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Journal articles on the topic 'Crash prediction'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Osman, Osama A., Mustafa Hajij, Peter R. Bakhit, and Sherif Ishak. "Prediction of Near-Crashes from Observed Vehicle Kinematics using Machine Learning." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 12 (2019): 463–73. http://dx.doi.org/10.1177/0361198119862629.

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This study introduces a machine learning model for near-crash prediction from observed vehicle kinematics data. The main hypothesis is that vehicles tend to experience discernible turbulence in their kinematics shortly before involvement in near-crashes. To test this hypothesis, the SHRP2 NDS vehicle kinematics data (speed, longitudinal acceleration, lateral acceleration, yaw rate, and pedal position) are utilized. Several machine learning algorithms are trained and comparatively analyzed including K nearest neighbor (KNN), random forest, support vector machine (SVM), decision trees, Gaussian

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Farooq, Muhammad Umer, and Aemal J. Khattak. "Investigating Highway–Rail Grade Crossing Inventory Data Quality’s Role in Crash Model Estimation and Crash Prediction." Applied Sciences 13, no. 20 (2023): 11537. http://dx.doi.org/10.3390/app132011537.

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The highway–rail grade crossings (HRGCs) crash frequency models used in the US are based on the Federal Railroad Administration’s (FRA) database for highway–rail crossing inventory. Inaccuracies or missing values within this database directly impact the estimated parameters of the crash models and subsequent crash predictions. Utilizing a set of 560 HRGCs in Nebraska, this research demonstrates variations in crash predictions estimated by the FRA’s 2020 Accident Prediction (AP) model under two scenarios: firstly, employing the unchanged, original FRA HRGCs inventory dataset as the input, and s

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3

Zhao, Liping, Feng Li, Dongye Sun, and Fei Dai. "Highway Traffic Crash Risk Prediction Method considering Temporal Correlation Characteristics." Journal of Advanced Transportation 2023 (February 15, 2023): 1–13. http://dx.doi.org/10.1155/2023/9695433.

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Crash risk analysis and prediction are considered the premise of highway traffic safety control, which directly affects the accuracy and effectiveness of traffic safety decisions. A highway traffic crash risk prediction method considering temporal correlation characteristics is proposed in this research. Firstly, the case-control sample analysis method is used to extract 6 time series sample data composed of crash traffic flow data and corresponding non-crash traffic flow data for crash risk analysis and prediction. Secondly, the multiparameter fusion clustering analysis method is used to indi

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Lei, Tian, Jia Peng, Xingliang Liu, and Qin Luo. "Crash Prediction on Expressway Incorporating Traffic Flow Continuity Parameters Based on Machine Learning Approach." Journal of Advanced Transportation 2021 (March 29, 2021): 1–13. http://dx.doi.org/10.1155/2021/8820402.

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Real-time crash prediction helps identify and prevent the occurrence of traffic crash. For years, various real-time crash prediction models have been investigated to provide effective information for proactive traffic management. When building real-time crash prediction model, a suitable variable space together with a specific time interval for traffic data aggregation and an appropriate modelling algorithm should be applied. Regarding the intercorrelation problem with variable space, comprehensive real-time crash prediction model considering available traffic data characteristics in applicabl

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Hassouna, Fady M. A., and Khaled Al-Sahili. "Practical Minimum Sample Size for Road Crash Time-Series Prediction Models." Advances in Civil Engineering 2020 (December 29, 2020): 1–12. http://dx.doi.org/10.1155/2020/6672612.

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Road crashes are problems facing the transportation sector. Crash data in many countries are available only for the past 10 to 20 years, which makes it difficult to determine whether the data are sufficient to establish reasonable and accurate prediction rates. In this study, the effect of sample size (number of years used to develop a prediction model) on the crash prediction accuracy using Autoregressive integrated moving average (ARIMA) method was investigated using crash data for years 1971–2015. Based on the availability of annual crash records, road crash data for four selected countries

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6

Liu, Miaomiao, and Yongsheng Chen. "Predicting Real-Time Crash Risk for Urban Expressways in China." Mathematical Problems in Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/6263726.

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We developed a real-time crash risk prediction model for urban expressways in China in this study. About two-year crash data and their matching traffic sensor data from the Beijing section of Jingha expressway were utilized for this research. The traffic data in six 5-minute intervals between 0 and 30 minutes prior to crash occurrence was extracted, respectively. To obtain the appropriate data training period, the data (in each 5-minute interval) during six different periods was collected as training data, respectively, and the crash risk value under different data conditions was defined. Then

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Naderan, Ali, and Jalil Shahi. "Aggregate crash prediction models: Introducing crash generation concept." Accident Analysis & Prevention 42, no. 1 (2010): 339–46. http://dx.doi.org/10.1016/j.aap.2009.08.020.

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Chen, Zhi, Xiao Qin, Renxin Zhong, Pan Liu, and Yang Cheng. "Predicting Imminent Crash Risk with Simulated Traffic from Distant Sensors." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 38 (2018): 12–21. http://dx.doi.org/10.1177/0361198118791379.

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The aim of this research was to investigate the performance of simulated traffic data for real-time crash prediction when loop detector stations are distant from the actual crash location. Nearly all contemporary real-time crash prediction models use traffic data from physical detector stations; however, the distance between a crash location and its nearest detector station can vary considerably from site to site, creating inconsistency in detector data retrieval and subsequent crash prediction. Moreover, large distances between crash locations and detector stations imply that traffic data fro

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Paja, W., M. Wrzesie&nacute;, R. Niemiec, and W. R. Rudnicki. "Application of all relevant feature selection for failure analysis of parameter-induced simulation crashes in climate models." Geoscientific Model Development Discussions 8, no. 7 (2015): 5419–35. http://dx.doi.org/10.5194/gmdd-8-5419-2015.

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Abstract. The climate models are extremely complex pieces of software. They reflect best knowledge on physical components of the climate, nevertheless, they contain several parameters, which are too weakly constrained by observations, and can potentially lead to a crash of simulation. Recently a study by Lucas et al. (2013) has shown that machine learning methods can be used for predicting which combinations of parameters can lead to crash of simulation, and hence which processes described by these parameters need refined analyses. In the current study we reanalyse the dataset used in this res

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Gill, G., T. Sakrani, W. Cheng, and J. Zhou. "COMPARISON OF ADJACENCY AND DISTANCE-BASED APPROACHES FOR SPATIAL ANALYSIS OF MULTIMODAL TRAFFIC CRASH DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 14, 2017): 1157–61. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1157-2017.

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Many studies have utilized the spatial correlations among traffic crash data to develop crash prediction models with the aim to investigate the influential factors or predict crash counts at different sites. The spatial correlation have been observed to account for heterogeneity in different forms of weight matrices which improves the estimation performance of models. But very rarely have the weight matrices been compared for the prediction accuracy for estimation of crash counts. This study was targeted at the comparison of two different approaches for modelling the spatial correlations among

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Torbic, Darren J., Douglas W. Harwood, and Karin M. Bauer. "Application of Highway Safety Manual Method for Ramp Crash Prediction to Loop and Diamond Ramps." Transportation Research Record: Journal of the Transportation Research Board 2636, no. 1 (2017): 43–52. http://dx.doi.org/10.3141/2636-06.

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The AASHTO Highway Safety Manual (HSM) now includes crash prediction procedures for ramps. Research was undertaken to assess how well these new crash prediction methods represented the safety performance of two ramp types with distinctly different geometrics: loop ramps and diamond ramps. The HSM crash prediction procedures were applied to 235 loop ramps and 243 diamond ramps in two states—California and Washington—and the results were compared with 5 years of actual crash data for the same ramps. The results indicate that the HSM crash prediction method can be applied to both loop and diamond

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Assi, Khaled, Syed Masiur Rahman, Umer Mansoor, and Nedal Ratrout. "Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol." International Journal of Environmental Research and Public Health 17, no. 15 (2020): 5497. http://dx.doi.org/10.3390/ijerph17155497.

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Predicting crash injury severity is a crucial constituent of reducing the consequences of traffic crashes. This study developed machine learning (ML) models to predict crash injury severity using 15 crash-related parameters. Separate ML models for each cluster were obtained using fuzzy c-means, which enhanced the predicting capability. Finally, four ML models were developed: feed-forward neural networks (FNN), support vector machine (SVM), fuzzy C-means clustering based feed-forward neural network (FNN-FCM), and fuzzy c-means based support vector machine (SVM-FCM). Features that were easily id

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Wang, Kai, Shanshan Zhao, and Eric Jackson. "Multivariate Poisson Lognormal Modeling of Weather-Related Crashes on Freeways." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 38 (2018): 184–98. http://dx.doi.org/10.1177/0361198118776523.

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Adverse weather conditions are one of the primary causes of motor vehicle crashes. To identify the factors contributing to crashes during adverse weather conditions and recommend cost-effective countermeasures, it is necessary to develop reliable crash prediction models to estimate weather-related crash frequencies. To account for the variations in crash count among different adverse weather conditions, crash types, and crash severities for both rain- and snow-related crashes, crash data on freeways was collected from the State of Connecticut, and crash prediction models were developed to esti

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Wang, Xiaofei, HuaQiao Pu, Xinwei Li, Ying Yan, and Jiangbei Yao. "A New GNB Model of Crash Frequency for Freeway Sharp Horizontal Curve Based on Interactive Influence of Explanatory Variables." Journal of Advanced Transportation 2018 (November 14, 2018): 1–9. http://dx.doi.org/10.1155/2018/8973581.

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Crash prediction of the sharp horizontal curve segment of freeway is a key method in analyzing safety situation of freeway horizontal alignment. The target of this paper is to improve predicting accuracy after considering the elastic influence of explanatory variables and interaction of explanatory variables on crash rate prediction. In the paper, flexibility and elasticity are defined to express the elastic influence of explanatory variables and interaction of explanatory variables on crash rate prediction. Thus, we proposed 6 types of models to predict crash frequency. These 6 types of model

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15

Alphonso Sequeira, Gerald Joy. "Prediction based activation of vehicle safety systems – a contribution to improve to occupant safety by validation of pre-crash information and crash severity plus restraint strategy prediction." at - Automatisierungstechnik 71, no. 3 (2023): 243–45. http://dx.doi.org/10.1515/auto-2022-0167.

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Abstract This work presents an approach to activate vehicle safety systems based on a pre-crash prediction. It focuses on the three major topics namely, crash validation, the geometry-estimation, and the crash severity and restraint strategy prediction. The topic of crash validation is administered by a proposal of a novel contact-based validation sensor along with the experimental investigations for comparison with other sensors.

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Naghawi, Hana. "Negative Binomial Regression Model for Road Crash Severity Prediction." Modern Applied Science 12, no. 4 (2018): 38. http://dx.doi.org/10.5539/mas.v12n4p38.

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In this paper, the Negative Binominal Regression (NBR) technique was used to develop crash severity prediction model in Jordan. The primary crash data needed were obtained from Jordan Traffic Institute for the year 2014. The collected data included number and severity of crashes. The data were organized into eight crash contributing factors including: age, age and gender, drivers’ faults, environmental factors, crash time, roadway defects and vehicle defects. First of all, descriptive analysis of the crash contributing factors was done to identify and quantify factors affecting crash severity,

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Shiran, Gholamreza, Reza Imaninasab, and Razieh Khayamim. "Crash Severity Analysis of Highways Based on Multinomial Logistic Regression Model, Decision Tree Techniques, and Artificial Neural Network: A Modeling Comparison." Sustainability 13, no. 10 (2021): 5670. http://dx.doi.org/10.3390/su13105670.

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The classification of vehicular crashes based on their severity is crucial since not all of them have the same financial and injury values. In addition, avoiding crashes by identifying their influential factors is possible via accurate prediction modeling. In crash severity analysis, accurate and time-saving prediction models are necessary for classifying crashes based on their severity. Moreover, statistical models are incapable of identifying the potential severity of crashes regarding influencing factors incorporated in models. Unlike previous research efforts, which focused on the limited

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Lee, Chris, Bruce Hellinga, and Frank Saccomanno. "Real-Time Crash Prediction Model for Application to Crash Prevention in Freeway Traffic." Transportation Research Record: Journal of the Transportation Research Board 1840, no. 1 (2003): 67–77. http://dx.doi.org/10.3141/1840-08.

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The likelihood of a crash or crash potential is significantly affected by the short-term turbulence of traffic flow. For this reason, crash potential must be estimated on a real-time basis by monitoring the current traffic condition. In this regard, a probabilistic real-time crash prediction model relating crash potential to various traffic flow characteristics that lead to crash occurrence, or “crash precursors,” was developed. In the development of the previous model, however, several assumptions were made that had not been clearly verified from either theoretical or empirical perspectives.

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Yang, Yang, Kun Wang, Zhenzhou Yuan, and Dan Liu. "Predicting Freeway Traffic Crash Severity Using XGBoost-Bayesian Network Model with Consideration of Features Interaction." Journal of Advanced Transportation 2022 (April 30, 2022): 1–16. http://dx.doi.org/10.1155/2022/4257865.

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In the field of freeway traffic safety research, there is an increasing focus in studies on how to reduce the frequency and severity of traffic crashes. Although many studies divide factors into “human-vehicle-road-environment” and other dimensions to construct models whichshowthe characteristic patterns of each factor's influence on crash severity, there is still a lack of research on the interaction effect of road and environment characteristics on the severity of a freeway traffic crash. This research aims to explore the influence of road and environmental factors on the severity of a freew

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Mintie Wubie, Tilahun, and Abeje Tilahun Fetene. "Road Crash Prediction Models: A Review of Methods and Applications." Put i saobraćaj 70, no. 4 (2024): 11–20. https://doi.org/10.31075/pis.70.04.02.

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Road traffic crashes are still the major road safety problems in the world causing for the death of more than 1 million people each year, although the problem is more serious in low- and middle-income countries. Therefore, road crash prediction models play an important role in road safety management to determining both the predicted crash frequency and the contributing factors that could then be addressed by transport policies. Many types of statistical crash prediction models have been proposed for estimating the predicted crash frequencies in road networks, ranging from basic Poisson and neg

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Dong, Chunjiao, Chunfu Shao, Juan Li, and Zhihua Xiong. "An Improved Deep Learning Model for Traffic Crash Prediction." Journal of Advanced Transportation 2018 (December 10, 2018): 1–13. http://dx.doi.org/10.1155/2018/3869106.

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Machine-learning technology powers many aspects of modern society. Compared to the conventional machine learning techniques that were limited in processing natural data in the raw form, deep learning allows computational models to learn representations of data with multiple levels of abstraction. In this study, an improved deep learning model is proposed to explore the complex interactions among roadways, traffic, environmental elements, and traffic crashes. The proposed model includes two modules, an unsupervised feature learning module to identify functional network between the explanatory v

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Fiorentini, Nicholas, and Massimo Losa. "Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms." Infrastructures 5, no. 7 (2020): 61. http://dx.doi.org/10.3390/infrastructures5070061.

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Crash severity is undoubtedly a fundamental aspect of a crash event. Although machine learning algorithms for predicting crash severity have recently gained interest by the academic community, there is a significant trend towards neglecting the fact that crash datasets are acutely imbalanced. Overlooking this fact generally leads to weak classifiers for predicting the minority class (crashes with higher severity). In this paper, in order to handle imbalanced accident datasets and provide a better prediction for the minority class, the random undersampling the majority class (RUMC) technique is

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Lu, Pan, Zijian Zheng, Yihao Ren, et al. "A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis." Journal of Advanced Transportation 2020 (June 19, 2020): 1–10. http://dx.doi.org/10.1155/2020/6751728.

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Highway-rail grade crossing (HRGC) crashes continue to be the major contributors to rail causalities in the United States and have been intensively researched in the past. Data-mining models focus on prediction while dominant general linear models focus on model and data fitness. Decision makers and traffic engineers rely on prediction models to examine at-grade crash frequency and make safety improvement. The gradient boosting (GB) model has gained popularity in many research areas. In this study, to fully understand the model performance on HRGC accident prediction performance, the GB model

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Wang, Bo, Chi Zhang, Yiik Diew Wong, Lei Hou, Min Zhang, and Yujie Xiang. "Comparing Resampling Algorithms and Classifiers for Modeling Traffic Risk Prediction." International Journal of Environmental Research and Public Health 19, no. 20 (2022): 13693. http://dx.doi.org/10.3390/ijerph192013693.

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Road infrastructure has significant effects on road traffic safety and needs further examination. In terms of traffic crash prediction, recent studies have started to develop deep learning classification algorithms. However, given the uncertainty of traffic crashes, predicting the traffic risk potential of different road sections remains a challenge. To bridge this knowledge gap, this study investigated a real-world expressway and collected its traffic crash data between 2013 and 2020. Then, according to the time-spatial density ratio (Pts), road sections were assigned into three classes corre

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Sharmin, Sadia, John N. Ivan, Shanshan Zhao, et al. "Incorporating Demographic Proportions into Crash Count Models by Quasi-Induced Exposure Method." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 9 (2020): 548–60. http://dx.doi.org/10.1177/0361198120930230.

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Quasi-induced exposure (QIE) is an effective technique for estimating the exposure of a specific driving or vehicle population when real exposure data are not available. Typically crash prediction models are carried out at the site level, that is, segment or intersection. Driving population characteristics are generally not available at this level, however, and thus are omitted from count models. Because of the sparsity of traffic crashes, estimating driving population distributions at the site level using crash data at individual sites is challenging. This study proposes a technique to obtain

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Xu, Chengcheng, Chen Wang, and Pan Liu. "Evaluating the Combined Effects of Weather and Real-Time Traffic Conditions on Freeway Crash Risks." Weather, Climate, and Society 10, no. 4 (2018): 837–50. http://dx.doi.org/10.1175/wcas-d-17-0124.1.

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Abstract The study presented in this paper investigated the combined effects of environmental factors and real-time traffic conditions on freeway crash risks. Traffic and weather data were collected from a 35-km freeway segment in the state of California, United States. The weather conditions were classified into five categories: clear, light rain, moderate/heavy rain, haze, and mist/fog. Logistic regression models using unmatched case-control data were developed to link the likelihood of crash occurrences to various traffic and environmental variables. The sample size requirements for case-co

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Deng, Shangkun, Yingke Zhu, Shuangyang Duan, Zhe Fu, and Zonghua Liu. "Stock Price Crash Warning in the Chinese Security Market Using a Machine Learning-Based Method and Financial Indicators." Systems 10, no. 4 (2022): 108. http://dx.doi.org/10.3390/systems10040108.

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Stock price crashes have occurred frequently in the Chinese security market during the last three decades. They have not only caused substantial economic losses to market investors but also seriously threatened the stability and financial safety of the security market. To protect against the price crash risk of individual stocks, a prediction and explanation approach has been proposed by combining eXtreme Gradient Boosting (XGBoost), the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and SHapley Additive exPlanations (SHAP). We assume that financial indicators can be adopted for stock c

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Liu, Xian, Jian Lu, Zeyang Cheng, and Xiaochi Ma. "A Dynamic Bayesian Network-Based Real-Time Crash Prediction Model for Urban Elevated Expressway." Journal of Advanced Transportation 2021 (May 13, 2021): 1–12. http://dx.doi.org/10.1155/2021/5569143.

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Traffic crash is a complex phenomenon that involves coupling interdependency among multiple influencing factors. Considering that interdependency is critical for predicting crash risk accurately and contributes to revealing the underlying mechanism of crash occurrence as well, the present study attempts to build a Real-Time Crash Prediction Model (RTCPM) for urban elevated expressway accounting for the dynamicity and coupling interdependency among traffic flow characteristics before crash occurrence and identify the most probable risk propagation path and the most significant contributors to c

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Huang, Weige, Hua Wang, and Qian Chen. "Neural Network Predictions Can Be Misleading Evidence From Predicting Crude Oil Futures Prices." E3S Web of Conferences 253 (2021): 02015. http://dx.doi.org/10.1051/e3sconf/202125302015.

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This paper applies Neural Network to predict WTI crude oil futures prices on basis of intraday minutely high frequency data. We looked into the recent market crash in the WTI crude oil futures market in April before the Delivery day. The results indicate that Neural Network could be misleading. More specifically, the paper shows that in normal situations Neural Network works well in sample and out of sample but it could give predictions with the opposite signs when the there exists a crash such as the one happened on April 20th, 2020. The evidence demonstrates that the prediction based on Neur

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Ma, Xiao-chi, Jian Lu, and Yiik Diew Wong. "Exploring the Behavior-Driven Crash Risk Prediction Model: The Role of Onboard Navigation Data in Road Safety." Journal of Advanced Transportation 2023 (December 26, 2023): 1–16. http://dx.doi.org/10.1155/2023/2780961.

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Driving behavior has frequently been overlooked in previous road traffic crash research. Hereby, abnormal (extreme) driving behavior data transmitted by the onboard navigation systems were collected for vehicles involved in traffic crashes, including sharp-lane-change, sharp-acceleration, and sudden-braking behaviors. Using these data in conjunction with expressway crash records, multiple classification learners were trained to establish a behavior-driven risk prediction model. To further investigate the influence of driving behavior on crash risk, partial dependence plots (PDPs) were applied.

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Karki, Bipin, Xiao Bo Qu, Kriengsak Panuwatwanich, Sherif Mohamed, and Partha Parajuli. "A GIS Based Crash Assignment Model for Signalized T Intersections." Applied Mechanics and Materials 543-547 (March 2014): 4472–75. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.4472.

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The crash assignment problem has long been considered as one of the most important components in an approach-level crash prediction model for intersections. A few pioneering studies have been carried out to properly assign the crashes in or nearby intersections to various approaches. However, the implementation of these models is very time consuming as it can only be done one by one manually. In this paper, a geographical information system (GIS) database is developed to complete the crash assignment. This tool has been applied in Queensland, Australia in the development of crash prediction mo

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Cai, Bowen, and Qianli Di. "Different Forecasting Model Comparison for Near Future Crash Prediction." Applied Sciences 13, no. 2 (2023): 759. http://dx.doi.org/10.3390/app13020759.

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A traffic crash is becoming one of the major factors that leads to unexpected death in the world. Short window traffic crash prediction in the near future is becoming more pragmatic with the advancements in the fields of artificial intelligence and traffic sensor technology. Short window traffic prediction can monitor traffic in real time, identify unsafe traffic dynamics, and implement suitable interventions for traffic conflicts. Crash prediction being an important component of intelligent traffic systems, it plays a crucial role in the development of proactive road safety management systems

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Bhattarai, Sujan. "Crash Prediction for Prioritization of Intersections for Safety Improvement: Case Study of Kathmandu Valley." Journal of Advanced College of Engineering and Management 5 (December 18, 2019): 165–79. http://dx.doi.org/10.3126/jacem.v5i0.26765.

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Every year globally 1.3 million people lose their lives from road traffic crashes (RTAs). Similarly, increasing rate of RTAs has been observed in Nepal including Kathmandu valley. This study is focused on the analysis of crash trends and respective site specific geometric features of urban road intersections in Kathmandu valley. Seventeen major intersections based on the data availability and traffic volume, are considered for the analysis of crash type. Previous crash data and traffic volume records of one year have been analysed. Common types of three and four legged intersections were taken

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Lekshmi, Sreekumar, and Nivedhitha M.G Sai. "Signalized Intersection Safety: A Case Study of Kollam Corporation." Journal of Transportation Engineering and Traffic Management 3, no. 3 (2022): 1–13. https://doi.org/10.5281/zenodo.7323046.

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In developing nations like India, motorization is increasing along with economic growth. Road traffic deaths in urban India have consistently been a serious issue of concern. National Crime Records Bureau (NCRB) 2014 reports show that urban road traffic crashes within the state of Kerala, India, increased by 37% from 2009 to 2012. Nearly 20% of those crashes occurred at intersections. 40% of significant traffic-related injuries and fatalities involved incidents at signalized intersections, which made up 24% of all recorded crashes at intersections. An urban road network's signalized crossi

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Acı, Çiğdem İnan, Gizen Mutlu, Murat Ozen, and Mehmet Acı. "Enhanced Multi-Class Driver Injury Severity Prediction Using a Hybrid Deep Learning and Random Forest Approach." Applied Sciences 15, no. 3 (2025): 1586. https://doi.org/10.3390/app15031586.

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Predicting driver injury severity and identifying factors influencing crash outcomes are crucial for developing effective traffic safety measures. This study focuses on estimating driver injury severity (uninjured, injured, or killed) and determining critical factors affecting crash outcomes. A hybrid framework combining Deep Neural Networks (DNNs) and Random Forest (RF) is proposed, where a DNN extracts features and RF performs the final classification, leveraging ensemble methods. The results were compared with those of well-known methods (e.g., kNN, XGBoost), with the hybrid approach achiev

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Kronprasert, Nopadon, Katesirint Boontan, and Patipat Kanha. "Crash Prediction Models for Horizontal Curve Segments on Two-Lane Rural Roads in Thailand." Sustainability 13, no. 16 (2021): 9011. http://dx.doi.org/10.3390/su13169011.

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The number of road crashes continues to rise significantly in Thailand. Curve segments on two-lane rural roads are among the most hazardous locations which lead to road crashes and tremendous economic losses; therefore, a detailed examination of its risk is required. This study aims to develop crash prediction models using Safety Performance Functions (SPFs) as a tool to identify the relationship among road alignment, road geometric and traffic conditions, and crash frequency for two-lane rural horizontal curve segments. Relevant data associated with 86,599 curve segments on two-lane rural roa

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Montella, Alfonso, Lucio Colantuoni, and Renato Lamberti. "Crash Prediction Models for Rural Motorways." Transportation Research Record: Journal of the Transportation Research Board 2083, no. 1 (2008): 180–89. http://dx.doi.org/10.3141/2083-21.

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Montana, J. Rhet, Luis A. Souto Arias, Pasquale Cirillo, and Cornelis W. Oosterlee. "Quantum Majorization in Market Crash Prediction." Risks 12, no. 12 (2024): 204. https://doi.org/10.3390/risks12120204.

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We introduce the Quantum Alarm System, a novel framework that combines the informational advantages of quantum majorization applied to tail pseudo-correlation matrices with the learning capabilities of a reinforced urn process, to predict financial turmoil and market crashes. This integration allows for a more nuanced analysis of the dependence structure in financial markets, particularly focusing on extreme events reflected in the tails of the distribution. Our model is tested using the daily log-returns of the 30 constituents of the Dow Jones Industrial Average, spanning from 2 January 1992

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Ding, Naikan, Linsheng Lu, and Nisha Jiao. "Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach." Discrete Dynamics in Nature and Society 2021 (November 20, 2021): 1–21. http://dx.doi.org/10.1155/2021/7028660.

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Rear-end crashes or crash risk is widely recognized as safety-critical state of vehicles under comprehensive conditions. This study investigated the association between traffic flow uncertainty, drivers’ visual perception, car-following behavior, roadway and vehicular characteristics, and rear-end crash risk variation and compared the crash risk variation prediction with and without specific flow-level data. Two datasets comprising 5055 individual vehicles in car-following state were collected through on-road experiments on two freeways in China. A hierarchical hybrid BN model approach was pro

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Khorsandi, Farzaneh, Guilherme De Moura Araujo, and Fernando Ferreira Lima dos Santos. "Artificial Intelligence-Driven All-Terrain Vehicle Crash Prediction and Prevention System." Journal of Agricultural Safety and Health 30, no. 4 (2024): 139–54. http://dx.doi.org/10.13031/jash.16079.

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HighlightsAn AI-driven system for predicting and preventing ATV crashes was developed.Machine learning model achieved rollover prediction accuracy of over 99%.The system has the potential to significantly reduce ATV-related injuries and fatalities by enabling preemptive actions.Abstract.All-Terrain Vehicle (ATV) crashes have become a public health concern in the U.S. over the past decades, resulting in numerous fatalities and hospitalizations. Most of those incidents could have been prevented if riders could better assess their ability to handle risks. Currently, risk factors associated with A

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Ji, Yang beibei, Rui Jiang, Ming Qu, and Edward Chung. "Traffic Incident Clearance Time and Arrival Time Prediction Based on Hazard Models." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/508039.

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Accurate prediction of incident duration is not only important information of Traffic Incident Management System, but also an effective input for travel time prediction. In this paper, the hazard based prediction models are developed for both incident clearance time and arrival time. The data are obtained from the Queensland Department of Transport and Main Roads’ STREAMS Incident Management System (SIMS) for one year ending in November 2010. The best fitting distributions are drawn for both clearance and arrival time for 3 types of incident: crash, stationary vehicle, and hazard. The results

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Cosgun, Taner, Mahmutcan Esenkalan, and Omer Kemal Kinaci. "Four-quadrant propeller hydrodynamic performance mapping for improving ship motion predictions." Brodogradnja 75, no. 3 (2024): 1–20. http://dx.doi.org/10.21278/brod75306.

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On the path toward fully autonomous sea vessels, forecasting a ship’s exact velocity and position during its route plays a crucial role in dynamic positioning, target tracking, and autopilot operations of the unmanned body navigating toward predetermined locations. This paper addresses the prediction of the operational performance of a free-running submarine advancing in a straight route (in surge motion). Along with the forward advancing vessel (straight-ahead motion) the study covers all possible scenarios of ship’s surge, including crash-ahead, crash-back, and astern motions. Conventional m

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Chanh Nghia, Nguyen, Tatacipta Dirgantara, Sigit P. Santosa, Annisa Jusuf, and Ichsan Setya Putra. "Impact Behavior of Square Crash Box Structures Having Holes at Corners." Applied Mechanics and Materials 660 (October 2014): 613–17. http://dx.doi.org/10.4028/www.scientific.net/amm.660.613.

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In this paper, an analytical prediction and numerical simulation of the behavior of square crash box structures having hole at corners on dynamic axial crushing are studied. The focus of the present theoretical prediction is to calculate the mean crushing force and maximum crushing force during the folding process subjected to axial impact loading. Then, the effect of hole size to the crushing response of square crash box structures was also evaluated. For validation, an explicit non-linear commercial finite element code LS-DYNA was used to predict the response of the structures subjected to a

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Samadi, Hossein, Omid Rahmani, Khaled Shaaban, Amir Saman Abdollahzadeh Nasiri, and Mehrzad Hasanvand. "Optimizing Equivalent Property-Damage-Only (EPDO) Prediction Models with Genetic Algorithms: A Case Study on Roundabout Geometric Characteristics." Infrastructures 10, no. 3 (2025): 61. https://doi.org/10.3390/infrastructures10030061.

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Roundabouts generally offer better traffic safety than other intersections, yet severe crashes still occur. They serve as a viable option to enhance intersection safety and reduce crash severity. Improving crash prediction models enhances the precision of prioritization and safety evaluation, ultimately lowering crash-related costs. This study examines the impact of geometric factors on crash frequency and severity in roundabouts. The equivalent property-damage-only (EPDO) index, which considers both severity and frequency, was included as an independent parameter. Increasing traffic volume si

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Hofmann, M., A. Oeckerath, T. Wallmersperger, and K. Wolf. "Improved Implementation of Material Behaviour Change due to Bake Hardening in the Simulation of the Process Chain." HTM Journal of Heat Treatment and Materials 77, no. 3 (2022): 228–39. http://dx.doi.org/10.1515/htm-2022-1009.

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Abstract The steadily increasing demands on the crash safety of automobiles with simultaneous weight reduction also mean higher requirements for crash simulation. In addition to manufacturability, the focus in component dimensioning is increasingly on crash properties. In order to improve the predictive accuracy of crash simulation, the individual process steps must be coupled in the simulations. To increase the quality of the predictions, thermal treatments such as curing of the paint, which normally takes place at 170 °C for around 20 minutes, should also be taken into account. In many steel

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Jiang, Yalan, Xianguo Qu, Weiwei Zhang, et al. "Analyzing Crash Severity: Human Injury Severity Prediction Method Based on Transformer Model." Vehicles 7, no. 1 (2025): 5. https://doi.org/10.3390/vehicles7010005.

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Traffic accident-related injuries and fatalities are a serious global public health and social development challenge. The accurate prediction of crash severity improves road safety and reduces casualties, as well as serving road managers and policy makers. Prediction models need to learn and analyze the various characteristic factors of traffic accidents and capture from them the inherent complex relationship between accident characteristics and the severity of traffic accidents. However, most accident prediction studies lack analytical predictions of injury severity, and predictive models rel

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Ho, Sheng-Chih, Kuo-Chi Yen, Chung-Yung Wang, and Yu Sun. "A Traffic Crash Warning Model for BOT E-Tolling Operations Based on Predictions Using a Data Association Framework." Applied Sciences 13, no. 10 (2023): 5973. http://dx.doi.org/10.3390/app13105973.

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As a result of the increasing use of artificial intelligence technology in transportation, numerous real-time crash prediction techniques have been developed. In the context of highway traffic management, machine learning models and classifiers are used to analyze electronic toll collection (ETC) and vehicle detector (VD) data to predict crash occurrences. However, traffic accidents are influenced by multiple factors, such as traffic speed differences, traffic density, and weather conditions, and direct associations may not exist between sensor data and crash incidents. Therefore, data integra

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Zhang, Cuiping, Xuedong Yan, Lu Ma, and Meiwu An. "Crash Prediction and Risk Evaluation Based on Traffic Analysis Zones." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/987978.

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Traffic safety evaluation for traffic analysis zones (TAZs) plays an important role in transportation safety planning and long-range transportation plan development. This paper aims to present a comprehensive analysis of zonal safety evaluation. First, several criteria are proposed to measure the crash risk at zonal level. Then these criteria are integrated into one measure-average hazard index (AHI), which is used to identify unsafe zones. In addition, the study develops a negative binomial regression model to statistically estimate significant factors for the unsafe zones. The model results

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Jia, Shuo, Jin Xu, Song Wang, and Xingliang Liu. "Connected multi-vehicle crash risk assessment considering probability and intensity." PLOS ONE 20, no. 2 (2025): e0313317. https://doi.org/10.1371/journal.pone.0313317.

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Accurate driving risk assessments are essential in vehicle collision avoidance and traffic safety. The uncertainty in driving intentions and behavior, coupled with the difficulty in accurately predicting future trajectories of vehicles, poses challenges in assessing collision risk among vehicles. Existing research on collision risk assessment has been limited to focusing on pre-crashes (e.g., time-to-collision) and ignoring the impact of crash severity on risk. Research integrating pre- and post-crash is needed to assess the collision risk comprehensively. Therefore, the objective of this stud

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Islam, Md Kamrul, Imran Reza, Uneb Gazder, Rocksana Akter, Md Arifuzzaman, and Muhammad Muhitur Rahman. "Predicting Road Crash Severity Using Classifier Models and Crash Hotspots." Applied Sciences 12, no. 22 (2022): 11354. http://dx.doi.org/10.3390/app122211354.

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The rapid increase in traffic volume on urban roads, over time, has altered the global traffic scenario. Additionally, it has increased the number of road crashes, some of which are severe and fatal in nature. The identification of hazardous roadway sections using the spatial pattern analysis of crashes and recognition of the primary and contributing factors may assist in reducing the severity of road traffic crashes (R.T.C.s). For crash severity prediction, along with spatial patterns, various machine learning models are used, and the spatial relations of R.T.C.s with neighboring areas are ev

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