Crowd Replication: Sensing-Assisted Quantification of Human Behaviour in Public Spaces

ing with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior speci c permission and/or a fee. Request permissions from permissions@acm.org. © 1 ACM. 2374-0353/1/1-ART1 $15.00 DOI: 0000001.0000001 ACM Transactions on Spatial Algorithms and Systems, Vol. 1, No. 1, Article 1. Publication date: January 1. 1:2 Samuli Hemminki, Keisuke Kuribayashi, Shin’ichi Konomi, Pe eri Nurmi, and Sasu Tarkoma also been linked with quality of life indicators [12]. Indeed, well-designed public spaces can promote individual well-being, and contribute to social and environmental values of neighbourhoods. With ever increasing motorization and urbanization, the role of public spaces has been transforming and their role as hubs for human activities has diminished. To counter this trend, urban planners, activists and architects have been advocating for new ways of enriching human experiences in public spaces by designing the environments so that positive qualities and liveliness of public spaces are increased [58]. Public space quality is intrinsically linked with the way it is used. According to Gehl [17], uses of public space can be categorized into necessary, optional, and social activities, with a successful space being one that actively promotes optional and social activities. Currently, however, urban planners struggle with quantifying whether a particular space promotes these activities. This is because current techniques for public space assessment are either too labour-intensive and provide insu ciently detailed information about usage of space, or require costly investment and high level of technical expertise. Indeed, despite technological advancements, direct observation remains the dominant approach for quantifying public space usage [26, 37, 57]. In direct observation, the researcher manually observes and logs activities of people within the space. From these observations, indicators related, e.g., to the amount of mobility and activities within the space can be derived [37]. The main limitations of this approach are the limited granularity of the collected information, and labour-intensiveness of the data collection process, usually resulting in a small sample size. While a range of pervasive sensing technologies, e.g., visual surveillance [14, 40], aerial photography [18], location tracking [41, 52], crowd sensing [46, 53, 56, 59], and analysis of cellular records [8], can support data collection, they have not been widely adopted due to high resource cost, need for expertise in interpreting the data, and privacy concerns. Additionally, pervasive sensing techniques are vulnerable to biases that limit generalizability of the conclusions that can be drawn. For example, visual surveillance is prone to losing data due to occlusions or illumination conditions whereas crowd sensing captures only users carrying devices with an active WiFi or Bluetooth interface. Accordingly, data collected through pervasive sensing is prone to contain temporal or spatial gaps, and may be biased to a speci c sub-population of the people using a space. To address the limitations of existing approaches, an approach that captures unbiased and detailed information of human behaviour with minimal resource cost is required. In this paper, as our rst contribution, we develop crowd replication as a novel sensor-assisted method for quantifying human behaviours within a public space. The idea in crowd replication is to augment direct observation through mobile sensing, enabling capture of detailed information about human behaviour. In crowd replication, the researcher carrying out an investigation is engaged into replicating the behaviours of target subjects while carrying a mobile device that collects a sensor trace of the movements and activities of the subjects; see Figure 2b for an illustration. From the sensor trace, we extract detailed movement trajectories and motions of people. These are then combined with manual annotations to derive indicators that relate motion characteristics (e.g., walking pace, motion intensity, and heading) with public space use on ne-grained spatial and temporal resolution. To relate these characteristics with the entire target population, the derived indicators can be extrapolated to capture the aggregate behaviour of the entire target population. Compared to pervasive sensing, crowd replication provides better control over sample selection and requires little additional investments or technical expertise. Indeed, a key design goal for crowd replication has been to make it easily adoptable by researchers and practitioners versed in direct observation. Another bene t of crowd replication is that it can be carried out using o -the-shelf smartphones without installing any sensors or other hardware within the public space being evaluated. This contrasts with mobile crowdsourcing where users need to download and install ACM Transactions on Spatial Algorithms and Systems, Vol. 1, No. 1, Article 1. Publication date: January 1. Crowd Replication: Sensing-Assisted antification of Human Behaviour in Public Spaces 1:3 an application on their smartphones. As crowd replication collects measurements only from the researcher(s) conducting the evaluation, privacy of people using the space can be protected. At the same time, this helps to improve quality of sensor data as placement of mobile device collecting sensor data can be better controlled. The main alternative to crowd replication, visual surveillance, provides a higher amount of measurements but requires either expensive visual analytics software or large amounts of manual e ort in annotating video data, making it costly and labour-intensive. Visual surveillance also su ers from high deployment cost due to privacy considerations, the need to obtain necessary permits, and the need to nd unobstructed locations for placing the cameras. We stress, however, that crowd replication does not aim at removing the need for visual surveillance, pervasive sensing, or other techniques. Rather, crowd replication has been designed as a cost-e ective and easy-to-adopt technique that researchers can use to capture high-quality data to quantify behaviour within public spaces. Crowd replication can also be used to complement other techniques as it provides detailed and unbiased information on the behaviour of people within the target site. Such samples could then be leveraged to interpret, validate and correct data and observations made through other data collection. A fundamental challenge for crowd replication is to ensure that the collected data is su ciently accurate and representative of the behaviour of people using the space. To ensure crowd replication captures high quality information of people using public space, as our second contribution, we develop a novel highly accurate pedestrian sensing solution for reconstructing movement trajectories from sensor traces captured during the replication process. What makes this particularly challenging is the high variability of motion, with walking pace, heading changes, step intensity and other walking characteristics di ering depending on the activities of the people being monitored. Capturing these subtle variations in walking characteristics is essential for understanding uses of public space as these changes are highly correlated with di erent types of activities taking place within the space [35]. Further complications are caused by stationary periods which can be frequently interleaved with periods of motion. To address accuracy requirements of crowd replication, our pedestrian modelling approach learns a personalized model of the stride patterns of a user. In crowd replication, only a small set of users are responsible for collecting data, i.e., the researchers carrying out the replication. Furthermore, the devices collecting sensor measurements are primarily used for sensor data collection instead of serving as personal communications devices. This provides better control over device placement and orientation, and reduces the impact of spontaneous user interactions and noise on the sensor measurements. Our sensing solution takes advantage of these factors, mining the collected sensor measurements for step cycles which then can be associated with indicators related to motion intensity, stride length, and walking pace. This contrasts with existing research on pedestrian behaviour sensing which has predominantly targeted scenarios where the focus is on generalization over various users and unknown device use and placement at the expense of sensing accuracy [7, 22]. We validate and demonstrate the bene ts of crowd replication through a case study carried out within a representative example of a metropolitan scale public space. We rst demonstrate that crowd replication captures data that is representative of the target population and their behaviours. We show this by assessing the convergence of the distributions of demographic variables and motion trajectories captured through crowd replication. Our results show that for most variables of interest (demographics and trajectories) the distributions can be learned with a reasonably small sample (50 − 100 replicated users). As can be expected, the slowest convergence is witnessed with infrequently occurring activities (social activities in our experiments), which can require up to 200 replicated users. This amount of data can be reasonably gathered in a matter of hours. For example, in our case each researcher collected 25 − 30 participants per hour, which means that ACM Transactions on Spatial Algorithms and Systems, Vol. 1, No. 1, Article 1. Publication date: January 1. 1:4 Samuli Hemminki, Keisuke Kuribayashi, Shin’ichi Konomi, Pe eri Nurmi, and Sasu Tarkoma two researchers could collect su cient amount of data in around 4 hours. In contrast, manually annotating 30 minutes of video surveillance data required over 50 hours of work,