which they walked. Interobserver reliability in categorising pedestrians according to these categories was high. All data were entered into a statistical software package for subsequent analysis.

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3 Results 3.1 Walking speed

A summary of all data is provided in table 1. The mean walking speed of individuals in our sample as a whole (1.47 m sÿ1) lay towards the higher end of the range reported in previous studies. The data were normally distributed about this mean, with minimum and maximum speeds of 0.45 and 5.56 m sÿ1, respectively, and standard deviation of 0.299 m sÿ1 (see figure 3). The highest reported speed values (namely, those above around 2.5 m sÿ1 ) typically came from the few individuals who trotted or ran through the sampling area. 500

Frequency

400 300 200 100 0

0.55

0.95 1.35 1.75 Walking speed (m sÿ1 )

2.15

Figure 3. Distribution of walking speeds. Frequency distribution of individual pedestrians' walking speeds averaged across the survey area (n 2613). The data are distributed normally about the mean (1.47 m sÿ1 ), with a standard deviation of 0.299 m sÿ1 (see table 1 for details).

In the following paragraphs the possible effects of a variety of personal, situational, and environmental factors on pedestrians' chosen walking speed are explored in turn. All data for each of these conditions are shown in table 1. 3.1.1 Effect of gender In line with several previous studies, we found that men walked, on average, faster than women. This difference (1.52 versus 1.42 m sÿ1) was highly significant (independentsamples t-test: t2611 ÿ8:755; p 5 0:001). Variability was slightly higher amongst males (as indicated by a larger standard deviation), but walking speeds were normally distributed around the mean in both groups (see figure 4, over). 3.1.2 Effect of age The speed at which participants chose to walk declined, on average, with increasing age. Differences in mean walking speed between age groups were highly significant [one-way analysis of variance (ANOVA): F4, 2608 75:9; p 5 0:001; see figure 5, over]. Post hoc tests confirmed that adults who appeared to fall within the second and third age groups (16 ^ 50 years) walked significantly faster than those in both older groups, and that pedestrians who appeared to be over 65 years walked significantly more slowly than everyone else (Dunnett's T; p 5 0:05). The youngest group (5 16 years) exhibited one of the highest mean walking speeds, but also the greatest variability in speed measurements: the latter was most likely the result, at least in part, of the relatively small sample size (n 114) and the greater inherent variability in walking speeds within a developmentally diverse age range (between approximately 10 and 15 years).


813

3.1.3 Effect of mobility Over 99% of participants (2592 out of a total of 2613) could be readily classified into one of five categories describing their overall level of mobility (see figure 6, over). The remainder (n 21) were typically associated with a particular activity, such as wheeling a wheelchair, pushing a bicycle, or walking a dogöeach of which contained too few observations to merit a separate category. Table 1. Summary of data: walking speed. Variable

Walking speed (m sÿ1 )

n

mean

median

SD

minimum

maximum

Gender Female Male

1.42 1.52

1.42 1.52

0.251 0.333

0.50 0.45

3.66 5.56

1314 1299

Age (approximate) 64

1.53 1.55 1.47 1.38 1.16

1.46 1.54 1.47 1.38 1.14

0.447 0.323 0.246 0.243 0.255

0.54 0.55 0.45 0.61 0.54

3.98 5.56 3.80 2.09 1.77

114 816 1196 323 164

1.50 1.46 1.40

1.48 1.47 1.38

0.320 0.266 0.266

0.45 0.58 0.65

5.56 3.80 2.40

1244 1032 242

1.39

1.40

0.248

0.50

1.82

54

0.98

0.94

0.169

0.76

1.39

20 21

Group size Singleton (1) Group (2 or 3)

1.52 1.36

1.52 1.37

0.312 0.245

0.45 0.50

5.56 3.13

1745 868

Group gender Male (alone) Female (alone) Male male Female female Female male

1.58 1.46 1.45 1.35 1.33

1.58 1.47 1.44 1.35 1.33

0.346 0.259 0.282 0.210 0.239

0.45 0.55 0.94 0.50 0.73

5.56 3.66 3.13 2.08 2.60

887 858 71 117 227

Time 07.00 ± 09.00 09.01 ± 11.00 11.01 ± 13.00 13.01 ± 15.00 15.01 ± 17.00 17.01 ± 19.00

1.50 1.48 1.44 1.46 1.47 1.51

1.51 1.49 1.45 1.44 1.48 1.52

0.240 0.301 0.310 0.295 0.358 0.296

0.55 0.61 0.50 0.63 0.54 0.45

2.97 3.25 3.42 3.98 5.56 3.48

305 251 377 1219 258 203

Location High Petergate (before) High Petergate (after) Stonegate Polwarth Gardens

1.38 1.45 1.47 1.50

1.41 1.44 1.48 1.51

0.290 0.267 0.227 0.333

0.74 0.63 0.78 0.45

1.95 3.98 2.04 5.56

273 993 209 1138

Total

1.47

1.47

0.299

0.45

5.56

2613

Mobility Unencumbered With small bag or case With larger shopping bags or luggage With small children, pram, or buggy With mobility aid Other

814


300 250

Frequency

200 150 100 50

(a)

0 300 250

Frequency

200 150 100 50 0

(b)

0.55


2.15

Figure 4. Effect of gender on walking speed. Frequency distribution of individual pedestrians' walking speeds plotted separately for (a) females (mean 1.42 m sÿ1 ; SD 0.251; n 1314) and (b) males (mean 1.52 m sÿ1 ; SD 0.333; n 1299) (see table 1 for details). Note how the entire distribution is shifted towards higher speeds for males compared with females: the difference in walking speed between the two groups was highly significant ( p 5 0:001). 500

64

Frequency

400 300 200 100 0

0.55


2.15

Figure 5. Effect of age on walking speed. Frequency distribution of individual pedestrians' walking speeds (m sÿ1 ) according to age (in years): note how the data are reasonably normally distributed within each of the four age groups under 65 years, but skewed towards lower speeds in the oldest age group. The effect of age on spontaneous walking speed was highly significant ( p 5 0:001) (see table 1 for details).


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Mobility classification

Figure 6. Effect of mobility on walking speed. Bar chart depicting mean walking speed (m sÿ1 ) for each of our five mobility classifications (see also table 1). Error bars indicate 1 standard error of the mean. The effect of mobility on walking speed was highly significant ( p 5 0:001).

These mobility classifications were developed throughout the data analysis procedure according to an iterative, bottom-up approach, and appeared to reflect differences in the degree of physical or attentional effort required in walking. Carrying large shopping bags, for example, or walking with young children typically demand more space and, arguably, greater attentional resources in negotiating space than walking unencumbered by baggage or other accoutrements. Unfortunately, the nature of covert observational studies precludes the consideration of mobility classifications based on physical health: as such, we could not assess the effects of various circulatory or ambulatory difficulties, nor visual or hearing impairments which we know from the substantial health-related literature can play an important role in decisions of whether and how to walk in urban environments. A one-way ANOVA revealed highly significant differences in mean walking speed between groups (F4;2587 22:1; p 5 0:001). Post hoc tests (Dunnett's T ) confirmed that unencumbered pedestrians walked significantly faster, on average, than any other group, and that people walking with a mobility aid (such as walking stick, frame, or crutches) walked significantly more slowly than anyone else ( p 5 0:05). Average walking speeds of people carrying larger shopping bags (1.40 m sÿ1 ) were also significantly lower than for those carrying only handbags or briefcases: although the mean speeds of participants with small children were very similar (1.39 m sÿ1 ), they were not significantly different from those with small bags or cases. We suspect that this nonsignificant result arises from the greater variability in walking speed measures among pedestrians with children and from its smaller sample size rather than from any real difference in speed preferences between these groups. 3.1.4 Effect of group size Single pedestrians walked faster, on average, than those walking with one or two companions (1.52 and 1.36 m sÿ1, respectively), with data for both populations falling normally about the mean (see figure 7, over). The difference in desired walking speed between singletons and groups (0.16 m sÿ1 ) was highly significant (independent-samples t-test: t2, 487 2:28; p 5 0:001).

816


300 250

Frequency

200 150 100 50

(a)

0 300 250

Frequency

200 150 100 50 0

(b)

0.55


2.15

Figure 7. Effect of group size on walking speed. Frequency distribution of individual pedestrians' walking speeds plotted separately for (a) singletons (mean 1.52 m sÿ1 ; SD 0.312; n 1745) and (b) groups of two or three (mean 1.36 m sÿ1 ; SD 0.245; n 868) (see table 1 for details). Note how both sets of data are normally distributed, but that for groups is shifted towards lower speeds compared with singletons: the difference in walking speed between single pedestrians and pairs or triplets was highly significant ( p 5 0:001).

The effect of walking in a group may have been confounded at least in part by gender, as proportionally more females were observed to walk in groups (54%) than males (46%). A separate analysis was therefore carried out on groups defined according to gender. A one-way ANOVA revealed significant differences in mean walking speed between these groups according to gender (F2, 412 7:34; p 5 0:01). Post hoc comparisons confirmed that all-male groups walked significantly faster than both female-only and mixed-gender groups (Dunnett's T ; p 5 0:05), but that the latter two groups walked at comparable speeds. 3.1.5 Effect of time of day Average walking speeds in our sample varied with time of day (see figure 8). Pedestrians in our sample walked faster, on average, during the early morning (07.00 ^ 09.00) and late afternoon (17.00 ^ 19.00), and slowest during the midday periods (11.00 ^ 15.00). The effect of time of day on walking speed was significant (one-way ANOVA: F5, 2607 2:98; p 5 0:05). However, post hoc analyses revealed that significant differences in mean speed were apparent only between the 11.00 ^ 13.00 slot and both the earliest (07.00 ^ 09.00) and latest (17.00 ^ 19.00) periods (Dunnett's T ; p 5 0:05). These differences most likely reflect a combination of trip purpose and group size, both of


817


1.55

1.50

1.45

1.40

1.35

07.00 ± 09.00

09.01 ± 11.00

11.01 ± 13.01 ± 15.01 ± 17.01 ± 13.00 15.00 17.00 19.00 Time of day

Figure 8. Effect of time of day on walking speed. Bar chart depicting mean walking speed (m sÿ1 ) for each of six, two-hour time periods between 7 am and 7 pm (see also table 1). Error bars indicate standard error of the mean. Pedestrians in our sample walked significantly faster, on average, during the first and last time periods compared with the midday period (11.01 ^ 13.00): p 5 0:05.

which may be disproportionately distributed across different times of day, and which may indeed interact with each other. It is not unreasonable to assume, for example, that a greater proportion of journeys made in the two hours before 09.00 and after 17.00 are made up of trips to and from work, and that, consequently, people will walk more quickly to reach their destination on time. Moreover, most commuting journeys are completed alone, rather than in groups: as such, there is probably a greater proportion of journeys made by singletons, who we know walk significantly faster than groups, between 07.00 and 09.00, and 17.00 and 19.00. 3.1.6 Effect of location Mean walking speed was found to differ quite considerably between our case-study locations (see figure 9, over), suggesting that the environmental context as a whole played an important part in people's decisions to walk at a certain pace. We suspected, however, that at least part of this difference could be attributed to group size, as groups accounted for a greater proportion of the population sampled in High Petergate, the slowest-moving location (44.7%), than they did in the faster-paced Polwarth Gardens (26.3%). As we suspected, a two-way, between-factors ANOVA identified a highly significant effect of location (F3, 2605 10:25; p 5 0:001), group size (F1, 2605 103:46; p 5 0:001), and the interaction between the two F3, 2605 5:42; p 5 0:01). In order to separate further the effects of group size from location, data for singletons and groups were subjected to separate statistical analyses (see table 2, over). Interestingly, singletons showed no differences in mean walking speeds between any of the locations surveyed [one-way ANOVA: F3, 1741 0:95; p 4 0:40). This finding suggests that the desired walking speed of pedestrians is remarkably stable across locationöat least for the limited range of environmental contexts studied here: if so, the similarity of speeds recorded between locations suggests a high degree of reliability in our measurement protocol. The effect of location on groups' walking speeds, however, was highly significant (F3, 864 18:75; p 5 0:001). Post hoc tests (Dunnett's T ; p 5 0:05) revealed that the mean walking speeds of groups in High Petergate before traffic restrictions were implemented (1.23 m sÿ1 ) were significantly lower than after the measures had been

818



1.55

1.50

1.45

1.40

1.35 te ga ter e' e P or f igh e H `b

e

t ga ter ' e P ter f igh à

S

H

e

at

eg

n to

rth

G

ns

de

ar

Po

a lw

Figure 9. Effect of location on walking speed. Bar chart depicting mean walking speed (m sÿ1) within each location studied (see also table 1). Error bars indicate 1 standard error of the mean. The effect of location was found to be highly significant ( p 5 0:001). However, separate analyses on singletons and groups revealed a significant effect of location for groups, but not for people walking on their own (see table 2). Table 2. Walking speed by location. Mean walking speed (m sÿ1 )

n

singletons

groups

singletons

High Petergate (before) High Petergate (after) Stonegate Polwarth Gardens

1.50 1.51 1.50 1.53

1.23 1.37 1.33 1.42

151 584 171 839

122 409 38 299

Total

1.52

1.36

1745

868

Location

groups

introduced (1.37 m sÿ1 ), and than in both other sites studied. On the other hand, walking speeds of groups in Polwarth Gardens, Edinburgh (1.42 m s ÿ1 ) were significantly higher than those of all other survey locations. Groups' walking speeds recorded in Stonegate and High Petergate after traffic restrictions did not differ significantly from each other. These environments differed according to two key, observable factors öthe presence of vehicles, and the amount or proportion of space reserved for pedestrians öeither or both of which may explain the differences in walking speed observed amongst groups between locations. The presence of vehicles on its own did not appear to be the principal factor: the volume of both traffic and cars parked at the kerbside was highest in the fastmoving Polwarth Gardens, yet next highest in the slowest-moving High Petergate before traffic was restricted. The most reasonable explanation seems to be the perceived amount of space reserved for pedestrians, which was certainly the least in High Petergate before traffic calming was introduced, exhibiting the narrowest pavements seen in our sample (1.3 m) and a substantial proportion of space taken up by parked cars. Pedestrians in Polwarth Gardens, on the other hand, had more space afforded them in terms of pavement width (approx. 1.75 m) than in either High Petergate (1.3 m) or Stonegate (1.35 m). Although such differences may not be a factor for singletons,


819

the smaller pavements may compound the physical and/or attentional demands of maintaining proximity with a companion while walking, resulting in significantly slowed speeds for groups, but not singletons, in areas within which exclusive space for pedestrians is limited. 3.2 Spacing behaviour

In the second part of this study, we investigated several aspects of microscopic behaviour that relate to where pedestrians position themselves with respect to features of their environment. In particular, we explored: (a) how people position themselves with respect to pavement ^ road boundaries; (b) how much space they maintain between themselves and their companion(s) while walking; and (c) the extent to which differences in these position preferences could be explained by the various personal, situational, and environmental characteristics we suspected may be important in shaping pedestrians' movement decisions. 3.2.1 Microscopic position To enable more effective comparisons between case-study locations, the microscopic position of each pedestrian in our sample was defined in categorical terms rather than absolute physical dimensions in space. These were: (1) middle of the pavement, (2) pavement, closest to road edge, (3) pavement, closest to building edge, (4) middle of the road, and (5) road, closest to the pavement edge. Taken together, the data as a whole showed that most participants (82%) walked on the pavement most of the time (see figure 10). Not unsurprisingly, perhaps, location was found to have a significant effect on people's positioning as they moved through space (see table 3, over): in particular, pedestrians appeared significantly more likely to walk on the pavement in Polwarth Gardens (98%),

Frequency

1200

800

400

0 il

bu

g

n di

ge

ed

e

dl

id

m

d oa

r

ge

ed

pavement

e

dl

id

m

b er

ge

ed

k

road

Position in space

Figure 10. Microscopic position of pedestrians. Bar chart depicting proportions of pedestrians occupying each of five positions in space within each location surveyed. Note how the vast majority of pedestrians walked within pavement areas located at the margins of the space (82%). Age, group size, and location, but not gender, were found to play a potential role in determining position preferences (see table 3).

820


a traditional single-carriageway road bounded by pavements either side, compared with Stonegate (46.3%), within which traffic is restricted to access only during the day. Interestingly, the proportion of people walking in the road in High Petergate almost doubled after traffic-restriction measures had been implemented (28.4% compared with 14.7%): this finding could reflect an increased likelihood of pedestrians venturing into the road space as a result of significantly decreased traffic flow, and the removal of barriers to entering the road presented by parked cars. Despite the increased tendency for pedestrians to walk in the road space within traffic-restricted areas such as High Petergate and Stonegate, it was noteworthy that the majority of pedestrians continued to occupy the footways at the margins: even in Stonegate, for example, in the heart of the pedestrianised footstreets area of York (see section 2), over half the population of walkers (53.7%) continued to occupy the edges of the total space available to them. Group size also appeared to have a sizeable effect on microscopic positioning (see table 3). In general, groups were more likely than singletons to walk in the road (22.3% compared with 15.2%) than on the pavement. Why this was the case is not clear, but could be explained by the perception of people in groups that the pavement did not afford them sufficient space to maintain proximity with their companion(s). Of those who walked on the pavement, singletons tended to position themselves in the centre (71%), suggesting people prefer approximately equal amounts of space on either side of them. Individuals in groups, however, were more likely to walk within the road edge or the building edge of the pavement (78.3%) compared with the centre (21.7%), suggesting that pedestrians with companions prefer to walk side by side rather than one behind another. Table 3. Summary of data: microscopic position. Variable

Microscopic position: proportion of sample

n

pavement (%)

road (%)


85.3 71.6 46.3 98.0

14.7 28.4 53.7 2.0

273 992 203 1108

Group size Singleton (1) Group (2 or 3)

84.8 77.7

15.2 22.3

1715 861

Age (approximate) 64

98.2 88.7 77.6 77.5 85.4

1.8 11.3 22.4 22.5 14.6

112 795 1185 320 164

Gender Female Male

84.4 80.4

15.6 19.6

1095 1028

Mobility Unencumbered With small bag or case With larger shopping bags or luggage With small children, pram, or buggy With mobility aid

81.5 85.0 79.5 83.3 90.5

18.5 15.0 20.5 16.7 9.5

1230 1011 239 54 21


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Age, gender, and mobility classification were also associated with different preferences for positioning in urban spaces (see table 3). Children and young adults, for example, were the least likely to walk in the road (1.8% and 11.3%, respectively) compared with both older adult groups (22.4%) and the eldest age group (14.6%). Gender appeared to be less important in determining individuals' positions in space, with males only slightly more likely to walk in the road than females (19.6% and 15.6%, respectively). Within our mobility classifications, those walking with a mobility aid were less likely to walk in the road (9.5%) compared with those with larger shopping bags (20.5%), no baggage (18.5%), small children (16.7%), or small bags (15.0%). Again, it is interesting to note that people taking up the most space (in this case, people with larger shopping bags) were the most likely out of all the groups to walk in the road (20.5%). 3.2.2 Interpersonal spacing The present study has already highlighted the importance of group size in determining the speed at which we prefer to walk, and where we choose to position ourselves within the space available to us. In this section we investigate how much space pedestrians maintain between themselves and their companions when walking, and the extent to which this interpersonal distance may be influenced by factors such as the gender, age, and mobility composition of the group, and the environment in which they walk. Interpersonal distance between members of a group was measured from the central point of each pedestrian on the ground (see figure 11). All measures were taken as the mean of three distances recorded at the beginning, middle, and end of each video clip in order to counteract any small variations in interpersonal distance as groups traversed the study area. The mean distance pedestrians maintained between themselves and their companion(s) was 0.85 m. This measure is very similar to the 0.75 m reported by Burgess (1983). The distribution contained somewhat large variation (SD 0.33 m), and was somewhat skewed towards shorter distances (see table 4; figure 12, over). At least part of this variation can be attributed to the effect of group size: people walking in groups of three maintained, on average, considerably greater distances x

y

(a)

z

x

y

(b) Figure 11. Measurement of interpersonal distance. (a) The interpersonal distance between groups of two people was measured from the midpoint of each pedestrian on the ground (that is, distance x ÿ y). (b) The interpersonal distance of groups of three people were taken as an average of the two shortest distances between the three people, that is, x ÿ y y ÿ z=2 (typically between the `central' pedestrian and his or her two companions). In all cases, three interpersonal distance measures, recorded on the start, middle, and end frames of the video clip, were averaged.

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100

Frequency

80 60 40 20 0 0.40

0.80 1.20 1.60 2.00 Interpersonal distance (m)

2.40

Figure 12. Distribution of interpersonal distances. Frequency distribution of each group's mean interpersonal distance for our population as a whole (see caption to figure 11 for details). The mean distance pedestrians chose to maintain between themselves while walking was 0.85 m (n 415). The distribution was skewed towards shorter distances and showed higher standard deviations than those recorded for walking speeds. Group size was found to be the most significant determinant of interpersonal distance, although location and age were also reported to have an effect (see table 4). Table 4. Summary of data: interpersonal distance. Variable

Interpersonal distance (m)

n

mean

median

SD

minimum maximum

Group size 2 3

0.83 1.06

0.75 0.93

0.317 0.366

0.27 0.63

2.41 1.93

374 35


0.75 0.83 0.83 0.91

0.68 0.77 0.80 0.78

0.220 0.293 0.261 0.398

0.41 0.41 0.49 0.27

1.74 1.93 1.60 2.41

61 196 20 139

Group gender Female female Female male Male male

0.84 0.82 0.90

0.74 0.74 0.81

0.350 0.305 0.313

0.27 0.40 0.54

2.41 2.11 2.28

115 221 71

Group age Children or children adults Adults Elderly or elderly adults

0.90 0.83 0.94

0.76 0.75 0.86

0.415 0.305 0.397

0.45 0.27 0.49

2.41 2.28 2.17

46 343 26

Total

0.85

0.76

0.325

0.27

2.41

415

between themselves compared with those walking in twos (1.06 m versus 0.83 m, respectively): this difference, which may reflect differences in intimacy between companions walking in twos and threes, was highly significant (Mann-Whitney U: z4:21 ; p 5 0:001). Location also had a significant effect on interpersonal spacing (Kruskall ^ Wallis: w23 8:32; p 5 0:05), with the shortest distances being observed in High Petergate (before traffic restrictions) and the largest in Polwarth Gardens (0.75 m versus


823

0.91 m, respectively). Again, this difference could be explained, at least in part, by the differing amounts of pavement space afforded to pedestrians within each of the environments studied. Smaller differences were found between groups according to their gender and age composition. In particular, all-male groups appeared to maintain significantly greater distances between themselves than both all-female and mixed groups (0.90 m versus 0.84 and 0.82 m, respectively; Kruskall ^ Wallis: w22 7:62; p 5 0:05). However, although groups comprising only adults tended to maintain shorter interpersonal distances compared with groups containing children or elderly pedestrians (0.83 m versus 0.90 and 0.94 m, respectively), these differences were not statistically significant (Kruskall ^ Wallis: w22 1:23; p 4 0:50). 3.3 Relationship between speed and spacing preferences

Interpersonal distance (m)

Having examined various speed and position preferences of pedestrians across a range of personal, situational, and environmental factors, we decided to investigate whether any relationships exist between pedestrians' desired walking speed and their spacing behaviour. An appreciation of how these may be related would prove helpful in the development of agent-based pedestrian models in two important regards: first, in providing a mechanism by which individual agents can be assigned values for several parameters in a realistic way; and second, in establishing rules that govern how individual agents will behave. If, for example, we found that faster walking speeds were associated with a greater likelihood of walking in the road, we could attempt to model this association by yoking the two variables together in assigning values of desired walking speed and initial starting position within an environment, or by assigning more `risky' or àggressive' behaviours to faster-walking agents. If no such association were found, modellers could be justified in assigning values of these parameters independently. Statistical analyses of our data revealed a small, but significant positive correlation between walking speed and interpersonal distance (Spearman's r 0:102; p 5 0:05; see figure 13), suggesting a tendency for interpersonal spacing to increase slightly with increasing walking speed in the sample as a whole. However, we found no consistent differences in walking speed according to microscopic position in space: people who

2.0

1.0

0.0

0

1.0 2.0 3.0 Walking speed of group (m sÿ1 )

Figure 13. Relationship between walking speed and interpersonal distance. Scatter plot of walking speed (m sÿ1 ) as a function of interpersonal distance (m) for groups of two or three people (n 415). A small, but statistically significant positive correlation (r 0:097; p 5 0:05) was found between the two variables, suggesting a tendency for faster-moving pedestrians to leave greater distances between themselves while walking.

824


walked in the road walked no faster, on average, than those who walked on the pavement (1.47 versus 1.46 m sÿ1, respectively: t2573 0:21; p 4 0:80). Taken together, these findings suggest a weak, but complex relationship between pedestrians' chosen walking speeds and their positioning behaviour with respect to salient features of their environment ö in particular, pavement ^ road boundaries and their companions: faster walking speeds do appear to be associated, albeit weakly, with greater space requirements between companions, but not their overall position in space. 4 General discussion In this paper we investigated the spontaneous walking speeds and spacing behaviours of pedestrians in urban environments uncluttered by obstacles or other people. We did this in order to appreciate better how people move when they are allowed the freedom to select their position in space and the speed at which they walköparameters central to all planning guidelines and agent-based models of pedestrian movement, yet so far poorly addressed in the empirical literature. The few empirical studies carried out in this area to date have tended to deal almost exclusively at the limits of human movement behaviour, within highly congested and obstructed spaces. We also attempted to elucidate the influence of various personal, situational, and environmental factors that may underpin the variation in pedestrians' movement preferences between individuals and environments. We did this in order to understand better how behaviour may vary between environments, and the extent to which any differences reflect differences in the overall structure of the environment, or the characteristics of the people that frequent them. Such an understanding is critical in assigning values of various parameters to individual model pedestrians and the rules that act upon them in any realistic, microscopic simulation model of pedestrian urban spaces. The mean walking speed of individuals across our sample as a whole (1.47 m sÿ1 ) lay towards the higher end of the range reported in previous studies (1.0 ^ 1.5 m sÿ1 ). This may reflect overall differences in the pace of life and climate between European compared with other cities; alternatively, the results may suggest that our participants were able to select their own walking speed, and, consequently, that desired walking speeds may be somewhat higher than expressed speeds recorded in many previous studies. Spontaneous walking speeds for the sample as a whole were normally distributed about the mean, but varied systematically according to a range of personal, situational, and environmental factors that characterised the journeys of our participants. Faster walking speeds, for example, had a significantly greater association with singletons (compared with groups), males (rather than females), adults (rather than children or the elderly), and those with no or few encumbrances (compared, say, with those with shopping bags or mobility aids). We also investigated the spacing preferences of pedestrians according to (1) where they positioned themselves within the overall space available to them; and (2) how much space they maintained between themselves and their companion(s) when walking with others. We found, not surprisingly, that the majority of people walk on the pavement most of the time. This was true even in locations where traffic was restricted, although there was a tendency for more people to use the road in these areas than those in which traffic flow was heavier. Group size was a further significant predictor of positioning behaviour, with individuals walking in groups of two or three apparently more likely to walk at the pavement margins rather than the centre, and in the road rather than the pavement in general compared with singletons. Position was also found to differ according to a range of other personal and situational factors, including age, level of mobility, and, to a lesser extent, gender. These differences presumably reflect differences in space requirements (those with large shopping bags, for example,


825

were more likely than anyone else to walk in the road), and in degree of caution (the youngest age groups were around ten times less likely than adults to leave the pavement). The distance maintained between pedestrians when walking in groups (0.85 m) was broadly similar to that reported by Burgess (approximately 0.75 m) twenty years ago. The distribution of interpersonal distances demonstrated a considerable negative skew: this most likely reflected the proportionally greater number of pairs in our sample, who maintained a significantly shorter distance between themselves and their companion compared with triplets. This could be explained by a tendency for greater intimacy among couples compared with triplets, as evidenced by the greater likelihood of couples holding hands. Other factors which appeared to have an effect on interpersonal distance were location (which we assume largely reflects differences in pavement width, and thus space available for pedestrians) and, to a lesser extent, the gender composition of the group. Of course, it is not necessary for model agents to be ascribed such human characteristics as gender or age in order for a microsimulation model to be capable of mimicking realistic pedestrian behaviour: indeed, such a model would be impossibly complex and not necessarily any more realistic as a result. However, it is important to have an appreciation of what factors contribute most significantly to our movement decisions, and how, in order to plan for and model them effectively. From our results, we argue that group size, age, mobility, and the perceived space allocated to pedestrians within an environment are the most important predictors of individuals' movement behaviour in urban spaces, and which potentially vary the most between different types of environment. As such, it is important that these factors are taken into account when designing effective urban spaces for pedestrians, or modelling the effects of putative design changes on pedestrian behaviour. Group size was one of the most significant factors underpinning pedestrians' speed and spacing preferences in the present study: not only did group size affect walking speed, positioning, and interpersonal spacing preferences, but groups appeared to be more significantly affected by the overall layout of their environment than singletonsö at least according to the speed at which they chose to walk. This has important implications in terms of planning for, and modelling pedestrians: a significant proportion of walking journeys, particularly those associated with leisure activities such as shopping or sightseeing, are completed by pedestrians walking with one or more companions, yet the effects of walking in a group have, until now, not received much attention in the academic or practical literature. Our results provide strong support for the consideration of group dynamics in any realistic design or model of pedestrian spaces in which significant proportions of the population step out with a companion. It may be enough, for example, to model groups as singular, extralarge agents with lower speed preferences and greater susceptibility to environmental influences compared with single agents; on the other hand, it is quite likely that other aspects of groups' movement behaviour, such as collision avoidance, will differ in other, more fundamental ways (indeed, our ongoing research provides strong evidence that this is the case), in which case it may be that the modelling of groups' behaviour may benefit from a more sophisticated treatment. Age and mobility were also found to be significant factors in both speed and positioning preferences. Adults, for example, tended to walk more slowly with increasing age, leave shorter distances between themselves and their companions when walking, and were more likely than either the seemingly more cautious children or the elderly to walk in the road. We also found that unencumbered pedestrians walked significantly faster, and those walking with a mobility aid significantly slower, than any other group. Interestingly, carrying large shopping bags was associated with the

826


greatest tendency to walk in the road, perhaps because the relatively narrow pavements in our sample locations were not perceived to afford them enough space. Again, these factors are important in planning and modelling terms: the yoking of factors like slower preferred walking speed and more conservative positioning, for example, as seen among the elderly or mobility impaired, should be taken into account in any model that seeks to predict movement behaviour in environments containing high proportions of pedestrians in these categories. We have argued that the perception of space available for walking was the critical factor underpinning differences in movement behaviour between study locations. The locations were chosen for their similarity in terms of use (mixed residential/retail), climate, and proximity to the city centre, but small differences in terms of vehicular access and pavement width. Interestingly, there was no difference in walking speed between these locations among the population of singletons: this suggested that the presence of light to moderate traffic flow and small differences in pavement width are not critical in determining the walking speed preferences of solo pedestrians. Significant differences in walking speed between study locations were found, however, among pedestrians walking with one or two companions: here, the smallest pavement widths (1.3 m, in High Petergate) were associated with the lowest average speeds. Interestingly, the implementation of a daytime traffic ban in High Petergate was associated with a significant increase in groups' walking speed, and a doubling of the proportion of pedestrians overall walking in the road. These results provide strong evidence for a relationship between group size, perceived space, and walking speed. 5 Conclusions Taken together, the findings presented here suggest a number of factors that appear to be critical in shaping individuals' preferred movement preferences in urban spaces. The results should prove useful both to designers and to modellers of urban spaces for pedestrians in several important regards. First, our description of several of the most fundamental elements of movement behaviour may be used by modellers to assign realistic values to each modelled pedestrian according to a range of simple parameters (for example, desired walking speed and starting position in space), and assist in confirming the efficacy of simple behavioural rules. The systematic investigation of which factors affect the values of these parameters (and how) also provides an insight into how a realistic distribution of values might be chosen for a particular environment: modelling a shopping street, for example, should take into account the greater proportion of pedestrians taking up more space on the pavement (for example, as a result of walking in groups, or transporting large shopping bags), and the effects this might have on other aspects of behaviour. Second, our results should prove useful not only in the calibration of movement parameters by planners and modellers of pedestrian spaces, but also in highlighting a number of behavioural phenomena that might be expected to emerge from any realistic model of individual movement behaviour, such as the relatively greater influence of environmental layout on the walking speed of groups as opposed to single pedestrians. Finally, of course, in characterising and attempting to explain behaviours that most closely represent those in ìdeal' (or at least, uncongested) scenarios, we are able to provide a more sound basis with which modellers may compare modelled, or òbserved' behaviours in order to estimate measures like the average delay experienced by model agents in the simulation. We believe that a more comprehensive understanding of how pedestrians negotiate urban spaces is essential to underpin the development of models that aim to help in the design of effective urban space, and recognise the importance of further empirical


827

study in order to deepen such an understanding. Of particular salience, it seems, are questions relating to how pedestrians negotiate obstacles, what factors attract or repel pedestrians to or from a particular location, and the significance to pedestrians of any differences between ìdeal' and àctual' paths taken through space. Research in these areas, to which we are currently contributing as part of the PEDFLOW project, should prove of benefit to planners, policymakers, and modellers interested in creating more pedestrian-friendly urban spaces for all of us to enjoy. Acknowledgements. This work was supported by EPSRC grant GQ/M59792 awarded to J M Kerridge and J Hine. Our most grateful thanks go to all members of the PEDFLOW steering group for their support and advice throughout the project: we should particularly like to thank Julian Hine (University of Ulster), Neil Johnstone (Halcrow Fox), Michael Carreno (Transport Research Institute, Napier University), and our colleagues in York City Council and the City of Edinburgh Council for helpful discussion and advice. References Al-Gadhi S A, Mahmassani H S, 1991, ``Simulation of crowd behavior and movement: fundamental relations and application'' Transportation Research Record number 1320, 260 ^ 268 Batty M, 1997, ``Predicting where we walk'' Nature 388 19 ^ 20 Batty M, 2001, `Àgent-based pedestrian modelling'' Environment and Planning B: Planning and Design 28 321 ^ 326 Blue V J, Adler J L, 1998, `Èmergent fundamental pedestrian flows from cellular automata microsimulation'' Transportation Research Record number 1644, 29 ^ 36 Boles W, 1981, ``The effects of density, sex and group size upon pedestrian walking velocity'' Man ^ Environment Systems 11 37 ^ 40 Bowman B L, Vecellio R L, 1994, ``Pedestrian walking speeds and conflicts at urban median locations'' Transportation Research Record number 1438, 67 ^ 72 Burgess J W, 1983, `Ìnterpersonal spacing between surrounding nearest neighbors reflects both familiarity and environmental density'' Ethology and Sociobiology 4 11 ^ 17 Ciolek M T, 1978, ``Spatial arrangements in social encounters: an attempt at a taxomomy'' Man ^ Environment Systems 8 52 ^ 59 Coffin A, Morrall J, 1995, ``Walking speeds of elderly pedestrians at crosswalks'' Transportation Research Record number 1487, 63 ^ 67 Dabbs J M, Stokes N A, 1975, ``Beauty is power: the use of space on the sidewalk'' Sociometry 38 551 ^ 557 Data Protection Act, 1998 Public General Actsö Elizabeth II chapter 29 (The Stationery Office, London) Fruin J J, 1971, ``Designing for pedestrians a level of service concept'' Highway Research Record number 355, 1 ^ 15 Fugger T F, Randles B C, Stein A C, Whiting W C, Gallagher B, 2000, `Ànalysis of pedestrian gait and perception/reaction at signal-controlled crosswalk intersections'' Transportation Research Record number 1705, 20 ^ 25 Galea E R, Galparsoro J M P, 1994, `À computer based simulation model for the prediction of evacuation from mass transport vehicles'' Fire Safety Journal 22 341 ^ 366 Gibson J J, 1979 The Ecological Approach to Visual Perception (Houghton Mifflin, Boston, MA) Habicht A T, Braaksma J P, 1984,`Èffective width of pedestrian corridors'' Journal of Transportation Engineering 110 80 ^ 93 Haklay M, O'Sullivan D, Thurstain-Goodwin M, Schelhorn T, 2001, ```So go downtown': simulating pedestrian movement in town centres'' Environment and Planning B: Planning and Design 28 343 ^ 359 Helbing D, Molna¨r P, Farkas I J, Bolay K, 2001, ``Self-organizing pedestrian movement'' Environment and Planning B: Planning and Design 28 361 ^ 383 Henderson L F, 1971, ``The statistics of crowd fluids'' Nature 229 381 ^ 383 Hillier B, Penn A, Hanson J, Grajewski T, Xu J, 1993, ``Natural movement: or, configuration and attraction in urban pedestrian movement'' Environment and Planning B: Planning and Design 20 29 ^ 66 Hoogendoorn S P, Bovy P H L, 2004, ``Pedestrian route-choice and activity scheduling theory and models'' Transportation Research B 38 169 ^ 190

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