Pedestrian Walking Speed Analysis: A Systematic Review

Abstract

(1) Background: Almost all trips include a walking leg. Pedestrian flow dynamics are an essential input to infrastructure design as well as efficient and safe operations. Pedestrian walking speed is the most influential traffic flow variable. This study examines the factors influencing pedestrian walking speed, categorizing them into pedestrian flow characteristics, pedestrian attributes, layout configuration, ambient conditions, and pedestrian behavioral patterns. (2) Methods: A comprehensive literature review was conducted, aggregating studies that investigate pedestrian walking speed across various environments and conditions. The identified factors were systematically categorized, and a meta-analysis was employed to synthesize the results. (3) Results: Speed measurements seem to be dependent on the method and technique employed, with experiments systematically overestimating speed and video recordings systematically underestimating it. Pedestrian density strongly influences speed as in motorized traffic. Being female, being of older age, walking in a group, engaging in social interactions or phone-related tasks, and moving under noise conditions are reported to have a negative impact on walking speed. Carrying baggage and moving under adverse weather conditions are also reported to have a statistically significant impact, but the direction of the impact is not always the same and seems to be very context dependent. (4) Conclusions: The findings highlight the significance of physiological, psychological, and environmental elements in shaping pedestrian behavior and thus speed. Valuable insights from this review can assist researchers, designers, and operators in providing safer, more inclusive, and reliable infrastructures for pedestrians. Future investigations should broaden the scope of data collection methods, particularly indoors.

1. Introduction

The pedestrian is often described as an individual traveling on foot, while individual walking speed is defined as the distance travelled over the time spent travelling and is measured in meters per second [1]. Walking is the oldest mode of transport and the most essential one as almost all trips include a walking trip leg. Pedestrians’ walking environments include pedestrian zones without vehicular traffic, shared spaces, and mixed traffic zones such as street intersections and crosswalks. In addition, pedestrians are referred to as passengers when using public transport infrastructure and rolling stock. Pedestrians are the most vulnerable road users as they are directly exposed to environmental conditions and crash risk against private cars or even bicycles and e-scooters in shared spaces. Their individual speed is dependent upon both exogenous and endogenous characteristics including age, gender, and physical condition.

Pedestrian movement dynamics have been extensively studied as they are valuable input for a large number of analyses such as emergency evacuations, pedestrian bridge dynamic design, transportation hub, and architectural design. Moving speed is a key element in all these studies as it is directly related to infrastructure operations and level of service, under both normal and disrupted conditions [2]. In such cases, walking speed is considered equal to the average individual speed of all pedestrians travelling over the same distance in a specific environment. Average speed depends on infrastructure design elements, ambient conditions, pedestrian density, and overall crowd dynamics. Unrestricted pace in optimal conditions, i.e., free-flow speed, is another metric used for infrastructure design. The Transit Capacity and Quality of Service Manual-2nd Edition [2,3] defines a baseline range for free-flow walking speed rate, which ranges from 0.75 m/s to 2.41 m/s. This range encompasses both restricted walking and jogging, reflecting the significant variability in pedestrian speed that arises from numerous factors influencing both desired and actual walking speed. The recommended pedestrian walking speed for design is 1.25 m/s [2], while the speed assumed for crosswalk signal timing is 1.2 m/s [4]. However, it is worth noting that those reference values differ according to the context.

Of course, average speed is also directly related to pedestrian density, as suggested by traffic flow theory. Under low-density regimes, individual pedestrians can travel at desired free-flow speeds, whereas increased densities impose reduced moving speeds [2]. Increased densities correspond to lower pedestrian comfort [5], infrastructure efficiency, and level of service [6]. Operations at capacity levels maximize pedestrian throughput but create instability in flow dynamics. Stability is particularly important under degraded conditions when incidents occur. The impact of minor incidents can be easily absorbed under stable flow, while the impact of major incidents can be mitigated, and emergency evacuation of critical facilities can be performed with safety.

Turning to transportation systems in particular, pedestrian dynamics become even more critical in terms of efficient design and operations. Nevertheless, relevant data are often unavailable, corresponding parameters are difficult to estimate, and analytical formulae are highly complex. Systematic data collection is not common practice, contrary to car traffic, and sensing equipment is not standardized. Even when densities and speeds are known, aggregate indicators may not be able to summarize traffic scenes because, in the case of pedestrians, stronger heterogeneity is observed across individuals while environmental factors have greater impact. Also, evolving and/or exogenous parameters prove to be more influential, as was the case with the recent COVID-19 pandemic when social distancing defined new behavioral patterns [7]. Moreover, space and time continuous models cannot sufficiently describe traffic scenes and behavioral choices due to the increased complexity level that increases with density. Thus, alternative modeling approaches, such as agent-based simulation, progressively gain the preference of researchers performing prospective urban studies.

From data collection to modeling and future projections, pedestrian speed is an essential parameter and an important input for transportation planning, infrastructure design, and sizing. The factors that affect speed have been extensively studied in the literature, but results are not always converging. Age [8], gender [9], and characteristics of the walking environment [10] are commonly reported as being very influential. Other analyzed factors include weather conditions, noise, or adjacent land use [11,12,13]. Despite the extensive literature on the topic, there is no systematic classification of those factors. To the best of the authors’ knowledge, there exists no comparative assessment of the relative importance of their influence upon pedestrian speed. A better understanding of walking speed determinants would be highly beneficial to infrastructure designers, researchers, and operators as capacity and level of service (LoS) result from real speeds.

In view of the above, the aim of this review is to address two emerging questions: (i) which are the main factors affecting pedestrian walking speed, and (ii) to what extent do these factors impact walking speed. In the review, the influencing factors are categorized into five distinct categories as follows:

(1)

Pedestrian flow characteristics: including pedestrian traffic states and the impact of other pedestrians upon speed (density, flow).

(2)

Pedestrian attributes: such as age, gender, and physical attributes that affect pedestrian walking speed.

(3)

Layout configuration: taking into consideration the layout of the walking environment (e.g., bottlenecks, corridors, inclination, surface), as well as the land use and permanent or temporary obstacles.

(4)

Ambient conditions: including noises and season/weather conditions.

(5)

Pedestrian behavioral patterns: including walking in a group, carrying baggage, and using a mobile phone.

Preferred Reporting Items for Systematic Reviews and Meta Analysis (PRISMA) was used as a basis for the systematic review. The Scopus database was employed to collect all the studies concerning the area of interest that were based exclusively on experimental or observational data sources. Medical studies, pedestrian safety, and simulated datasets were excluded from the analysis. Both average real speeds and free-flow horizontal walking speeds were considered, inside a facility or at outdoor areas.

The paper is structured as follows: Section 2 describes the review methodology followed, Section 3 presents the factors that affect walking speed along with the extent of their impact, and Section 4 discusses the main conclusions.

2. Materials and Methods

2.1. Search Strategy

The Scopus database was used in May 2022 to identify the relevant literature. The keywords used were “pedestrian walking speed”, and the search was conducted in the articles’ title, abstract, and keywords. The limitations’ set for these searches were “English language” and “journal articles”. The search provided 863 records in total. Lastly, a keywords selection was made in order to limit the next step of full paper reading. The keywords which were excluded are listed below per discipline:

• Medical studies: wounds and injuries, injury scale, craniocerebral trauma;
• Pedestrian safety: navigation, collision avoidance; and
• Simulated data: forecasting.

This search step provided 641 records. Afterwards, duplicates were removed, and the abstracts were reviewed to check their validity. Categories considered as invalid were as follows:

• Data provided from simulation models or prediction models;
• Vertical movement and evacuation experiments;
• Lateral oscillation structure;
• Studies on pedestrian speed after trauma/stroke or studies on pedestrians with disabilities or on rehab conditions;
• Studying the effect of speed on behavior (e.g., probability of collision);
• Studies on stride interval or gait gap;
• Monitoring the accessibility or walkability of a city/neighborhood.

Finally, 143 papers proceeded for full-text reading. The abovementioned procedure is presented in Figure 1.

2.2. Synthesis Method

The eligible literature considered includes pedestrian movement or behavior studies through field observations or structured experiments that report descriptive statistics or quantitative analysis of walking speed. Regarding the factors that have an impact on walking speed, the process followed is based on whether the literature is associated with the categories mentioned previously. After detailed full text reading, 96 studies were finally included in the review. Information collected from the literature responds to the following information:

• Year when data collection was conducted, if reported, and the country;
• Location, whether it was a public facility (indoor) or public space (outdoor);
• Research objective;
• Data collection method (field observation/experiment), number of participants, duration of recordings, and technical means (video camera, stopwatch, etc.);
• Data extraction method;
• Results, including the influential factor and walking speed rates, if reported.

Walking speeds were converted into m∗s⁻¹, and averages were calculated if needed. Regarding data collection, the identification and validation of abnormal data are beyond the scope of this paper. Additionally, not all papers included in this review provided detailed descriptions of their data collection and analysis methodologies.

Lastly, the review includes a meta-analysis of the literature related to walking speed rates and influencing factors. The aim of the meta-analysis was to synthesize the results from multiple studies to provide a comprehensive understanding of the impact of different factors on walking speed rates. The analysis utilized the mean walking speed rates reported in the literature and calculated the effect size for each influencing factor. The results of the meta-analysis are presented and discussed in the following sections.

In the 1970s, the first steps were made in pedestrian behavior and speed analysis with the aid of manual counts. However, most of the studies were performed after the year 2000. Field observations with photographic techniques are the most dominant, with the technical means evolving overtime. Indoor experimental studies are more recent but account for as much as 41% of the collected literature. They employ emerging methods for pedestrian data collection, such as detection systems and virtual reality. In terms of geographical distribution, the largest effort to understand pedestrian dynamics is observed in China (21% of total studies). United States of America and United Kingdom follow, with approximately 8% each. In Figure 1, the contribution of each country to the discipline of interest is displayed.

3. Results

The influencing factors were categorized into five distinct categories as mentioned earlier: (i) pedestrian flow characteristics, (ii) pedestrian attributes, (iii) layout configuration, (iv) ambient conditions, and (v) pedestrian behavioral patterns. They are presented in the subsections below.

3.1. Pedestrian Flow Characteristics

Traffic flow theory primarily concerns studies on the movement of vehicular means of transport and employs the fundamental relationship and variables: flow (Q), density (k), and speed (V) [1]. The fundamental diagram was first introduced in pedestrian dynamics in 1971 when field observations, with the aid of photographic techniques, were employed and the relationship of density with speed was defined [3]. The difference to car traffic is that density is here measured in number of pedestrians over a squared metric of walkway length. As presented by [14,15], flow (Q) is an essential variable in pedestrian flow theory; however, density (k) serves as the key influencing factor on pedestrian speed.

The inverse relationship between speed and density was confirmed in the posterior field and experimental studies. Field studies using various video recording techniques were implemented in different street environments including walkways [16,17], signalized intersections [9], unsignalized intersections [18], and sidewalks [19,20,21,22,23]. Similar conclusions were reached with manual counts in [24]. Interestingly, two field studies report a positive correlation between density and speed for low density levels [15,25]. The negative correlation is proven only for higher densities, presumably corresponding to the congested part of the fundamental diagram. Turning to controlled experiments, all studies confirm the negative correlation between speed and density either using videographic techniques [9,26,27,28,29,30,31,32,33,34] or manual counting [25]. All the reported relationships are presented in

Table 1. Summary of fundamental relationships obtained from literature for speed (m/s) and density (ped/m²). * Walking speed is presented in (m/min).

Reference	Country	Location	Method	Data Collection	Fundamental Relationship
[19]	India	Sidewalk	Field Observation	Videographic Techniques	$\mathrm{V}=-0.6102\mathrm{k}+1.325$
[22]	India	Sidewalk	Field Observation	Videographic Techniques	$\mathrm{V}=1.298-0.6814\mathrm{k}$ (linear)
					$\mathrm{V}=1.33{\mathrm{e}}^{-0.606\mathrm{k}}$ (exponential)
					$\mathrm{V}=0.7913-0.237\ln \mathrm{k}$ (logarithmic)
[24]	Hong Kong	Walkway/Crosswalks	Field Observation	Videographic Techniques and Manual Counts	$\ast \mathrm{V}=77.4-21.5\mathrm{k}$ (indoor walkway)
					$\mathrm{V}={\mathrm{e}}^{\left(4.47-0.572\mathrm{k}\right)}$ (outdoor walkway),
					$\ast \mathrm{V}=85{\mathrm{e}}^{-0.347{\mathrm{k}}^2}$ (signalized crosswalk),
					$\ast \mathrm{V}=100{\mathrm{e}}^{-0.5\mathrm{k}}$ (light rail transit crosswalk)
[15]	England and Australia	Walkway	Field Observation	Manual Counts	$\mathrm{V}=1.588+0.015\log \mathrm{k}$
[28]	Scotland	Walkway	Field Observation	Videographic Techniques	$\ln \mathrm{V}=0.674\ln \mathrm{Q}-0.448\ln \mathrm{k}$
[32]	China	Laboratory	Field Observation	Videographic Techniques	$\mathrm{V}=1.99232{\mathrm{e}}^{\left(-\frac{\mathrm{k}}{1.43385}+0.03378\right)}$
[35]	Nigeria	Walkway	Field Observation	Videographic Techniques	$\ast \mathrm{V}=59.547-0.137\mathrm{k}$
[36]	Nigeria	Walkway	Field Observation	Videographic Techniques	$\ast \mathrm{V}=68.052-4.1826\mathrm{k}$ (university environment)
					$\ast \mathrm{V}=75.099-10.307\mathrm{k}$ (hospital environment)
[37]	Singapore	Sidewalk	Field Observation	Videographic Techniques & Manual Counts	$\ast \mathrm{V}=73.9-15.3\mathrm{k}$

.

This subsection summarizes the effect of density characteristics on walking speed. Two main categories for data collection were indicated, video recording techniques and manual tracking. Dense crowds in a walkable space prevent pedestrians from walking freely at a desired speed and increase the travel time, which is also stated by [2].

3.2. Pedestrian Attributes

This subsection considers two crucial pedestrian attributes found in the literature, i.e., gender and age, and discusses their influence on pedestrian walking speed.

Starting from gender differences, almost all studies find that males generally walk faster than females under all circumstances and street environments: signalized or non-signalized crosswalks [38,39,40,41,42,43,44,45,46,47,48,49], walkways [23,35,50,51,52,53,54,55,56], sidewalks [25,57,58], and inside public facilities [10,34,59,60,61]. This difference in speed is attributed to gait characteristics, which lead to bigger stride for men [60], the fearless behavior of men [62], the security distance that appears to be higher for women [34], or the fact that women tend to chat more on the mobile phone or when walking in a group which distracts their movement [9].

Interestingly, some studies report higher speeds for females, under specific circumstances. The authors of [36] manually counted the walking speed on a walkway at a teaching hospital facility and found the mean walking speed for females to be 3.5% higher. Crossing speeds at signalized crosswalks are higher for females according to [40,63]. Lastly, female dyads had higher speed when walking unrestricted on a campus walkway during a field observation in China [64].

Table 2. Summary of speeds for males and females as reported in the literature.

Reference	Country	Location	Method	Data Collection	Gender	Walking Speed
[59]	UK	Public Facility	Experiment	Manual Counts	Female	0.80
					Male	0.90
[25]	Pennsylvania	Sidewalk	Experiment	Manual Counts	Female	3.10
					Male	2.79
[50]	Iran	Walkway	Experiment	Manual Counts	Female	1.31
					Male	1.40
[34]	India	Public Facility	Experiment	Videographic Techniques	Female	1.24
					Male	1.27
[38]	Turkey	Sidewalk	Field Observation	Detection System	Male	1.34
					Female	1.27
[61]	UK	Public Facility	Field Observation	Manual Counts	Female	1.04
					Male	1.11
[43]	Canada	Signalized Crosswalk	Field Observation	Manual Counts	Female	1.44
					Male	1.50
	China	Signalized Crosswalk	Field Observation	Manual Counts	Female	1.23
					Male	1.26
[48]	Palestine	Signalized Crosswalk	Field Observation	Manual Counts	Female	1.30
					Male	1.39
[42]	Jordan	Signalized Crosswalk	Field Observation	Manual Counts	Female	1.33
					Male	1.35
[65]	India	Street	Field Observation	Manual Counts	Female	1.20
					Male	1.30
[48] *	Palestine	Unsignalized Crosswalk	Field Observation	Manual Counts	Female	1.32
					Male	1.43
[66]	North Carolina	Walkway	Field Observation	Manual Counts	Female	1.20
					Male	1.39
[53]	Saudi Arabia	Walkway	Field Observation	Manual Counts	Female	0.88
					Male	1.17
[56]	Australia	Walkway	Field Observation	Manual Counts	Female	1.10
					Male	1.19
[37]	Singapore	Sidewalk	Field Observation	Mixed Methods	Female	1.15
					Male	1.32
[40]	Uganda	Walkway	Field Observation	Mixed Methods	Male	1.04
					Female	0.89
	USA	Walkway	Field Observation	Mixed Methods	Male	0.8
					Female	0.84
[60]	Kuala Lumpur	Public Facility	Field Observation	Videographic Techniques	Female	0.81
					Male	0.93
[51] *	Iran	Sidewalk	Field Observation	Videographic Techniques	Female	0.98
					Male	1.10
[57]	Bangladesh	Sidewalk	Field Observation	Videographic Techniques	Female	1.24
					Male	1.07
[58]	India	Sidewalk	Field Observation	Videographic Techniques	Female	1.11
					Male	1.15
[23]	Hong Kong	Sidewalk	Field Observation	Videographic Techniques	Female	1.10
					Male	1.21
[51]	Iran	Signalized Crosswalk	Field Observation	Videographic Techniques	Female	1.19
					Male	1.29
	Iran	Midblock Crosswalk	Field Observation	Videographic Techniques	Female	1.02
					Male	1.13
[9]	United Arab Emirates	Signalized Crosswalk	Field Observation	Videographic Techniques	Female	1.10
					Male	1.27
[67]	Croatia	Signalized Crosswalk	Field Observation	Videographic Techniques	Female	1.48
					Male	1.47
[46]	Qatar	Signalized Crosswalk	Field Observation	Videographic Techniques	Female	1.39
					Male	1.50
[63]	Qatar	Signalized Crosswalk	Field Observation	Videographic Techniques	Female	1.38
					Male	1.32
[39]	India	Signalized Crosswalk	Field Observation	Videographic Techniques	Male	1.52
					Female	1.45
[62]	Bangladesh	Street	Field Observation	Videographic Techniques	Female	0.71
					Male	0.78
[51]	Iran	Street	Field Observation	Videographic Techniques	Female	0.92
					Male	1.04
[41]	Turkey	Unsignalized Crosswalk	Field Observation	Videographic Techniques	Male	1.21
					Female	1.13
[35]	Nigeria	Walkway	Field Observation	Videographic Techniques	Female	1.07
					Male	1.22
[36]	Nigeria	Walkway	Field Observation	Videographic Techniques	Female	1.13
					Male	1.15
[52]	New Zealand	Walkway	Field Observation	Videographic Techniques	Female	1.43
					Male	1.50

* References are duplicated because they may refer to different sites or data collection methods.

provides average speed values found in the collected literature per gender while considering different data collection methods. The table is sorted by method, data collection, and location.

Concerning the influence of age, the literature supports that it affects pedestrian speed following a general inverted U-shaped curve. Children seem to walk slower than young adults who walk faster than seniors [15,37,38,48,52,58,65,66,67], which is primarily attributed to health conditions in the elderly and differences in body stamina and structure [60]. However, in some cases, higher speeds for children comparing to adults were reported [37,39,43].

The walking speed of younger and middle-aged pedestrians is greater than that of seniors, with the speed decreasing as age increases [27,35,39,42,44,47,48,54,56,57,63,68,69,70]. The abovementioned statement has been established by controlled experiments as well [50,59,71,72,73,74]. Lastly, crossing times have been thoroughly studied concerning seniors resulting in insufficient time for them when crossing an intersection due to their lower speed and their need for larger personal space [49,60,75,76].

This subsection underlines the influence of pedestrians’ characteristics, gender, and age on speed. In general, males are reported to walk faster; however, this statement can be rebutted under some circumstances. Regarding age parameter, speed can be characterized by an inverted U-shaped curve. In

Table 3. Speed variations across different age groups as found in literature.

Reference	Country	Location	Method	Data Collection	Age Group	Speed (m/s)
[50]	Iran	Walkway	Experiment	-	Young Adults	1.45
					Middle-aged	1.42
					Seniors	1.19
[75]	-	-	Experiment	Detection System	Young Adults	1.57
					Seniors	1.38
[59]	UK	Public Facility	Experiment	Manual Counts	Seniors	0.85
[71]	Cape Town	Signalized Crosswalk	Experiment	Manual Counts	Seniors	0.86
[38]	Turkey	Public Space	Field Observation	Detection System	Children	1.12
					Young Adults	1.34
					Adults	1.3
					Seniors	1.17
[48] *	Palestine	Signalized Crosswalk	Field Observation	Manual Counts	Children	1.31
					Young Adults	1.45
					Middle-aged	1.29
					Seniors	1.13
[43]	Canada	Signalized Crosswalk	Field Observation	Manual Counts	Children	1.65
					Adults	1.53
					Seniors	1.41
	China	Signalized Crosswalk	Field Observation	Manual Counts	Children	1.25
					Adults	1.26
					Seniors	1.19
[42]	Jordan	Signalized Crosswalk	Field Observation	Manual Counts	Children	1.29
					Young Adults	1.49
					Adults	1.47
					Middle-aged	1.29
					Seniors	1.17
[49]	South Florida	Signalized Crosswalk	Field Observation	Manual Counts	Seniors	0.74
[48]	Palestine	Unsignalized Crosswalk	Field Observation	Manual Counts	Children	1.23
					Young Adults	1.42
					Middle-aged	1.37
					Seniors	1.09
[65]	India	Walkway	Field Observation	Manual Counts	Children	1.24
					Young Adults	1.39
					Adults	1.20
					Middle-aged	1.01
					Seniors	0.92
[66]	North Carolina	Walkway	Field Observation	Manual Counts	Middle-aged	1.30
					Seniors	1.29
[37]	Singapore	Sidewalk	Field Observation	Mixed Methods	Children	1.27
					Young Adults	1.23
					Seniors	0.90
[60]	Kuala Lumpur	Public Facility	Field Observation	Videographic Techniques	Children	0.77
					Adults	0.96
					Seniors	0.78
[35]	Nigeria	Public Space	Field Observation	Videographic Techniques	Young Adults	1.17
					Adults	1.13
[39]	India	Public Space	Field Observation	Videographic Techniques	Children	1.64
					Adults	1.54
					Seniors	1.24
[68]	Italy	Public Space	Field Observation	Videographic Techniques	Adults	1.28
					Seniors	1.03
[58]	India	Sidewalk	Field Observation	Videographic Techniques	Children	1.17
					Young Adults	1.29
					Middle-aged	1.15
					Seniors	0.92
[57]	Bangladesh	Sidewalk	Field Observation	Videographic Techniques	Young Adults	1.26
					Adults	1.16
					Seniors	1.04
[44] *	Israel	Sidewalk	Field Observation	Videographic Techniques	Young Adults	1.67
					Middle-aged	1.42
					Seniors	1.19
[69]	Italy	Sidewalk	Field Observation	Videographic Techniques	Children	1.04
					Young Adults	1.00
					Middle-aged	0.99
					Seniors	0.84
[63]	Qatar	Signalized Crosswalk	Field Observation	Videographic Techniques	Children	-
					Middle-aged	1.37
					Seniors	1.24
[67]	Croatia	Signalized Crosswalk	Field Observation	Videographic Techniques	Children	1.44
[44]	Israel	Signalized Crosswalk	Field Observation	Videographic Techniques	Young Adults	1.48
					Middle-aged	1.33
					Seniors	1.09
[70]	China	Signalized Crosswalk	Field Observation	Videographic Techniques	Children	1.24
					Middle-aged	1.18
					Seniors	1.08
[44]	Israel	Unsignalized Crosswalk	Field Observation	Videographic Techniques	Young Adults	1.49
					Middle-aged	1.39
					Seniors	1.11
[52]	New Zealand	Walkway	Field Observation	Videographic Techniques	Children	1.38
					Young Adults	1.46
					Middle-aged	1.49
					Seniors	1.37

* References are duplicated because they may refer to different sites or data collection methods.

, age-based walking speed rates are presented. The table is sorted by method, data collection, and location.

3.3. Layout Configuration

In the environment configuration subsection, we identify two main categories relevant to the study of the factors that influence pedestrian walking speed: (i) walking outdoors (signalized or not intersections, pavements, etc.) and (ii) walking indoors. Within these categories, we consider several key factors that may influence pedestrian behavior, including the slope and angle of the walking surface, the land use and size of the city, and design elements such as obstacles and the condition of pavement.

An early study analyzed pedestrian walking behavior when walking both indoors and outdoors and claimed that pedestrians seem to be faster when walking outdoors [24] for safety and weather conditions issues [60]. Walking elements and their design have impact on speed including street sections, crosswalks, pavements, etc. [44,51,58,62]; crosswalk length and front distance length [27,67,77]; crossing markings [45,78]; street width [42]; and on street motor vehicle parking [22]. Walking speed is positively correlated with city size according to [15,55]. Moreover, the land use in terms of walkability plays an important role in walking speed [12,26,43,57,58]. The speed decrease with the inclination [28,50,52,54,56,79] and cold weather effect on pavement such as ice [54]. Contrary to the abovementioned, no impact of pavement width and its condition [23] as well as traffic control [48] were reported.

Regarding indoor walking behavior, a negative correlation between the degree of the angle and the speed [80,81,82,83,84], as well as the presence of obstacles [75,79], are reported. On the other hand, a positive correlation between (i) the height of the walls [29] that pedestrians are reported to walk away from [85] and (ii) the presence of guiding signs [10] when walking within an enclosed passageway with the speed are reported.

In conclusion, walking environment configuration has several attributes that influence pedestrian walking behavior and, hence, their speed. The difference between walking indoors and outdoors is not clear because it depends on many different factors such as the condition of the pavement, the turning angle, the slope, and moving or stable constraints.

3.4. Ambient Conditions

The ambient conditions identified as influential are (i) sound and light and (ii) weather conditions.

The sound environment may concern “nature sounds” such as birdsong and rustling leaves and “noise” including disruptive sounds, like traffic and human activities. Studies on the impact of noise or nature sounds on pedestrian behavior showed speed differentiations [11,25,86]. Significant correlation between walking speed and environmental sounds was established by [87], who reported higher speed rates for nature sounds than traffic noises. Apart from soundscape conditions, Pedersen and Johansson (2018) found that lighting conditions significantly impact pedestrian walking characteristics, revealing that lower lighting levels led to a decrease in walking speed. [76].

Regarding the weather conditions, the literature revealed a significant impact of weather conditions on speed [15], although the results do not converge. Weather conditions may have negative effects on pavement condition (ice, snow, etc.) and result in more carefully walking behavior and, hence, lower walking speed rates [13,54]. However, several studies report higher speeds for pedestrians under adverse weather in order to reach the destination quickly and protect themselves [9,13,51,60,61]. Contrarily to the latter, Brščić and Kanda [88] stated no weather effect on pedestrian speed when individuals were walking in a shopping center.

3.5. Pedestrian Behavioral Patterns

This subsection analyzes the effect of pedestrian behavior in walking speed. More specifically, walking in a group, smartphone usage, and carrying a bag are the main categories mentioned.

Walking in a group seems to affect pedestrian speed [30,89]. A noteworthy speed reduction is mentioned when walking in a group, in most cases, regardless the number of the members [9,17,38,40,41,47,51,58,63,65,67,90]. A reason for this lies in the social relationship and interaction that leads to gait alterations in order to obtain similar speed [91]. Some researchers report a lower influence of the group effect when moving in pairs [15,42,45].

Mobile phone usage seems to decrease walking speed. A noteworthy number of authors report that all technological uses (including mobile-phone-related tasks, headphones, etc.) have a negative impact on pedestrian walking behavior [62,92,93,94,95,96,97,98,99]. Moreover, reduced speed and distraction is known to have significant safety implications, especially when pedestrians cross the road.

Findings are not conclusive regarding the impact of carrying luggage on walking speed. The literature reports both a negative [9,38,40,41,57,100] and positive effect [52,58,63] on luggage-laden pedestrians’ speed as well as pedestrians around them [100]. Some researchers even report an absence of impact on walking speed [39,47,61,71,101], which may be due to the combination of some positive and negative parameters. The size of the luggage and travel purpose may be determinant in the direction of this relationship but has not been explored so far. Commuters carrying briefcases may walk faster than the average flow stream, whereas tourists carrying heavy luggage may walk slower than the average flow stream.

Additional distractive factors that were found to change pedestrian walking speed are facial expressions and staring [89], the surrounding speed [33], the fear of falling when crossing an intersection [44], the waiting time at a crosswalk [102], and smoking [59]. In

Table 4. Behavioral influencing factors and their impact on walking speed (+ for positive, − for negative).

Reference	Country	Location	Walking in a Group	Carrying a Baggage	Cell Phone Use
[34]	Turkey	Sidewalk	−	−
[36]	Uganda	Walkway	−	−
[36]	USA	Walkway	−	+
[37]	Turkey	Unsignalized Crosswalk	−	−
[38]	Jordan	Signalized Crosswalk	−
[41]	USA	Unsignalized Crosswalk	−		−
[48]	New Zealand	Walkway		+
[54]	Bangladesh	Sidewalk		−
[55]	India	Sidewalk	−	−	−
[61]	Qatar	Signalized Crosswalk	−	+	−
[64]	India	Street	−
[66]	Croatia	Signalized Crosswalk	−
[89]	USA	Signalized Crosswalk	−
[92]	China	Walkway		−
[94]	United Kingdom	Public Facility			−
[95]	Canada	Signalized Crosswalk			−
[96]		Signalized Crosswalk			−
[98]	Slovenia	Signalized Crosswalk			−
[99]		Public Facility			−
[100]	-	Public Facility			−

, the abovementioned influencing factors are presented.

3.6. Meta Analysis

A meta-analysis was conducted on the collected literature in an effort to gain additional insight on both the results and methods used to obtain them as well as on the potential influence of the collection method on results. On the means of observation, the meta-analysis reveals that the most frequently utilized method is field observations, accounting for 61%. Cameras are predominantly employed for both field observations (60%) and experiments (38%), while “other methods”, such as detection systems and virtual reality (VR) technology, are reported for experiments (24%), which offer more flexibility compared to other methods, as the environment and scenarios can be replicated. An insightful observation is that the majority of studies were conducted after 2010, comprising 76% of the total (41% of the experiments and 59% of the observations). On the contrary, before 2010, manual tracking was mostly used for field observations. Regarding the location of the studies, a significant percentage of the research was conducted in China (21%), followed by the United States of America (8%) and the United Kingdom (7%). The lowest pedestrian velocity was reported at signalized crossings, likely due to the higher density caused by traffic light cycle, which potentially results in restricted movement, while the highest velocity was observed at walkways.

Turning to the influential factors and the fundamental relationship of traffic, walking speed rates were estimated for given density values using different reported relationships (Figure 2). The findings show a consistent pattern confirming the well-established negative correlation between density and speed. However, a single study reported an inverse relationship [15].

Pedestrian characteristics, specifically gender and age, were examined using two main data collection methods: video recording and manual tracking. Although both methods showed that men walk 9% faster than women (Figure 3), under specific circumstances (such as traffic control at crosswalks), the opposite is reported. Markedly, controlled experiments report 30% higher average for both genders’ walking speeds compared to field observations (Figure 3 and Figure 4). Manual counting (vs. video recordings) provides higher speed values in all cases. This is particularly true in the case of experimental studies and needs further investigation. Also, outdoor velocity rates were reported to be 38% higher than indoor facilities’ speeds (

Table 5. Pedestrian walking speed (m/s) regarding gender for field observations and controlled experiments. Rates were concluded by the meta-analysis performed.

Gender	Indoor		Outdoor
	Field Observation	Controlled Experiment	Field Observation	Controlled Experiment
Males	0.93	1.09	1.26	2.00
Females	0.81	1.02	1.17	1.80

).

Turning to age, speed is generally reduced when age increases among adults. However, disparities are observed in the magnitude of the speed reduction. Again, manual counts provide higher speed rates compared to video recordings for all age groups and independently of the data collection method. Interestingly, detection systems used provide values close to manual counting and not video recordings that seem to be less reliable and to consistently underestimate speed rates at around 20% (Figure 5). Last but not least, people aged 55 and more reported to have 17% lower speed than the average of the studies and 11% than the proposed speed by Highway Capacity Manual [1].

Walking in groups and carrying loads were identified as factors influencing pedestrian speed. Social interactions and group size were found to consistently reduce walking speed, with the highest reduction being observed in larger groups. The highest reduction was found in groups with five or more members and is approximately 27%. Field observation using video cameras is the most common method used to investigate this issue, although manual tracking methods also support this finding (

Table 6. Pedestrian walking speed (m/s) regarding pedestrian behavior characteristics for field observations and controlled experiments.

Pedestrian Behavior	Field Observations	Experiments	Differences
Group	1.35	-	-
No Group	1.40	-	-
Baggage	1.28	1.80	−41%
No Baggage	1.28	1.82	−43%
Phone	1.30	1.09	16%
No Phone	1.37	1.27	7%

). The influence of carrying baggage on pedestrian speed behavior is still inconclusive, regardless of the method employed to collect pedestrian data. Some studies suggest that carrying loads has a negative effect on speed, while others found higher speeds for luggage-laden pedestrians. Significant differences between controlled experiments and field observations emerged according to meta-analysis, with 41% lower average speed for field observations. Lastly, observations made in different contexts strongly indicate that any phone-related task has only a negative effect on walking speed.

4. Conclusions and Discussion

Pedestrian walking speed is an essential input to infrastructure design and an important variable determining the level of service and the safety of operations. The objective of this study is to provide a comprehensive review of the literature on pedestrian walking speed and the factors having an impact on speeds. Influential factors were organized in the following categories: (i) pedestrian flow characteristics, (ii) pedestrian attributes, (iii) layout configuration, (iv) ambient conditions, and (v) pedestrian behavioral patterns.

In terms of observational methods, the literature includes both field observations and controlled experiments. The data collection techniques were mostly video recordings, manual counts, or detection systems. This trend reflects advancements in observational technology and methodologies post 2010, underscoring the increasing precision of data collection in pedestrian research. However, the meta-analysis performed indicates that results are dependent upon the observational method and the data collection technique. The impact concerns the value of estimated walking speed and not the direction of the impact of influential factors. Field observations report 40% lower average walking speed compared to experiments. Detection systems and manual counts are related with higher speed and videographic techniques with lower. This finding is important as it suggests that future experimental protocols should be revised to address the issue. For example, all measurements, field or experimental, should include at least some manual counts as a benchmark, and it is also important to explore methods for the optimal placement of sensors [103,104] or measurement uncertainties [105] to ensure the accuracy of data collection.

Turning to influential factors, the fundamental flow characteristics (i.e., mainly density) are found to have a strong impact on speeds, similarly to motorized traffic. The meta-analysis confirms the negative correlation between speed and density. The higher negative gradient in speed curve occurs at signalized crosswalks when using videographic methods and manual counts. In contrast, a negligible increase is reported in walkways when using solely manual counts. Outdoor walkways experience a smaller decrease compared to indoor walkways and sidewalks. Lastly, experimental settings show in general a higher rate of speed decrease compared to observations in the field. These findings underscore the interplay between infrastructure design, traffic management, and pedestrian behavior, emphasizing the need for integrated approaches in urban planning. In general, the pedestrian flow relationship is data collection method and infrastructure dependent and is fundamental for designing pedestrian facilities. Recent research, as highlighted in [106], has contributed significantly to addressing this challenge by developing a tool to generate situation-specific fundamental diagrams. However, other factors come into play. They are related to pedestrians’ physiology and psychology as well as to the built environment and ambient conditions. Being female, being of older age, walking in a group, engaging in social interactions or phone-related tasks, and moving under noise conditions are reported to mostly have a negative impact on walking speed. Males are reported to walk faster, although this statement can be rebutted. Interestingly, carrying baggage also has a non-conclusive influence as, in some cases, it increases while in others it decreases speed. We may assume that the direction of the influence depends on travel purpose and baggage size. Regarding the age factor, the review considers the influence follows an inverse U-shape curve with the average walking speed of seniors to be the lowest walking speed reported, followed by children. Walking environment and weather conditions are reported to have a statistically significant impact, but the direction of the impact is not always the same and seems to be very context dependent.

This review offers valuable insights for researchers, designers, and operators. In the research community, there is a need for the development of experimental and field observation protocols that incorporate benchmarks regarding walking speed to ensure accurate data collection and analysis. Furthermore, designers and operators should consider not only passenger volume but also the specific profile of pedestrians to enhance capacity and safety. However, this review suffers from certain limitations and gaps that need to be addressed. Methodological variations in speed estimation highlight the importance of further exploration not only of the physiological and psychological dynamics in pedestrian studies but also the methodological framework. Understanding these discrepancies is essential for designing pedestrian-friendly environments and addressing potential safety hazards associated with distractions. Enhancing pedestrian safety and mobility requires an understanding of influential factors and their interactions within urban environments. Addressing these gaps will advance our understanding of pedestrian dynamics and inform evidence-based interventions for creating safer and more inclusive pedestrian environments. First, the literature examined does not include new emerging data collection techniques/methods as they are not yet well-established, and they present important heterogeneity making them difficult to classify. Regarding data collection methods and extraction processes, it is crucial to consider the disturbances and challenges encountered to ensure the reliability and validity of research findings. Factors such as environmental conditions, technological limitations, and human errors can introduce biases or inaccuracies in the collected data. These disturbances can significantly impact the outcomes of studies, leading to misleading conclusions or unreliable results. Additionally, the literature should broaden its scope to encompass a wider range of influencing factors, particularly in indoor environments. It is also important to acknowledge that the methods employed in the literature review may have inherent limitations. Considering the meta-analysis, shortcomings in result reliability and accuracy should be examined. Novel methods presented by [107,108] can be helpful in improving the accuracy and reliability of the analysis.