Assessment on the crash risk factors of a typical long-span bridge using oversampling-based classification method and considering bridge structure movement
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Abdel-Aty, 1897, Predicting freeway crashes from loop detector data by matched case-control logistic regression, Transp. Res. Rec. J. Transp. Res. Board, 1, 88
Abdel-Aty, 2007, Crash risk assessment using intelligent transportation systems data and real-time intervention strategies to improve safety on freeways, J. Intell. Transp. Syst. Technol. Plan. Oper., 11, 107, 10.1080/15472450701410395
Abdel-aty, 2006, Calibrating a real-time traffic crash-prediction model using archived weather and ITS traffic data, IEEE Trans. Intell. Transp. Syst., 7, 167, 10.1109/TITS.2006.874710
Abidine, M.B., Fergani, B., 2016. Comparing HMM, LDA, SVM and Smote-SVM Algorithms in Classifying Human Activities, pp. 639–644. doi: 10.1007/978-3-319-30298-0.
Ahmed, 2012, The viability of using automatic vehicle identification data for real-time crash prediction, IEEE Trans. Intell. Transp. Syst., 13, 459, 10.1109/TITS.2011.2171052
Ahmed, M.M., Abdel-aty, M., Yu, R., 2012. Assessment of Interaction of Crash Occurrence, Mountainous Freeway Geometry, Real-Time Weather , and Traffic Data, (December). doi: 10.3141/2280-06.
Basso, 2018, Real-time crash prediction in an urban expressway using disaggregated data’, Transp. Res. Part C., 86, 202, 10.1016/j.trc.2017.11.014
Bennet, 2015, Motor-vehicle collisions involving child pedestrians at intersection and mid-block locations, Accident Anal. Prevent., 78, 94, 10.1016/j.aap.2015.03.001
Chawla, 2006, SMOTE: synthetic minority over-sampling technique, J. Artif. Intell. Res., 2009, 321
Chen, 2011, Injury severities of truck drivers in single- and multi-vehicle accidents on rural highways, Accid. Anal. Prev., 43, 1677, 10.1016/j.aap.2011.03.026
Chen, 2014, Refined-scale panel data crash rate analysis using random-effects tobit model, Accident Anal. Prevent., 73, 323, 10.1016/j.aap.2014.09.025
Chen, 2016, Crash frequency modeling using real-time environmental and traffic data and unbalanced panel data models, Int. J. Environ. Res. Public Health, 13, 1, 10.3390/ijerph13060609
Chen, 2018, Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data, J. Saf. Res., 153, 10.1016/j.jsr.2018.02.010
Davis, 2001, Relating severity of pedestrian injury to impact speed in vehicle-pedestrian crashes: simple threshold model, Transp. Res. Rec., 108, 10.3141/1773-13
Gates, 2006, The safety and cost-effectiveness of approach guardrail for bridges on low volume roads, Transp. Res. Rec., 10.1177/0361198106196700106
Golob, 2003, Relationships among urban freeway accidents, traffic flow, weather, and lighting conditions, J. Transp. Eng., 129, 342, 10.1061/(ASCE)0733-947X(2003)129:4(342)
Golob, 2004, A method for relating type of crash to traffic flow characteristics on urban freeways, Transp. Res. Part A: Policy Pract., 38, 53
He, 2009, Learning from Imbalanced Data, IEEE Trans. Knowl. Data Eng., 21, 1263, 10.1109/TKDE.2008.239
Hossain, M., Muromachi, Y. (no date) Understanding crash mechanism and selecting appropriate interventions for real-time hazard mitigation on urban expressways. Transp. Res. Rec. 2213, 53–62.
Hossain, 2012, A Bayesian network based framework for real-time crash prediction on the basic freeway segments of urban expressways, Accident Anal. Prevent., 45, 373, 10.1016/j.aap.2011.08.004
Huang, 2012, Characteristics anaysis and safety countermeasures of long span highway bridge traffic accidents of Yangtze river delta, Highway, 4, P160
Kadilar, 2016, Effect of driver, roadway, collision, and vehicle characteristics on crash severity: a conditional logistic regression approach, Int. J. Injury Control Saf. Promot., 23, 135, 10.1080/17457300.2014.942323
Lee, 2006, Potential real-time indicators of sideswipe crashes on freeways, Transp. Res. Rec. J. Transp. Res. Board, 41, 10.1177/0361198106195300105
Leo, 2011, Random Forests, Machine Learning, 45, 5
Lord, 2010, The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives, Transp. Res. Part A: Policy Pract., 44, 291
Ma, 2018, The impact of aggressive driving behavior on driver-injury severity at highway-rail grade crossings accidents, J. Adv. Transp., 2018, 10.1155/2018/9841498
Ma, 2019, Risk riding behaviors of urban e-bikes: A literature review, Int. J. Environ. Res. Public Health
Mehta, 2015, Safety performance function development for analysis of bridges, J. Transp. Eng., 141, 1, 10.1061/(ASCE)TE.1943-5436.0000776
Murthy, 1990, A fuzzy set approach for bridge traffic safety evaluation, Civil Eng. Syst., 7, 36, 10.1080/02630259008970568
Oqab, 2016, Bayes classifiers for imbalanced traffic accidents datasets, Accident Anal. Prevent., 88, 37, 10.1016/j.aap.2015.12.003
Pande, 2007, Multiple-model framework for assessment of real-time crash risk, Transp. Res. Rec. J. Transp. Res. Board, 2019, 99, 10.3141/2019-13
Roshandel, 2015, Impact of real-time traffic characteristics on freeway crash occurrence: Systematic review and meta-analysis, Int. J. Infectious Dis. Int. Soc. Infectious Dis., 79, 198
Sun, J. et al., 2018. Imbalanced enterprise credit evaluation with DTE-SBD: decision tree ensemble based on SMOTE and bagging with differentiated sampling rates. Elsevier Inc., 425, 76–91. doi: 10.1016/j.ins.2017.10.017.
Turner, 1984, Prediction of bridge accident rates, J. Transp. Eng., 110, 45, 10.1061/(ASCE)0733-947X(1984)110:1(45)
Wang, 2016, Analysis of traffic accident characteristics of Hangzhou Bay sea-crossing bridge, Highway, 5, P152
Washington, 2003
Wustrow, 1994, 65.Jahresversammlung Der Deutschen Gesellschaft Fur Hno-Heilkunde, Kopf- Und Halschirurgie, Laryngo- Rhino- Otologie, 73, 556, 10.1055/s-2007-997194
Ximiao, 2016, Investigating macro-level hotzone identification and variable importance using big data: a random forest models approach, Neurocomputing, 181, 53
Xu, 2016, Real-time identification of traffic conditions prone to injury and non-injury crashes on freeways using genetic programming, J. Adv. Transp., 50, 701, 10.1002/atr.1370
Xu, 2013, Analysis on influencing factors identification of crash rates using tobit model with endogenous variable analysis of influencing factors identification of crash rates using tobit model with endogenous variable, Promet Traff. Transp.
Xu, 2013, Identifying crash-prone traffic conditions under different weather on freeways, J. Saf. Res., 46, 135, 10.1016/j.jsr.2013.04.007
Yu, 2013, Bayesian random effect models incorporating real-time weather and traffic data to investigate mountainous freeway hazardous factors’, Accid. Anal. Prev., 50, 371, 10.1016/j.aap.2012.05.011
Yu, 2014, Analyzing crash injury severity for a mountainous freeway incorporating real-time traffic and weather data, Saf. Sci., 63, 50, 10.1016/j.ssci.2013.10.012
Zheng, 2010, Impact of traffic oscillations on freeway crash occurrences, Accid. Anal. Prev., 42, 626, 10.1016/j.aap.2009.10.009