Abstract In large crowd events, there is always a potential possibility that a stampede accident will occur. The accident may cause injuries or even death. Approaches for controlling crowd flows and predicting dangerous congestion spots would be a boon to on-site authorities to manage the crowd and to prevent fatal accidents. One of the most popular approaches is real-time crowd simulation based on position data from personal Global Positioning System (GPS) devices. However, the accuracy of spatial data varies for different GPS devices, and it is also affected by an environment in which an event takes place. In this paper, we would like to assess the effect of position errors on stampede prediction. We propose an Automatic Real-time dEtection of Stampedes (ARES) method to predict stampedes for large events. We implement three different stampede assessment methods in Menge framework and incorporate position errors. Our analysis suggests that the probability of simulated stampede changes significantly with the increase of the magnitude of position errors, which cannot be eliminated entirely with the help of classic techniques, such as the Kalman filter. Thus, it is our position that novel stampede assessment methods should be developed, focusing on the detection of position noise and the elimination of its effect.
@cite_22: The paper presents a comprehensive survey of contemporary methods, technologies and systems for localization and tracking of moving objects in indoor environment and gives their comparison according to various criteria, such as accuracy, privacy, scalability and type of location data. Some representative examples of indoor LBS applications available on the market are presented that are based on reviewed localization technologies. The prominent research directions in this domain are categorized and discussed.
@cite_36: Autoregressive moving average (ARMA) method is applied to modeling the time series of position changes of GPS sites, obtained by the Geographical Survey Institute (GSI) of Japan during the period from April 1996 to March 1998. The present application is focused on denoising of the GPS time series data where only white noise is considered, and detection of data discontinuities and outliers in order to obtain time-averaged velocity and strain fields in central Japan. The data discontinuities are detected by a typical Kalman filter algorithm. The outliers are eliminated by using robust estimation techniques during the ARMA process. The averaged strain field, calculated by the least-squares collocation method from the improved two-year time series data, distinguishes clearly between the tectonically active and inactive regions. Higher maximum shear strain rates were detected in the southern area of the Kanto district. In the areas with very high seismicities, however, the difference between the maximum shear strain rates, that were estimated from the raw time series data and the ARMA-analyzed data, amounted to about 0.2 microstrain yr. This indicates that the existence of noise and discontinuities can lead to an over-prediction of the strain field.
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@cite_5: This book has been developed to provide greatly improved capabilities that help global positioning systems (GPS) work better and faster in almost any location. Offering a detailed look at all the technical aspects and underpinnings of assisted-global positioning system (A-GPS), the book emphasizes practical implementation. The book reviews standard GPS design, helping the reader understand why GPS requires assistance in the first place. The reader discovers how A-GPS enables the computing of a position from navigation satellites in the absence of precise time, a topic that has not yet been covered in any other book. Moreover, the reader learns how to design and analyze a high sensitivity GPS receiver and determine the achievable sensitivity. The book provides detailed worksheets that show how to compute, analyze, and improve the processing gain from the input signal at the antenna to the signal after the correlators. These worksheets are used to generate families of curves that completely characterize receiver sensitivity, parameterized in terms of front end noise figure, and coherent and noncoherent integration times. The book discusses special forms of assistance data, industry standards for A-GPS, and government mandates for location of mobile phones. The reader also finds coverage of future global navigation satellite systems (GNSS) and how they can be designed specifically for instant-fixes and high sensitivity. The book features numerous tables, worksheets, and graphs that illustrate key topics and provide the equivalent of a technical handbook for engineers who design or use A-GPS.
@cite_16: Employing partially overlapping overhead sensors and automatic pedestrian tracking algorithms we recorded the crowd traffic in a rectilinear section of the main walkway of Eindhoven train station on a 24 7 basis. Beside giving access to the train platforms (it passes underneath the railways), the walkway plays an important connection role in the city. Several crowding scenarios occur during the day, including high- and low-density dynamics in uni- and bi-directional regimes. In this paper we discuss our recording technique and we illustrate preliminary data analyses. Via fundamental diagrams-like representations we report pedestrian velocities and fluxes vs. pedestrian density. Considering the density range @math - @math ped m @math , we find that at densities lower than @math ped m @math pedestrians in unidirectional flows walk faster than in bidirectional regimes. On the opposite, velocities and fluxes for even bidirectional flows are higher above @math ped m @math .
@cite_20: The next generation of navigation and positioning systems must provide greater accuracy and reliability in a range of challenging environments to meet the needs of a variety of mission-critical applications. No single navigation technology is robust enough to meet these requirements on its own, so a multisensor solution is required. Although many new navigation and positioning methods have been developed in recent years, little has been done to bring them together into a robust, reliable, and cost-effective integrated system. To achieve this, four key challenges must be met: complexity, context, ambiguity, and environmental data handling. This paper addresses each of these challenges. It describes the problems, discusses possible approaches, and proposes a program of research and standardization activities to solve them. The discussion is illustrated with results from research into urban GNSS positioning, GNSS shadow matching, environmental feature matching, and context detection.
@cite_11: This paper describes a precision positioning technique that can be applied to vehicles or mobile robots in urban or leafy environments. Currently, the availability of satellite positioning is anticipated to improve because of the presence of various positioning satellites such as GPS of the U.S., GLONASS of Russia and GALILEO of Europe. However, because of the serious impact of multipath on their positioning accuracy in urban or leafy areas, such improvements in the availability of satellite positioning do not necessarily also facilitate high precision positioning. Our proposed technique mitigates GPS and GLONASS multipath by means of an omnidirectional infrared (IR) camera that can eliminate the need for invisible satellites by using IR images. With an IR camera, the sky appears distinctively dark. This facilitates the detection of the borderline between the sky and the surrounding buildings, which are captured in white, because of the difference in the atmospheric transmittance rates between visible light and IR rays. The proposed technique can automatically and robustly mitigate GPS and GLONASS multipath by excluding the invisible satellites. Positioning evaluation was carried out only with visible satellites that have less multipath errors and without using invisible satellites. The evaluation results confirm the effectiveness of the proposed technique and the feasibility of its highly accurate positioning.
Recent research has attempted to combine data from GPS and external sensors to improve the tracking accuracy. @cite_20 developed a positioning technique by integrating GPS and Wi-Fi data (to improve positioning indoors) and accelerometers (to distinguish pedestrian behavior). proposed an approach to utilize overhead Microsoft Kinect depth sensors placed in a walkway to provide reliable automatic pedestrian positioning for tracking @cite_16 . @cite_11 proposed to use infrared cameras to eliminate the multi-path errors which are caused by different positioning systems. Assisted-GPS (A-GPS) technique is also widely used to improve the performance of GPS-enabled smartphones @cite_5 . In general, combining GPS, WiFi, and sensors data improves tracking accuracy indoors (see @cite_22 for review). Besides employing external sensors, various algorithms were developed to mitigate GPS measurement noise, e.g., the Kalman filter @cite_43 @cite_54 and the autoregressive moving average filter @cite_36 .