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Cited by 15Urban Transport and AccessibilityEvacuation and Crowd DynamicsTransportation Planning and Optimization

The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index; none of the 4 sources its claims cite is checked for support yet.

Analysis of Factors Affecting Walking Speed Based on Natural Field Data: Considering the Attributes of Travelers and the Travel Environment

Open access
Miao, Shuqi, Li, Tinghao, Zheng, Lili, Tan, Bowen, Ma, Qianjun·2023·Sustainability
DOI 10.3390/su151411433
Open this paper in CoChat — free accountOpen PDF

Abstract

In Mobility as a Service (MaaS), walking plays a crucial role in connecting various modes of transportation. In order to provide more accurate predictions of walking travel time, a comprehensive and in-depth study is required to examine the factors that influence walking speed. Many existing studies focus on exploring various factors affecting walking speed, but there is limited research on further investigating the magnitude of their impact and the reasons for differences among different pedestrians. This study examines the relationship between personal characteristics and the degree of influence of environmental factors on walking speed. We recruited 31 volunteers and investigated their traveler characteristics such as height, weight, and age, as well as environmental factors such as weather conditions, ground conditions, and sidewalk Level of Service (LOS). Descriptive statistics were performed on walking speed, revealing the influence of these factors. For example, the speed of females is 89% of that of males. When in a hurry, the speed increases by 17%, while on uneven roads, the speed decreases by 11%. We then proposed the influence coefficient f to represent the degree of influence and analyzed its correlation with personal characteristics. We discovered some strong correlations. For instance, the greater the body weight, the more significant the reduction in walking speed due to precipitous weather or uneven roads. Similarly, the taller the person, the greater the increase in walking speed under the influence of a rushed situation. Finally, we constructed a series of regression models for “f” and a speed estimation model. Our findings provide support for predicting personalized speeds in various scenarios, based solely on the traveler’s personal characteristics and speeds in controlled group scenarios in the travel service system, and contribute to the study and development of MaaS in terms of travel time prediction, travel route planning, and personalized services.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This study uses one month of natural walking-trip data from 31 volunteers to quantify how traveler attributes and travel-environment factors affect walking speed, introducing an influence coefficient \(f\) to capture individual differences. It then builds regression models for \(f\) and a personalized speed-estimation model intended to support walking-time prediction in MaaS and travel service systems.

Key findings

  • Across 325 walking-trip samples, mean walking speed was 1.28 m/s, with 15th, 50th, and 85th percentiles of 0.94, 1.27, and 1.58 m/s; speeds ranged from 0.43 to 2.52 m/s.
  • The abstract reports female speed as 89% of male speed, while the Discussion reports 90%; rushed walking increased speed by 17% in the abstract and 13% in the Discussion, and uneven roads decreased speed by 11%.
  • Influence coefficients varied strongly by person: under puddles, the most affected individual’s speed decreased by 45% while the least affected decreased by 2%; with tailwind, some individuals increased speed by 57% while others increased by 32%.
  • Significant correlations included \(f_{\text{Rushed level,Rushed}}\) with height (\(r=0.734\)) and \(f_{\text{Weather,Precipitous}}\) with weight (\(r=-0.849\)); the highest reported adjusted \(R^2\) was 0.933 for travel distance above 1000 m versus weight, corresponding to a 0.043 m/s speed increase per 1 kg compared with distances under 500 m.

Methods

  • Recruited 31 volunteers who submitted walking-trip data multiple times over a one-month survey period on a university campus and surrounding area; each walking trip was a sample, yielding 325 samples.
  • Collected average walking speed, personal characteristics (age, gender, height, weight), and environmental/self-variable factors including travel distance, road smoothness, weather, wind influence, road condition, sidewalk LOS, rushed level, and intersections per 100 m.
  • Used descriptive statistics, bivariate correlation, Welch’s test, and cumulative frequency curves; defined influence coefficient \(f_{i,j}=(v_{i,j}-v_{i,0})/v_{i,0}\), then regressed \(f\) on height/weight and proposed a speed-estimation model \(v_{i_1,j_1,\ldots}=v_0\prod_{k=1}^{n}(1+f_{i_k,j_k})\).

Limitations

  • Stated: the age distribution was narrow, mostly 20–28 years old, so age was excluded and findings have highest credibility among young individuals; some factor levels had limited sample sizes, especially weather; data are not publicly available due to privacy concerns.
  • Stated: further research should categorize rushed level and road conditions more finely, consider more personal attributes, and expand the survey scope and age range.
  • Evident from the text: the abstract and Discussion report different values for female/male speed ratio and rushed-speed increase; several variables were self-reported subjectively; weather and wind were not significant in Welch’s test; and some \(f\) regression models had low or negative adjusted \(R^2\).

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3390/su151411433

Cites

20 of 20 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

0 of 4 cited sources checked · 0 supported · 0 flagged

  • Findingnone given

    The taller the person, the greater the increase in walking speed under the influence of a rushed situation.

    “the taller the person, the greater the increase in walking speed under the influence of a rushed situation.”
    Quote in the paperSubstantial claim
  • Findingown data

    As travel distance increases, walking speed also increases.

    “as travel distance increases, speed also increases.”
    Quote in the paperSubstantial claim
  • Findingown data

    Under the conditions of headwind high influence, other influences of wind, LOS level 2, LOS level 3, travel distances of over 1000 m, and rushed situations, f is positively correlated with weight.

    “Under the conditions of headwind high influence, other influences of wind, LOS level 2, LOS level 3, travel distances of over 1000 m, and rushed situations, f is positively correlated with weight.”
    Quote in the paperSubstantial claim
  • Findingreasoning

    The volunteers' ages were predominantly concentrated between 20 and 28 years old.

    “the volunteers' ages were predominantly concentrated between 20 and 28 years old”
    Quote in the paperSubstantial claim
  • Findingown data

    The highest negative correlation is observed for f_Weather,Precipitous, w, which is -0.849.

    “The highest negative correlation is observed for f Weather,Precipitous , w , which is -0.849.”
    Quote in the paperSubstantial claim
  • It may state two separate claims as one. 19
  • Probably not a substantial claim. 5

  • It may state two separate claims as one.

    Findingown data

    Significant negative correlations are observed for f_Weather,Precipitous, w; (f_Sidewalk LOS,Level 3, w); and (f_Road smoothness,uneven, w).

    “Significant negative correlations are observed for f Weather,Precipitous , w , ( f Sidewalk LOS,Level 3 , w), and ( f Road smoothness,uneven , w).”
    Quote in the paperSubstantial claim

  • It may state two separate claims as one.

    Findingown data

    Under the factor of road conditions, the walking speed of the individual most influenced by puddles decreased by 45%, while the least influenced individual only decreased by 2%.

    “under the factor of road conditions, the walking speed of the individual most influenced by puddles decreased by 45%, while the least influenced individual only decreased by 2%.”
    Quote in the paperNot a substantial claim

  • It may state two separate claims as one.

    Findingnone given

    Walking speeds are similar in rainy and snowy weather, with slightly faster speeds observed in dry weather.

    “Walking speeds are similar in rainy and snowy weather, with slightly faster speeds observed in dry weather.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim. The paper’s conclusions may not depend on it.

    Findingown data

    The survey collected 325 walking trip samples.

    “The histogram of average walking speeds obtained from the survey of 325 samples is shown in Figure 2.”
    Quote in the paperNot a substantial claim

  • It may state two separate claims as one.

    Findingown data

    Travel distance, height, and sidewalk LOS are all significantly correlated with walking speed.

    “Travel distance, height, and sidewalk LOS are all significantly correlated with walking speed.”
    Quote in the paperSubstantial claim

  • It may state two separate claims as one.

    Findingreasoning

    Walking speed increases with height and weight.

    “Furthermore, this study found that walking speed increases with height and weight, which is not entirely consistent with existing research findings [4].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [4] Bosina, 2017, Estimating pedestrian speed using aggregated literature data, Phys. A Stat. Mech. Its Appl. source

  • It may state two separate claims as one.

    Findingown data

    The highest adjusted R2 value, 0.933, was observed for the relationship between travel distance of over 1000 m and weight w, indicating that for every 1 kg increase in weight, walking speed increases by 0.043 m/s compared to travel distances of less than 500 m.

    “Among them, the highest adjusted R 2 value, 0.933, was observed for the relationship between travel distance of over 1000 m and weight w, indicating that for every 1 kg increase in weight, walking speed increases by 0.043 m/s compared to travel distances of less than 500 m.”
    Quote in the paperNot a substantial claim

  • It may state two separate claims as one.

    Findingown data

    Significant positive correlations are observed for (f_Rushed level,Rushed, h), (f_Travel distance,Above 1000 m, w), and (f_Rushed level,Rushed, w).

    “Significant positive correlations are observed for ( f Rushed level,Rushed , h), ( f Travel distance,Above 1000 m , w), and ( f Rushed level,Rushed , w).”
    Quote in the paperSubstantial claim

  • It may state two separate claims as one.

    Findingown data

    Regarding the influence of wind, when walking with a tailwind, some individuals experienced a 57% increase in walking speed, while others only experienced a 32% increase.

    “regarding the influence of wind, when walking with a tailwind, some individuals experienced a 57% increase in walking speed, while others only experienced a 32% increase.”
    Quote in the paperNot a substantial claim

  • It may state two separate claims as one.

    Findingown data

    Walking speed is significantly influenced by travelers' gender, urgency, road smoothness, and travel distance.

    “This indicates that walking speed is significantly influenced by travelers' gender, urgency, road smoothness, and travel distance.”
    Quote in the paperSubstantial claim

  • It may state two separate claims as one.

    Findingown data

    Walking speed is fastest during dry weather, followed by rainy weather and snowy weather.

    “the walking speed is fastest during dry weather, followed by rainy weather and snowy weather.”
    Quote in the paperNot a substantial claim

  • It may state two separate claims as one.

    Findingown data

    Compared to the no-influence condition, walking speed is noticeably lower under the high headwind condition, while in other situations, walking speeds are relatively similar and higher than the no-influence condition.

    “Figure 2j indicates that compared to the no-influence condition, walking speed is noticeably lower under the high headwind condition, while in other situations, walking speeds are relatively similar and higher than the no-influence condition.”
    Quote in the paperSubstantial claim

  • It may state two separate claims as one.

    Findingnone given

    In the survey, the distribution of volunteer ages was highly concentrated, and their physical fitness was similar.

    “In our survey, the distribution of volunteer ages was highly concentrated, and their physical fitness was similar.”
    Quote in the paperSubstantial claim

  • It may state two separate claims as one.

    Findingown data

    The fastest road surface condition for walking speed is dry, while the slowest is icy.

    “Figure 3i shows that the fastest road surface condition for walking speed is dry, while the slowest is icy.”
    Quote in the paperNot a substantial claim

  • It may state two separate claims as one.

    Findingown data

    There is an increasing trend in walking speed with an increase in pedestrian height and weight, a decrease in the number of intersections per 100 m, and an improvement in sidewalk LOS, with the exception when the number of intersections is zero.

    “The position of the curves also indicates an increasing trend in walking speed with an increase in pedestrian height and weight, a decrease in the number of intersections per 100 m, and an improvement in sidewalk LOS, with the exception when the number of intersections is zero.”
    Quote in the paperSubstantial claim

  • It may state two separate claims as one.

    Findingcitation

    Road conditions have a significant impact on walking speed: the highest walking speed is observed on dry surfaces, followed by snow-covered surfaces, puddles, and ice-water mixture, while the slowest walking speed is observed on icy surfaces.

    “Road conditions also have a significant impact on walking speed: the highest walking speed is observed on dry surfaces, followed by snow-covered surfaces, puddles, and ice-water mixture, while the slowest walking speed is observed on icy surfaces, which is consistent with previous research [13,22].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [13] Fossum, 2021, The walking speed of pedestrians on various pavement surface conditions during winter, Transp. Res. Part D Transp. Environ. source

      Checked before — its earlier verdict is held back until a new check runs.

    • cites [22] Fujita, 2019, Traffic flow in a crowd of pedestrians walking at different speeds, Phys. Rev. E source

  • It may state two separate claims as one.

    Findingown data

    Some influence coefficient combinations that did not show significant correlation in tests showed correlation trends in scatter plots: one combination showed a negative correlation with height, and three combinations showed a positive correlation with height; three combinations showed a positive correlation with weight.

    “some other combinations also demonstrated correlation trends in scatter plots: 𝑓 , showed a negative correlation with height, while 𝑓 , , 𝑓 , , and 𝑓 , showed a positive correlation with height.Furthermore, 𝑓 , , 𝑓 , , and 𝑓 , showed a positive correlation with weight.”
    Quote in the paperNot a substantial claim

  • It may state two separate claims as one.

    Findingown data

    Sidewalk LOS has a moderate correlation with walking speed, while travel distance and height have weak correlations.

    “Sidewalk LOS has a moderate correlation, while the others have weak correlations.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim. It may be too vague to check against evidence.

    Interpretationreasoning

    These models, together with the speed estimation model, can provide support for speed prediction in travel service systems or MaaS.

    “These models, together with the speed estimation model, can provide support for speed prediction in travel service systems or MaaS.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.

    Study design and conductnone given

    The sample sizes, averages, and percentiles are weighted to fairly consider each volunteer.

    “The sample sizes, averages, and percentiles are weighted to fairly consider each volunteer.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim. The paper’s conclusions may not depend on it.

    Interpretationown data

    The study findings have the highest credibility among young individuals.

    “the study findings have the highest credibility among young individuals.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim.

    Interpretationreasoning

    The findings contribute to the study and development of MaaS in terms of travel time prediction, travel route planning, and personalized services.

    “contribute to the study and development of MaaS in terms of travel time prediction, travel route planning, and personalized services.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: The influence coefficients at the control level are zero.
    • Rests on an assumption: The sample of 31 volunteers is representative of the population for which personalized speed predictions are made.
    • Rests on an assumption: The relationship between influence coefficients and personal characteristics (height and weight) is stable across different scenarios and populations.
    • Rests on an assumption: The walking speed at the control group level (v0) is known or can be accurately obtained for a traveler.

  • It may state two separate claims as one.

    Interpretationreasoning

    If height has a positive impact on speed, weight also demonstrates a positive influence on speed.

    “Therefore, if height has a positive impact on speed, weight also demonstrates a positive influence on speed.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The physical fitness of volunteers was similar.

  • It may state two separate claims as one.

    Interpretationown data

    Even though test results show significant correlation between these coefficients and personal characteristics, they cannot be perfectly explained by a single line or curve.

    “From Figure 7, it is evident that even though the test results show a significant correlation between these coefficients and personal characteristics, they cannot be perfectly explained by a single line or curve.”
    Quote in the paperNot a substantial claim
24 of 24

  • The authors state this as an assumption.

    The influence coefficients at the control level are zero.

    “By setting the coefficients at the control level, f i,0 , to zero, we can obtain a personalized speed estimation model as follows:”

  • The paper relies on this without support.

    The sample of 31 volunteers is representative of the population for which personalized speed predictions are made.

    “We recruited 31 volunteers”
    “provide support for predicting personalized speeds in various scenarios”

  • The paper relies on this without support.

    The relationship between influence coefficients and personal characteristics (height and weight) is stable across different scenarios and populations.

    “we can predict a traveler's personalized walking speed at other levels of these influencing factors based solely on their walking speed at a partial level of these factors”

  • The paper relies on this without support.

    The walking speed at the control group level (v0) is known or can be accurately obtained for a traveler.

    “based solely on the traveler's personal characteristics and speeds in controlled group scenarios”

  • The paper relies on this without support.

    The exclusion of outliers and high-influence points does not bias the correlation results.

    “After excluding outliers and high-influence points from the data, the paper conducted bivariate linear correlation tests”

  • The paper relies on this without support.

    Existing studies have rarely considered the influences of weather, rushed level, road smoothness, travel distance, and wind on walking speed.

    “In addition, the existing studies have rarely considered the influences of weather, rushed level, road smoothness, travel distance, and wind on walking speed, making the results of this study a valuable reference.”

  • The paper relies on this without support.

    Many existing studies have not taken into consideration the influence of environmental factors and variable factors on walking speed, represented by the coefficient f, and its relationship with the personal characteristics of travelers.

    “This is what many existing studies have not taken into consideration.”

  • The paper relies on this without support.

    The sample of volunteers is representative of young individuals in general.

    “We found that among young individuals, walking speed is influenced to some extent by gender, height, weight, rushed level, road smoothness, travel distance, sidewalk LOS, weather conditions, road conditions, influence of wind, and the number of intersections per 100 m.”

  • The paper relies on this without support.

    The physical fitness of volunteers was similar.

    “In our survey, the distribution of volunteer ages was highly concentrated, and their physical fitness was similar.”
9 of 9

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Existing methods to calculate walking speeds require tuning for use in real-world scenarios, as many factors affect an individual's walking speed beyond slope and obstruction level (such as weather, fitness level or age).

    “The existing methods to calculate walking speeds require tuning for use in real-world scenarios, as there are many factors which can affect an individual’s walking speed beyond the slope and obstruction level (such as the weather, fitness level or age) [2–4].”

    From Improved prediction of hiking speeds using a data driven approach (2023) · cites this paper as [3]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • One moderate-snow sample has different minimum and maximum speeds

    Minor

    Table 3 assigns moderate snow a sample size of one but gives it a minimum of 1.09 m/s and a maximum of 1.22 m/s. A group containing one walking-trip sample cannot have different minimum and maximum values.

  • Rushed-speed increase is reported as both 17% and 13%

    Minor

    The abstract reports a 17% increase when travelers are rushed, whereas the discussion reports 13% for rushed versus non-rushed situations. Table 3's averages of 1.45 and 1.24 m/s support approximately 17%.

  • Influence-coefficient example contradicts its definition

    Minor

    Equation (1) defines the coefficient as the speed change divided by the control-group speed. With 1.2 m/s as the control and 0.9 m/s at LOS level 4, the coefficient is (0.9 − 1.2)/1.2 = −0.25, not +0.33.

  • Road-surface speed ranking disagrees with Table 3

    Minor

    The discussion ranks snow-covered surfaces ahead of puddles and ice-water mixtures. Table 3 instead gives ice-water mixtures an average of 1.25 m/s, above the 1.24 m/s reported for each of snow-covered surfaces and puddles.

  • LOS level 3-weight correlation has the opposite sign

    Minor

    The narrative calls the correlation between weight and the LOS level 3 coefficient significantly negative, but Table 7 reports a significant positive coefficient of 0.829.

5 of 5

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. Yao, 2010, Characteristics of Pedestrian Walking Speeds in Sidewalk in Xi’an of China, Prog. Saf. Sci. Technol., 8, 1961
  2. Franek, 2008, Influence of urban vegetation on the walking speed of pedestrians, Ceskoslovenska Psychol., 52, 597

Cited by

Sorted by citations

Related

Metadata

Authors
Miao, S., Li, T., Zheng, L., Tan, B., & Ma, Q.
Affiliations
  • Jilin University (CN)
  • College of Transportation, Jilin University, Changchun 130022, China
Venue
Sustainability
Publisher
MDPI AG
Published
2023-07-24
Volume
15
Issue
14
Pages
11433
ISSN
2071-1050 (ISSN-L)
DOI
10.3390/su151411433
OpenAlex
W4385241726
Cited by
15
References
20
Citations by year
2026: 7 · 2025: 5 · 2024: 2 · 2023: 1
FWCI
1.91
Type
journal article
Language
en
Open access
Licence
Locations
  • Sustainability (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Transportation › Urban Transport and Accessibility
  • Social Sciences › Safety Research › Evacuation and Crowd Dynamics
  • Social Sciences › Transportation › Transportation Planning and Optimization
Keywords
walking speed, Mobility as a Service, travel time prediction, weather conditions, uneven terrain, body weight, height

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