Pokemon GO in Melbourne CBD: A case study of the cyber-physical symbiotic social networks

[EN] The recent popular game, Pokemon GO, created two symbiotic social networks by location-based mobile augmented reality (LMAR) technique. One is in the physical world among players, and another one is in the cyber world among players' avatars. To date, there is no study that has explored the formation of each social network and their symbiosis. In this paper, we carried out a data-driven research on the Pokemon GO game to solve this problem. We accordingly organised the collection of two real datasets. For the first dataset, we designed a questionnaire to collect players' individual behaviours in Pokemon GO, and used maps of Melbourne (Australia) to track and record their usual playing areas. Based on the data that we collected, we modelled the formation of the symbiotic social networks in both physical world (i.e. for players) and cyber world (i.e. for avatars) as well as interactions between players and Pokemon GO elements (i.e. 'bridges' of the two worlds). By investigating the mechanism of network formation, we revealed the relatively weak correlation between the formation processes of the two networks. We further incorporated the real-world pedestrian dataset collected by sensors across Melbourne CBD into the study of their symbiosis. Based on the second dataset, we examined the changes of people's social behaviours in terms of most visited places. The results suggested that the existence of the cyber social network has reciprocally changed the structure of the symbiotic physical social network. (C) 2017 Elsevier B.V. All rights reserved.This research is partially supported by the Australian Research Council projects DP150103732, DP140103649, and LP140100816. The authors extend their appreciation to the International Scientific Partnership Program (ISPP) at King Saud University, Riyadh, Saudi Arabia for funding this work through the project No. ISPP#0069.Wang, D.; Wu, T.; Wen, S.; Liu, D.; Xiang, Y.; Zhou, W.; Hassan Mohamed, H.... (2018). Pokemon GO in Melbourne CBD: A case study of the cyber-physical symbiotic social networks. Journal of Computational Science. 26:456-467. https://doi.org/10.1016/j.jocs.2017.06.009S4564672

Stochastic Sampling Simulation for Pedestrian Trajectory Prediction

Urban environments pose a significant challenge for autonomous vehicles (AVs) as they must safely navigate while in close proximity to many pedestrians. It is crucial for the AV to correctly understand and predict the future trajectories of pedestrians to avoid collision and plan a safe path. Deep neural networks (DNNs) have shown promising results in accurately predicting pedestrian trajectories, relying on large amounts of annotated real-world data to learn pedestrian behavior. However, collecting and annotating these large real-world pedestrian datasets is costly in both time and labor. This paper describes a novel method using a stochastic sampling-based simulation to train DNNs for pedestrian trajectory prediction with social interaction. Our novel simulation method can generate vast amounts of automatically-annotated, realistic, and naturalistic synthetic pedestrian trajectories based on small amounts of real annotation. We then use such synthetic trajectories to train an off-the-shelf state-of-the-art deep learning approach Social GAN (Generative Adversarial Network) to perform pedestrian trajectory prediction. Our proposed architecture, trained only using synthetic trajectories, achieves better prediction results compared to those trained on human-annotated real-world data using the same network. Our work demonstrates the effectiveness and potential of using simulation as a substitution for human annotation efforts to train high-performing prediction algorithms such as the DNNs.Comment: 8 pages, 6 figures and 2 table

Stochastic Sampling Simulation for Pedestrian Trajectory Prediction

Urban environments pose a significant challenge for autonomous vehicles (AVs) as they must safely navigate while in close proximity to many pedestrians. It is crucial for the AV to correctly understand and predict the future trajectories of pedestrians to avoid collision and plan a safe path. Deep neural networks (DNNs) have shown promising results in accurately predicting pedestrian trajectories, relying on large amounts of annotated real-world data to learn pedestrian behavior. However, collecting and annotating these large real-world pedestrian datasets is costly in both time and labor. This paper describes a novel method using a stochastic sampling-based simulation to train DNNs for pedestrian trajectory prediction with social interaction. Our novel simulation method can generate vast amounts of automatically-annotated, realistic, and naturalistic synthetic pedestrian trajectories based on small amounts of real annotation. We then use such synthetic trajectories to train an off-the-shelf state-of-the-art deep learning approach Social GAN (Generative Adversarial Network) to perform pedestrian trajectory prediction. Our proposed architecture, trained only using synthetic trajectories, achieves better prediction results compared to those trained on human-annotated real-world data using the same network. Our work demonstrates the effectiveness and potential of using simulation as a substitution for human annotation efforts to train high-performing prediction algorithms such as the DNNs.Comment: 8 pages, 6 figures and 2 table

Design of pedestrian network friendliness maps

This article introduces the concept of pedestrian, or walking friendliness, and presents a methodology for obtaining maps thereof. Walking friendliness is a quality of walking indicator, defined for any given origin in a city, which combines accessibility measures, based on impedance between that origin and destinations, with performance scores for the pedestrian infrastructure linking those origins and destinations. The methodology uses geographic information systems to obtain walking friendliness values and represent them in a map. The approach is demonstrated through a case study for the city of Coimbra, Portugal, for which friendliness maps were derived. The procedure and maps that were produced can be scaled to any size of city.info:eu-repo/semantics/publishedVersio

Towards Smarter Management of Overtourism in Historic Centres Through Visitor-Flow Monitoring

Historic centres are highly regarded destinations for watching and even participating in diverse and unique forms of cultural expression. Cultural tourism, according to the World Tourism Organization (UNWTO), is an important and consolidated tourism sector and its strong growth is expected to continue over the coming years. Tourism, the much dreamt of redeemer for historic centres, also represents one of the main threats to heritage conservation: visitors can dynamize an economy, yet the rapid growth of tourism often has negative effects on both built heritage and the lives of local inhabitants. Knowledge of occupancy levels and flows of visiting tourists is key to the efficient management of tourism; the new technologies—the Internet of Things (IoT), big data, and geographic information systems (GIS)—when combined in interconnected networks represent a qualitative leap forward, compared to traditional methods of estimating locations and flows. A methodology is described in this paper for the management of tourism flows that is designed to promote sustainable tourism in historic centres through intelligent support mechanisms. As part of the Smart Heritage City (SHCITY) project, a collection system for visitors is developed. Following data collection via monitoring equipment, the analysis of a set of quantitative indicators yields information that can then be used to analyse visitor flows; enabling city managers to make management decisions when the tourism-carrying capacity is exceeded and gives way to overtourism.Funded by the Interreg Sudoe Programme of the European Regional Development Funds (ERDF

Improving Livability Using Green and Active Modes: A Traffic Stress Level Analysis of Transit, Bicycle, and Pedestrian Access and Mobility

Understanding the relative attractiveness of alternatives to driving is vitally important toward lowering driving rates and, by extension, vehicle miles traveled (VMT), traffic congestion, greenhouse gas (GHG) emissions, etc. The relative effectiveness of automobile alternatives (i.e., buses, bicycling, and walking) depends on how well streets are designed to work for these respective modes in terms of safety, comfort and cost, which can sometimes pit their relative effectiveness against each other. In this report, the level of traffic stress (LTS) criteria previously developed by two of the authors was used to determine how the streets functioned for these auto alternative modes. The quality and extent of the transit service area was measured using a total travel time metric over the LTS network. The model developed in this study was applied to two transit routes in Oakland, California, and Denver, Colorado

Examining trade-offs between social, psychological, and energy potential of urban form

Urban planners are often challenged with the task of developing design solutions which must meet multiple, and often contradictory, criteria. In this paper, we investigated the trade-offs between social, psychological, and energy potential of the fundamental elements of urban form: the street network and the building massing. Since formal methods to evaluate urban form from the psychological and social point of view are not readily available, we developed a methodological framework to quantify these criteria as the first contribution in this paper. To evaluate the psychological potential, we conducted a three-tiered empirical study starting from real world environments and then abstracting them to virtual environments. In each context, the implicit (physiological) response and explicit (subjective) response of pedestrians were measured. To quantify the social potential, we developed a street network centrality-based measure of social accessibility. For the energy potential, we created an energy model to analyze the impact of pure geometric form on the energy demand of the building stock. The second contribution of this work is a method to identify distinct clusters of urban form and, for each, explore the trade-offs between the select design criteria. We applied this method to two case studies identifying nine types of urban form and their respective potential trade-offs, which are directly applicable for the assessment of strategic decisions regarding urban form during the early planning stages

Examining the Relationship Between Road Structure and Burglary Risk Via Quantitative Network Analysis

OBJECTIVES: To test the hypothesis that the spatial distribution of residential burglary is shaped by the configuration of the street network, as predicted by, for example, crime pattern theory. In particular, the study examines whether burglary risk is higher on street segments with higher usage potential. METHODS: Residential burglary data for Birmingham (UK) are examined at the street segment level using a hierarchical linear model. Estimates of the usage of street segments are derived from the graph theoretical metric of betweenness, which measures how frequently segments feature in the shortest paths (those most likely to be used) through the network. Several variants of betweenness are considered. The geometry of street segments is also incorporated—via a measure of their linearity—as are several socio-demographic factors. RESULTS: As anticipated by theory, the measure of betweenness was found to be a highly-significant predictor of the burglary victimization count at the street segment level for all but one of the variants considered. The non-significant result was found for the most localized measure of betweenness considered. More linear streets were generally found to be at lower risk of victimization. CONCLUSIONS: Betweenness offers a more granular and objective means of measuring the street network than categorical classifications previously used, and its meaning links more directly to theory. The results provide support for crime pattern theory, suggesting a higher risk of burglary for streets with more potential usage. The apparent negative effect of linearity suggests the need for further research into the visual component of target choice, and the role of guardianship