Another Look at Pedestrian Walking Speed

Fitzpatrick, Kay; Brewer, Marcus A; Turner, Shawn

doi:10.1177/0361198106198200104

Cited by 123 publications

(74 citation statements)

References 4 publications

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“…Overall, the speed threshold (i.e., the fastest pace that the user can walk without causing system localization failures) was found to be around 6.4 km/h to 7.6 km/h. Previous studies found that the general walking speed is around 3.4 km/h to 5.5 km/h [36], which is below the threshold speed. As a result, we could expect our system to be feasible for practical use at normal walking speed.…”

Section: Technical Assessment Results and Discussionmentioning

confidence: 82%

Augmented-Reality-Based Indoor Navigation: A Comparative Analysis of Handheld Devices Versus Google Glass

Rehman

Cao

2016

IEEE Trans. Human-Mach. Syst.

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Abstract-Navigation systems have been widely used in outdoor environments, but indoor navigation systems are still in early development stages. In this paper, we introduced an augmented reality-based indoor navigation application to assist people navigate in indoor environments. The application can be implemented on electronic devices such as a smartphone or a head-mounted device. In particular, we examined Google Glass as a wearable head-mounted device in comparison to handheld navigation aids including a smartphone and a paper map. We conducted both a technical assessment study and a human factors study. The technical assessment established the feasibility and reliability of the system. The human factors study evaluated human-machine system performance measures including perceived accuracy, navigation time, subjective comfort, subjective workload, and route memory retention. The results showed that the wearable device was perceived to be more accurate, but other performance and workload results indicated that the wearable device was not significantly different from the handheld smartphone. We also found that both digital navigation aids were better than the paper map in terms of shorter navigation time and lower workload, but digital navigation aids resulted in worse route retention. These results could provide empirical evidence supporting future designs of indoor navigation systems. Implications and future research were also discussed.

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Section: Technical Assessment Results and Discussionmentioning

confidence: 82%

Augmented-Reality-Based Indoor Navigation: A Comparative Analysis of Handheld Devices Versus Google Glass

Rehman

Cao

2016

IEEE Trans. Human-Mach. Syst.

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“…To further understand the role of user speed, we examined the distribution of user speeds for both alcoholic and nonalcoholic user-nights. A participant was considered stationary if the computed speed was below 1 km/h, while a participant was assumed to be walking if the speed was in the range of 2-5 km/h [49]. In Figure 7, we show the percentage of time (as a fraction of a user night) when the user was stationary or walking for both night types.…”

Section: Resultsmentioning

confidence: 99%

DrinkSense: Characterizing Youth Drinking Behavior Using Smartphones

Santani

Labhart

et al. 2018

IEEE Trans. on Mobile Comput.

122

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Abstract-Alcohol consumption is the number one risk factor for morbidity and mortality among young people. In late adolescence and early adulthood, excessive drinking and intoxication are more common than in any other life period, increasing the risk of adverse physical and psychological health consequences. In this paper, we examine the feasibility of using smartphone sensor data and machine learning to automatically characterize and classify drinking behavior of young adults in an urban, ecologically valid nightlife setting. Our work has two contributions. First, we use previously unexplored data from a large-scale mobile crowdsensing study involving 241 young participants in two urban areas in a European country, which includes phone data (location. accelerometer, Wifi, Bluetooth, battery, screen, and app usage) along with self-reported, fine-grain data on individual alcoholic drinks consumed on Friday and Saturday nights over a three-month period. Second, we build a machine learning methodology to infer whether an individual consumed alcohol on a given weekend night, based on her/his smartphone data contributed between 8PM and 4AM. We found that accelerometer data is the most informative single cue, and that a combination of features results in an overall accuracy of 76.6%.

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“…For instance Indians maintain higher speeds and less headways when compared to Germans [45]. From the previous studies, [49][50][51][52][53] it is evident that younger pedestrians maintain higher speeds when compared to old pedestrians. Also men walk at higher speeds while compared to women [17,[49][50][51][52][53][54][55].…”

Section: Walking Speeds At Various Facilitiesmentioning

confidence: 99%

“…From the previous studies, [49][50][51][52][53] it is evident that younger pedestrians maintain higher speeds when compared to old pedestrians. Also men walk at higher speeds while compared to women [17,[49][50][51][52][53][54][55]. Tanaboriboon et al [49] reported the mean walking speed in Singapore as 74 m/min which is relatively slower than the walking speeds in American counterparts.…”

Section: Walking Speeds At Various Facilitiesmentioning

confidence: 99%

Fundamental diagrams of pedestrian flow characteristics: A review

Vanumu

Rao

Tiwari

2017

Eur. Transp. Res. Rev.

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Introduction The dimensionality of pedestrian infrastructure facilities have a great influence on pedestrian movements and a considerable impact on natural environment of the facility. Understanding the pedestrian movements are crucial to estimate the capacity of the system accurately, especially in the transportation terminals such as railway stations, bus terminals, airports and so forth, where large crowd gathers and transfers. To have a safe and comfortable movement in normal situation and also a quick evacuation in emergency situation, pedestrian movement patterns should be analysed and modelled properly. Purpose Once the behaviour of pedestrians is established in terms of speed and density with respect to the environment, even for the colossal systems, the pedestrian flow characteristics can be modelled by applying extremely efficient simulations. The main modelling element in the context of flow models is the fundamental relationship among speed, flow and density. The objective of this study is to review the fundamental diagrams of pedestrian flow characteristics developed for various flow types and geometric elements. This paper also discusses the design values of flow parameters and walking speeds of pedestrians at various facilities.Methods In order to achieve the goal of this paper, we presented a systematic review of fundamental diagrams of pedestrian flow characteristics developed by using various approaches such as field, experimental and simulation.Conclusions After a thorough review of literature, this paper identifies certain research gaps which provides an opportunity to enhance the understanding of fundamental diagrams of pedestrian flow characteristics.

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Another Look at Pedestrian Walking Speed

Cited by 123 publications

References 4 publications

Augmented-Reality-Based Indoor Navigation: A Comparative Analysis of Handheld Devices Versus Google Glass

Augmented-Reality-Based Indoor Navigation: A Comparative Analysis of Handheld Devices Versus Google Glass

DrinkSense: Characterizing Youth Drinking Behavior Using Smartphones

Fundamental diagrams of pedestrian flow characteristics: A review

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