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Traffic accidents present substantial challenges to human safety and socio-economic development in urban areas.
Spatial risk estimation in tweedie compound poisson double generalized linear models
Halder, A., Mohammed, S., Chen, K., and Dey, D. (2019) · 1912
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An index which distinguishes between some important exponential families
Tweedie, M. C. et al. (1984) · 1984
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Exponential dispersion models
Jørgensen, B. (1987) · 1987
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Accident prediction models for roads with minor junctions
Mountain, L., Fawaz, B., and Jarrett, D. (1996) · 1996
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Comparison of fuzzy and neural classifiers for road accidents analysis
Sayed, T. and Abdelwahab, W. (1998) · 1998
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Fitting tweedie’s compound poisson model to insurance claims data: dispersion modelling
Smyth, G. K. and Jørgensen, B. (2002) · 2002
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Accident prediction models for urban roads
Greibe, P. (2003) · 2003
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Traffic accident analysis using decision trees and neural networks
Chong, M. M., Abraham, A., and Paprzycki, M. (2004) · 2004
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Occurrence and quantity of precipitation can be modelled simultaneously
Dunn, P. K. (2004) · 2004
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A summary of traffic flow forecasting methods
Liu, J. and Guan, W. (2004) · 2004
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Analysis of freeway accident frequencies: negative binomial regression versus artificial neural network
Chang, L.-Y. (2005) · 2005
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Data mining of tree-based models to analyze freeway accident frequency
Chang, L.-Y. and Chen, W.-C. (2005) · 2005
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Poisson, poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory
Lord, D., Washington, S. P., and Ivan, J. N. (2005) · 2005
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Traffic-incident detection-algorithm based on nonparametric regression
Tang, S. and Gao, H. (2005) · 2005
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A crash-prediction model for multilane roads
Caliendo, C., Guida, M., and Parisi, A. (2007) · 2007
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Kernel density estimation and k-means clustering to profile road accident hotspots
Anderson, T. K. (2009) · 2009
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Accident prediction models with random corridor parameters
El-Basyouny, K. and Sayed, T. (2009) · 2009
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Real-time highway traffic accident prediction based on the k-nearest neighbor method
Lv, Y., Tang, S., and Zhao, H. (2009) · 2009
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A bayesian network analysis of workplace accidents caused by falls from a height
Martin, J. E., Rivas, T., Matías, J., Taboada, J., and Argüelles, A. (2009) · 2009
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Lower upper bound estimation method for construction of neural network-based prediction intervals
Khosravi, A., Nahavandi, S., Creighton, D., and Atiya, A. F. (2010) · 2010
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The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives
Lord, D. and Mannering, F. (2010) · 2010
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Incident tree model and incident tree analysis method for quantified risk assessment: an in-depth accident study in traffic operation
Wang, W., Jiang, X., Xia, S., and Cao, Q. (2010) · 2010
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Random parameter models for accident prediction on two-lane undivided highways in india
Dinu, R. and Veeraragavan, A. (2011) · 2011
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A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using bayesian networks
Mujalli, R. O. and De Oña, J. (2011) · 2011
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The evolving structure of the southeast asian air transport network through the lens of complex networks, 1979–2012
Dai, L., Derudder, B., and Liu, X. (2018) · 2012
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A bayesian network based framework for real-time crash prediction on the basic freeway segments of urban expressways
Hossain, M. and Muromachi, Y. (2012) · 2012
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Explaining the road accident risk: Weather effects
Bergel-Hayat, R., Debbarh, M., Antoniou, C., and Yannis, G. (2013) · 2013
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Using geographically weighted poisson regression for county-level crash modeling in california
Li, Z., Wang, W., Liu, P., Bigham, J. M., and Ragland, D. R. (2013) · 2013
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The effect of traffic and road characteristics on road safety: A review and future research direction
Wang, C., Quddus, M. A., and Ison, S. G. (2013) · 2013
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014) · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
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Crash prediction and risk evaluation based on traffic analysis zones
Zhang, C., Yan, X., Ma, L., and An, M. (2014) · 2014
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A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction
Lin, L., Wang, Q., and Sadek, A. W. (2015) · 2015
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Application of the poisson-tweedie distribution in analyzing crash frequency data
Saha, D., Alluri, P., Dumbaugh, E., and Gan, A. (2020) · 2020
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Portraying the spatial dynamics of urban vibrancy using multisource urban big data
Tu, W., Zhu, T., Xia, J., Zhou, Y., Lai, Y., Jiang, J., and Li, Q. (2020) · 2020
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Graph deep learning model for network-based predictive hotspot mapping of sparse spatio-temporal events
Zhang, Y. and Cheng, T. (2020) · 2020
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Multi-modal urban transit network design considering reliability: multi-objective bi-level optimization
Barahimi, A. H., Eydi, A., and Aghaie, A. (2021) · 2021
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Predicting cycle-level traffic movements at signalized intersections using machine learning models
Mahmoud, N., Abdel-Aty, M., Cai, Q., and Yuan, J. (2021) · 2021
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Modeling crash spatial heterogeneity: Random parameter versus geographically weighting
Xu, P. and Huang, H. (2015) · 2015
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Learning deep representation from big and heterogeneous data for traffic accident inference
Chen, Q., Song, X., Yamada, H., and Shibasaki, R. (2016) · 2016
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Insurance ratemaking using a copula-based multivariate tweedie model
Shi, P. (2016) · 2016
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A non-negative matrix factorization model based on the zero-inflated tweedie distribution
Abe, H. and Yadohisa, H. (2017) · 2017
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Tweedie distributions for fitting semicontinuous health care utilization cost data
Kurz, C. F. (2017) · 2017
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Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (2017) · 2017
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Sdcae: Stack denoising convolutional autoencoder model for accident risk prediction via traffic big data
Chen, C., Fan, X., Zheng, C., Xiao, L., Cheng, M., and Wang, C. (2018) · 2018
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Ai and deep learning for urban computing
Wang, S. and Cao, J. (2021) · 2021
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Deep spatio-temporal graph convolutional network for traffic accident prediction
Yu, L., Du, B., Hu, X., Sun, L., Han, L., and Lv, W. (2021) · 2021
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Network spacetime ai: Concepts, methods and applications
Cheng, T., Zhang, Y., and Haworth, J. (2022) · 2022
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A deep generative approach for crash frequency model with heterogeneous imbalanced data
Ding, H., Lu, Y., Sze, N., Chen, T., Guo, Y., and Lin, Q. (2022) · 2022
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A novel stfsa-cnn-gru hybrid model for short-term traffic speed prediction
Ma, C., Zhao, Y., Dai, G., Xu, X., and Wong, S.-C. (2022) · 2022
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Differential expression of single-cell rna-seq data using tweedie models
Mallick, H., Chatterjee, S., Chowdhury, S., Chatterjee, S., Rahnavard, A., and Hicks, S. C. (2022) · 2022
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Uncertainty quantification for traffic forecasting: A unified approach
Qian, W., Zhang, D., Zhao, Y., Zheng, K., and Yu, J. J. (2022) · 2022
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A multi-head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networks
Reza, S., Ferreira, M. C., Machado, J., and Tavares, J. M. R. (2022) · 2022
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Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data
Tang, J. and Zeng, J. (2022) · 2022
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Attention based spatiotemporal graph attention networks for traffic flow forecasting
Wang, Y., Jing, C., Xu, S., and Guo, T. (2022) · 2022
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A multi-attention dynamic graph convolution network with cost-sensitive learning approach to road-level and minute-level traffic accident prediction
Wu, M., Jia, H., Luo, D., Luo, H., Zhao, F., and Li, G. (2022) · 2022
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Quantifying the spatial homogeneity of urban road networks via graph neural networks
Xue, J., Jiang, N., Liang, S., Pang, Q., Yabe, T., Ukkusuri, S. V., and Ma, J. (2022) · 2022
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Cross-area travel time uncertainty estimation from trajectory data: a federated learning approach
Zhu, Y., Ye, Y., Liu, Y., and James, J. (2022) · 2022
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Uncertainty quantification of sparse travel demand prediction with spatial-temporal graph neural networks
Zhuang, D., Wang, S., Koutsopoulos, H., and Zhao, J. (2022) · 2022
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Mst-gat: A multimodal spatial–temporal graph attention network for time series anomaly detection
Ding, C., Sun, S., and Zhao, J. (2023) · 2023
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Jiang, X., Zhuang, D., Zhang, X., Chen, H., Luo, J., and Gao, X. (2023) · 2023
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Global status report on road safety 2023
Organization, W. H. (2023) · 2023
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: Multi-view graph convolutional networks for traffic accident risk prediction
Trirat, P., Yoon, S., and Lee, J.-G. (2023) · 2023
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Uncertainty quantification of spatiotemporal travel demand with probabilistic graph neural networks
Wang, Q., Wang, S., Zhuang, D., Koutsopoulos, H., and Zhao, J. (2023) · 2023
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Gmat-du: Traffic anomaly prediction with fine spatiotemporal granularity in sparse data
Zhao, S., Zhao, D., Liu, R., Xia, Z., Cheng, B., and Chen, J. (2023) · 2023
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Network-wide road crash risk screening: a new framework
Bonera, M., Barabino, B., Yannis, G., and Maternini, G. (2024) · 2024
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Space-time analysis of accident frequency and the role of built environment in mitigation
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