[1]张延孔,卢家品,张帅超,等.基于路网结构的城市交通事故短期风险预测方法[J].智能系统学报,2020,15(4):663-671.[doi:10.11992/tis.201910002]
 ZHANG Yankong,LU Jiapin,ZHANG Shuaichao,et al.A short-term risk prediction method for urban traffic accidents based on road network[J].CAAI Transactions on Intelligent Systems,2020,15(4):663-671.[doi:10.11992/tis.201910002]
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基于路网结构的城市交通事故短期风险预测方法

参考文献/References:
[1] World Health Organization. Global status report on road safety 2015[R]. World Health Organization, 2015.
[2] ZHANG Guangnan, KELVIN KW Yau, CHEN Guanghan. Risk factors associated with traffic violations and accident severity in China[J]. Accident analysis & prevention, 2013, 59: 18-25.
[3] 敖曼, 翟润平. 气象条件对道路交通的影响分析[J]. 公路与汽运, 2011(2): 58-62
AO Man, ZHAI Runping. Analysis of the influence of meteorological conditions on road traffic[J]. Highways & automotive applications, 2011(2): 58-62
[4] NOLAND R B. Traffic Fatalities and Injuries: the effect of changes in infrastructure and other trends[J]. Accident analysis & prevention, 2003, 35: 599-611.
[5] LI Z, WANG W, LIU P, et al. Using geographically weighted roisson regression for county-level crash modeling in california[J]. Safety science, 2013, 58: 89-97.
[6] 秦利燕, 邵春福, 赵亮. 道路交通事故宏观预测模型[J]. 武汉理工大学学报: 交通科学与工程版, 2010, 34(1): 158-161
QIN Liyan, SHAO Chunfu, ZHAO Liang. Macro prediction model of road traffic accident based on neural network[J]. Journal of Wuhan University of Technology (transportation science & engineering edition), 2010, 34(1): 158-161
[7] 张辉, 陈柯羽. 高速公路减速波事故的前兆特征与概率模型[J]. 交通运输研究, 2017, 3(5): 26-32
ZHANG Hui, CHEN Keyu. Precursory characteristics and probability model of deceleration wave accidents on expressways[J]. Transportation research, 2017, 3(5): 26-32
[8] 王健, 卢锡凤. 基于聚类分析的高速公路事故黑点鉴别及成因分析研究[J]. 公路交通技术, 2016, 32(5): 114-119
WANG Jian, LU Xifeng. Identification and cause analysis of expressway accident black spots based on cluster analysis[J]. Highway traffic technology, 2016, 32(5): 114-119
[9] BAO J, LIU P, YU H, et al. Incorporating twitter-based human activity information in spatial analysis of crashes in urban areas[J]. Accident analysis & prevention, 2017, 106: 358-369.
[10] SINNOTT R O, YIN S. Accident black spot identification and verification through social media[C]// IEEE International Conference on Data Science & Data Intensive Systems. Sydney, Australia, 2015.
[11] 李娟, 邵春福. 基于BP神经网络的交通事故预测模型[J]. 交通与计算机, 2006, 24(2): 34-37
LI Juan, SHAO Chunfu. Traffic accident prediction model based on BP neural network[J]. Traffic and computer, 2006, 24(2): 34-37
[12] 张志豪, 杨文忠, 袁婷婷, 等. 基于LSTM神经网络模型的交通事故预测[J]. 计算机工程与应用, 2019, 55(14): 249-253
ZHANG Zhihao, YANG Wenzhong, YUAN Tingting, et al. Traffic accident prediction based on LSTM neural network model[J]. Computer engineering and applications, 2019, 55(14): 249-253
[13] 胡立伟, 张婷, 郭凤香. 基于灰色BP神经网络的道路交通事故车型分担率预测及其预防策略研究[J]. 武汉理工大学学报(交通科学与工程版), 2018, 42(3): 388-392
HU Liwei, ZHANG Ti, GUO Fengxiang. Traffic accident split rate of vehicle types prediction and prevention Strategies study based on gray BP neural network[J]. Journal of Wuhan university of technology (transportation science & engineering edition), 2018, 42(3): 388-392
[14] MA X, TAO Z, WANG Y, et al. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data[J]. Transportation research part C: emerging technologies, 2015, 54: 187-197.
[15] CHEN Quanjun. Learning deep representation from big and heterogeneous data for traffic accident inference[C]//Thirtieth AAAI Conference on Artificial Intelligence. Phoenix, Arizona, United States, 2016: 338-344.
[16] REN Honglei. A deep learning approach to the citywide traffic accident risk prediction[C]// 2018 21st International Conference on Intelligent Transportation Systems (ITSC). Maui, United States, 2018: 3346-3351.
[17] BAO J, LIU P, UKKUSURI S V. A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data[J]. Accident analysis & prevention, 2019, 122: 239-254.
[18] DEFFERRARD Micha?l, XAVIER Bresson, PIERRE rgheynst. Convolutional neural networks on graphs with fast localized spectral filtering[J]. Advances in neural information processing systems, 2016: 3844-3852.
[19] KIPF T N, MAX W. Semi-supervised classification with graph convolutional network[J]. arXiv preprint arXiv: 1609.02907, 2016.
[20] LIN Lei, HE Zhengbing, SRINIVAS Peeta. Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach[J]. Transportation research part C: emerging technologies, 2018, 97: 258-276.
[21] GEORGE E P, DAVID A. Pierce. Distribution of residual autocorrelations in autoregressive-integrated moving average time series models[J]. Journal of the american statistical association, 1970, 65: 1509-1526.
[22] NAZNIN F. Application of a random effects negative binomial model to examine tram-involved crash frequency on route sections in Melbourne, Australia[J]. Accident analysis & prevention, 2016, 92: 15-21.
[23] BAO Jie. Incorporating twitter-based human activity information in spatial analysis of crashes in urban areas[J]. Accident analysis & prevention, 2017, 106: 358-369.

备注/Memo

收稿日期:2019-10-08。
基金项目:国家自然科学基金项目(61602146);浙江大学CAD&CG国家重点实验室开放课题(A1814);中央高校基本科研业务费专项(75104-036002)
作者简介:张延孔,讲师,博士研究生,主要研究方向为数据可视化、数据可解释分析;卢家品,硕士研究生,主要研究方向为数据可视化;张帅超,博士研究生,主要研究方向为交通利益预测与评估、大数据分析
通讯作者:张延孔.E-mail:zhangyankong@hfut.edu.cn

更新日期/Last Update: 2020-07-25
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