Academic literature on the topic 'Analysis crowd'
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Journal articles on the topic "Analysis crowd"
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Husman, Muhammad Afif, Waleed Albattah, Zulkifli Zainal Abidin, et al. "Unmanned Aerial Vehicles for Crowd Monitoring and Analysis." Electronics 10, no. 23 (2021): 2974. http://dx.doi.org/10.3390/electronics10232974.
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Crowd monitoring and analysis has become increasingly used for unmanned aerial vehicle applications. From preventing stampede in high concentration crowds to estimating crowd density and to surveilling crowd movements, crowd monitoring and analysis have long been employed in the past by authorities and regulatory bodies to tackle challenges posed by large crowds. Conventional methods of crowd analysis using static cameras are limited due to their low coverage area and non-flexible perspectives and features. Unmanned aerial vehicles have tremendously increased the quality of images obtained for crowd analysis reasons, relieving the relevant authorities of the venues’ inadequacies and of concerns of inaccessible locations and situation. This paper reviews existing literature sources regarding the use of aerial vehicles for crowd monitoring and analysis purposes. Vehicle specifications, onboard sensors, power management, and an analysis algorithm are critically reviewed and discussed. In addition, ethical and privacy issues surrounding the use of this technology are presented.
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Elbishlawi, Sherif, Mohamed H. Abdelpakey, Agwad Eltantawy, Mohamed S. Shehata, and Mostafa M. Mohamed. "Deep Learning-Based Crowd Scene Analysis Survey." Journal of Imaging 6, no. 9 (2020): 95. http://dx.doi.org/10.3390/jimaging6090095.
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Recently, our world witnessed major events that attracted a lot of attention towards the importance of automatic crowd scene analysis. For example, the COVID-19 breakout and public events require an automatic system to manage, count, secure, and track a crowd that shares the same area. However, analyzing crowd scenes is very challenging due to heavy occlusion, complex behaviors, and posture changes. This paper surveys deep learning-based methods for analyzing crowded scenes. The reviewed methods are categorized as (1) crowd counting and (2) crowd actions recognition. Moreover, crowd scene datasets are surveyed. In additional to the above surveys, this paper proposes an evaluation metric for crowd scene analysis methods. This metric estimates the difference between calculated crowed count and actual count in crowd scene videos.
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Bhuiyan, Roman, Junaidi Abdullah, Noramiza Hashim, et al. "Deep Dilated Convolutional Neural Network for Crowd Density Image Classification with Dataset Augmentation for Hajj Pilgrimage." Sensors 22, no. 14 (2022): 5102. http://dx.doi.org/10.3390/s22145102.
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Almost two million Muslim pilgrims from all around the globe visit Mecca each year to conduct Hajj. Each year, the number of pilgrims grows, creating worries about how to handle such large crowds and avoid unpleasant accidents or crowd congestion catastrophes. In this paper, we introduced deep Hajj crowd dilated convolutional neural network (DHCDCNNet) for crowd density analysis. This research also presents augmentation technique to create additional dataset based on the hajj pilgrimage scenario. We utilized a single framework to extract both high-level and low-level features. For creating additional dataset we divide the process of images augmentation into two routes. In the first route, we utilized magnitude extraction followed by the polar magnitude. In the second route, we performed morphological operation followed by transforming the image into skeleton. This paper presented a solution to the challenge of measuring crowd density using a surveillance camera pointed at a distance. An FCNN-based technique for crowd analysis is included in the proposed methodology, particularly for classifying crowd density. There are several obstacles in video analysis when there are a large number of pilgrims moving around the tawaf area, with densities of between 7 and 8 per square meter. The proposed DHCDCNNet method has achieved accuracy of 97%, 89% and 100% for the JHU-CROWD dataset, the UCSD dataset and the proposed Hajj-Crowd dataset, respectively. The proposed Hajj-Crowd dataset, the UCSD dataset, and the JHU-CROW dataset all had accuracy of 98%, 97% and 97%, respectively, using the VGGNet approach. Using the ResNet50 approach, the proposed Hajj-Crowd dataset, the UCSD dataset, and the JHU-CROW dataset all had an accuracy of 99%, 91% and 97%, respectively.
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Yugendar, Poojari, and K. V. R. Ravishankar. "Crowd Behavioural Analysis at a Mass Gathering Event." Journal of KONBiN 46, no. 1 (2018): 5–20. http://dx.doi.org/10.2478/jok-2018-0020.
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Abstract Religious occasions, gathering at fairs and terminals, are the events of crowd gatherings. Such gatherings act as severe threats for crowds because of high density in less space, which ends up in adverse outcomes resulting in crowd stampedes. The movement of an individual person in a crowd is influenced by the physical factors. In the present study, characteristics like age, gender, group size, child holding, child carrying, people with luggage and without luggage are considered for crowd behaviour analysis. The average speed of the crowd movement was observed as 0.86 m/s. The statistical analysis concluded that there was a significant effect of age, gender, density and luggage on the crowd walking speed. Multi-linear regression (MLR) model was developed between crowd speed and significant factors observed from the statistical analysis. Location 1 data was used for the model development. This developed model was validated using Location 2 data. Gender has more significant effect on speed followed by luggage and age. This study helps in proper dispersal of crowd in a planned manner to that of diversified directional flow that exist during crowd gathering events.
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R, Shaamili. "A Research Perceptive on Deep Learning Framework for Pedestrian Detection in a Crowd." Computational Intelligence and Machine Learning 3, no. 2 (2022): 9–14. http://dx.doi.org/10.36647/ciml/03.02.a002.
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In populated cities, we often find crowded events like political meetings, religious festivals, music concerts, and events in shopping malls, which have more safety issues. Smart surveillance systems are used in big cities to keep crowds safe and make crowd security less complicated and more accurate. However, the surveillance systems proposed for a crowd are monitored by human agents, which are inefficient, error-prone, and overwhelming. Even with deep learning-based feature engineering in crowds, many variants of crowd analysis still lack attention and are technically unaddressed. Considering this scenario, the smart system requires the most advanced techniques to monitor the security of the crowd. Crowd analysis is commonly divided into crowd statics and behavior analysis. This paper explores more about crowd behaviour analysis, pedestrian and group detection which describes the movements that are noticed in the crowd image. Subsequently, the issues of the current methodology of pedestrian detection, datasets, and evaluation criteria are analyzed. Keyword : Crowd Analysis, Pedestrian and group detection, deep learning, Crowd IoT analysis, Human Activity Recognition.
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JOHANSSON, ANDERS, DIRK HELBING, HABIB Z. AL-ABIDEEN, and SALIM AL-BOSTA. "FROM CROWD DYNAMICS TO CROWD SAFETY: A VIDEO-BASED ANALYSIS." Advances in Complex Systems 11, no. 04 (2008): 497–527. http://dx.doi.org/10.1142/s0219525908001854.
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The study of crowd dynamics is interesting because of the various self-organization phenomena resulting from the interactions of many pedestrians, which may improve or obstruct their flow. Besides formation of lanes of uniform walking direction and oscillations at bottlenecks at moderate densities, it was recently discovered that stop-and-go waves [D. Helbing et al., Phys. Rev. Lett.97 (2006) 168001] and a phenomenon called "crowd turbulence" can occur at high pedestrian densities [D. Helbing et al., Phys. Rev. E75 (2007) 046109]. Although the behavior of pedestrian crowds under extreme conditions is decisive for the safety of crowds during the access to or egress from mass events as well as for situations of emergency evacuation, there is still a lack of empirical studies of extreme crowding. Therefore, this paper discusses how one may study high-density conditions based on suitable video data. This is illustrated at the example of pilgrim flows entering the previous Jamarat Bridge in Mina, 5 kilometers from the Holy Mosque in Makkah, Saudi-Arabia. Our results reveal previously unexpected pattern formation phenomena and show that the average individual speed does not go to zero even at local densities of 10 persons per square meter. Since the maximum density and flow are different from measurements in other countries, this has implications for the capacity assessment and dimensioning of facilities for mass events. When conditions become congested, the flow drops significantly, which can cause stop-and-go waves and a further increase of the density until critical crowd conditions are reached. Then, "crowd turbulence" sets in, which may trigger crowd disasters. For this reason, it is important to operate pedestrian facilities sufficiently below their maximum capacity and to take measures to improve crowd safety, some of which are discussed in the end.
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Andriyanto, Sidhiq, M. Suyanto, and Sukoco Sukoco. "Implementasi Metode Reynolds menggunakan Simulasi Kerumunan Bebek." INTENSIF 1, no. 2 (2017): 75. http://dx.doi.org/10.29407/intensif.v1i2.788.
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 "Simulation of Duck Crows Using Reynolds Method" is a study with the aim to find out the behavior of duck breeding crowd. The next goal is to make a crowd simulation using the Reynolds method. Limitations of this research variable is the object of research on adult duck Turi, the method used is Reynolds method. The simulations are made using Unity3D software in the form of 3D and the animation provided is just a running gesture. The method of analysis of this research is using research and development method. The result of the research is the data of duck walking in the crowd to be applied in 3D animation. The end result of the study is a simulation of duck crowds that run on flat fields. Destination directions are affected by mouse input and can avoid obstacles when walking. This simulation uses Reynolds basic rules of cohesion, alignment and separation.The conclusion of the research is that there is a similarity between the simulation of the crowd with the movement of the original duck crowd and the Reynolds method can be applied in the simulation of the duck crowd in 3D. Research produces 3D animation of duck crowds that are given the ability to avoid obstacles and target goals determined by mouse input.
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Denis, Stijn, Ben Bellekens, Abdil Kaya, Rafael Berkvens, and Maarten Weyn. "Large-Scale Crowd Analysis through the Use of Passive Radio Sensing Networks." Sensors 20, no. 9 (2020): 2624. http://dx.doi.org/10.3390/s20092624.
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The creation of an automatic crowd estimation system capable of providing reliable, real-time estimates of human crowd sizes would be an invaluable tool for organizers of large-scale events, particularly so in the context of safety management. We describe a set of experiments in which we installed a passive Radio Frequency (RF) sensor network in different environments containing thousands of human individuals and discuss the accuracy with which the resulting measurements can be used to estimate the sizes of these crowds. Depending on the selected training approach, a median crowd estimation error of 184 people could be obtained for a large scale environment which contained 3227 people at its peak. Additionally, we look into the potential benefits of dividing one of our experimental environments into multiple subregions and open up a potentially interesting new topic of research regarding the estimation of crowd flows. Finally, we investigate the combination of our measurements with another sources of crowd-related data: sales data from drink stands within the environment. In doing so, we aim to integrate the concept of an automatic RF-based crowd estimation system into the broader domain of crowd analysis.
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Aiello, Lucia. "Digital Skill Evolution in an Industrial Relationship." International Journal of R&D Innovation Strategy 1, no. 1 (2019): 1–15. http://dx.doi.org/10.4018/ijrdis.2019010101.
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Crowdsourcing is a powerful mechanism for doing online work and allows for collaboration. By now, in the media and in business on the Internet, crowdsourcing is recognized as an innovative form of value creation that needs taken seriously. This article provides a framework to propose the relation between crowds and tutorship; it considers the tutor of a crowd as a strategic professional figure in an online community. This is done by the consideration of the different roles, activities and tasks of a tutor through the field analysis of a platform of one company that uses crowdsourcing. This tutor is examined based on the middle-of-the-road theoretical positioned from Porter and Kramer, the value creation, and Suermann and Franzoni, the crows science user contribution patterns. The recently evolution of crowd platforms considers the interaction between companies and crowds based on a “Community of Practice” model of Zucchermaglio and Talamo. Value analysis also considers the differences in roles and tasks in relation to where crowd activity is placed into the value chain of company. In crowdsourcing, “digital people” live in a digital society where every individual has a role and operates in an online community, and those have force points and weakness points. The tutor cans also monitor these points, and push interaction and activities of the crowd. The main theoretical contribution is the looking gap in literature and contributing work to this. Through a qualitative analysis, this article provides evidence of the main activities and the role of digital tutors in an online community. The method utilized is netnography through an online participation and observation of a researcher. In this work, professional figures and new technologies are weighed, and human resource management must consider this as it creates value. This article concludes that a tutor of crowds has a positive behavior, they can stimulate crowds. These positive and active behaviors effect crowd attitudes about the benefits of the community, their expectations, and opinions all of which are fundamental for the growth of online community.
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Negied, Nermin Kamal Abdel-Wahab, Elsayed B. Hemayed, and Magda Fayek. "HSBS: A Human’s Heat Signature and Background Subtraction Hybrid Approach for Crowd Counting and Analysis." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 08 (2016): 1655025. http://dx.doi.org/10.1142/s0218001416550259.
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This work presents a new approach for crowd counting and classification based upon human thermal and motion features. The technique is efficient for automatic crowd density estimation and type of motion determination. Crowd density is measured without any need for camera calibration or assumption of prior knowledge about the input videos. It does not need any human intervention so it can be used successfully in a fully automated crowd control systems. Two new features are introduced for crowd counting purpose: the first represents thermal characteristics of humans and is expressed by the ratio between their temperature and their ambient environment temperature. The second describes humans motion characteristics and is measured by the ratio between humans motion velocity and the ambient environment rigidity. Each ratio should exceed a certain predetermined threshold for human beings. These features have been investigated and proved to give accurate crowd counting performance in real time. Moreover, the two features are combined and used together for crowd classification into one of the three main types, which are: fully mobile, fully static, or mix of both types. Last but not least, the proposed system offers several advantages such as being a privacy preserving crowd counting system, reliable for homogeneous and inhomogeneous crowds, does not depend on a certain direction in motion detection, has no restriction on crowd size. The experimental results demonstrate the effectiveness of the approach.
Dissertations / Theses on the topic "Analysis crowd"
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Mehran, Ramin. "Analysis of behaviors in crowd videos." Doctoral diss., University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4801.
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In this dissertation, we address the problem of discovery and representation of group activity of humans and objects in a variety of scenarios, commonly encountered in vision applications. The overarching goal is to devise a discriminative representation of human motion in social settings, which captures a wide variety of human activities observable in video sequences. Such motion emerges from the collective behavior of individuals and their interactions and is a significant source of information typically employed for applications such as event detection, behavior recognition, and activity recognition. We present new representations of human group motion for static cameras, and propose algorithms for their application to variety of problems. We first propose a method to model and learn the scene activity of a crowd using Social Force Model for the first time in the computer vision community. We present a method to densely estimate the interaction forces between people in a crowd, observed by a static camera. Latent Dirichlet Allocation (LDA) is used to learn the model of the normal activities over extended periods of time. Randomly selected spatio-temporal volumes of interaction forces are used to learn the model of normal behavior of the scene. The model encodes the latent topics of social interaction forces in the scene for normal behaviors. We classify a short video sequence of $n$ frames as normal or abnormal by using the learnt model. Once a sequence of frames is classified as an abnormal, the regions of anomalies in the abnormal frames are localized using the magnitude of interaction forces. The representation and estimation framework proposed above, however, has a few limitations. This algorithm proposes to use a global estimation of the interaction forces within the crowd. It, therefore, is incapable of identifying different groups of objects based on motion or behavior in the scene. Although the algorithm is capable of learning the normal behavior and detects the abnormality, but it is incapable of capturing the dynamics of different behaviors. To overcome these limitations, we then propose a method based on the Lagrangian framework for fluid dynamics, by introducing a streakline representation of flow. Streaklines are traced in a fluid flow by injecting color material, such as smoke or dye, which is transported with the flow and used for visualization. In the context of computer vision, streaklines may be used in a similar way to transport information about a scene, and they are obtained by repeatedly initializing a fixed grid of particles at each frame, then moving both current and past particles using optical flow. Streaklines are the locus of points that connect particles which originated from the same initial position. This approach is advantageous over the previous representations in two aspects: first, its rich representation captures the dynamics of the crowd and changes in space and time in the scene where the optical flow representation is not enough, and second, this model is capable of discovering groups of similar behavior within a crowd scene by performing motion segmentation. We propose a method to distinguish different group behaviors such as divergent/convergent motion and lanes using this framework. Finally, we introduce flow potentials as a discriminative feature to recognize crowd behaviors in a scene. Results of extensive experiments are presented for multiple real life crowd sequences involving pedestrian and vehicular traffic. The proposed method exploits optical flow as the low level feature and performs integration and clustering to obtain coherent group motion patterns. However, we observe that in crowd video sequences, as well as a variety of other vision applications, the co-occurrence and inter-relation of motion patterns are the main characteristics of group behaviors. In other words, the group behavior of objects is a mixture of individual actions or behaviors in specific geometrical layout and temporal order. We, therefore, propose a new representation for group behaviors of humans using the inter-relation of motion patterns in a scene. The representation is based on bag of visual phrases of spatio-temporal visual words. We present a method to match the high-order spatial layout of visual words that preserve the geometry of the visual words under similarity transformations. To perform the experiments we collected a dataset of group choreography performances from the YouTube website. The dataset currently contains four categories of group dances.<br>ID: 031001560; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Title from PDF title page (viewed August 26, 2013).; Thesis (Ph.D.)--University of Central Florida, 2011.; Includes bibliographical references (p. 100-104).<br>Ph.D.<br>Doctorate<br>Electrical Engineering and Computer Science<br>Engineering and Computer Science<br>Electrical Engineering
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Jan, Yasir. "Novel architectures for spectator crowd image analysis." Thesis, Jan, Yasir (2020) Novel architectures for spectator crowd image analysis. PhD thesis, Murdoch University, 2020. https://researchrepository.murdoch.edu.au/id/eprint/59147/.
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Crowd image analysis can involve various tasks, such as head detection, head pose estimation, and body detection. These tasks face a range of issues including low resolution, varying crowd density, overlapping bodies, and image corruptions. Additionally, the techniques performing these tasks have computational overhead as well.
Existing head detection techniques perform poorly for very low-resolution images. They also cannot perform head detection and head pose estimation simultaneously for multiple heads. To address these issues, we propose a novel neural network architecture (WNet), which performs joint tasks on low-resolution multiple heads. Experiments on the spectator crowd dataset (S-HOCK) show that fewer images can be used for the simultaneous tasks.
Existing body detection techniques rely on the visibility of body parts. But in dense spectator crowd images, the bodies are occluded, and not visible clearly. We propose “pixel matching based body detection” (PMBD) technique, to reduce the effect of occlusion in body parts. It can locate the body region by color matching and proximity. Experiments are performed on the S-HOCK dataset to accurately detect occluded bodies in a crowd image.
When low-resolution images, such as spectator head images, get distorted with corrup-tions e.g. blurriness, pixelation, and fog, they are poorly classified by the neural networks. Existing robustness techniques aim towards a specific type of corruption and improve the accuracy. We propose a novel technique, called “Edge to edge scanline smoothing” (ESS), for image enhancement to mitigate the effect of a wide range of corruption.
Training of neural networks is a computationally extensive task. The proposed structure of neurons called the “Multiple Output Neurons” (MON), in contrast to conventional single output neurons, can be used in the training process to reduce computation in the neural networks. We demonstrate a reduction in computation for various tasks, such as spectator crowd head pose classification.
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Holmer, Torsten, and Jörg Rainer Noennig. "Listening to the Crowd." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-234390.
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In der Stadtplanung hat sich in der jüngeren Vergangenheit Partizipation als neues Paradigma durchgesetzt. Projektentwickler, Planer und Politiker haben erkannt, dass Bauprojekte mit großem Einfluss auf Stadtgesellschaft und Stadtentwicklung nicht mehr ohne umfassende Bürgerbeteiligung durchgeführt werden können. Vorfälle wie die Unruhen um das Bahnhofsprojekt Stuttgart 21 haben gezeigt, dass die regulären Verfahren der Bauleitplanung mit ihren Instrumenten der formalen Bürgerbeteiligung (Anzeige und Auslage von Planungsunterlagen) nicht ausreichen, um einen gesellschaftlichen Konsens zu kontroversen Projekten zu erzielen. Die Problematik lässt sich zu einem großen Teil auf das sogenannte „Planungsparadox“ zurückführen: dezidierte Meinungen und Kritik aus der Bevölkerung bilden sich oft erst, wenn das jeweilige Projekt zur Ausführung kommt und konkrete Formen annimmt – also wenn die Planungen bereits abgeschlossen sind und jegliche weitere Änderung mit erheblichen Aufwendungen verbunden ist. [... aus dem Text]
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Holmer, Torsten, and Jörg Rainer Noennig. "Listening to the Crowd." TUDpress, 2017. https://tud.qucosa.de/id/qucosa%3A30888.
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In der Stadtplanung hat sich in der jüngeren Vergangenheit Partizipation als neues Paradigma durchgesetzt. Projektentwickler, Planer und Politiker haben erkannt, dass Bauprojekte mit großem Einfluss auf Stadtgesellschaft und Stadtentwicklung nicht mehr ohne umfassende Bürgerbeteiligung durchgeführt werden können. Vorfälle wie die Unruhen um das Bahnhofsprojekt Stuttgart 21 haben gezeigt, dass die regulären Verfahren der Bauleitplanung mit ihren Instrumenten der formalen Bürgerbeteiligung (Anzeige und Auslage von Planungsunterlagen) nicht ausreichen, um einen gesellschaftlichen Konsens zu kontroversen Projekten zu erzielen. Die Problematik lässt sich zu einem großen Teil auf das sogenannte „Planungsparadox“ zurückführen: dezidierte Meinungen und Kritik aus der Bevölkerung bilden sich oft erst, wenn das jeweilige Projekt zur Ausführung kommt und konkrete Formen annimmt – also wenn die Planungen bereits abgeschlossen sind und jegliche weitere Änderung mit erheblichen Aufwendungen verbunden ist. [... aus dem Text]
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Guler, Puren. "Automated Crowd Behavior Analysis For Video Surveillance Applications." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614659/index.pdf.
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Automated analysis of a crowd behavior using surveillance videos is an important issue for public security, as it allows detection of dangerous crowds and where they are headed. Computer vision based crowd analysis algorithms can be divided into three groups<br>people counting, people tracking and crowd behavior analysis. In this thesis, the behavior understanding will be used for crowd behavior analysis. In the literature, there are two types of approaches for behavior understanding problem: analyzing behaviors of individuals in a crowd (object based) and using this knowledge to make deductions regarding the crowd behavior and analyzing the crowd as a whole (holistic based). In this work, a holistic approach is used to develop a real-time abnormality detection in crowds using scale invariant feature transform (SIFT) based features and unsupervised machine learning techniques.
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KHAN, SULTAN DAUD. "Automatic Detection and Computer Vision Analysis of Flow Dynamics and Social Groups in Pedestrian Crowds." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2016. http://hdl.handle.net/10281/102644.
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Computer vision played a vital role in the field of video surveillance. However, recent developed computer vision algorithms rarely solve the problems related to real time crowd management. The phenomena of crowd like sports, festivals, concerts, political gatherings etc, are mostly observed in urban areas, which attracts hundreds of thousands people. In this thesis, we have developed algorithms that overcome some of the challenges encountered in videos of crowded environments such as sporting events, religious festivals, parades, concerts, train stations, airports, and malls. The main theme of this thesis is two fold ,i.e, understanding crowd dynamics in videos of (i), high density crowds and (ii) low density crowds. Typical examples of high density crowds include marathons, religious festivals while malls, airports, subways etc covers low dense situations. In this thesis, we adopt different approaches in order to deal with different kinds of problems coming from these two categories of crowd.
In particular, first part of the thesis, we adopt holistic approach to generate a global representation of the scene that captures both dynamics of the crowd and structure of the scene. This was achieved by extracting global features, i.e optical flow from the scene. For the crowd flow segmentation problem, the optical flows vectors are clustered by using K-means clustering followed by the blob absorption approach. Using the segmentation information, we continue to estimate the number of people in each segment by carrying out the blob analysis and blob size optimization approach. This approach however, provide useful information for understanding crowd dynamics yet it lacks significant information for understanding crowd behavior. Therefore, in this thesis, the current crowd flow segmentation and counting approach is further extended in order to coup the challenges of crowd behavior understanding. The extension adopts optical flow for the identification of pedestrian movements, and it considers the analyzed video as a set of sequences. The latter are analyzed separately, producing tracklets that are then clustered to produce global trajectories, defining both sources and sinks, but also characterizing the movement of pedestrians in the scene.
In the second part of the thesis, We propose a novel approach for automatic detection of social groups of pedestrians in crowds by considering only start (source) and stop (sink) locations of pedestrian trajectories. We build an Association Matrix that captures the joint probability distribution of starts and stops locations of all pedestrian trajectories to all other pedestrian trajectories in the scene. Pedestrians exhibiting similar distribution are combining in a group, where as similarity among the distributions is measuread by KL Divergence We adopt bottom-up hierarchical clustering approach, which is three step processes. In first step, we treat all the individuals as independent clusters, In the second step, couples are detected and after pruning of bad couples, Adjacency matrix is generated. Later on, in step three, using the Adjacency Matrix, groups of couples, those have strong intergroup closeness (similarity) are merged into a larger group..
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Bisagno, Niccolò. "On simulating and predicting pedestrian trajectories in a crowd." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/256722.
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Crowds of people are gathering at multiple venues, such as concerts, political rallies, as well as in commercial malls, or just simply walking on the streets. More and more people are flocking to live in urban areas, thus generating a lot of scenarios of crowds. As a consequence, there is an increasing demand for automatic tools that can analyze and predict the behavior of crowds to ensure safety.
Crowd motion analysis is a key feature in surveillance and monitoring applications, providing useful hints about potential threats to safety and security in urban and public spaces. It is well known that people gatherings are generally difficult to model, due to the diversity of the agents composing the crowd. Each individual is unique, being driven not only by the destination but also by personality traits and attitude.
The domain of crowd analysis has been widely investigated in the literature. However, crowd gatherings have sometimes resulted in dangerous scenarios in recent years, such as stampedes or during dangerous situations.
To take a step toward ensuring the safety of crowds, in this work we investigate two main research problems: we try to predict each person future position and we try to understand which are the key factors for simulating crowds. Predicting in advance how a mass of people will fare in a given space would help in ensuring the safety of public gatherings.
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McAllister, Kevin Michael. "Analysis of sport crowd behavior adapting Smelser's theory of collective behavior." Thesis, Boston University, 2002. https://hdl.handle.net/2144/32796.
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Thesis (Ed.D.)--Boston University<br>PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.<br>The purpose of this study was to validate a new protocol for examining spectator behavior at high school sporting events. The methodology was based on the construct of Smelser's (1962) six determinants of collective behavior - conduciveness, strain, growth of beliefs, precipitating factors, mobilization , and social controls - and operationalised incorporating both qualitative (interviews and observations) and quantitative methods that included a behavioral assessment, an identification scale, and pre- and post-game emotional scales. Fourteen games (3 hockey, 6 soccer, 5 football) were observed over three stages of methodological development. Data were collected by teams of researchers at the games, and then were organized by themes relating to the six determinants. The themes were then examined against social and psychological theories attributed to spectator research, and examined for new construct relationships of the determinants. The research yielded a valid methodology for further spectator research, and suggestions for understanding spectator behavior are offered.<br>2031-01-01
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Solmaz, Berkan. "Holistic Representations for Activities and Crowd Behaviors." Doctoral diss., University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5870.
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In this dissertation, we address the problem of analyzing the activities of people in a variety of scenarios, this is commonly encountered in vision applications. The overarching goal is to devise new representations for the activities, in settings where individuals or a number of people may take a part in specific activities. Different types of activities can be performed by either an individual at the fine level or by several people constituting a crowd at the coarse level. We take into account the domain specific information for modeling these activities. The summary of the proposed solutions is presented in the following. The holistic description of videos is appealing for visual detection and classification tasks for several reasons including capturing the spatial relations between the scene components, simplicity, and performance [1, 2, 3]. First, we present a holistic (global) frequency spectrum based descriptor for representing the atomic actions performed by individuals such as: bench pressing, diving, hand waving, boxing, playing guitar, mixing, jumping, horse riding, hula hooping etc. We model and learn these individual actions for classifying complex user uploaded videos. Our method bypasses the detection of interest points, the extraction of local video descriptors and the quantization of local descriptors into a code book; it represents each video sequence as a single feature vector. This holistic feature vector is computed by applying a bank of 3-D spatio-temporal filters on the frequency spectrum of a video sequence; hence it integrates the information about the motion and scene structure. We tested our approach on two of the most challenging datasets, UCF50 [4] and HMDB51 [5], and obtained promising results which demonstrates the robustness and the discriminative power of our holistic video descriptor for classifying videos of various realistic actions. In the above approach, a holistic feature vector of a video clip is acquired by dividing the video into spatio-temporal blocks then concatenating the features of the individual blocks together. However, such a holistic representation blindly incorporates all the video regions regardless of their contribution in classification. Next, we present an approach which improves the performance of the holistic descriptors for activity recognition. In our novel method, we improve the holistic descriptors by discovering the discriminative video blocks. We measure the discriminativity of a block by examining its response to a pre-learned support vector machine model. In particular, a block is considered discriminative if it responds positively for positive training samples, and negatively for negative training samples. We pose the problem of finding the optimal blocks as a problem of selecting a sparse set of blocks, which maximizes the total classifier discriminativity. Through a detailed set of experiments on benchmark datasets [6, 7, 8, 9, 5, 10], we show that our method discovers the useful regions in the videos and eliminates the ones which are confusing for classification, which results in significant performance improvement over the state-of-the-art. In contrast to the scenes where an individual performs a primitive action, there may be scenes with several people, where crowd behaviors may take place. For these types of scenes the traditional approaches for recognition will not work due to severe occlusion and computational requirements. The number of videos is limited and the scenes are complicated, hence learning these behaviors is not feasible. For this problem, we present a novel approach, based on the optical flow in a video sequence, for identifying five specific and common crowd behaviors in visual scenes. In the algorithm, the scene is overlaid by a grid of particles, initializing a dynamical system which is derived from the optical flow. Numerical integration of the optical flow provides particle trajectories that represent the motion in the scene. Linearization of the dynamical system allows a simple and practical analysis and classification of the behavior through the Jacobian matrix. Essentially, the eigenvalues of this matrix are used to determine the dynamic stability of points in the flow and each type of stability corresponds to one of the five crowd behaviors. The identified crowd behaviors are (1) bottlenecks: where many pedestrians/vehicles from various points in the scene are entering through one narrow passage, (2) fountainheads: where many pedestrians/vehicles are emerging from a narrow passage only to separate in many directions, (3) lanes: where many pedestrians/vehicles are moving at the same speeds in the same direction, (4) arches or rings: where the collective motion is curved or circular, and (5) blocking: where there is a opposing motion and desired movement of groups of pedestrians is somehow prohibited. The implementation requires identifying a region of interest in the scene, and checking the eigenvalues of the Jacobian matrix in that region to determine the type of flow, that corresponds to various well-defined crowd behaviors. The eigenvalues are only considered in these regions of interest, consistent with the linear approximation and the implied behaviors. Since changes in eigenvalues can mean changes in stability, corresponding to changes in behavior, we can repeat the algorithm over clips of long video sequences to locate changes in behavior. This method was tested on over real videos representing crowd and traffic scenes.<br>Ph.D.<br>Doctorate<br>Electrical Engineering and Computer Science<br>Engineering and Computer Science<br>Electrical Engineering
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Jones, Kyle Thomas. "Innovation Management and Crowdsourcing| A Quantitative Analysis of Sponsor and Crowd Assessments." Thesis, The George Washington University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10686345.
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<p> Crowdsourcing is an increasingly common method used for new product development in large engineering-focused companies. While effective at generating a large number of ideas, previous research has noted that there is not an efficient mechanism to sort ideas based on the sponsor's desired outcomes. Without such a mechanism, the sponsor is left to evaluate ideas individually in a labor-intensive effort. This paper evaluates the extent to which information revealed by the crowd during the course of a crowdsourcing event can be used to accurately predict sponsor selection of submitted ideas. The praxis reviews current literature relevant to new product development, innovation management, and crowdsourcing as well as methods for efficient sorting. Using a quantitatively-based methodology, the author develops and evaluates several predictive models using various attributes of the crowd reaction to crowdsourced ideas. Ultimately, the praxis proposes a model that can significantly reduce the burden of sorting through submissions and determining the submissions which merit further review. </p><p>
Books on the topic "Analysis crowd"
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Plummer, Tony. The psychology of technical analysis: Profiting from crowd behavior and the dynamics of price. Probus Pub. Co., 1993.
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Sentiment in the Forex market: Indicators and strategies to profit from crowd behavior and market extremes. John Wiley & Sons, 2008.
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Ali, Saad, Ko Nishino, Dinesh Manocha, and Mubarak Shah, eds. Modeling, Simulation and Visual Analysis of Crowds. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8483-7.
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N, Pathak P., and Bhabha Atomic Research Centre, eds. Extraction chromatographic studies on a strontium selective crown ether. Bhabha Atomic Research Centre, 1998.
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Associates, Golder, and Canada Centre for Mineral and Energy Technology., eds. Crown pillar stability back-analysis: Report to CANMET. Golder Associates Ltd., 1990.
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Sunkin, Maurice. The nature of the crown: A legal and political analysis. Oxford University Press, 1999.
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Tonasket Ranger District (Wash.), Washington (State). Dept. of Ecology., and TerraMatrix Inc, eds. Crown Jewel Mine, draft environmental impact statement. TerraMatrix, 1995.
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Crow-Omaha: New light on a classic problem of kinship analysis. University of Arizona Press, 2012.
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Ontario. Ministry of Natural Resources. An environmental assessment of timber management on crown lands in the Megisan Lake area. The Committee, 1996.
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Cerrah, Ibrahim. Crowds and public order policing: An analysis of crowds and interpretations of their behaviour based on observational studies in Turkey, England, and Wales. Ashgate/Dartmouth, 1998.
Find full textBook chapters on the topic "Analysis crowd"
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Still, G. Keith. "RAMP analysis." In Applied Crowd Science. CRC Press, 2021. http://dx.doi.org/10.1201/9781351053068-5.
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Still, G. Keith. "Crowd risk analysis." In Applied Crowd Science. CRC Press, 2021. http://dx.doi.org/10.1201/9781351053068-9.
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Still, G. Keith. "Event egress analysis." In Applied Crowd Science. CRC Press, 2021. http://dx.doi.org/10.1201/9781351053068-10.
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Still, G. Keith. "Strategic and tactical analysis." In Applied Crowd Science. CRC Press, 2021. http://dx.doi.org/10.1201/9781351053068-11.
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Amsterdamer, Yael, and Tova Milo. "Crowd Mining and Analysis." In Encyclopedia of Database Systems. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_80657-2.
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Amsterdamer, Yael, and Tova Milo. "Crowd Mining and Analysis." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_80657.
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Ryan, David, Simon Denman, Sridha Sridharan, and Clinton Fookes. "Scene Invariant Crowd Counting and Crowd Occupancy Analysis." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28598-1_6.
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Feliciani, Claudio, Kenichiro Shimura, and Katsuhiro Nishinari. "Analysis of Past Crowd Accidents." In Introduction to Crowd Management. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90012-0_3.
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Wąs, Jarosław, and Krzysztof Kułakowski. "Social Groups in Crowd." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_255-1.
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Wąs, Jarosław, and Krzysztof Kułakowski. "Social Groups in Crowd." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_255.
Full textConference papers on the topic "Analysis crowd"
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De Luca, Antonio, Scott Lomax, and Marguerite Jeansonne Pinto. "Advanced analysis of a pedestrian bridge and considerations on crowd-structure interaction." In IABSE Symposium, Prague 2022: Challenges for Existing and Oncoming Structures. International Association for Bridge and Structural Engineering (IABSE), 2022. http://dx.doi.org/10.2749/prague.2022.1427.
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<p>Pedestrian bridges, grandstands, and other long-span structures may be subjected to crowd loading. Crowds have the dual effect to produce large forces and alter the structure’s modal properties and damping. Vibration testing of full-scale structures allows the verification of the modelling assumptions and design criteria.</p><p>This paper discusses the case study of a pedestrian bridge structure subjected to crowd loading. The results of the design finite element model of the bridge and the bridge performance were validated through field testing. The theoretical effects of crowds were analysed and compared to the experimental test data.</p>
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Deshpande, N. P., and R. Gupta. "Crowd management using fuzzy logic and G.I.S." In RISK ANALYSIS 2010. WIT Press, 2010. http://dx.doi.org/10.2495/risk100281.
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Cao, Tian, Xinyu Wu, Jinnian Guo, Shiqi Yu, and Yangsheng Xu. "Abnormal crowd motion analysis." In 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2009. http://dx.doi.org/10.1109/robio.2009.5420408.
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Mahata, Debanjan, and Nitin Agarwal. "Learning from the crowd." In ASONAM '13: Advances in Social Networks Analysis and Mining 2013. ACM, 2013. http://dx.doi.org/10.1145/2492517.2492661.
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Dupont, Camille, Luis Tobias, and Bertrand Luvison. "Crowd-11: A Dataset for Fine Grained Crowd Behaviour Analysis." In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2017. http://dx.doi.org/10.1109/cvprw.2017.271.
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Kishore, P. V. V., R. Rahul, K. Sravya, and A. S. C. S. Sastry. "Crowd Density Analysis and tracking." In 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2015. http://dx.doi.org/10.1109/icacci.2015.7275777.
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H.Y., Swathi, G. Shivakumar, and H. S. Mohana. "Crowd Behavior Analysis: A Survey." In 2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT). IEEE, 2017. http://dx.doi.org/10.1109/icraect.2017.66.
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Zhong, Zhi, Weizhong Ye, Shengshu Wang, Ming Yang, and Yangsheng Xu. "Crowd Energy and Feature Analysis." In 2007 IEEE International Conference on Integration Technology. IEEE, 2007. http://dx.doi.org/10.1109/icitechnology.2007.4290448.
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Christophe, Emmanuel, Jordi Inglada, and Jerome Maudlin. "Crowd-sourcing satellite image analysis." In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5648833.
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Ihaddadene, Nacim, and Chabane Djeraba. "Real-time crowd motion analysis." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761041.
Full textReports on the topic "Analysis crowd"
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Petty, Mikel D., Ryland C. Gaskins, and Frederic D. McKenzie. Crowd Modeling in Military Simulations: Requirements Analysis, Survey, and Design Study. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada474641.
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Drury, J., S. Arias, T. Au-Yeung, et al. Public behaviour in response to perceived hostile threats: an evidence base and guide for practitioners and policymakers. University of Sussex, 2023. http://dx.doi.org/10.20919/vjvt7448.
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Background: Public behaviour and the new hostile threats • Civil contingencies planning and preparedness for hostile threats requires accurate and up to date knowledge about how the public might behave in relation to such incidents. Inaccurate understandings of public behaviour can lead to dangerous and counterproductive practices and policies. • There is consistent evidence across both hostile threats and other kinds of emergencies and disasters that significant numbers of those affected give each other support, cooperate, and otherwise interact socially within the incident itself. • In emergency incidents, competition among those affected occurs in only limited situations, and loss of behavioural control is rare. • Spontaneous cooperation among the public in emergency incidents, based on either social capital or emergent social identity, is a crucial part of civil contingencies planning. • There has been relatively little research on public behaviour in response to the new hostile threats of the past ten years, however. • The programme of work summarized in this briefing document came about in response to a wave of false alarm flight incidents in the 2010s, linked to the new hostile threats (i.e., marauding terrorist attacks). • By using a combination of archive data for incidents in Great Britain 2010-2019, interviews, video data analysis, and controlled experiments using virtual reality technology, we were able to examine experiences, measure behaviour, and test hypotheses about underlying psychological mechanisms in both false alarms and public interventions against a hostile threat. Re-visiting the relationship between false alarms and crowd disasters • The Bethnal Green tube disaster of 1943, in which 173 people died, has historically been used to suggest that (mis)perceived hostile threats can lead to uncontrolled ‘stampedes’. • Re-analysis of witness statements suggests that public fears of Germany bombs were realistic rather than unreasonable, and that flight behaviour was socially structured rather than uncontrolled. • Evidence for a causal link between the flight of the crowd and the fatal crowd collapse is weak at best. • Altogether, the analysis suggests the importance of examining people’s beliefs about context to understand when they might interpret ambiguous signals as a hostile threat, and that. Tthe concepts of norms and relationships offer better ways to explain such incidents than ‘mass panic’. Why false alarms occur • The wider context of terrorist threat provides a framing for the public’s perception of signals as evidence of hostile threats. In particular, the magnitude of recent psychologically relevant terrorist attacks predicts likelihood of false alarm flight incidents. • False alarms in Great Britain are more likely to occur in those towns and cities that have seen genuine terrorist incidents. • False alarms in Great Britain are more likely to occur in the types of location where terrorist attacks happen, such as shopping areass, transport hubs, and other crowded places. • The urgent or flight behaviour of other people (including the emergency services) influences public perceptions that there is a hostile threat, particularly in situations of greater ambiguity, and particularly when these other people are ingroup. • High profile tweets suggesting a hostile threat, including from the police, have been associated with the size and scale of false alarm responses. • In most cases, it is a combination of factors – context, others’ behaviour, communications – that leads people to flee. A false alarm tends not to be sudden or impulsive, and often follows an initial phase of discounting threat – as with many genuine emergencies. 2.4 How the public behave in false alarm flight incidents • Even in those false alarm incidents where there is urgent flight, there are also other behaviours than running, including ignoring the ‘threat’, and walking away. • Injuries occur but recorded injuries are relatively uncommon. • Hiding is a common behaviour. In our evidence, this was facilitated by orders from police and offers from people staff in shops and other premises. • Supportive behaviours are common, including informational and emotional support. • Members of the public often cooperate with the emergency services and comply with their orders but also question instructions when the rationale is unclear. • Pushing, trampling and other competitive behaviour can occur,s but only in restricted situations and briefly. • At the Oxford Street Black Friday 2017 false alarm, rather than an overall sense of unity across the crowd, camaraderie existed only in pockets. This was likely due to the lack of a sense of common fate or reference point across the incident; the fragmented experience would have hindered the development of a shared social identity across the crowd. • Large and high profile false alarm incidents may be associated with significant levels of distress and even humiliation among those members of the public affected, both at the time and in the aftermath, as the rest of society reflects and comments on the incident. Public behaviour in response to visible marauding attackers • Spontaneous, coordinated public responses to marauding bladed attacks have been observed on a number of occasions. • Close examination of marauding bladed attacks suggests that members of the public engage in a wide variety of behaviours, not just flight. • Members of the public responding to marauding bladed attacks adopt a variety of complementary roles. These, that may include defending, communicating, first aid, recruiting others, marshalling, negotiating, risk assessment, and evidence gathering. Recommendations for practitioners and policymakers • Embed the psychology of public behaviour in emergencies in your training and guidance. • Continue to inform the public and promote public awareness where there is an increased threat. • Build long-term relations with the public to achieve trust and influence in emergency preparedness. • Use a unifying language and supportive forms of communication to enhance unity both within the crowd and between the crowd and the authorities. • Authorities and responders should take a reflexive approach to their responses to possible hostile threats, by reflecting upon how their actions might be perceived by the public and impact (positively and negatively) upon public behaviour. • To give emotional support, prioritize informative and actionable risk and crisis communication over emotional reassurances. • Provide first aid kits in transport infrastructures to enable some members of the public more effectively to act as zero responders.
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Anilkumar, Rahul, Benjamin Melone, Michael Patsula, et al. Canadian jobs amid a pandemic : examining the relationship between professional industry and salary to regional key performance indicators. Department of Systems and Computer Engineering, Carleton University, 2022. http://dx.doi.org/10.22215/dsce/220608.
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The COVID-19 pandemic has contributed to massive rates of unemployment and greater uncertainty in the job market. There is a growing need for data-driven tools and analyses to better inform the public on trends within the job market. In particular, obtaining a “snapshot” of available employment opportunities mid-pandemic promises insights to inform policy and support retraining programs. In this work, we combine data scraped from the Canadian Job Bank and Numbeo globally crowd-sourced repository to explore the relationship between job postings during a global pandemic and Key Performance Indicators (e.g. quality of life index, cost of living) for major cities across Canada. This analysis aims to help Canadians make informed career decisions, collect a “snapshot” of the Canadian employment opportunities amid a pandemic, and inform job seekers in identifying the correct fit between the desired lifestyle of a city and their career. We collected a new high-quality dataset of job postings from jobbank.gc.ca obtained with the use of ethical web scraping and performed exploratory data analysis on this dataset to identify job opportunity trends. When optimizing for average salary of job openings with quality of life, affordability, cost of living, and traffic indices, it was found that Edmonton, AB consistently scores higher than the mean, and is therefore an attractive place to move. Furthermore, we identified optimal provinces to relocate to with respect to individual skill levels. It was determined that Ajax, Marathon, and Chapleau, ON are each attractive cities for IT professionals, construction workers, and healthcare workers respectively when maximizing average salary. Finally, we publicly release our scraped dataset as a mid-pandemic snapshot of Canadian employment opportunities and present a public web application that provides an interactive visual interface that summarizes our findings for the general public and the broader research community.
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Schomaker, Michael E., Stanley J. Zarnoch, William A. Bechtold, David J. Latelle, William G. Burkman, and Susan M. Cox. Crown-condition classification: a guide to data collection and analysis. U.S. Department of Agriculture, Forest Service, Southern Research Station, 2007. http://dx.doi.org/10.2737/srs-gtr-102.
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Randolph, KaDonna C. Descriptive statistics of tree crown condition in the Southern United States and impacts on data analysis and interpretation. U.S. Department of Agriculture, Forest Service, Southern Research Station, 2006. http://dx.doi.org/10.2737/srs-gtr-94.
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Randolph, KaDonna C. Descriptive statistics of tree crown condition in the Southern United States and impacts on data analysis and interpretation. U.S. Department of Agriculture, Forest Service, Southern Research Station, 2006. http://dx.doi.org/10.2737/srs-gtr-94.
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Chen, Li, Yu Ji, QiPeng Wang, and Peng Chen. Effects of traditional Chinese exercise on the treatment of COVID-19: a protocol for a systematic review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.1.0089.
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Review question / Objective: Since the end of December 2019, COVID-19 has caused a huge impact in many countries and has attracted great attention from countries around the world. In fact, many studies have shown that during the fight against the COVID-19 epidemic. Chinese traditional exercise play an active role in promoting human health. The main purpose of this study is to provide a reliable method and credible evidence to improve the prognosis of patients with COVID-19 through traditional Chinese exercise. Condition being studied: In recent years, a large number of studies have shown that traditional Chinese sports, as the traditional culture of the Chinese nation, have played a huge role in this new crown epidemic. Therefore, we will study the effect of traditional Chinese exercise on the prognosis and treatment effect of COVID-19 patients in a systematic review and analysis.
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Arce, Eliécer, and Edgar A. Robles. Fiscal Rules and the Behavior of Public Investment in Costa Rica and Panama: Towards Growth-Friendly Fiscal Policy? Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003071.
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This paper aims to provide evidence on the effects of fiscal rules on public investment, fiscal results and growth in Costa Rica and Panama. First, we find that the budget formulation process and the political economy behind the adoption and compliance of fiscal rules explain that Panama has a bias to create and sequentially pile up rules, while Costa Rica has a tendency not to comply with them. Second, a retrospective analysis of the 2018 fiscal rules in both nations finds asymmetric effects on the fiscal results. In Panama it is difficult to separate the effect of fiscal rule designs on public investment; and, in Costa Rica, the application of the fiscal rule will decrease public investment, if the debt to GDP ratio exceeds 60 percent and current expenditure crowds out capital expenditure. Two lessons emerge. First, an effective fiscal rule compliance requires time consistent institutions, solid monitoring, enforcement schemes and improving the quality of public financial management systems. Second, it is necessary to review the design of fiscal rules in both countries to ensure they are investment and growth friendly.
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Lu, Tianjun, Jian-yu Ke, Fynnwin Prager, and Jose N. Martinez. “TELE-commuting” During the COVID-19 Pandemic and Beyond: Unveiling State-wide Patterns and Trends of Telecommuting in Relation to Transportation, Employment, Land Use, and Emissions in Calif. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2147.
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Telecommuting, the practice of working remotely at home, increased significantly (25% to 35%) early in the COVID-19 pandemic. This shift represented a major societal change that reshaped the family, work, and social lives of many Californians. These changes also raise important questions about what factors influenced telecommuting before, during, and after COVID-19, and to what extent changes in telecommuting have influenced transportation patterns across commute modes, employment, land use, and environment. The research team conducted state-level telecommuting surveys using a crowd-sourced platform (i.e., Amazon Mechanical Turk) to obtain valid samples across California (n=1,985) and conducted state-level interviews among stakeholders (n=28) across ten major industries in California. The study leveraged secondary datasets and developed regression and time-series models. Our surveys found that, compared to pre-pandemic levels, more people had a dedicated workspace at home and had received adequate training and support for telecommuting, became more flexible to choose their own schedules, and had improved their working performance—but felt isolated and found it difficult to separate home and work life. Our interviews suggested that telecommuting policies were not commonly designed and implemented until COVID-19. Additionally, regression analyses showed that telecommuting practices have been influenced by COVID-19 related policies, public risk perception, home prices, broadband rates, and government employment. This study reveals advantages and disadvantages of telecommuting and unveils the complex relationships among the COVID-19 outbreak, transportation systems, employment, land use, and emissions as well as public risk perception and economic factors. The study informs statewide and regional policies to adapt to the new patterns of telecommuting.
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Wright, Alex, Peter Sanders, John Proust, Robin Workman, Tadas Andriejauskas, and Hao Ye. The new toolkit for highways asset management. TRL, 2021. http://dx.doi.org/10.58446/ojct3729.
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The application of consistent, reliable information is a key component of highway asset management. However, the tools to understand asset performance have developed rapidly over the last decade. These include asset surveys, intelligent infrastructure monitoring, crowd sourcing, remote sensing, data analytics and visualisation. However, their potential is not yet being fully exploited within the highway environment. By bringing these components of sensing and measurement together we could better understand highway assets and improve reactive and proactive decisions. This paper discusses the tools now available to understand the performance of highway assets. It explores their current and future capabilities, the benefits they bring, and the possibilities that could be achieved through their application within an integrated toolkit. Whilst these tools are not in themselves “new”, a key objective of the paper has been to highlight their emerging capabilities, bring awareness to highway asset managers, and encourage their take up. Increased application will inevitably lead to further development in capability and, importantly, accessibility. There are a number of challenges to overcome to draw full value from these technologies. These include the technical, commercial, and social barriers that influence development and accessibility. The paper discusses actions that could help overcome these, which are presented within the context of a roadmap to the implementation of an integrated toolkit. The roadmap is not definitive - it aims to stimulate further thinking, debate and discussion. The effective management of infrastructure assets is essential to deliver a clean, efficient, safe, reliable and accessible network. A joined up and collaborative approach will help the community achieve the benefits of the integrated toolkit that will help asset managers achieve this.