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This paper presents an investigation into automated data collection, applied to pedestrians in public spaces. Three case studies conducted in Brno, Czech Republic, a typical, medium-sized city in Central Europe, were used to determine the accuracy of the proposed method. Data were recorded in two ways: (i) automated data collection, using a data logger constructed on the principle of a minicomputer to measure the intensity of Wi-Fi signals from mobile devices; and (ii) in situ observation. Data from in situ observation provided a basis for the comparison and verification of corresponding values from the automated data collection. The research framework of the paper comprises the determination of exact values for optimum characterization of pedestrian behavior in a given locality, taking into consideration conventions from previously published works: (i) the number of pedestrians (N); (ii) speed (u); (iii) flow (q); and (iv) density (k). The results of this study confirm that as the density of the street network increases, the accuracy of the data collected by the digital method decreases significantly. These findings indicate that the method is more suitable for projects focused on identifying major trends or shifts in pedestrian preferences when navigating city centers than for projects that require exact counts in specific locations at a given time.

This paper presents an investigation into automated data collection, applied to pedestrians in public spaces. Three case studies conducted in Brno, Czech Republic, a typical, medium-sized city in Central Europe, were used to determine the accuracy of the proposed method. Data were recorded in two ways: (i) automated data collection, using a data logger constructed on the principle of a minicomputer to measure the intensity of Wi-Fi signals from mobile devices; and (ii) in situ observation. Data from in situ observation provided a basis for the comparison and verification of corresponding values from the automated data collection. The research framework of the paper comprises the determination of exact values for optimum characterization of pedestrian behavior in a given locality, taking into consideration conventions from previously published works: (i) the number of pedestrians (N); (ii) speed (u); (iii) flow (q); and (iv) density (k). The results of this study confirm that as the density of the street network increases, the accuracy of the data collected by the digital method decreases significantly. These findings indicate that the method is more suitable for projects focused on identifying major trends or shifts in pedestrian preferences when navigating city centers than for projects that require exact counts in specific locations at a given time.

This paper presents an investigation into automated data collection, applied to pedestrians in public spaces. Three case studies conducted in Brno, Czech Republic, a typical, medium-sized city in Central Europe, were used to determine the accuracy of the proposed method. Data were recorded in two ways: (i) automated data collection, using a data logger constructed on the principle of a minicomputer to measure the intensity of Wi-Fi signals from mobile devices; and (ii) in situ observation. Data from in situ observation provided a basis for the comparison and verification of corresponding values from the automated data collection. The research framework of the paper comprises the determination of exact values for optimum characterization of pedestrian behavior in a given locality, taking into consideration conventions from previously published works: (i) the number of pedestrians (N); (ii) speed (u); (iii) flow (q); and (iv) density (k). The results of this study confirm that as the density of the street network increases, the accuracy of the data collected by the digital method decreases significantly. These findings indicate that the method is more suitable for projects focused on identifying major trends or shifts in pedestrian preferences when navigating city centers than for projects that require exact counts in specific locations at a given time.