Crowdedness estimation in public pedestrian space for pedestrian guidance

Abstract

A degree of crowdedness in public pedestrian space is obtained from sequences of greyscale images and still colour images in order to provide a route selection hint to pedestrians. Two measures are employed: amount of motion and colour entropy, and they are used selectively depending on the nature of target space. The pedestrian space is categorised into two: smooth and stagnated. The smooth space urges pedestrians to flow smoothly without pausing, while the stagnated space invites pedestrians to pause for socialising. A degree of stagnation, obtained from accumulated differentiated images, is used to distinguish these two types of space. The amount of motion and the colour entropy are compared against human's sense of crowdedness for evaluation. The experiments have shown that the amount of motion represents the smooth space's crowdedness well, while the colour entropy is suitable for evaluating the stagnated space. It has also shown tendency of significance between the degree of stagnation and the types of space.

BlueEye

“…Using the standard free-space radio propagation formulation, it is possible to represent the measured received signal strength indicator (RSSIM) as (1) In equation 1, n = 2 for free-space and n > 2 (empirically adjusted) for RF challenged environment like office indoor, as example. In typical path loss model [10], n takes multiple values, for office buildings, ranging from 2.76 to 4.33, based on location type.…”

“…Toyosawa et al [1] approached the problem of crowdedness measurement using infrastructure based sensing, but it makes the solution cost ineffective. Liu et al [2] makes an attempt to detect crowded spots in a city traffic using participatory sensing of vehicles.…”

A State-Space Model for Assimilating Passenger and Vehicle Flow Data with User Feedback in a Transit Network

“…Noteworthy, interpreting user perceptions of crowdedness is a complex problem that has been researched over the years from psychological/behavioural angles (Stokols 1972;Kalb and Keating 1981;Mohd Mahudin, Cox, and Griffiths 2012;Li and Hensher 2013). In a wider context, human perception of numerosity is a factor here with suggested geometric or logarithmic (Weber-Fechner-like) principles (Toyosawa and Kawai 2005;Cicchini, Anobile, and Burr 2016). In the present study, from the point of view of the data assimilation approach, such "measurements" are treated as any other noisy data on crowdedness and the methodology is, in principle, applicable for assimilation of data from vehicle weighing (Frumin 2010;Nielsen et al 2014;Ball 2016), data obtained by counting distinct personal electronic devices (e.g., Schauer, Werner, and Marcus 2014), data available in smartcard-based ticketing systems (e.g., Zhang, Jenelius, and Kottenhoff 2017) or data from automatic passenger counters (APCs, e.g., Pinna, Dalla Chiara, and Deflorio 2010).…”