Fusion convolutional neural network-based interpretation of unobserved heterogeneous factors in driver injury severity outcomes in single-vehicle crashes
暂无分享,去创建一个
Guohui Zhang | Tianwei Ma | Pan Liu | Zhenning Li | Hao Yu | Guohui Zhang | Pan Liu | Hao Yu | Zhenning Li | T. Ma
[1] David T. Ma,et al. Identifying heterogeneous factors for driver injury severity variations in snow-related rural single-vehicle crashes. , 2020, Accident; analysis and prevention.
[2] Jie Bao,et al. A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data. , 2019, Accident; analysis and prevention.
[3] Biswajeet Pradhan,et al. Severity Prediction of Traffic Accidents with Recurrent Neural Networks , 2017 .
[4] Franco Turini,et al. A Survey of Methods for Explaining Black Box Models , 2018, ACM Comput. Surv..
[5] Dominique Lord,et al. Comparing Three Commonly Used Crash Severity Models on Sample Size Requirements: Multinomial Logit, Ordered Probit, and Mixed Logit Models , 2014 .
[6] Nathan Huynh,et al. Analysis of driver injury severity in rural single-vehicle crashes. , 2012, Accident; analysis and prevention.
[7] Dominique Lord,et al. The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives. , 2011, Accident; analysis and prevention.
[8] Chandra R. Bhat,et al. Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis , 2020, Analytic Methods in Accident Research.
[9] Yunpeng Wang,et al. Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory , 2015, PloS one.
[10] Yunpeng Wang,et al. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data , 2015 .
[11] Xiqun Chen,et al. Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach , 2017, ArXiv.
[12] Jing Chen,et al. Traffic Accident’s Severity Prediction: A Deep-Learning Approach-Based CNN Network , 2019, IEEE Access.
[13] Feng Chen,et al. Investigating the Impacts of Real-Time Weather Conditions on Freeway Crash Severity: A Bayesian Spatial Analysis , 2020, International journal of environmental research and public health.
[14] Guohui Zhang,et al. Investigation of driver injury severities in rural single-vehicle crashes under rain conditions using mixed logit and latent class models. , 2019, Accident; analysis and prevention.
[15] Sven Behnke,et al. Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition , 2010, ICANN.
[16] Mohamed Abdel-Aty,et al. Development of Artificial Neural Network Models to Predict Driver Injury Severity in Traffic Accidents at Signalized Intersections , 2001 .
[17] Changxi Ma,et al. Temporal stability of driver injury severity in single-vehicle roadway departure crashes: A random thresholds random parameters hierarchical ordered probit approach , 2020 .
[18] Faming Liang,et al. Crash Injury Severity Analysis Using a Bayesian Ordered Probit Model , 2007 .
[19] Fred L. Mannering,et al. The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity , 2014 .
[20] Yannis Dimopoulos,et al. Use of some sensitivity criteria for choosing networks with good generalization ability , 1995, Neural Processing Letters.
[21] Loo Hay Lee,et al. Enhancing transportation systems via deep learning: A survey , 2019, Transportation Research Part C: Emerging Technologies.
[22] Amin Keramati,et al. A crash severity analysis at highway-rail grade crossings: The random survival forest method. , 2020, Accident; analysis and prevention.
[23] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[24] Guohui Zhang,et al. Examining driver injury severity in intersection-related crashes using cluster analysis and hierarchical Bayesian models. , 2018, Accident; analysis and prevention.
[25] Mohamed Abdel-Aty,et al. Crash injury severity analyses with multilevel thresholds of change modelling approach for at-fault out-of-state drivers , 2020, Journal of Transportation Safety & Security.
[26] Helai Huang,et al. A stable and optimized neural network model for crash injury severity prediction. , 2014, Accident; analysis and prevention.
[27] Qiong Wu,et al. Mixed logit model-based driver injury severity investigations in single- and multi-vehicle crashes on rural two-lane highways. , 2014, Accident Analysis and Prevention.
[28] F. Mannering. Temporal instability and the analysis of highway accident data , 2018 .
[29] Fred L. Mannering,et al. The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives , 2010 .
[30] Changxi Ma,et al. The temporal stability of factors affecting driver injury severity in run-off-road crashes: A random parameters ordered probit model with heterogeneity in the means approach. , 2020, Accident; analysis and prevention.
[31] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[32] C J O'Donnell,et al. Predicting the severity of motor vehicle accident injuries using models of ordered multiple choice. , 1996, Accident; analysis and prevention.
[33] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[34] Le Minh Kieu,et al. Deep learning methods in transportation domain: a review , 2018, IET Intelligent Transport Systems.
[35] Asad J. Khattak,et al. Injury Severity in Multivehicle Rear-End Crashes , 2001 .
[36] Eric T. Donnell,et al. Median barrier crash severity: some new insights. , 2010, Accident; analysis and prevention.
[37] Xiugang Li,et al. Predicting motor vehicle crashes using Support Vector Machine models. , 2008, Accident; analysis and prevention.
[38] Viswanathan Shankar,et al. Modeling the Simultaneity in Injury Causation in Multivehicle Collisions , 2002 .
[39] Yanyong Guo,et al. Modeling correlation and heterogeneity in crash rates by collision types using full bayesian random parameters multivariate Tobit model. , 2019, Accident; analysis and prevention.
[40] F. Mannering,et al. Driver aging and its effect on male and female single-vehicle accident injuries: some additional evidence. , 2006, Journal of safety research.
[41] Fei-Yue Wang,et al. Traffic Flow Prediction With Big Data: A Deep Learning Approach , 2015, IEEE Transactions on Intelligent Transportation Systems.
[42] Eduard Zaloshnja,et al. The Economic and Societal Impact of Motor Vehicle Crashes, 2010 (Revised) , 2015 .
[43] Guohui Zhang,et al. Exploratory multinomial logit model–based driver injury severity analyses for teenage and adult drivers in intersection-related crashes , 2016, Traffic injury prevention.
[44] Qiang Zeng,et al. Rule extraction from an optimized neural network for traffic crash frequency modeling. , 2016, Accident; analysis and prevention.
[45] Hussain Hamid,et al. Applications of Deep Learning in Severity Prediction of Traffic Accidents , 2017 .
[46] Li-Yen Chang,et al. Analysis of traffic injury severity: an application of non-parametric classification tree techniques. , 2006, Accident; analysis and prevention.
[47] Julian D. Olden,et al. Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks , 2002 .
[48] G. David Garson,et al. Interpreting neural-network connection weights , 1991 .
[49] Hongzhi Guan,et al. A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes. , 2015, Accident; analysis and prevention.
[50] Mohamed Abdel-Aty,et al. Artificial Neural Networks and Logit Models for Traffic Safety Analysis of Toll Plazas , 2002 .
[51] Jinwoo Lee,et al. Analyzing freeway crash severity using a Bayesian spatial generalized ordered logit model with conditional autoregressive priors. , 2019, Accident; analysis and prevention.
[52] F Mannering,et al. Statistical analysis of accident severity on rural freeways. , 1996, Accident; analysis and prevention.
[53] Shing Chung Josh Wong,et al. Modeling nonlinear relationship between crash frequency by severity and contributing factors by neural networks , 2016 .
[54] J. Estellé,et al. Chronic Trichuris muris Infection Decreases Diversity of the Intestinal Microbiota and Concomitantly Increases the Abundance of Lactobacilli , 2015, PloS one.
[55] Ramesh Sharda,et al. Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. , 2006, Accident; analysis and prevention.
[56] Yuanchang Xie,et al. Predicting motor vehicle collisions using Bayesian neural network models: an empirical analysis. , 2007, Accident; analysis and prevention.
[57] Panagiotis Ch. Anastasopoulos,et al. Analysis of accident injury-severities using a correlated random parameters ordered probit approach with time variant covariates , 2018, Analytic Methods in Accident Research.
[58] Jean Ponce,et al. A Theoretical Analysis of Feature Pooling in Visual Recognition , 2010, ICML.
[59] A. Shibata,et al. Risk factors of fatality in motor vehicle traffic accidents. , 1994, Accident; analysis and prevention.
[60] Yoshua Bengio,et al. On the Expressive Power of Deep Architectures , 2011, ALT.
[61] Guohui Zhang,et al. Examining driver injury severity outcomes in rural non-interstate roadway crashes using a hierarchical ordered logit model. , 2016, Accident; analysis and prevention.
[62] Mohamed Abdel-Aty,et al. Presence of passengers: does it increase or reduce driver's crash potential? , 2008, Accident; analysis and prevention.
[63] M. Abdel-Aty,et al. Analysis of left-turn crash injury severity by conflicting pattern using partial proportional odds models. , 2008, Accident; analysis and prevention.
[64] Tameru Hailesilassie,et al. Rule Extraction Algorithm for Deep Neural Networks: A Review , 2016, ArXiv.
[65] Hao Yu,et al. Short-term FFBS demand prediction with multi-source data in a hybrid deep learning framework , 2019 .
[66] Yin Yang,et al. Taxi-Based Mobility Demand Formulation and Prediction Using Conditional Generative Adversarial Network-Driven Learning Approaches , 2019, IEEE Transactions on Intelligent Transportation Systems.
[67] Fred Mannering,et al. Impact of roadside features on the frequency and severity of run-off-roadway accidents: an empirical analysis. , 2002, Accident; analysis and prevention.
[68] Qiong Wu,et al. Analysis of driver injury severity in single-vehicle crashes on rural and urban roadways. , 2016, Accident; analysis and prevention.