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Добірка наукової літератури з теми "Traffic accidents Mathematical models"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 31 січня 2023

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Статті в журналах з теми "Traffic accidents Mathematical models"

MOUSSA, NAJEM. "SIMULATION STUDY OF TRAFFIC ACCIDENTS IN BIDIRECTIONAL TRAFFIC MODELS." International Journal of Modern Physics C 21, no. 12 (December 2010): 1501–15. http://dx.doi.org/10.1142/s0129183110016007.

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Conditions for the occurrence of bidirectional collisions are developed based on the Simon–Gutowitz bidirectional traffic model. Three types of dangerous situations can occur in this model. We analyze those corresponding to head-on collision; rear-end collision and lane-changing collision. Using Monte Carlo simulations, we compute the probability of the occurrence of these collisions for different values of the oncoming cars' density. It is found that the risk of collisions is important when the density of cars in one lane is small and that of the other lane is high enough. The influence of different proportions of heavy vehicles is also studied. We found that heavy vehicles cause an important reduction of traffic flow on the home lane and provoke an increase of the risk of car accidents.

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Sysoev, Anton, Vladimir Klyavin, Alexandra Dvurechenskaya, Albert Mamedov, and Vladislav Shushunov. "Applying Machine Learning Methods and Models to Explore the Structure of Traffic Accident Data." Computation 10, no. 4 (March 31, 2022): 57. http://dx.doi.org/10.3390/computation10040057.

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The problem of reducing the increasing number of road traffic accidents has become more relevant in recent years. According to the United Nations plan this number has to be halved by 2030. A very effective way to handle it is to apply the machine learning paradigm to retrospective road traffic accident datasets. This case study applies machine learning techniques to form typical “portraits” of drivers violating road traffic rules by clustering available data into seven homogeneous groups. The obtained results can be used in forming effective marketing campaigns for different target groups. Another relevant problem under consideration is to use available retrospective statistics on mechanical road traffic accidents without victims to estimate the probable type of road traffic accident for the driver taking into account her/his personal features (such as social characteristics, typical road traffic rule violations, driving experience, and age group). For this purpose several machine learning models were applied and the results were discussed.

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Planić, Jovana. "Defining risks on road sections during the transport of dangerous goods in the Serbian army using the linear mathematical programming model." Vojnotehnicki glasnik 70, no. 4 (2022): 939–71. http://dx.doi.org/10.5937/vojtehg70-36071.

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Introduction/purpose: The paper presents a model for the selection of a route for the transport of dangerous goods using DEA (Data Envelopment Analysis) models and fuzzy logic systems. The presented model is used to define the risk on road sections during the transport of dangerous goods as well as to select the optimal route for the realization of the transport task. Methods: The model consists of two phases. The first phase includes the application of DEA models in which formed input and output models are connected in the output DEA final form which shows routes with a satisfactory level of traffic safety and at the same time eliminates routes with low traffic safety. The second phase involves the application of fuzzy logic systems, and as a way out of the fuzzy system, preference is given to one route. Route evaluation is based on six criteria, namely: route length, number of access points, AADT (annual average daily traffic), the number of traffic accidents with fatalities, the number of traffic accidents with the injured and the number of traffic accidents with material damage. When the values of the input criteria are entered, a calculation and evaluation is performed, and, as an exit from the fuzzy system, preference is given to one of the entered routes (the route with the lowest level of risk). The criteria used were defined on the basis of expert assessments. Results: A user program that represents decision support in traffic service. Conclusion: The user platform was created for the Matlab R2015a software package with the ability to be adapted to specific problems.

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Droj, Gabriela, Laurențiu Droj, and Ana-Cornelia Badea. "GIS-Based Survey over the Public Transport Strategy: An Instrument for Economic and Sustainable Urban Traffic Planning." ISPRS International Journal of Geo-Information 11, no. 1 (December 30, 2021): 16. http://dx.doi.org/10.3390/ijgi11010016.

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Traffic has a direct impact on local and regional economies, on pollution levels and is also a major source of discomfort and frustration for the public who have to deal with congestion, accidents or detours due to road works or accidents. Congestion in urban areas is a common phenomenon nowadays, as the main arteries of cities become congested during peak hours or when there are additional constraints such as traffic accidents and road works that slow down traffic on road sections. When traffic increases, it is observed that some roads are predisposed to congestion, while others are not. It is evident that both congestion and urban traffic itself are influenced by several factors represented by complex geospatial data and the spatial relationships between them. In this paper were integrated mathematical models, real time traffic data with network analysis and simulation procedures in order to analyze the public transportation in Oradea and the impact on urban traffic. A mathematical model was also adapted to simulate the travel choices of the population of the city and of the surrounding villages. Based on the network analysis, traffic analysis and on the traveling simulation, the elements generating traffic congestion in the inner city can be easily determined. The results of the case study are emphasizing that diminishing the traffic and its effects can be obtained by improving either the public transport density or its accessibility.

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Budzyński, Marcin, Kazimierz Jamroz, Łukasz Jeliński, and Anna Gobis. "The Effects of Roadside Hazards on Road Accident Severity." Journal of KONBiN 49, no. 2 (June 1, 2019): 319–39. http://dx.doi.org/10.2478/jok-2019-0038.

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Abstract The risk of becoming involved in an accident emerges when elements of the transport system do not operate properly (man – vehicle – road – roadside). The road, its traffic layout and safety equipment have a critical impact on road user safety. This gives infrastructural work a priority in road safety strategies and programmes. Run-off-road accidents continue to be one of the biggest problems of road safety with consequences including vehicle roll-over or hitting a roadside object. This type of incident represents more than 20% of rural accidents and about 18% of all road deaths in Poland. Mathematical models must be developed to determine how selected roadside factors affect road safety and provide a basis for new roadside design rules and guidelines.

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TSENG, JIE-JUN, MING-JER LEE, and SAI-PING LI. "HEAVY-TAILED DISTRIBUTIONS IN FATAL TRAFFIC ACCIDENTS: ROLE OF HUMAN ACTIVITIES." International Journal of Modern Physics C 20, no. 08 (August 2009): 1281–90. http://dx.doi.org/10.1142/s0129183109014345.

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Human activities can play a crucial role in the statistical properties of observables in many complex systems such as social, technological, and economic systems. We demonstrate this by looking into the heavy-tailed distributions of observables in fatal plane and car accidents. Their origin is examined and can be understood as stochastic processes that are related to human activities. Simple mathematical models are proposed to illustrate such processes and compared with empirical results obtained from existing databanks.

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Najafi Moghaddam Gilani, Vahid, Seyed Mohsen Hosseinian, Meisam Ghasedi, and Mohammad Nikookar. "Data-Driven Urban Traffic Accident Analysis and Prediction Using Logit and Machine Learning-Based Pattern Recognition Models." Mathematical Problems in Engineering 2021 (May 26, 2021): 1–11. http://dx.doi.org/10.1155/2021/9974219.

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Modeling the severity of accidents based on the most effective variables accounts for developing a high-precision model presenting the possibility of occurrence of each category of future accidents, and it could be utilized to prioritize the corrective measures for authorities. The purpose of this study is to identify the variables affecting the severity of the injury, fatal, and property damage only (PDO) accidents in Rasht city by collecting information on urban accidents from March 2019 to March 2020. In this regard, the multiple logistic regression and the pattern recognition type of artificial neural network (ANN) as a machine learning solution are used to recognize the most influential variables on the severity of accidents and the superior approach for accident prediction. Results show that the multiple logistic regression in the forward stepwise method has R2 of 0.854 and an accuracy prediction power of 89.17%. It turns out that the accidents occurred between 18 and 24 and KIA Pride vehicle has the highest effect on increasing the severity of accidents, respectively. The most important result of the logit model accentuates the role of environmental variables, including poor lighting conditions alongside unfavorable weather and the dominant role of unsafe and poor quality of vehicles on increasing the severity of accidents. In addition, the machine learning model performs significantly better and has higher prediction accuracy (98.9%) than the logit model. In addition, the ANN model’s greater power to predict and estimate future accidents is confirmed through performance and sensitivity analysis.

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Borucka, Anna, Edward Kozłowski, Piotr Oleszczuk, and Andrzej Świderski. "Predictive analysis of the impact of the time of day on road accidents in Poland." Open Engineering 11, no. 1 (December 12, 2020): 142–50. http://dx.doi.org/10.1515/eng-2021-0017.

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AbstractThe steady increase in the number of road users and their growing mobility mean that the issue of road safety is still a topical one. Analyses of factors influencing the number of road traffic accidents contribute to the improvement of road safety. Because changes in traffic volume follow a daily rhythm, hour of the day is an important factor affecting the number of crashes. The present article identifies selected mathematical models which can be used to describe the number of road traffic accidents as a function of the time of their occurrence during the day. The study of the seasonality of the number of accidents in particular hours was assessed. The distributions of the number of accidents in each hour were compared using the Kruskal-Wallis and Kolmogorov-Smirnov tests. Multidimensional scaling was used to present the found similarities and differences. Similar hours were grouped into clusters, which were used in further analysis to construct the ARMAXmodel and the Holt-Winters model. Finally, the predictive capabilities of each model were assessed.

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Göttlich, Simone, and Thomas Schillinger. "Microscopic and macroscopic traffic flow models including random accidents." Communications in Mathematical Sciences 19, no. 6 (2021): 1579–609. http://dx.doi.org/10.4310/cms.2021.v19.n6.a6.

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Zong, Fang, Huiyong Zhang, Hongguo Xu, Xiumei Zhu, and Lu Wang. "Predicting Severity and Duration of Road Traffic Accident." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/547904.

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This paper presents a model system to predict severity and duration of traffic accidents by employing Ordered Probit model and Hazard model, respectively. The models are estimated using traffic accident data collected in Jilin province, China, in 2010. With the developed models, three severity indicators, namely, number of fatalities, number of injuries, and property damage, as well as accident duration, are predicted, and the important influences of related variables are identified. The results indicate that the goodness-of-fit of Ordered Probit model is higher than that of SVC model in severity modeling. In addition, accident severity is proven to be an important determinant of duration; that is, more fatalities and injuries in the accident lead to longer duration. Study results can be applied to predictions of accident severity and duration, which are two essential steps in accident management process. By recognizing those key influences, this study also provides suggestive results for government to take effective measures to reduce accident impacts and improve traffic safety.

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Дисертації з теми "Traffic accidents Mathematical models"

Lierkamp, Darren. "Simulating the effects of following distance on a high-flow freeway." Full text available online (restricted access), 2003. http://images.lib.monash.edu.au/ts/theses/Lierkamp.pdf.

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"CP830 Research Project and Thesis 2". Includes bibliographical references (p. 80-93) Electronic reproduction.[S.l. :s.n.],2003.Electronic data.Mode of access: World Wide Web.System requirements: Adobe Acrobat reader software for PDF files.Access restricted to institutions with a subscription.

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Sze, Nang-ngai, and 施能藝. "Quantitative analyses for the evaluation of traffic safety and operations." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39707398.

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Zhou, Dingshan Sam. "An integrated traffic incident detection model /." Full text (PDF) from UMI/Dissertation Abstracts International, 2000. http://wwwlib.umi.com/cr/utexas/fullcit?p9992952.

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Brizendine, Laora Dauberman. "Low probability-high consequence considerations in a multiobjective approach to risk management." Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-07112009-040353/.

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Yau, C. P. Eric. "Using GIS and statistical models for traffic accidents analysis : a case study of the Tuen Mun town centre." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37639110.

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Milton, John Calvin. "Generalized extreme value and mixed logit models : empirical applications to vehicle accident severities /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/10152.

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Agdeppa, Rhoda Padua. "Studies on Mathematical Models of Traffic Equilibria." 京都大学 (Kyoto University), 2009. http://hdl.handle.net/2433/123831.

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Shankar, Venkataraman N. "Limited dependent variable and structural equations models : empirical applications to traffic operations and safety /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/10179.

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Yue, Yang, and 樂陽. "Spatial-temporal dependency of traffic flow and its implications for short-term traffic forecasting." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B35507366.

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Mason, Anthony David. "Mathematical models of road traffic, and related problems." Thesis, University of Cambridge, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.624189.

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Книги з теми "Traffic accidents Mathematical models"

Fricke, Lynn B. Traffic accident reconstruction. Evanston, Ill: Northwestern University, Traffic Institute, 1990.

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Sylvain, Lassarre, ed. Structural road accident models: The international DRAG family. New York: Pergamon, 2000.

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Rivers, R. W. Speed analysis for traffic accident investigation. 2nd ed. Jacksonville, Fla: Institute of Police Technology and Management, University of North Florida, 1997.

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Cooper, Gary W. Work, energy, and speed from damage in traffic accidents: Topic 870 of The traffic-accident investigation manual. Evanston, Ill. (P.O. Box 1409, Evanston 60204): Northwestern University Traffic Institute, 1989.

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Lofgren, M. J. Handbook for the accident reconstructionist. 3rd ed. Jacksonville, Fla. (4567 St. Johns Bluff Road, South, Jacksonville 32216): Institute of Police Technology and Management, University of North Florida, 1988.

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Bonnett, George M. Anatomy of the collision: Energy, momentum, restitution and the reconstructionist. 2nd ed. Jacksonville, Fla: Institute of Police Technology and Management, University of North Florida, 2006.

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Baker, J. Stannard. Speed estimates for vehicles that fall, flip, or vault: Topic 866 of the Traffic-accident investigation manual. Evanston, Ill. (P.O. Box 1409, Evanston 60204): Northwestern University Traffic Institute, 1989.

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Cooper, Gary W. Derivations of equations for traffic accident reconstruction: Topic 890 of the Traffic-accident investigation manual. Evanston, Ill: Northwestern University Traffic Institute, 1987.

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Equations and formulas for the traffic accident investigator and reconstructionist. 2nd ed. Tucson, AZ: Lawyers & Judges Pub. Co., 2006.

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Engineers, Society of Automotive, ed. Motor vehicle accident reconstruction: Review and update. Warrendale, PA: Society of Automotive Engineers, 1989.

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Частини книг з теми "Traffic accidents Mathematical models"

Elefteriadou, Lily. "Mathematical and Empirical Models." In An Introduction to Traffic Flow Theory, 129–35. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8435-6_6.

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Schmand, Daniel. "Recent Developments in Mathematical Traffic Models." In Dynamics in Logistics, 71–87. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88662-2_4.

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AbstractPredictions such as forecasts of congestion effects in transportation networks can be based on complex simulations that include many aspects of actual transportation systems. On the other hand, rigorous mathematical traffic models give rise to theoretical analyses, very general statements, and various traffic optimization opportunities. There has been a huge development in the last years to make mathematical traffic models more realistic. This chapter provides an overview of the mathematical traffic models developed recently and some state-of-the-art results.

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Jing, Teng Mun, and Rohayu Mohd Salleh. "Analyzing of Traffic Accidents in Skudai City Using Stochastic Models." In Springer Proceedings in Physics, 309–23. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8903-1_28.

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Piccoli, Benedetto, and Andrea Tosin. "Vehicular Traffic: A Review of Continuum Mathematical Models." In Encyclopedia of Complexity and Systems Science, 1–37. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-3-642-27737-5_576-3.

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Piccoli, Benedetto, and Andrea Tosin. "Vehicular Traffic: A Review of Continuum Mathematical Models." In Mathematics of Complexity and Dynamical Systems, 1748–70. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1806-1_112.

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Piccoli, Benedetto, and Andrea Tosin. "Vehicular Traffic: A Review of Continuum Mathematical Models." In Encyclopedia of Complexity and Systems Science, 9727–49. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-30440-3_576.

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Kachroo, Pushkin, and Kaan M. A. Özbay. "Traffic Assignment: A Survey of Mathematical Models and Techniques." In Advances in Industrial Control, 25–53. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-69231-9_2.

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Turdibekov, Kamolbek, Mirjalil Yakubov, Absaid Sulliev, and Amangul Sanbetova. "Mathematical Models of Asymmetric Modes in High-Speed Traffic." In XIV International Scientific Conference “INTERAGROMASH 2021”, 1051–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80946-1_95.

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Chiarello, Felisia Angela. "An Overview of Non-local Traffic Flow Models." In Mathematical Descriptions of Traffic Flow: Micro, Macro and Kinetic Models, 79–91. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66560-9_5.

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Kachroo, Pushkin, and Neveen Shlayan. "Dynamic Traffic Assignment: A Survey of Mathematical Models and Techniques." In Complex Networks and Dynamic Systems, 1–25. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6243-9_1.

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Тези доповідей конференцій з теми "Traffic accidents Mathematical models"

Zamzuri, Zamira Hasanah. "Selected models for correlated traffic accident count data." In ADVANCES IN INDUSTRIAL AND APPLIED MATHEMATICS: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences (SKSM23). Author(s), 2016. http://dx.doi.org/10.1063/1.4954629.

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Bodnar, Larysa, and Alexander Kanin. "A model for assessing the priority of the bridges within their repair strategy." In 6th International Conference on Road and Rail Infrastructure. University of Zagreb Faculty of Civil Engineering, 2021. http://dx.doi.org/10.5592/co/cetra.2020.1212.

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The efficiency of the road network operation significantly depends on ensuring the durability and operational reliability of road bridges. To prevent accidents on bridges, ensure their reliability and durability, it is necessary to perform a set of measures, including the following: inspection of bridges, determination of their operational condition, calculation of residual life, development of recommendations for their further operation, and performance of operational measures. Considering the significant quantity of bridges in Ukraine, most of these activities have to be performed using modern information technology. Therefore, an Analytical Expert Bridge Management System Software Complex (AESUM) for bridges of Ukraine on public roads was developed and implemented. An important component of this software complex is the procedures for development of a strategy of repairs in the system of road bridges operation. One of the components of the mathematical model, which is the basis for justifying the repairing strategy, is a system of priorities for bridges repairs, taking into account their importance. This scientific paper considers the current system of priority, which takes into account the technical and operational condition of bridges and the category of roads on which they are located. A new model for evaluation of the priorities of repairs is proposed which takes into account such factors as importance of the bridge, technical and operational condition of the bridge, traffic capacity of the bridge and the cost-effectiveness of repairs. The factors mentioned above consist of a set of sub-factors. For example, the bridge importance factor consists of such sub-factors as the average daily traffic volume, the bypass influence, the bridge's affiliation with international transport corridors, etc.

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Tamura, Atsutaka, Jun-ichi Hongu, Tomomi Yamamoto, and Takao Koide. "Dynamic Tensile Behavior of Fiber Bundles Freshly Isolated From Nerve Roots." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-70796.

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Viscoelasticity of the nerve root may play a significant role in biomechanical stability of the spine. To date, however, relatively few studies have been conducted to characterize and elucidate this complex mechanical behavior. Thus, a series of tensile stress relaxation tests with a ramp-hold phase was performed using fiber bundles isolated from the nerve roots. In addition, the current study presents the application of a curve fitting technique, i.e., a stress relaxation response of the fiber bundles was theoretically predicted based on the measured data obtained at moderate to sub-traumatic loading conditions. To do that, a least squares optimization method was employed, and we revealed that this technique is applicable to reasonably predict even an instantaneous “elastic” response as well as subsequent slow stress decay of the neural fiber bundles. The resultant fitted coefficients also suggested that the viscoelastic tensile behavior of the nerve root is mainly dominated by the long-term time constants (100–1000 s) rather than the short-term time constants (0.1–1 s). Since a mathematical human body model is a powerful tool to investigate injury mechanisms involving high-contact sports and traffic accidents, our results will be useful in predicting potential spinal injuries and alleviating mechanical damage of the nerve roots, while preventing neck/low back pain due to such traumatic events.

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Saharan, Sabariah, and Roberto Baragona. "A cluster analysis on road traffic accidents using genetic algorithms." In THE 4TH INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES: Mathematical Sciences: Championing the Way in a Problem Based and Data Driven Society. Author(s), 2017. http://dx.doi.org/10.1063/1.4980927.

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Bedane, Tarikwa Tesfa, Beakal Gizachew Assefa, and Sudhir Kumar Mohapatra. "Preventing Traffic Accidents Through Machine Learning Predictive Models." In 2021 International Conference on Information and Communication Technology for Development for Africa (ICT4DA). IEEE, 2021. http://dx.doi.org/10.1109/ict4da53266.2021.9672249.

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Klyavin, Vladimir, Anton Sysoev, Alexandra Drurechenskaya, and Albert Mamedov. "Approaches to Traffic Accidents Clustering to Form Effective Marketing Campaign." In 2021 3rd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA). IEEE, 2021. http://dx.doi.org/10.1109/summa53307.2021.9632028.

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AL-Hasani, Ghanim, Abdel-Hamid Soliman, and Md Asaduzzaman. "Comparison of spatial regression models with Road Traffic Accidents Data r." In International Conference on Statistics: Theory and Applications (ICSTA'19). Avestia Publishing, 2019. http://dx.doi.org/10.11159/icsta19.31.

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Abdullah, Lazim, and Nurnadiah Zamri. "Road traffic accidents models using threshold levels of fuzzy linear regression." In 2012 International Conference on Statistics in Science, Business and Engineering (ICSSBE2012). IEEE, 2012. http://dx.doi.org/10.1109/icssbe.2012.6396563.

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Zhang, Shukui, Keyin Miao, and Jing Li. "Selection of Ship Traffic Accidents Forecasting Models in Downstream Waterway of Yangtze River." In 2018 International Conference on Sports, Arts, Education and Management Engineering (SAEME 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/saeme-18.2018.47.

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Sleurs, Kristof, Dagang Li, Emmanuel Van Lil, and Antoine Van de Capelle. "Bridging the Gap between Mathematical Traffic Models and Operational Parameters." In GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference. IEEE, 2009. http://dx.doi.org/10.1109/glocom.2009.5425557.

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Звіти організацій з теми "Traffic accidents Mathematical models"

Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, November 2021. http://dx.doi.org/10.31979/mti.2021.2102.

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In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records from February 2016 to December 2020, as well as logistic regression, tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train the models. These models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.

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Hovakimyan, Naira, Hunmin Kim, Wenbin Wan, and Chuyuan Tao. Safe Operation of Connected Vehicles in Complex and Unforeseen Environments. Illinois Center for Transportation, August 2022. http://dx.doi.org/10.36501/0197-9191/22-016.

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Autonomous vehicles (AVs) have a great potential to transform the way we live and work, significantly reducing traffic accidents and harmful emissions on the one hand and enhancing travel efficiency and fuel economy on the other. Nevertheless, the safe and efficient control of AVs is still challenging because AVs operate in dynamic environments with unforeseen challenges. This project aimed to advance the state-of-the-art by designing a proactive/reactive adaptation and learning architecture for connected vehicles, unifying techniques in spatiotemporal data fusion, machine learning, and robust adaptive control. By leveraging data shared over a cloud network available to all entities, vehicles proactively adapted to new environments on the proactive level, thus coping with large-scale environmental changes. On the reactive level, control-barrier-function-based robust adaptive control with machine learning improved the performance around nominal models, providing performance and control certificates. The proposed research shaped a robust foundation for autonomous driving on cloud-connected highways of the future.

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