Journal articles on the topic 'Pedestrian Flows Monitoring'
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1
Kurkcu, Abdullah, and Kaan Ozbay. "Estimating Pedestrian Densities, Wait Times, and Flows with Wi-Fi and Bluetooth Sensors." Transportation Research Record: Journal of the Transportation Research Board 2644, no. 1 (January 2017): 72–82. http://dx.doi.org/10.3141/2644-09.
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Monitoring nonmotorized traffic is gaining more attention in the context of transportation studies. Most of the traditional pedestrian monitoring technologies focus on counting pedestrians passing through a fixed location in the network. It is thus not possible to anonymously track the movement of individuals or groups as they move outside each particular sensor’s range. Moreover, most agencies do not have continuous pedestrian counts mainly because of technological limitations. Wireless data collection technologies, however, can capture crowd dynamics by scanning mobile devices. Data collection that takes advantage of mobile devices has gained much interest in the transportation literature as a result of its low cost, ease of implementation, and richness of the captured data. In this paper, algorithms to filter and aggregate data collected by wireless sensors are investigated, as well as how to fuse additional data sources to improve the estimation of various pedestrian-based performance measures. Procedures to accurately filter the noise in the collected data and to find pedestrian flows, wait times, and counts with wireless sensors are presented. The developed methods are applied to a 2-month-long collection of public transportation terminal data carried out with the use of six sensors. Results point out that if the penetration rate of discoverable devices is known, then it is possible to accurately estimate the number of pedestrians, pedestrian flows, and average wait times in the detection zone of the developed sensors.
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Wang, Xianing, Zhan Zhang, Ying Wang, Jun Yang, and Linjun Lu. "A Study on Safety Evaluation of Pedestrian Flows Based on Partial Impact Dynamics by Real-Time Data in Subway Stations." Sustainability 14, no. 16 (August 19, 2022): 10328. http://dx.doi.org/10.3390/su141610328.
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With the rapid development of urban rail transit, the scientific assurance of pedestrian safety has become an important issue. Orderly behavior is a crucial factor affecting pedestrian safety. Therefore, in-depth research into pedestrian behavior is needed. This study carries out an evaluation of safety in pedestrian flows by establishing a new force model based on real-time data. In this model, we consider the microscopic characteristics of pedestrians and define four force influence mechanisms for simulating pedestrian behavior. Compared with existing models, this model incorporates partial impact dynamics to make it applicable to the particular environment of subway stations. Through the validation of real-world data, it is demonstrated that the model can accurately describe pedestrian behavior and better reproduce the characteristics of pedestrians. The influence of pedestrians and of environmental factors on the model are also discussed. Using our model, we propose a risk evaluation system based on pedestrian volatility. By using real-time pedestrian information from subway stations, the potential risk to pedestrians can be discerned and assessed in advance. This research advances the management of pedestrian safety and provides a framework for studying behavior models and for safety evaluation.
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Kim, Jeongyun, Sehyun Tak, Michel Bierlaire, and Hwasoo Yeo. "Trajectory Data Analysis on the Spatial and Temporal Influence of Pedestrian Flow on Path Planning Decision." Sustainability 12, no. 24 (December 13, 2020): 10419. http://dx.doi.org/10.3390/su122410419.
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The modeling of walking behavior and design of walk-friendly urban pathways have been of interest to many researchers over the past decades. One of the major issues in pedestrian modeling is path planning decision-making in a dynamic walking environment with different pedestrian flows. While previous studies have agreed that pedestrian flow influences path planning, only a few studies have dealt with the empirical data to show the relationship between pedestrian flow and path planning behavior. This study introduces a new methodology for analyzing pedestrian trajectory data to find the dynamic walking conditions that influence the path planning decision. The comparison of the pedestrians’ path shows that the higher proportion of opposite flows are, the greater they influence the path selection decision. In this study, we investigate the relationship between the opposite flow changes and path planning behavior and find the spatial and temporal ranges of the opposite flow that affects the path planning behavior. Lastly, we find the ratio of pedestrians that update their paths with respect to the opposite flow rate.
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Yao, Shenjun, Jinzi Wang, Lei Fang, and Jianping Wu. "Identification of Vehicle-Pedestrian Collision Hotspots at the Micro-Level Using Network Kernel Density Estimation and Random Forests: A Case Study in Shanghai, China." Sustainability 10, no. 12 (December 13, 2018): 4762. http://dx.doi.org/10.3390/su10124762.
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The improvement of pedestrian safety plays a crucial role in developing a safe and friendly walking environments, which can contribute to urban sustainability. A preliminary step in improving pedestrian safety is to identify hazardous road locations for pedestrians. This study proposes a framework for the identification of vehicle-pedestrian collision hot spots by integrating the information about both the likelihood of the occurrence of vehicle-pedestrian collisions and the potential for the reduction in vehicle-pedestrian crashes. First, a vehicle-pedestrian collision density surface was produced via network kernel density estimation. By assigning a threshold value, possible vehicle-pedestrian hot spots were identified. To obtain the potential for vehicle-pedestrian collision reduction, random forests was employed to model the density with a set of variables describing vehicle and pedestrian flows. The potential for crash reduction was then measured as the difference between the observed vehicle-pedestrian crash density and the prediction produced by the random forests models. The final hotspots were determined by excluding those with a crash reduction value of no more than zero. The method was applied to the identification of hazardous road locations for pedestrians in a district in Shanghai, China. The result indicates that the method is useful for decision-making support.
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Gruden, Chiara. "The evaluation of the surrogate safety measures along a pedestrian confined ramp of an old bridge." European Transport/Trasporti Europei, no. 77 (May 2020): 1–13. http://dx.doi.org/10.48295/et.2020.77.7.
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Pedestrians walk daily to meet their basic needs. Therefore, over the years it has been attempted to make urban context more livable, respecting the needs of weak road users. Pedestrian mobility is also growing strongly from the point of view of tourism, especially in the areas where government measures are taken to reduce vehicular flow in order to let the architectural and landscape assets more usable. Often tourist itineraries are made of narrow areas crossed by numerous pedestrians, leading to possible critical circumstances. The criticality is connected to a likely reduction of safety and use (comfort and possible delays). Confined spaces such as small streets or pedestrian bridges well exemplify the described situation. Particularly, pedestrian bridges are often composed of ramps with variable slopes and consist of different floors, making walking difficult and therefore reducing travel speed and limiting the mobility of users such as handicapped or elderly people. Starting from the geometrical evaluation of the old bridge of Mostar (BIH), this article provides some ideas for evaluating the safety of pedestrians during the journey on the bridge, under different flow conditions. The analyses were carried out starting from an estimate of pedestrian flows from video monitoring. Safety was then assessed thanks to the calculation of surrogate measures, based on the trajectories of individuals, obtained through tracking tools. Camera monitoring and inspections to survey infrastructure’s geometric features ease the procedure of data entering into image processing software. The evaluation of individual pedestrian trajectories and/or their interactions allows to estimate the movement variations through the study of parameters such as speed variation, pedestrian density and surrogate safety factors. Surrogate safety measures, indeed, are indicators calculated for a chosen pair of users interacting in the detected scene. Among these factors there are: relative speed (Delta-V), Time-To-Collision (TTC), Time Advantage (TAdv) and T2. All the above-mentioned kinds of measures are interesting to study generally the behavioural aspects of road traffic and, specifically, safety level and factors influencing it. The purpose of this work is to estimate the effects of induced pedestrian mobility on Mostar’s Ottoman bridge in terms of safety, with the aim of preventing negative scenarios that could lead to a bad infrastructural level of service. Keywords: road safety; pedestrian trajectories; surrogate safety measures.
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Gruden, Chiara, Irena Ištoka Otković, and Matjaž Šraml. "An Eye-Tracking Study on the Effect of Different Signalized Intersection Typologies on Pedestrian Performance." Sustainability 14, no. 4 (February 12, 2022): 2112. http://dx.doi.org/10.3390/su14042112.
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Pedestrian safety is a well-known issue, such that many road safety associations emphasize measures to improve this aspect. One of the main conflict points is pedestrian crossings, where motorized and non-motorized traffic flows directly interact and where pedestrian attention and reaction are two elements that can strongly influence their safety. Nowadays, these aspects are often deviated by the use of smartphones to surf the Internet or social media. The aim of the present study is to find out (1) whether and how intersection typology affects pedestrian behavior, both in terms of attention and crossing performance, and (2) whether and how gaze and behavioral characteristics are affected by smartphone use and social media browsing. To achieve this goal, eye-tracking technology was used to obtain qualitative and quantitative information on the number of fixations, their duration, and reaction times of pedestrians. Additionally, from the eye-tracking videos, it was possible to derive pedestrian waiting times, crossing times, and speeds. Statistical tests were conducted to determine if there is a significant difference in pedestrian behavior at the three different types of intersections and in their behavior when using or not using their device. Results confirm the initial hypotheses and quantify the difference in pedestrian gaze behavior and crossing performance when walking across three different types of signalized crosswalks.
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Ye, Qiming, Yuxiang Feng, Eduardo Candela, Jose Escribano Macias, Marc Stettler, and Panagiotis Angeloudis. "Spatial-Temporal Flows-Adaptive Street Layout Control Using Reinforcement Learning." Sustainability 14, no. 1 (December 23, 2021): 107. http://dx.doi.org/10.3390/su14010107.
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Complete streets scheme makes seminal contributions to securing the basic public right-of-way (ROW), improving road safety, and maintaining high traffic efficiency for all modes of commute. However, such a popular street design paradigm also faces endogenous pressures like the appeal to a more balanced ROW for non-vehicular users. In addition, the deployment of Autonomous Vehicle (AV) mobility is likely to challenge the conventional use of the street space as well as this scheme. Previous studies have invented automated control techniques for specific road management issues, such as traffic light control and lane management. Whereas models and algorithms that dynamically calibrate the ROW of road space corresponding to travel demands and place-making requirements still represent a research gap. This study proposes a novel optimal control method that decides the ROW of road space assigned to driveways and sidewalks in real-time. To solve this optimal control task, a reinforcement learning method is introduced that employs a microscopic traffic simulator, namely SUMO, as its environment. The model was trained for 150 episodes using a four-legged intersection and joint AVs-pedestrian travel demands of a day. Results evidenced the effectiveness of the model in both symmetric and asymmetric road settings. After being trained by 150 episodes, our proposed model significantly increased its comprehensive reward of both pedestrians and vehicular traffic efficiency and sidewalk ratio by 10.39%. Decisions on the balanced ROW are optimised as 90.16% of the edges decrease the driveways supply and raise sidewalk shares by approximately 9%. Moreover, during 18.22% of the tested time slots, a lane-width equivalent space is shifted from driveways to sidewalks, minimising the travel costs for both an AV fleet and pedestrians. Our study primarily contributes to the modelling architecture and algorithms concerning centralised and real-time ROW management. Prospective applications out of this method are likely to facilitate AV mobility-oriented road management and pedestrian-friendly street space design in the near future.
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López Baeza, Jesús, José Carpio-Pinedo, Julia Sievert, André Landwehr, Philipp Preuner, Katharina Borgmann, Maša Avakumović, Aleksandra Weissbach, Jürgen Bruns-Berentelg, and Jörg Rainer Noennig. "Modeling Pedestrian Flows: Agent-Based Simulations of Pedestrian Activity for Land Use Distributions in Urban Developments." Sustainability 13, no. 16 (August 18, 2021): 9268. http://dx.doi.org/10.3390/su13169268.
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Pedestrian activity is a cornerstone for urban sustainability, with key implications for the environment, public health, social cohesion, and the local economy. Therefore, city planners, urban designers, and decision-makers require tools to predict pedestrian mobility and assess the walkability of existing or planned urban environments. For this purpose, diverse approaches have been used to analyze different inputs such as the street network configuration, density, land use mix, and the location of certain amenities. This paper focuses on the location of urban amenities as key elements for pedestrian flow prediction, and, therefore, for the success of public spaces in terms of the social life of city neighborhoods. Using agent-based modeling (ABM) and land use floor space data, this study builds a pedestrian flow model, which is applied to both existing and planned areas in the inner city of Hamburg, Germany. The pedestrian flows predicted in the planned area inform the ongoing design and planning process. The flows simulated in the existing area are compared against real-world pedestrian activity data for external validation to report the model accuracy. The results show that pedestrian flow intensity correlates to the density and diversity of amenities, among other KPIs. These correlations validate our approach and also quantify it with measurable indicators.
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Santilli, Daniela, Mauro D’Apuzzo, Azzurra Evangelisti, and Vittorio Nicolosi. "Towards Sustainability: New Tools for Planning Urban Pedestrian Mobility." Sustainability 13, no. 16 (August 20, 2021): 9371. http://dx.doi.org/10.3390/su13169371.
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Background: Since the beginning of the new millennium, sensitivity towards the environment has been spreading globally. In fact, countries are adopting measures to develop new decision support tools that can evaluate the impact of interventions to promote and encourage sustainable mobility. To reduce the levels of pollution related to road traffic, policies that favor multimodal transport alternatives have been strengthened. This involves the combined use of public transport, cycling and walking paths, as well as sharing services where available. Regardless of the type of transport, the pedestrian component remains relevant in cities, even if the infrastructures are often not adequate to accommodate it and conflicts arise that must be managed. It is, therefore, necessary to assess the exposure to risk in terms of road safety. Methods: To this end, the work proposes a forecasting model to estimate the pedestrian flows that load the network. The methodology employs a hybrid approach that appears to better capture the movements of pedestrians. Results: By comparing the results of the model with the real data collected on the study area, satisfactory estimates were obtained. Conclusions: Therefore, this can be an effective tool to help road managers to evaluate the actions to protect vulnerable users.
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Matos, Bárbara, and Carlos Lobo. "The Barrier Effect and Pedestrian Mobility/Accessibility on Urban Highways: An Analysis Based on the Belo Horizonte/Minas Gerais/Brazil Ring Road." Sustainability 15, no. 4 (February 13, 2023): 3408. http://dx.doi.org/10.3390/su15043408.
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Historically, public policies in Brazil and many developing countries have prioritized individual motorized modes of transport. The urban space of Brazilian cities has been repeatedly remodeled, often to meet the demands of vehicle flow, to the detriment of spaces for pedestrian use. Part of the transport infrastructure, particularly the highways, are obstacles to the population’s mobility, constituting the barrier effect and expanding the physical separation of the communities that reside or transit between the adjacent neighborhoods. Based on the proposed mobility and accessibility indicators extracted from the Origin and Destination Survey databases, this article aims to analyze pedestrian mobility and access to cross the Celso Mello Azevedo Ring Road, located in Belo Horizonte/Brazil. The results indicate that, over the period analyzed, the flows of foot crossings were significantly reduced, even in areas where population growth was recorded. The evidence also shows the prevalence of crossings by motorized modes, whose mobility and access are characterized by a tangible inequality in spatial distribution, linked to the occupation pattern of the surroundings, one consequence of which is the expressive volume of accidents involving pedestrians on the highway.
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Ivić, Majda, Jelena Kilić, Katarina Rogulj, and Nikša Jajac. "Decision Support to Sustainable Parking Management—Investment Planning through Parking Fines to Improve Pedestrian Flows." Sustainability 12, no. 22 (November 14, 2020): 9485. http://dx.doi.org/10.3390/su12229485.
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With the urbanization and expansion of cities, which have taken place over recent decades, new demands and problems are emerging, among which is the problem of inadequate transport infrastructure. The number of motor vehicles is growing, while transport infrastructure is not following that growth fast enough. One of the problems that arises is the insufficient number of garages and parking lots, causing an increase in illegal parking on sidewalks, which impedes and endangers pedestrian traffic. This paper proposes a new decision support concept (DSC) for the management of illegally parked cars in urban centers, which offers a method that can contribute to solving this problem and improving the flow of pedestrians on city roads. Due to its complexity, the problem addressed in this research is recognized as a multicriteria one and therefore the proposed model is based on the use of multicriteria analysis methods—more precisely, the Preference Ranking Organization Method for Enrichment Evaluation—PROMETHEE, and the analytic hierarchy process—AHP. The proposed DSC is validated in the city of Split (Croatia), more precisely in the neighborhood of Sućidar, which shows that this methodology is applicable and effective for finding not a temporary but a permanent solution to the problem described.
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Duives, Dorine, Guangxing Wang, and Jiwon Kim. "Forecasting Pedestrian Movements Using Recurrent Neural Networks: An Application of Crowd Monitoring Data." Sensors 19, no. 2 (January 18, 2019): 382. http://dx.doi.org/10.3390/s19020382.
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Currently, effective crowd management based on the information provided by crowd monitoring systems is difficult as this information comes in at the moment adverse crowd movements are already occurring. Up to this moment, very little forecasting techniques have been developed that predict crowd flows a longer time period ahead. Moreover, most contemporary state estimation methods apply demanding pre-processing steps, such as map-matching. The objective of this paper is to design, train and benchmark a data-driven procedure to forecast crowd movements, which can in real-time predict crowd movement. This procedure entails two steps. The first step comprises of a cell sequence derivation method that allows the representation of spatially continuous GPS traces in terms of discrete cell sequences. The second step entails the training of a Recursive Neural Network (RNN) with a Gated Recurrent Unit (GRU) and six benchmark models to forecast the next location of pedestrians. The RNN-GRU is found to outperform the other tested models. Some additional tests of the ability of the RNN-GRU to forecast illustrate that the RNN-GRU preserves its predictive power when a limited amount of data is used from the first few hours of a multi-day event and temporal information is incorporated in the cell sequences.
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Xie, Kefan, Benbu Liang, Yu Song, and Xueqin Dong. "Analysis of Walking-Edge Effect in Train Station Evacuation Scenarios: A Sustainable Transportation Perspective." Sustainability 11, no. 24 (December 15, 2019): 7188. http://dx.doi.org/10.3390/su11247188.
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Due to the highly developed rail transit over the past decades, the phenomena of complex individual self-organized behaviors and mass crowd dynamics have become a great concern in the train station. In order to understand passengers’ walking-edge effect and analyze the relationship between the layout and sustainable service abilities of the train station, a heuristics-based social force model is proposed to elaborate the crowd dynamics. Several evacuation scenarios are implemented to describe the walking-edge effect in a train station with the evacuation efficiency, pedestrian flow, and crowd density map. The results show that decentralizing crowd flow can significantly increase the evacuation efficiency in different scenarios. When the exits are far away from the central axis of the railway station, the walking-edge effect has little influence on the evacuation efficiency. Obstacles can guide the movement of passengers by channelizing pedestrian flows. In addition, a wider side exit of the funnel-shaped corridors can promote walking-edge effect and decrease the pressure among a congested crowd. Besides providing a modified social force model with considering walking-edge effect, several suggestions are put forward for managers and architects of the train station in designing sustainable layouts.
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Royko, Yuriy, Yevhen Fornalchyk, Eugeniusz Koda, Ivan Kernytskyy, Oleh Hrytsun, Romana Bura, Piotr Osinski, et al. "Public Transport Prioritization and Descriptive Criteria-Based Urban Sections Classification on Arterial Streets." Sustainability 15, no. 3 (January 28, 2023): 2363. http://dx.doi.org/10.3390/su15032363.
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The present paper is aimed at improving minimization methods in traffic flows, particularly reducing the costs of civil transportation through sections of the transport network by giving priority to public transport in densely developed areas. In cities with a radial and radial–circular planning scheme of the road network, where arterial traffic flows converge in the central part, the challenge of street congestion with traffic often arises. As a result, delays of all types of vehicles increase, which causes excessive travel time for users of private and public transport. In this regard, it is proposed to divide the sections of the transport network into eight types based on their geometric parameters and traffic conditions. This differentiation of sections improves the existing methods for determining the spatial delay of traffic flows on sections of the transport network with different parameters. It was achieved by considering the duration of vehicles passing signalized intersections and pedestrian crosswalks and the sections of streets between them, while simultaneously recording the duration of public transport movement, as well as the time they spend at stopping points, using GPS receivers. The results of onsite monitoring and further computations revealed that there are particular urban sections with specific, different distances between adjacent stop lines that are critical for public transport operation. Furthermore, based on the delay criterion, there were three different passage modes proposed to improve the efficiency of the traffic.
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Zaleckis, Kestutis, Szymon Chmielewski, Jūratė Kamičaitytė, Indre Grazuleviciute-Vileniske, and Halina Lipińska. "Walkability Compass—A Space Syntax Solution for Comparative Studies." Sustainability 14, no. 4 (February 11, 2022): 2033. http://dx.doi.org/10.3390/su14042033.
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The ongoing discourse on air quality and climate changes positions walkability as a pivotal point of sustainable urban planning. Urban studies examine a city’s walkability in terms of pedestrian flows, design qualities, and street network topology, leaving walkability comparative frameworks under development. Building on the space syntax theory, this research introduces a “walkability compass”, a four spatial indicator-designed tool for city walkability assessment and comparison. The tools are being tested on eight Baltic region cities: Vilnius, Kaunas (LT), Malmö (SE), Riga (LV), Tallinn (ES), Gdansk, Bialystok, Lublin (PL). The nine-step method framework integrates four indexes: Gravity (Gr), Reach (Re), Straightness (St), and Population density (Pop). The “walkability compass” results reveal significant Re and St correlations; thus, visual and cultural aspects become the main factors in pedestrian-friendly cities. The spatial pattern typology has matched similar cities (Malmö and Kaunas) to work closely on sustainable urban planning development. In all case studies, specific walkability zones were mapped, but the Gr zones turned out to be the most compact ones (the Z-score of Gr was ranged from 355.4 to 584; other indexes oscillated between 209.4 and 542.6). The walkability mapping results are publicly shared via WebMap to stimulate the participatory discussion on case studies cities further development.
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Lavrov, L. P., and E. G. Molotkova. "Intra-quarter development as a unique component of urban environment in the St. Petersburg historical center." Вестник гражданских инженеров 17, no. 2 (2020): 5–21. http://dx.doi.org/10.23968/1999-5571-2020-17-2-5-21.
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The living environment, which constitutes a significant part of the historical center development in St. Petersburg, is assumed to be a unique object of urban planning activity, since its potential is exceptional, but the strategy for further use has not yet been determined. The active usage of huge residential areas in the city center is hindered due to the uncertainty of property relations, some missteps in the area of the cultural heritage conservation system, defects in the system of land use and land tenure. Taking into consideration that the intra-quarter environment has been hardly ever studied, the authors propose using the stagnation period to «stock-take» the quarters of the St. Petersburg center. In connection with the envisaged orientation of the city transport system to increasing the role of pedestrian flows in the city center, the role of the unique system of passage yards is emphasized and it is proposed to take it into account as an integral component of the city center as UNESCO historical heritage object. The decisive role of intra-quarter communications for the formation of urban development characteristic types is substantiated. It is pointed out that in order to assess the mass development potential in the center of St. Petersburg, it is necessary to provide each historical quarter with a corresponding certificate and organize a monitoring of possible evolutions.
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Hristov, P. O., D. Petrova-Antonova, S. Ilieva, and R. Rizov. "ENABLING CITY DIGITAL TWINS THROUGH URBAN LIVING LABS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2022 (May 30, 2022): 151–56. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2022-151-2022.
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Abstract. The population density in urban areas is rapidly rising, leading to a constant need for new infrastructure and services for citizens. To reduce the time to implementation and optimise the monetary cost of various solutions, the plans and policies of local authorities and stakeholders would benefit from undergoing a series of virtual stress tests. To this end, prescriptive and predictive technologies are widely adopted to optimise city planning and to understand the urban processes and environment such as air pollution and transportation. Nevertheless, holistic sandboxes tightly integrated with cities are still largely lacking. The city digital twin is a promising concept that provides a tool for exploration of new solutions in a controlled environment before their deployment. The digital twin is a virtual replica of the real city, which collects data from the infrastructure, processes and services using not only the available systems, but also purposely built connected devices and sensors. In this context, the establishment of urban living labs facilitates the monitoring and understanding of urban processes and enriches the digital twin with highly-relevant data. This paper presents an urban living lab, under deployment in the district of Lozenets in Sofia, Bulgaria. It is part of a larger initiative for developing a city digital twin of Sofia to support the design, exploration, and experimentation of different solutions. The living lab is equipped with sensors for monitoring air quality, atmospheric parameters, noise pollution and pedestrian flows. In addition, a Light Detection and Ranging (LiDAR) system is realised as an edge computing facility at one of the busiest intersections of the district. Along with the equipment, the paper describes the architecture and components of the platform for data collection, storage, processing, and visualization. Finally, high-priority studies are presented, and their demographic and economic impact is discussed.
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Wang, Wenguang, Xiyuan Chang, Jihuang Yang, and Gaofei Xu. "LiDAR-Based Dense Pedestrian Detection and Tracking." Applied Sciences 12, no. 4 (February 9, 2022): 1799. http://dx.doi.org/10.3390/app12041799.
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LiDAR-based pedestrian detection and tracking (PDT) with high-resolution sensing capability plays an important role in real-world applications such as security monitoring, human behavior analysis, and intelligent transportation. The problem of LiDAR-based PDT suffers from the complex gathering movements and the phenomenon of self- and inter-object occlusions. In this paper, the detection and tracking of dense pedestrians using three-dimensional (3D) real-measured LiDAR point clouds in surveillance applications is studied. To deal with the problem of undersegmentation of dense pedestrian point clouds, the kernel density estimation (KDE) is used for pedestrians center estimation which further leads to a pedestrian segmentation method. Three novel features are defined and used for further PDT performance improvements, which takes advantage of the pedestrians’ posture and body proportion. Finally, a new track management strategy for dense pedestrians is presented to deal with the tracking instability caused by dense pedestrians occlusion. The performance of the proposed method is validated with experiments on the KITTI dataset. The experiment shows that the proposed method can significantly increase F1 score from 0.5122 to 0.7829 compared with the STM-KDE. In addition, compared with AB3DMOT and EagerMOT, the tracking trajectories from the proposed method have the longest average survival time of 36.17 frames.
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Pervaiz, Mahwish, Yazeed Yasin Ghadi, Munkhjargal Gochoo, Ahmad Jalal, Shaharyar Kamal, and Dong-Seong Kim. "A Smart Surveillance System for People Counting and Tracking Using Particle Flow and Modified SOM." Sustainability 13, no. 10 (May 11, 2021): 5367. http://dx.doi.org/10.3390/su13105367.
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Based on the rapid increase in the demand for people counting and tracking systems for surveillance applications, there is a critical need for more accurate, efficient, and reliable systems. The main goal of this study was to develop an accurate, sustainable, and efficient system that is capable of error-free counting and tracking in public places. The major objective of this research is to develop a system that can perform well in different orientations, different densities, and different backgrounds. We propose an accurate and novel approach consisting of preprocessing, object detection, people verification, particle flow, feature extraction, self-organizing map (SOM) based clustering, people counting, and people tracking. Initially, filters are applied to preprocess images and detect objects. Next, random particles are distributed, and features are extracted. Subsequently, particle flows are clustered using a self-organizing map, and people counting and tracking are performed based on motion trajectories. Experimental results on the PETS-2009 dataset reveal an accuracy of 86.9% for people counting and 87.5% for people tracking, while experimental results on the TUD-Pedestrian dataset yield 94.2% accuracy for people counting and 94.5% for people tracking. The proposed system is a useful tool for medium-density crowds and can play a vital role in people counting and tracking applications.
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Wang, Le, Junwei Su, Zhaolin Gu, and Qingxiang Shui. "Effect of Street Canyon Shape and Tree Layout on Pollutant Diffusion under Real Tree Model." Sustainability 12, no. 5 (March 9, 2020): 2105. http://dx.doi.org/10.3390/su12052105.
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Trees have a significant impact on the airflow and pollutant diffusion in the street canyon and are directly related to the comfort and health of residents. In this paper, OpenFOAM is used for simulating the airflow and pollutant diffusion in the street canyon at different height–width ratios and tree layouts. Different from the drag source model in the previous numerical simulation, this study focuses on the characterization of the blocking effect of tree branches on airflow by using more precise and real tree models. It is found that the airflow is blocked by the tree branches in the canopy, resulting in slower airflow and varying velocity direction; the air flows in the pore area between trees more easily, and the vortex centers are different in cases where the street canyon shape and tree layout are different. Low-velocity airflow distributes around and between two tree canopies, especially under the influence of two trees with different spacing. At the height of the pedestrian, the tree branches change the vortex structure of airflow, and thereby high pollutant concentration distribution on both sides of the bottom of the leeward side of the street canyon changes constantly. In the street canyon, the small change in tree spacing has a very limited influence on the pollutant concentration. The street canyon has the lowest average pollutant concentration at the largest y-axis direction spacing between two trees.
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Liu, Mianfang, Dongchu Han, Dongmei Li, and Ming Wang. "Route guidance during evacuations integrated with perimeter control strategy in large-scale mixed traffic flow networks." International Journal of Modern Physics C 29, no. 11 (November 2018): 1850112. http://dx.doi.org/10.1142/s0129183118501127.
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Recent efficient monitoring and traffic management of large-scale mixed traffic networks still remain a challenge for both traffic researchers and practitioners. The difficulty in modeling route guidance evacuation of pedestrian-vehicle mixed flow lies in mixed flow and uneven or heterogeneous network flow. Existing studies have demonstrated that multi-region control can display different layers of traffic state measurement and control, and incorporate heterogeneity effect in the large-scale network dynamics. The optimal perimeter control can manipulate the percentages of flows that transfer between two regions, offering real-time traffic management strategies to improve the network performance. However, the effect of route guidance evacuation integrated with perimeter control strategies in case of heterogeneous traffic networks is still unexplored. The paper advances in this direction by firstly extending route choice behavior aggregation with perimeter control. For an evacuation study, we consider a campus and its surrounding traffic network that can be classified into two types of networks: the first includes emergency areas that involve a large number of evacuees, and the second includes roads that lead to different destinations. The second network consists of some regions with different evacuation directions. Based on the configuration, this paper proposes a route evacuation guidance control strategy that addresses traffic flow first assignment between regions by controlling perimeter flow with the help of Macroscopic fundamental diagram (MFD) representation and to guide evacuates’ route choice at intersections by LOGIT model in regions. In addition, comparison results show that the proposed route guidance strategy has considerable potential to improve performances and equilibrium conditions (i.e. system optimum and user equilibrium) on the overall network.
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Kim, Yeanjae, Jieun Baek, and Yosoon Choi. "Smart Helmet-Based Personnel Proximity Warning System for Improving Underground Mine Safety." Applied Sciences 11, no. 10 (May 11, 2021): 4342. http://dx.doi.org/10.3390/app11104342.
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A smart helmet-based wearable personnel proximity warning system was developed to prevent collisions between equipment and pedestrians in mines. The smart helmet worn by pedestrians receives signals transmitted by Bluetooth beacons attached to heavy equipment, light vehicles, or dangerous zones, and provides visual LED warnings to the pedestrians and operators simultaneously. A performance test of the proposed system was conducted in an underground limestone mine. It was confirmed that as the transmission power of the Bluetooth beacon increased, the Bluetooth low energy (BLE) signal detection distance of the system also increased. The average BLE signal detection distance was at least 10 m, regardless of the facing angle between the smart helmet and Bluetooth beacon. The subjective workload for the smartphone-, smart glasses-, and smart helmet-based proximity warning system (PWS) was evaluated using the National Aeronautics and Space Administration task load index. All six workload parameters were the lowest when using the smart helmet-based PWS. The smart helmet-based PWS can provide visual proximity warning alerts to both the equipment operator and the pedestrian, and it can be expanded to provide worker health monitoring and hazard awareness functions by adding sensors to the Arduino board.
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Venuti, Fiammetta, Marco Domaneschi, Marc Lizana, and Branko Glisic. "Finite Element Models of a Benchmark Footbridge." Applied Sciences 11, no. 19 (September 28, 2021): 9024. http://dx.doi.org/10.3390/app11199024.
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Modern footbridges are often lively structures, characterized by natural frequencies that fall in the range of pedestrian activities, such as walking, running, and jumping. Therefore, serviceability assessment under human-induced excitation is crucial both at the design stage and during the footbridge lifetime. This paper presents and validates two different FE models of an existing footbridge with very complex geometry: the Streicker Footbridge at the Princeton University Campus. It represents a benchmark in the field as a testbed for vibration serviceability assessments under pedestrian excitation. The real structure is equipped with strain and temperature sensors that are currently used to collect measurements in both static and dynamic modes for research and educational purposes in Structural Health Monitoring (SHM). Based on detailed drawings of the Streicker Footbridge, a three-dimensional beam-based model was developed to represent the complex behavior of the full-scale benchmark bridge. Subsequently, a more refined discretization of the bridge deck adopting shell elements was inserted. The bridge Finite Element models were validated against available SHM data concerning static and dynamic tests. The relevant ANSYS APDL script files along with an example of pedestrian jumping application are available upon request for further research developments on the relationship between pedestrians and the benchmark footbridge.
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Kofanov, Oleksii, Olena Kofanova, Alla Chepel, Anatolii Kriuchkov, Iryna Rabosh, and Nataliia Zhukova. "Modeling of the Car Traffic Air Pollution on the Territories Neighboring Multi-level Interchanges." Environmental Research, Engineering and Management 78, no. 4 (December 12, 2022): 17–38. http://dx.doi.org/10.5755/j01.erem.78.4.31583.
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Ensuring the proper quality of the environment is a foreground and important task for achieving sustainable development goals in contemporary urban societies. Therefore, the aim of the research is to study the air pollution on the territories neighboring multi-level interchanges and assess the additional load on the lower levels based on the analysis of physico-chemical properties of harmful substances, which are exhausts components. The scientific novelty of the paper and the authors’ contribution is in determining the level of ecological danger of territories neighboring multi-level interchanges as a result of the summation effect from emissions of vehicle flows, physical and chemical properties of pollutants, and processes of their sedimentation. To predict the pollution level of the roadside air, the model of torch approximation based on the K-theory and the equations of turbulent diffusion was used. Both mathematical modeling and visualization were performed by the Mathcad software package. Particular attention was paid to the analysis of the environmental situation neighboring multi-level interchanges and urban objects in terms of their pollution with nitrogen oxides and particulate matter PM10. Research has shown that due to the sedimentation processes a significant increase of the technogenic load on the interchange first level road will be observed. It was found that, compared to the upper road, the technogenic load in the center of the lower road increases on average by 1.4–1.6 times in terms of nitrogen oxides and by 1.5–1.6 times in terms of PM10. The territory with the harmful substances maximum permissible concentration excess is significantly increased. So, taking into account that multi-level interchanges are much more environmentally acceptable than surface sections of roads with traffic lights and pedestrian crossings, they still pose danger to the health of people who stay for a long time at the transport stops, move on the sidewalks and underground crossings, as well as for vehicle drivers and passengers. Thus, it is recommended not to place on the territories neighboring multi-level interchanges public transport stops, cafes, shops, and other city infrastructure objects, as well as to remove the existing ones.
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Nourian, Pirouz, Samaneh Rezvani, Kotryna Valeckaite, and Sevil Sariyildiz. "Modelling walking and cycling accessibility and mobility." Smart and Sustainable Built Environment 7, no. 1 (April 3, 2018): 101–16. http://dx.doi.org/10.1108/sasbe-10-2017-0058.
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Purpose The most sustainable forms of urban mobility are walking and cycling. These modes of transportation are the most environmental friendly, the most economically viable and the most socially inclusive and engaging modes of urban transportation. To measure and compare the effectiveness of alternative pedestrianization or cycling infrastructure plans, the authors need to measure the potential flows of pedestrians and cyclists. The paper aims to discuss this issue. Design/methodology/approach The authors have developed a computational methodology to predict walking and cycling flows and local centrality of streets, given a road centerline network and occupancy or population density data attributed to building plots. Findings The authors show the functionality of this model in a hypothetical grid network and a simulated setting in a real town. In addition, the authors show how this model can be validated using crowd-sensed data on human mobility trails. This methodology can be used in assessing sustainable urban mobility plans. Originality/value The main contribution of this paper is the generalization and adaptation of two network centrality models and a trip-distribution model for studying walking and cycling mobility.
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Sohail, Rafiya, Yousaf Saeed, Abid Ali, Reem Alkanhel, Harun Jamil, Ammar Muthanna, and Habib Akbar. "A Machine Learning-Based Intelligent Vehicular System (IVS) for Driver’s Diabetes Monitoring in Vehicular Ad-Hoc Networks (VANETs)." Applied Sciences 13, no. 5 (March 6, 2023): 3326. http://dx.doi.org/10.3390/app13053326.
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Diabetes is a chronic disease that is escalating day by day and requires 24/7 continuous management. It may cause many complications, precisely when a patient moves, which may risk their and other drivers’ and pedestrians’ lives. Recent research shows diabetic drivers are the main cause of major road accidents. Several wireless non-invasive health monitoring sensors, such as wearable continuous glucose monitoring (CGM) sensors, in combination with machine learning approaches at cloud servers, can be beneficial for monitoring drivers’ diabetic conditions on travel to reduce the accident rate. Furthermore, the emergency condition of the driver needs to be shared for the safety of life. With the emergence of the vehicular ad-hoc network (VANET), vehicles can exchange useful information with nearby vehicles and roadside units that can be further communicated with health monitoring sources via GPS and Internet connectivity. This work proposes a novel approach to the health care of drivers’ diabetes monitoring using wearable sensors, machine learning, and VANET technology. Several machine learning (ML) algorithms assessed the proposed prediction model using the cross-validation method. Performance metrics precision, recall, accuracy, F1-score, sensitivity, specificity, MCC, and AROC are used to validate our method. The result shows random forest (RF) outperforms and achieves the highest accuracy compared to other algorithms and previous approaches ranging from 90.3% to 99.5%.
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Skoczyński, Przemysław. "Analysis of Solutions Improving Safety of Cyclists in the Road Traffic." Applied Sciences 11, no. 9 (April 22, 2021): 3771. http://dx.doi.org/10.3390/app11093771.
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Cycling safety management is particularly important due to the increasing use of this mode of transport and increasing car traffic flows. Cyclists—travelling on roadways or sharing the space with pedestrians—are exposed to considerable risks for their safety, as well as for the safety of other road users. Comprehensive and effective management of bicycle traffic safety is therefore essential for the protection of this group of road users. The article presents procedures, the implementation of which is aimed at increasing the safety of cyclists and effective implementation of measures to achieve that. The analyses aimed at the selection of appropriate measures and solutions to improve safety of cyclists in the road traffic have been presented. The procedures include: Selection of devices and measures, risk assessment and estimation of the potential to reduce the risk of collisions/accidents involving cyclists, selection of investment measures and devices reducing the risk of cyclists and other road users, implementation methods and monitoring of selected measures reducing the risk of collisions/accidents involving cyclists. The proposals contained in the article fill in the gap existing in this area of knowledge. The analyses conducted and the presented results show that the construction of infrastructure for cyclists is not the only way to improve the safety of this group of road users. Due to the high costs and deficit of the road surface in the road-street cross-section—other forms of measures in this area should also be analyzed.
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Kim, Hyunsoo. "Feasibility of DRNN for Identifying Built Environment Barriers to Walkability Using Wearable Sensor Data from Pedestrians’ Gait." Applied Sciences 12, no. 9 (April 26, 2022): 4384. http://dx.doi.org/10.3390/app12094384.
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Identifying built environment barriers to walkability is the first step toward monitoring and improving our walking environment. Although conventional approaches (i.e., surveys by experts or pedestrians, walking interviews, etc.) to identify built environment barriers have contributed to improving the walking environment, these approaches may require time and effort. To address the limitations of conventional approaches, wearable sensing technologies and data analysis techniques have recently been adopted in the investigation of the built environment. Among various wearable sensors, an inertial measurement unit (IMU) can continuously capture gait-related data, which can be used to identify built environment barriers to walkability. To propose a more efficient method, the author adopts a cascaded bidirectional and unidirectional long short-term memory (LSTM)-based deep recurrent neural network (DRNN) model for classifying human gait activities (normal and abnormal walking) according to walking environmental conditions (i.e., normal and abnormal conditions). This study uses 101,607 gait data collected from the author’s previous study for training and testing a DRNN model. In addition, 31,142 gait data (20 participants) have been newly collected to validate whether the DRNN model is feasible for newly added gait data. The gait activity classification results show that the proposed method can classify normal gaits and abnormal gaits with an accuracy of about 95%. The results also indicate that the proposed method can be used to monitor environmental barriers and improve the walking environment.
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Kim, Hyunsoo. "Feasibility of DRNN for Identifying Built Environment Barriers to Walkability Using Wearable Sensor Data from Pedestrians’ Gait." Applied Sciences 12, no. 9 (April 26, 2022): 4384. http://dx.doi.org/10.3390/app12094384.
Full textAbstract:
Identifying built environment barriers to walkability is the first step toward monitoring and improving our walking environment. Although conventional approaches (i.e., surveys by experts or pedestrians, walking interviews, etc.) to identify built environment barriers have contributed to improving the walking environment, these approaches may require time and effort. To address the limitations of conventional approaches, wearable sensing technologies and data analysis techniques have recently been adopted in the investigation of the built environment. Among various wearable sensors, an inertial measurement unit (IMU) can continuously capture gait-related data, which can be used to identify built environment barriers to walkability. To propose a more efficient method, the author adopts a cascaded bidirectional and unidirectional long short-term memory (LSTM)-based deep recurrent neural network (DRNN) model for classifying human gait activities (normal and abnormal walking) according to walking environmental conditions (i.e., normal and abnormal conditions). This study uses 101,607 gait data collected from the author’s previous study for training and testing a DRNN model. In addition, 31,142 gait data (20 participants) have been newly collected to validate whether the DRNN model is feasible for newly added gait data. The gait activity classification results show that the proposed method can classify normal gaits and abnormal gaits with an accuracy of about 95%. The results also indicate that the proposed method can be used to monitor environmental barriers and improve the walking environment.
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Boyarshinov, M. G., A. S. Vavilin, and A. G. Shumkov. "FOURIER ANALYSIS OF THE TRAFFIC FLOW INTENSITY." Intelligence. Innovations. Investment, no. 4 (2021): 46–59. http://dx.doi.org/10.25198/2077-7175-2021-4-46.
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The relevance of this work is determined by the need to find modern ways to process the information about traffic flows for regulating and controlling the movement of transport and pedestrians, to reduce congestion, road accidents, etc. The object of study is a part of road with heavy two-way traffic, equipped with a software and hardware complex that allows to measure the characteristics of the transport flow. The subject of the study is the daily intensity of the cars flow during the week, from Monday to Sunday. The purpose of this study is to analyze the amplitudes, frequencies, and periods of harmonic functions obtained by decomposing the time series of road traffic intensities to identify the main patterns of traffic flow formation. As a theoretical and methodological approach, the decomposition of the function of the traffic flow intensity in the Fourier series with respect to harmonic functions is used. The approach developed by the authors using the fast Fourier transform procedure made it possible to determine the amplitude-frequency characteristics of the time series under consideration, which is a scientific novelty of the analysis. It is proposed to use the «period-amplitude» characteristics as physically more meaningful instead of the «frequency-amplitude» dependencies traditionally used for the analysis. The processing of data obtained from software and hardware complexes allowed us to determine dependences of the car flow intensity on the road of the Perm city at different averaging intervals, to describe the features of the motor transport movement on the road under consideration. As a result of the study, the amplitude-frequency characteristics of time series are obtained. It is shown that the individual harmonics of the Fourier series expansion of the traffic flow intensity, which exhibits the properties of a random function, duplicate the periodicity of the global, local, and intermediate extremes of the original function and have similar periods. The practical significance consists in the use of the decomposition of the function of the traffic flow intensity in the Fourier series of harmonic functions for predicting traffic flows, controlling the operation of traffic lights, monitoring the operation of equipment, as well as in the reconstruction, design and construction of roads and road objects. The study will continue in the direction of obtaining, processing and determining the «period-amplitude» characteristics for time series of traffic flow intensity for other road networks.
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Cho, Kuk, and Dooyong Cho. "Autonomous Driving Assistance with Dynamic Objects Using Traffic Surveillance Cameras." Applied Sciences 12, no. 12 (June 20, 2022): 6247. http://dx.doi.org/10.3390/app12126247.
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This paper describes a method that precisely estimates the position of images of traffic surveillance camera objects. We suggest a projection method with multiple traffic surveillance cameras through a local coordinate system into a global coordinate system. The transformation of coordinates uses detected objects, parameters of the camera and the geometric information of high- definition (HD) maps. Traffic surveillance cameras that pursue traffic safety and convenience use various sensors to generate traffic information. We suggest a transformation method with images of the camera and HD maps and an evaluation method. Therefore, it is necessary to improve the sensor-related technology to increase the efficiency and reliability of the traffic information. Recently, the role of the camera in collecting video information has become more important due to advances in artificial intelligence (AI) technology. The objects projected from the traffic surveillance camera domain to the HD domain are helpful to identify imperceptible zones, such as blind spots, on roads for autonomous driving assistance. In this study, we proposed to identify and track dynamic objects (vehicles, pedestrian, etc.) with traffic surveillance cameras, and to analyze and provide information about them in various environments. To this end, we conducted the identification of dynamic objects using the Yolov4 and DeepSort algorithms, established real-time multi-user support servers based on Kafka, defined transformation matrices between images and spatial coordinate systems, and implemented map-based dynamic object visualization. In addition, a positional consistency evaluation was performed to confirm its usefulness. Through the proposed scheme, we confirmed that multiple traffic surveillance cameras can serve as important sensors to provide relevant information by analyzing road conditions in real-time in terms of road infrastructure beyond a simple monitoring role.
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Zambrano Nájera, Jeannette, and Diana Marcela Rey. "Assessment of Storm Floods in a Small Tropical Andean Basins." Frontiers in Climate 4 (August 8, 2022). http://dx.doi.org/10.3389/fclim.2022.760894.
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The increase in urbanization processes has considerably changed urban flows, as basin impermeabilization generates increased surface runoff volumes. This induces storm floods that simultaneously generate mobility problems, infrastructure damage, and in extreme conditions, pedestrian deaths. This problem has been widely studied. For small basins with steep watersheds, however, there is little literature to be found. In these basins, the problem is even more complex, as flows are faster and more energized, given the surface slopes, which generates further problems, including drainage infrastructure damage, landslides, and fallen trees. Many solutions have been proposed, which include Sustainable Urban Drainage Systems (SUDS), which work together with the drainage network. These were studied in the present investigation, via the modeling of a basin with those characteristics typical of mountains, using SWMM's Low Impact Development LID model. It was found that rain barrels and green roofs were viable solutions, independently, although the application of additional techniques, such as cisterns and bioretention cells, is also indispensable. The present study also concludes that, although the techniques studied are implemented on a small scale, these should be both planned and implemented by municipal authorities.
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Hurst, Stance, and Eileen Johnson. "Adair-Steadman (41FS2) — Survey at a Folsom Site in the Brazos River drainage on the Southern Plains." Index of Texas Archaeology Open Access Grey Literature from the Lone Star State, 2019. http://dx.doi.org/10.21112/ita.2019.1.26.
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Excavations led by Texas State Archeologist Curtis Tunnell from 1969-1974 identified Adair-Steadman (41FS2) as a Folsom period tool production workshop primarily aimed at producing Folsom points. The Lubbock Lake Landmark’s regional research program continued the exploration of Adair-Steadman through five annual 1-day surveys from 2015-2019. An Unmanned Aerial Vehicle (UAV) drone was flown to document the site’s surface using photogrammetry. Dating sediment samples collected in 2013, using the Optical Stimulated Luminescence (OSL) technique, was completed in 2016. Blossoming mesquite trees were treated with alcohol-based herbicides to maintain the natural landscape at Adair-Steadman in the absence of fire. Results from pedestrian survey indicates that the exposure of new lithic objects from erosion has slowed down over the last five years. A new lithic cluster area was identified that could be an indicator of an activity area with a subsurface component. A 3D dense cloud, a high resolution digital elevation model, and a high resolution orthomosiac map was created from the overlapping images captured by the UAV. This information was useful in documenting the current surface at Adair-Steadman and monitoring changes in the future. Results from OSL dating indicated that the clay band (a lamellae layer) identified and sampled in 2013 may provide a reliable stratigraphic marker. The sediments above the lamellae layer were late Holocene in age. In contrast, the sediments below the lamellae layer dated to the middle Holocene.