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Relevant bibliographies by topics / Accident Prediction Models

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Academic literature on the topic 'Accident Prediction Models'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 June 2025

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Journal articles on the topic "Accident Prediction Models"

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Abioye, Olumide F., Maxim A. Dulebenets, Junayed Pasha, et al. "Accident and hazard prediction models for highway–rail grade crossings: a state-of-the-practice review for the USA." Railway Engineering Science 28, no. 3 (2020): 251–74. http://dx.doi.org/10.1007/s40534-020-00215-w.

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Abstract Highway–rail grade crossings (HRGCs) are one of the most dangerous segments of the transportation network. Every year numerous accidents are recorded at HRGCs between highway users and trains, between highway users and traffic control devices, and solely between highway users. These accidents cause fatalities, severe injuries, property damage, and release of hazardous materials. Researchers and state Departments of Transportation (DOTs) have addressed safety concerns at HRGCs in the USA by investigating the factors that may cause accidents at HRGCs and developed certain accident and hazard prediction models to forecast the occurrence of accidents and crossing vulnerability. The accident and hazard prediction models are used to identify the most hazardous HRGCs that require safety improvements. This study provides an extensive review of the state-of-the-practice to identify the existing accident and hazard prediction formulae that have been used over the years by different state DOTs. Furthermore, this study analyzes the common factors that have been considered in the existing accident and hazard prediction formulae. The reported performance and implementation challenges of the identified accident and hazard prediction formulae are discussed in this study as well. Based on the review results, the US DOT Accident Prediction Formula was found to be the most commonly used formula due to its accuracy in predicting the number of accidents at HRGCs. However, certain states still prefer customized models due to some practical considerations. Data availability and data accuracy were identified as some of the key model implementation challenges in many states across the country.

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Alicioglu, Gulsum, Bo Sun, and Shen Shyang Ho. "An Injury-Severity-Prediction-Driven Accident Prevention System." Sustainability 14, no. 11 (2022): 6569. http://dx.doi.org/10.3390/su14116569.

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Traffic accidents are inevitable events that occur unexpectedly and unintentionally. Therefore, analyzing traffic data is essential to prevent fatal accidents. Traffic data analysis provided insights into significant factors and driver behavioral patterns causing accidents. Combining these patterns and the prediction model into an accident prevention system can assist in reducing and preventing traffic accidents. This study applied various machine learning models, including neural network, ordinal regression, decision tree, support vector machines, and logistic regression to have a robust prediction model in injury severity. The trained model provides timely and accurate predictions on accident occurrence and injury severity using real-world traffic accident datasets. We proposed an informative negative data generator using feature weights derived from multinomial logit regression to balance the non-fatal accident data. Our aim is to resolve the bias that happens in the favor of the majority class as well as performance improvement. We evaluated the overall and class-level performance of the machine learning models based on accuracy and mean squared error scores. Three hidden layered neural networks outperformed the other models with 0.254 ± 0.038 and 0.173 ± 0.016 MSE scores for two different datasets. A neural network, which provides more accurate and reliable results, should be integrated into the accident prevention system.

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Hildebrand, Eric, Karen Robichaud, and Hong Ye. "Evaluation of accident prediction for rural highways." Canadian Journal of Civil Engineering 35, no. 6 (2008): 647–51. http://dx.doi.org/10.1139/l08-008.

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This paper evaluates the accuracy of three commonly used models that predict accidents on two-lane, rural, arterial highways. The retrospective evaluation compared model outputs with empirical collision results for a sample of highway sections in the Province of New Brunswick. The analysis determined historical accident rates, identified key predictive variables, and compared the observed results with estimates from each safety model. All three models were found to significantly overestimate accident frequencies on the highway sections under study. The model generally employed in New Brunswick, MicroBENCOST, was found to yield the highest errors in estimated collisions. These findings suggest that the benefits from accident reduction are generally overestimated on highway improvement projects analyzed with these accident prediction models.

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Sawalha, Ziad, and Tarek Sayed. "Transferability of accident prediction models." Safety Science 44, no. 3 (2006): 209–19. http://dx.doi.org/10.1016/j.ssci.2005.09.001.

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Zhang, Cheng, Xiong Zou, and Chuan Lin. "Fusing XGBoost and SHAP Models for Maritime Accident Prediction and Causality Interpretability Analysis." Journal of Marine Science and Engineering 10, no. 8 (2022): 1154. http://dx.doi.org/10.3390/jmse10081154.

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In order to prevent safety risks, control marine accidents and improve the overall safety of marine navigation, this study established a marine accident prediction model. The influences of management characteristics, environmental characteristics, personnel characteristics, ship characteristics, pilotage characteristics, wharf characteristics and other factors on the safety risk of maritime navigation are discussed. Based on the official data of Zhejiang Maritime Bureau, the extreme gradient boosting (XGBoost) algorithm was used to construct a maritime accident classification prediction model, and the explainable machine learning framework SHAP was used to analyze the causal factors of accident risk and the contribution of each feature to the occurrence of maritime accidents. The results show that the XGBoost algorithm can accurately predict the accident types of maritime accidents with an accuracy, precision and recall rate of 97.14%. The crew factor is an important factor affecting the safety risk of maritime navigation, whereas maintaining the equipment and facilities in good condition and improving the management level of shipping companies have positive effects on improving maritime safety. By explaining the correlation between maritime accident characteristics and maritime accidents, this study can provide scientific guidance for maritime management departments and ship companies regarding the control or management of maritime accident prevention.

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Persaud, Bhagwant N. "Accident prediction models for rural roads." Canadian Journal of Civil Engineering 21, no. 4 (1994): 547–54. http://dx.doi.org/10.1139/l94-056.

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Following on earlier work, the development of additional models for estimating the accident potential of rural road sections is described. In the modelling procedure presented, Ontario data are used to develop regression model estimates of a section's accident potential based on its traffic and geometric characteristics, and an empirical Bayesian procedure is used for refining these estimates using the section's accident count. These refined estimates are shown in a validation exercise to be superior to estimates based on accident counts or regression models only. This method of estimating accident potential is recommended for use in the identification of accident blackspots and in the evaluation of safety treatments on rural roads. To facilitate this use, detailed example applications are presented. Key words: safety, empirical Bayesian, accident prediction, rural roads, generalized linear modelling, accident modelling.

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Olszewski, Piotr, Beata Osińska, Piotr Szagała, and Paweł Włodarek. "Development of accident prediction models for pedestrian crossings." MATEC Web of Conferences 231 (2018): 03002. http://dx.doi.org/10.1051/matecconf/201823103002.

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In large Polish cities like Warsaw, pedestrians constitute almost 60% of road fatalities. Although traffic safety situation in general is improving, the numbers of pedestrians hit when crossing a road have not significantly decreased over the last six years. A negative binomial model was estimated for predicting accidents at unsignalised pedestrian crossings based on accident data from 52 crossings in Warsaw. A total of 58 pedestrian accidents were recorded at these crossings during the last seven years. The model shows that the number of accidents is less-than-proportional to both pedestrian and motorised traffic daily volumes. Other risk factors affecting pedestrian safety are: higher proportion of heavy vehicles and location in a mixed land use area. The model can be used with the Empirical Bayes method for an unbiased identification of high risk locations.

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Zhang, Zhihao, Wenzhong Yang, and Silamu Wushour. "Traffic Accident Prediction Based on LSTM-GBRT Model." Journal of Control Science and Engineering 2020 (March 5, 2020): 1–10. http://dx.doi.org/10.1155/2020/4206919.

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Road traffic accidents are a concrete manifestation of road traffic safety levels. The current traffic accident prediction has a problem of low accuracy. In order to provide traffic management departments with more accurate forecast data, it can be applied in the traffic management system to help make scientific decisions. This paper establishes a traffic accident prediction model based on LSTM-GBRT (long short-term memory, gradient boosted regression trees) and predicts traffic accident safety level indicators by training traffic accident-related data. Compared with various regression models and neural network models, the experimental results show that the LSTM-GBRT model has a good fitting effect and robustness. The LSTM-GBRT model can accurately predict the safety level of traffic accidents, so that the traffic management department can better grasp the situation of traffic safety levels.

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Kim, Daeseong, Sangyun Jung, and Sanghoo Yoon. "Risk Prediction for Winter Road Accidents on Expressways." Applied Sciences 11, no. 20 (2021): 9534. http://dx.doi.org/10.3390/app11209534.

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Road accidents caused by weather conditions in winter lead to higher mortality rates than in other seasons. The main causes of road accidents include human carelessness, vehicle defects, road conditions, and weather factors. If the risk of road accidents with changes in road weather conditions can be quantitatively evaluated, it will contribute to reducing the road accident fatalities. The road accident data used in this study were obtained for the period 2017 to 2019. Spatial interpolation estimated the weather information; geographic information system (GIS) and Shuttle Radar Topography Mission (SRTM) data identified road geometry and accident area altitude; synthetic minority oversampling technique (SMOTE) addressed the data imbalance problem between road accidents due to weather conditions and from other causes, and finally, machine learning was performed on the data using various models such as random forest, XGBoost, neural network, and logistic regression. The training- to test data ratio was 7:3. Random forest model exhibited the best classification performance for road accident status according to weather risks. Thus, by applying weather data and road geometry to machine learning models, the risk of road accidents due to weather conditions in the winter season can be predicted and provided as a service.

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Zhang, Yihang, and Yunsick Sung. "Hybrid Traffic Accident Classification Models." Mathematics 11, no. 4 (2023): 1050. http://dx.doi.org/10.3390/math11041050.

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Traffic closed-circuit television (CCTV) devices can be used to detect and track objects on roads by designing and applying artificial intelligence and deep learning models. However, extracting useful information from the detected objects and determining the occurrence of traffic accidents are usually difficult. This paper proposes a CCTV frame-based hybrid traffic accident classification model that enables the identification of whether a frame includes accidents by generating object trajectories. The proposed model utilizes a Vision Transformer (ViT) and a Convolutional Neural Network (CNN) to extract latent representations from each frame and corresponding trajectories. The fusion of frame and trajectory features was performed to improve the traffic accident classification ability of the proposed hybrid method. In the experiments, the Car Accident Detection and Prediction (CADP) dataset was used to train the hybrid model, and the accuracy of the model was approximately 97%. The experimental results indicate that the proposed hybrid method demonstrates an improved classification performance compared to traditional models.

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Dissertations / Theses on the topic "Accident Prediction Models"

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Tian, Zhun. "Investigating speed-accident relationship at urban signalized intersections using accident prediction models." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/32928.

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Motor vehicle speed is a key risk factor contributing to many road accidents. Historical data shows that speed-related accidents account for a significant proportion of all the fatal and serious injury accidents and result in considerable social and economic costs. The objective of this thesis is to understand and quantify the relationship between traffic speed and accident frequency at urban signalized intersections in the city of Edmonton and Vancouver, Canada. This objective is achieved by developing accident prediction models which relate accident frequency to speed variables and other intersection characteristics. Road accident, traffic speed, traffic flow and road geometric data were obtained from the two cities for the purpose of the models development. The generalized linear modelling techniques are used to develop the accident prediction models assuming negative binomial error structure. A total of 15 models are developed relating accident frequency with five speed variables: average speed, mode speed, 85th percentile speed, speed standard deviation and percent of vehicles speeding. The results show that all five speed variables are positively correlated with accident frequency. A quantitative relationship between the change in the value of speed variables and the change in accident frequency is derived from the developed models.

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Lord, Dominique. "The prediction of accidents on digital networks, characteristics and issues related to the application of accident prediction models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0020/NQ53687.pdf.

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Oh, Jutaek. "Evaluation and enhancement of accident prediction models and accident modification factors of rural intersections." Diss., Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/32844.

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Wen, Keyao. "Traffic Accident Prediction Model Implementation in Traffic Safety Management." Thesis, Linköping University, Communications and Transport Systems, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-52203.

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<p>As one of the highest fatalities causes, traffic accidents and collisions always requires a large amounteffort to be reduced or prevented from occur. Traffic safety management routines therefore always needefficient and effective implementation due to the variations of traffic, especially from trafficengineering point of view apart from driver education.Traffic Accident Prediction Model, considered as one of the handy tool of traffic safety management,has become of well followed with interested. Although it is believed that traffic accidents are mostlycaused by human factors, these accident prediction models would help from traffic engineering point ofview to enlarge the traffic safety level of road segments. This thesis is aiming for providing a guidelineof the accident prediction model implementation in traffic safety management, regarding to trafficengineering field. Discussion about how this prediction models should merge into the existing routinesand how well these models would perform would be given. As well, cost benefit analysis of theimplementation would be at the end of this thesis. Meanwhile, a practical field study would bepresented in order to show the procedures of the implementation of traffic accident prediction model.The field study is about this commercial model set SafeNET, from TRL Limited UK, implemented inRoad Safety Audit procedures combined with microscopic simulation tool. Detailed processing andinput and output data will be given accompany with the countermeasures for accident frequencyreduction finalization.</p>

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Malheiro, de Magalhaes Fernando Jose. "Prediction in Poisson and other errors in variables models." Thesis, University of Sheffield, 1997. http://etheses.whiterose.ac.uk/2971/.

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We want to be able to use information about the traffic flows at road junctions and covariates describing those junctions to predict the number of accidents occurring there. We develop here a Bayesian predictive approach. Initially we considered three simpler but related problems to assess the efficiency of some approximation techniques, namely: (I) Given a treatment with an effect that can be described mathematically as of a multiplicative form, we record Poisson countings before and after the treatment is applied. Then, given a new individual with a known counting before the treatment is used, we want to predict the outcome on that individual after the treatment is applied. (II) After observing the value on an individual before any treatment is applied, we decide, based on that value, which of two treatments to apply, and then register the post- treatment outcome. Given a new individual, with an observed value before he receives any treatment, we aim to derive the predictive distribution for the outcome after one of the treatments is used. (This problem is also considered when several possible treatments are available). (III) We compare the effects of two treatments, through a two-period crossover design. We assume that both the treatment effect and the period effect are of multiplicative forms. Estimative and approximation methods are developed for each of these problems. We use the Gibbs sampling approach, normal asymptotic approximations for the posterior distributions and the Laplace approximations. Examples are presented to compare the efficiency and performance of the different methods. We find that the Laplace method performs well, and has computational advantages over the other methods. Using the knowledge obtained solving these simpler problems we develop solutions for the traffic accidents problem and analyse a real data set. Stepwise procedures for the incorporation of the covariates through the use of Kullback-Leibler measure of divergence are developed. We also consider the three simpler problems assuming that the observations are exponentially and binomially distributed.

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Van, Espen Adinda. "Evaluating traffic safety performance of countries using data envelopment analysis and accident prediction models." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/44552.

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Road safety is an issue of global importance, receiving both national and international attention. According to the World Health Organization, road traffic injuries are extrapolated to become the fifth leading cause of death in the world by 2030. Studies conducted to gain better insight into how countries can improve their road safety performance levels often use one single variable – the number of fatalities per million inhabitants – and focus predominantly on European countries. This thesis looks to develop and analyze models incorporating a wider range of countries as well as a wider range of road safety performance indicators using data envelopment analysis and accident prediction models. The first method, initially calculate the efficiency scores using three input variables (percentage of seatbelt use in front seat, road density, and total health expenditure as percentage of GDP) and two output variables (number of fatalities per million inhabitants and fatalities per million passenger cars). It was found that the addition of the percentage of seatbelt use in rear seats (fourth input variable) and the percentage of roads paved (fifth input variable) improved the efficiency scores and rankings. Overall, the percentage of seat belt use in front seats and the total health expenditure variables had the greatest importance. The second method developed three accident prediction models using the generalized linear modeling approach with the negative binomial error structure. The elasticity analysis revealed that, for Model 1 and Model 2, the health expenditure variable had the greatest impact on the number of fatalities. For Model 3, the seatbelt wearing rate in front seats and the seatbelt wearing rate in rear seats had the greatest effect on the number of fatalities.

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Fawcett, Lee, Neil Thorpe, Joseph Matthews, and Karsten Kremer. "A novel Bayesian hierarchical model for road safety hotspot prediction." Elsevier, 2016. https://publish.fid-move.qucosa.de/id/qucosa%3A72268.

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In this paper, we propose a Bayesian hierarchical model for predicting accident counts in future years at sites within a pool of potential road safety hotspots. The aim is to inform road safety practitioners of the location of likely future hotspots to enable a proactive, rather than reactive, approach to road safety scheme implementation. A feature of our model is the ability to rank sites according to their potential to exceed, in some future time period, a threshold accident count which may be used as a criterion for scheme implementation. Our model specification enables the classical empirical Bayes formulation – commonly used in before-and-after studies, wherein accident counts from a single before period are used to estimate counterfactual counts in the after period – to be extended to incorporate counts from multiple time periods. This allows site-specific variations in historical accident counts (e.g. locally-observed trends) to offset estimates of safety generated by a global accident prediction model (APM), which itself is used to help account for the effects of global trend and regression-to-mean (RTM). The Bayesian posterior predictive distribution is exploited to formulate predictions and to properly quantify our uncertainty in these predictions. The main contributions of our model include (i) the ability to allow accident counts from multiple time-points to inform predictions, with counts in more recent years lending more weight to predictions than counts from time-points further in the past; (ii) where appropriate, the ability to offset global estimates of trend by variations in accident counts observed locally, at a site-specific level; and (iii) the ability to account for unknown/unobserved site-specific factors which may affect accident counts. We illustrate our model with an application to accident counts at 734 potential hotspots in the German city of Halle; we also propose some simple diagnostics to validate the predictive capability of our model. We conclude that our model accurately predicts future accident counts, with point estimates from the predictive distribution matching observed counts extremely well.

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Boffo, Gabriela Holz. "Formatos e técnicas de modelos de previsão de acidentes de trânsito." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/31414.

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A ampliação acelerada da demanda por transporte, mais especificamente pelo transporte rodoviário, tem provocado um aumento expressivo no número de acidentes de trânsito nesse ambiente. Consequentemente, a redução dos acidentes de trânsito tem sido um grande desafio para os pesquisadores e gestores da área rodoviária. Porém, os acidentes de trânsito são eventos complexos se considerados os diversos fatores que podem influenciá-los. Dentro desse contexto esta dissertação apresenta um estudo de modelos de previsão de acidentes, que podem ser utilizados para a avaliação do potencial de segurança em determinados locais, identificação e classificação de localidades perigosas ou com propensão a acidentes e avaliação da eficácia de medidas de melhoria da segurança. Nessa dissertação é apresentado um levantamento teórico e metodológico dos modelos de previsão de acidentes, identificando as principais variáveis adotadas bem como as técnicas utilizadas. Para cada modelo revisado foram verificadas as principais diferenças e limitações, e ainda, a análise das variáveis mais influentes presentes nesses modelos. Após, é feita uma comparação de duas abordagens distintas para estimar modelos de previsão de acidentes. A primeira consiste em estimar a ocorrência de acidentes em segmentos da via com as mudanças de características dos elementos de infraestrutura. O segundo relaciona a frequência de acidentes para um único elemento de infraestrutura da via, chamado na literatura internacional de entidade (ex: interseção, curva, tangente, etc.), com base apenas na variável relacionada ao volume de tráfego. O estudo baseado na comparação dessas duas abordagens para a previsão de acidentes revelou que a utilização do volume de tráfego como única variável independente apresenta resultados semelhantes ou até melhores que os modelos baseados em diversos elementos de infraestrutura da rodovia.<br>The enlargement and the accelerated development of transportation systems, more specifically the land system, have caused the number of road accidents to increase significantly. Therefore, the reduction of road accidents has been a great challenge for researchers and managers in the field of land transportation. However, considering the various factors that may influence them, road accidents are complex events. In this context, this paper presents a study of accident prediction models that can be used to assess the safety potential in certain locations, identify and rank dangerous locations or areas prone to accidents and evaluate the effectiveness of safety improvement measures. Initially, a theoretical and methodological review of accident prediction models is presented, and both the main variables adopted and the methodologies employed are identified. The main differences between all models reviewed and their limitations are presented, and the most influential variables are analyzed. In a second moment, a comparison of two different accident prediction methods is performed. The first method consists in estimating the occurrence of accidents in road sections with changes in the characteristics of infrastructure elements. The second one relates the frequency of accidents based on a single infrastructure element (intersection, curve, tangent, etc.) based on traffic volume only. The study based on the comparison of these two methods found that the use of traffic volume as the only independent variable yields similar or even better results than the models based on various road infrastructure elements.

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Adedokun, Adeyemi. "Application of Road Infrastructure Safety Assessment Methods at Intersections." Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-127334.

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Traffic safety at intersections is a particularly difficult phenomenon to study, given the fact that accidents occur randomly in time and space thereby making short-term measurement, assessment and comparison difficult. The EU directive 2008/96/EC introduced road infrastructure safety management, which offers a five layer structure for developing safer road infrastructure has been used to develop tools for accident prediction and black spot management analysis which has been applied in this work to assess the safety level of intersections in Norrköping city in Sweden. Accident data history from STRADA (Swedish Traffic Accident Data Acquisition) and the network demand model for Norrköping city were used to model black spots and predict the expected number of accidents at intersections using PTV Visum Safety tool, after STRADA accident classification was restructured and the Swedish accident prediction model (APM) was configured and tested to work within the tool using the model from the Swedish road administration (SRA). The performance of the default (Swiss) and the Swedish APM was compared and identified locations with the high accident records, predicted accident counts and traffic volumes were audited using qualitative assessment checklist from Street-Audit tool. The results from these methods were analysed, validated and compared. This work provides recommendations on the used quantitative and qualitative methods to prevent accident occurrence at the identified locations.

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Andeta, Jemal Ahmed. "Road-traffic accident prediction model : Predicting the Number of Casualties." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20146.

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Efficient and effective road traffic prediction and management techniques are crucial in intelligent transportation systems. It can positively influence road advancement, safety enhancement, regulation formulation, and route planning to save living things in advance from road traffic accidents. This thesis considers road safety by predicting the number of casualties if an accident occurs using multiple traffic accident attributes. It helps individuals (drivers) or traffic offices to adjust and control their contributions for the occurrence of an accident before emerging it. Three candidate algorithms from different regression fit patterns are proposed and evaluated to conduct the thesis: the bagging, linear, and non-linear fitting patterns. The gradient boosting machines (GBoost) from the bagging, Linearsupport vector regression (LinearSVR) from the linear, and extreme learning machines (ELM) also from the non-linear side are the selected algorithms. RMSE and MAE performance evaluation metrics are applied to evaluate the models. The GBoost achieved a better performance than the other two with a low error rate and minimum prediction interval value for 95% prediction interval. A SHAP (SHapley Additive exPlanations) interpretation technique is applied to interpret each model at the global interpretation level using SHAP’s beeswarm plots. Finally, suggestions for future improvements are presented via the dataset and hyperparameter tuning.

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Books on the topic "Accident Prediction Models"

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Hadayeghi, Alireza. Accident prediction models for safety evaluation of urban transportation network. National Library of Canada, 2002.

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1947-, Persaud Bhagwant Naraine, and Ontario. Ministry of Transportation. Safety Research Office., eds. Accidents, convictions and demerit points: An Ontario driver records study : accident prediction models for older drivers. Safety Research Office, Safety Policy Branch, Ontario, 1994.

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Shoumaker, Wayne. Accident prediction models for signalized and unsignalized intersections: Update on CART models and the APSI program. University of California, 1990.

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1959-, Klein R., and Rehm Werner, eds. Models and criteria for prediction of deflagration-to-detonation transition (DDT) in hydrogen-air-steam systems under severe accident conditions. ForsForschungszentrum Jülich, Zentralbibliothek, 2000.

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Wright, D. E. Fitting predictive accident models in GLIM with uncertainty in the flow estimates. Transport and Road Research Laboratory, 1991.

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Authority, New Zealand Land Transport Safety. Safety directions: Predicting and costing road safety outcomes. Land Transport Safety Authority, 2000.

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Lord, Dominique. The prediction of accidents on digital networks: Characteristics and issues related to the application of accident prediction models. 2000.

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Kulmala, Risto. Safety at rural three- and four-arm junctions: Development and application of accident prediction models. 1995.

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Nachimuthu, K. System Simulation Model Based Road Accidents and Its Cost Prediction. Lulu Press, Inc., 2016.

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Bentil, Kweku K. A model approach for predicting commercial construction site accidents. 1990.

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Book chapters on the topic "Accident Prediction Models"

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Jasiūnienė, Vilma, Kornelija Ratkevičiūtė, and Harri Peltola. "Road Network Safety Ranking Using Accident Prediction Models." In Vision Zero for Sustainable Road Safety in Baltic Sea Region. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22375-5_19.

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Zhao, HaoZhe, and Guozheng Rao. "Traffic Accident Prediction Methods Based on Multi-factor Models." In Knowledge Science, Engineering and Management. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82153-1_4.

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Huang, Yuanlin, and Adolf D. May. "Accident Prediction Models and Applications for Unsignalized and Signalized Intersections." In Intersections without Traffic Signals II. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-84537-6_20.

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Khekare, Ganesh, Anil V. Turukmane, Chetan Dhule, Pooja Sharma, and Lokesh Kumar Bramhane. "Experimental Performance Analysis of Machine Learning Algorithms." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_104.

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AbstractMachine Learning models and algorithms have become quite common these days. Deep Learning and Machine Learning algorithms are utilized in various projects, and now, it has opened the door to several opportunities in various fields of research and business. However, identifying the appropriate algorithm for a particular program has always been an enigma, and that necessitates to be solved ere the development of any machine learning system. Let’s take the example of the Stock Price Prediction system, it is used to identify the future asset prediction of a industry or other financial aspects traded on a related transaction. Now, it is a daunting task to find the right algorithm or model for such a purpose that can predict accurate values. There are several other systems such as recommendation systems, sales prediction of a mega-store, or predicting what are the chances of a driver meeting an accident based on his past records and the road they’ve taken. These problem statements require to be built using the most suitable algorithm and identifying them is a necessary task. This is what the system does, it compares a set of machine learning algorithms while determining the appropriate algorithm for the selected predictive system using the required data sets. The objective is to develop an interface that can be used to display the result matrix of different machine learning algorithms after being exposed to different datasets with different features. Besides that, one can determine the most suitable (or optimal) models for their operations, using these fundamentals. For experimental performance analysis several technologies and tools are used including Python, Django, Jupyter Notebook, Machine Learning, Data Science methodologies, etc. The comparative performance analysis of best known five time series forecasting machine learning algorithms viz. linear regression, K – nearest neighbor, Auto ARIMA, Prophet, and Support Vector Machine is done. Stock market, earth and sales forecasting data is used for analysis.

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Marcillo, Pablo, Lorena Isabel Barona López, Ángel Leonardo Valdivieso Caraguay, and Myriam Hernández-Álvarez. "A Review of Learning-Based Traffic Accident Prediction Models and Their Opportunities to Improve Information Security." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68285-9_37.

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Pisharody, N. N., Kanchan D. Bahukhandi, Prashant S. Rawat, and R. K. Elangovan. "Development of Accident Prediction Model for Low Frequency and High Severity (LFHS) Industrial Accidents." In Springer Proceedings in Earth and Environmental Sciences. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05335-1_24.

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Ting, Choo-Yee, Nicholas Yu-Zhe Tan, Hizal Hanis Hashim, Chiung Ching Ho, and Akmalia Shabadin. "Malaysian Road Accident Severity: Variables and Predictive Models." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0058-9_67.

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Al-shanini, Ali, Arshad Ahmad, and Faisal Khan. "Grey Model for Accident Prediction in Data-Scarce Environment." In ICGSCE 2014. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-505-1_48.

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Wang, Hao, Lai Zheng, and Xianghai Meng. "Traffic Accidents Prediction Model Based on Fuzzy Logic." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22418-8_14.

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Simaiya, Sarita, Umesh Kumar Lilhore, Himanshu Pandey, Naresh Kumar Trivedi, Abhineet Anand, and Jasminder Sandhu. "An Improved Deep Neural Network-Based Predictive Model for Traffic Accident’s Severity Prediction." In Ambient Communications and Computer Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7952-0_17.

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Conference papers on the topic "Accident Prediction Models"

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Justo-Silva, Rita, and Adelino Ferreira. "Accident prediction models considering pavements quality." In Fifth International Conference on Road and Rail Infrastructure. University of Zagreb Faculty of Civil Engineering, 2018. http://dx.doi.org/10.5592/co/cetra.2018.796.

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Gokasar, Ilgin, and Kaan Aytekin. "Accident Lane Prediction Using Probabilistic Inference." In 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). IEEE, 2019. http://dx.doi.org/10.1109/mtits.2019.8883385.

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Vieira Gomes, S., C. Carvalheira, J. Cardoso, and L. Picado Santos. "Accident prediction models in urban areas: Lisbon case study." In URBAN TRANSPORT 2008. WIT Press, 2008. http://dx.doi.org/10.2495/ut080601.

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Meng, Xianghai, Qinzhong Hou, and Yongyi Shi. "Research on Accident Prediction Models for Freeways in Mountainous and Rolling Areas." In 2015 Seventh International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). IEEE, 2015. http://dx.doi.org/10.1109/icmtma.2015.205.

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Richmond, Mary C., Ping K. Wan, Brady P. Dague, and Kyra W. Davis. "Selection of Chemical Hazard Prediction Models for Plant Safety and Control Room Habitability Evaluations." In 2013 21st International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icone21-16392.

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Assuring the protection of people and the environment from unnecessary exposure to radiation is of great concern to nuclear electric power generators and regulators. In order to secure and maintain a license for operation of a nuclear power plant in the United States, the applicant is required to assess postulated scenarios to determine if an accidental chemical release either onsite or offsite will result in a design-basis event. When determining design-basis events, accident categories such as fire, explosion and/or toxic vapor cloud formation are considered. Evaluations must consider whether the postulated accidental chemical release will result in operator impairment or damage to safety related structures, systems or components (SSCs), either of which may prevent safe shutdown of the nuclear plant. A critical step in performing such safety evaluations is selection of an appropriate model which will most closely reflect the behavior of a chemical release in the scenario under consideration. Many chemical dispersion models are available for use in safety evaluations for accidental chemical releases; however, it is imperative that the model selected be appropriate for the postulated release conditions. Model selection should be based on careful evaluation of factors such as release location, meteorological conditions, terrain, chemical inventory and storage conditions, as well as the physical and chemical properties of the chemical under consideration in the postulated release. Failure to select a model suitable for the conditions under which the chemical is released or to appropriately evaluate the physical properties of the chemical and the corresponding limitations of the model may result in underpredicting or overpredicting the impact of a fire, explosion or toxic chemical release. Underpredicting could leave the facility susceptible to damage of safety related SSCs or lead to operator impairment both of which may affect the ability of the plant to safely operate following an accident. While overpredicting the impacts could lead to unnecessary and costly overdesign. This paper will address the considerations that must be evaluated when selecting a chemical dispersion model and will illustrate the importance of model selection in performing nuclear safety evaluations through examples and case studies.

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Boyd, Christopher, and Kenneth Armstrong. "Modeling Improvements for System Code Evaluation of Inlet Plenum Mixing Under Severe Accident Conditions Using CFD Predictions." In 18th International Conference on Nuclear Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/icone18-30262.

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An updated mixing model is developed for application to system codes used for predicting severe accident-induced failures of steam generator (SG) U-tubes in a pressurized-water reactor. Computational fluid dynamics is used to predict the natural circulation flows between a simplified reactor vessel and the primary side of an SG during a hypothesized severe accident scenario. The results from this analysis are used to extend earlier experimental results and predictions. These new predictions benefit from the inclusion of the entire natural circulation loop between the reactor vessel upper plenum and the SG. Tube leakage and mass flow into the pressurizer surge line also are considered. The predictions are utilized as a numerical experiment to improve the basis for simplified models applied in one-dimensional system codes that are used during the prediction of severe accident natural circulation flows. An updated inlet plenum mixing model is proposed that accounts for mixing in the hot leg as well as the inlet plenum region. The new model is consistent with the predicted behavior and can account for flow into a side-mounted pressurizer surge line if present. Sensitivity studies demonstrate the applicability of the approach over a range of conditions. The predictions are most sensitive to changes in the SG secondary side temperatures or heat-transfer rates at the SG tubes. Grid independence is demonstrated through comparisons with previous models and by increasing the number of cells in the model. This work supports the U.S. Nuclear Regulatory Commission (NRC) studies of SG tube integrity under severe accident conditions.

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Teixeira, A. P., and C. Guedes Soares. "Risk of Maritime Traffic in Coastal Waters." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-77312.

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This paper addresses the broad aspects of safety of maritime transportation from the identification to the management of risks related particularly to the maritime traffic in coastal waters. A brief overview of present-day maritime accident statistics are presented and the methodologies that have been adopted in the maritime sector to analyze ship accidents are reviewed. The paper also reviews the models and tools that have been used for simulation of ship navigation and for accident probability prediction based on Automatic Identification System (AIS) data and the analysis and modelling of the influence of human and organisational factors on ship accidents. The development of maritime risk models based on Bayesian Networks and the various elements that influence an effective response to maritime accidents are also addressed.

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Xu, Weifeng, Fangqing Yang, Peng Chen, and Yehong Liao. "A Method for Online Calibration of MAAP Simulations in a Severe Accident Management System Database." In 2017 25th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/icone25-66431.

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During a nuclear plant accident, five accident events are usually considered, including core uncovery, core outlet temperature arrived at 650 °C, core support plate failure, reactor vessel failure and containment failure. In accident emergency aspect, when an accident happens, the initial event can be utilized in the severe accident management system which is based on MAAP to simulate the long process of the accident, so as to provide support for operators to take actions. However, in MAAP, many sensitivity parameters exist, which reflect phenomenological uncertainty or models uncertainty and will influence the happening time of the five accident events above. In this paper, based on MAAP5 and LOCAs, the CPR1000 is simulated to analyze the influences of MAAP5’s sensitivity parameters reflecting phenomenological uncertainty on the accident process, which is aimed to find out the sensitivity parameters associated to the five important accident events and build the database between these sensitivity parameters and five accident events’ happening time. Then, based on the research above, a preliminary approach to optimize the MAAP5’s accidents simulation is introduced, which is realized by adjusting sensitivity parameters. Finally, the application of this research will be showed in a severe accident management system developed by us. The research results offer great reference significance for the severe accident simulation and prediction in MAAP5.

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Sperry, Benjamin R., Bhaven Naik, and Jeffery E. Warner. "Evaluation of Grade Crossing Hazard Ranking Models." In 2017 Joint Rail Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/jrc2017-2271.

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Public agencies involved with highway-railroad grade crossing safety must allocate available funding to projects which are considered the most in need for improvements. Mathematical models provide a ranking of hazard risk at crossings and support the project selection process. This paper reports the results of a research study sponsored by the Ohio Rail Development Commission (ORDC) and the Ohio Department of Transportation (ODOT) examining hazard ranking models for grade crossing project selection. The goal of the research was to provide ORDC, ODOT, and other stakeholders with a better understanding of the grade crossing hazard ranking formulas and other methods used by States to evaluate grade crossing hazards and select locations for hazard elimination projects. A comprehensive literature review along with personal interviews of state DOT personnel from eight states yielded best practices for hazard ranking and project selection. The literature review found that more than three-quarters of states utilize some type of hazard ranking formula or other systematic method for project prioritization. The most commonly-used hazard ranking model in use is the U.S. DOT Accident Prediction Model; however, at least eleven states utilize state-specific hazard ranking models. Detailed evaluation of several different hazard ranking models determined that the existing hazard ranking model used in Ohio, the U.S. DOT Accident Prediction Model, should continue to be used. The research also recommends greater use of sight distance information at crossings and expanding the preliminary list of crossings to be considered in the annual program as enhancements to the existing project selection process used by the ORDC and ODOT.

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Saeed, M., Yu Jiyang, B. X. Hou, Aniseh A. A. Abdalla, and Zhang Chunhui. "Numerical Simulation of Hydrogen Dispersion Inside a Compartment Using HYDRAGON Code." In 2016 24th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/icone24-60610.

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During severe accident in the nuclear power plant, a considerable amount of hydrogen can be generated by an active reaction of the fuel-cladding with steam within the pressure vessel which may be released into the containment of nuclear power plant. Hydrogen combustion may occur where there is sufficient oxygen, and the hydrogen release rates exceed 10% of the containment. During hydrogen combustion, detonation force and short term pressure may be produced. The production of these gas species can be detrimental to the structural integrity of the safety systems of the reactor and the containment. In 1979, the Three Mile Island (1979) accident occurred. This accident compelled experts and researchers to focus on the study of distribution of hydrogen inside the containment of nuclear power plant. However after the Fukushima Dai-ichi nuclear power plant accident (2011), the modeling of the gas behavior became important topic for scientists. For the stable and normal operation of the containment, it is essential to understand the behavior of hydrogen inside the containment of nuclear power plant in order to mitigate the occurrence of these types of accidents in the future. For this purpose, it is important to identify how burnable hydrogen clouds are produced in the containment of nuclear power plant. The combustion of hydrogen may occur in different modes based on geometrical complexity and gas composition. Reliable turbulence models must be used in order to obtain an accurate estimation of the concentration distribution as a function of time and other physical phenomena of the gas mixture. In this study, a small scale hydrogen-dispersion case is selected as a benchmark to address turbulence models. The computations are performed using HYDRAGON code developed by Department of Engineering Physics, Tsinghua University, China. HYDRAGON code is a three dimensional thermal-hydraulics analysis code. The purpose of this code is to predict the behavior of hydrogen gas and multiple gas species inside the containment of nuclear power plant during severe accident. This code mainly adopts CFD models and structural correlations used for wall flow resistance instead of using boundary layer at a wall. HYDROGAN code analyzes many processes such as hydrogen diffusion condensation, combustion, gas stratification, evaporation, mixing process. The main purpose of this research is to study the influence of turbulence models to the concentration distribution and to demonstrate the code thermal-hydraulic simulation capability during nuclear power plant accident. The calculated results of various turbulence models have different prediction values in different compartments. The results of k–ε turbulence model are in reasonable agreement as compared to the benchmark experimental data.

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Reports on the topic "Accident Prediction Models"

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Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, 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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Rest, J., and S. A. Zawadzki. FASTGRASS: A mechanistic model for the prediction of Xe, I, Cs, Te, Ba, and Sr release from nuclear fuel under normal and severe-accident conditions. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/7255051.

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