Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis
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Ahangari, 2019, A Machine Learning Distracted Driving Prediction Model, International Symposium of Intelligent Unmanned Systems on Artificial Intelligence
Alwan, 2016, Car accident detection and notification system using smartphone, Int. J. Comput. Sci. Mob. Comput.
Amin, 2012, Accident detection and reporting system using GPS, GPRS and GSM technology, Int. Conf. Informatics, Electron. Vis, 640
Arvin, 2019, How instantaneous driving behavior contributes to crashes at intersections: extracting useful information from connected vehicle message data, Accid. Anal. Prev., 127, 118, 10.1016/j.aap.2019.01.014
Arvin, 2019, The role of pre-crash driving instability in contributing to crash intensity using naturalistic driving data, Accident Analysis & Prevention, 132, 10.1016/j.aap.2019.07.002
Azimi, 2019, Investigation of heterogeneity in severity analysis for large truck crashes, 98th Annu. Meet. Transp. Res. Board
Azimi, 2020, Severity analysis for large truck rollover crashes using a random parameter ordered logit model, Accident Analysis & Prevention, 135, 105355, 10.1016/j.aap.2019.105355
Badr, 2019, Why feature correlation matters…. A lot! Towar, Data Sci.
Boulange, 2017, Examining associations between urban design attributes and transport mode choice for walking, cycling, public transport and private motor vehicle trips, J. Transp. Health, 6, 155, 10.1016/j.jth.2017.07.007
Chawla, 2002, SMOTE: synthetic minority over-sampling technique, J. Artif. Intell. Res., 16, 321, 10.1613/jair.953
Chen, 2016, Learning deep representation from big and heterogeneous data for traffic accident inference, 30th AAAI Conf. Artif. Intell., 338
Chen, 2016, Xgboost: a scalable tree boosting system, Proc. 22nd acm sigkdd Int. Conf. Knowl. Discov. Data Min., 785, 10.1145/2939672.2939785
Chen, 2016, A vision based traffic accident detection method using extreme learning machine, Int. Conf. Adv. Robot. Mechatronics, 567
Clark, 2016
Dong, 2015, Support vector machine in crash prediction at the level of traffic analysis zones: assessing the spatial proximity effects, Accid. Anal. Prev., 82, 192, 10.1016/j.aap.2015.05.018
Fernandes, 2016, Automatic accident detection with multi-modal alert system implementation for ITS, Veh. Commun., 3, 1
Friedman, 2001, Greedy function approximation: a gradient boosting machine, Ann. Stat., 1189
Global status report on road safety 2015, 2015. World Heal. Organ.
Golshani, 2018, Modeling travel mode and timing decisions: comparison of artificial neural networks and copula-based joint model, Travel Behav. Soc., 21, 10.1016/j.tbs.2017.09.003
Gu, 2016, From Twitter to detector: real-time traffic incident detection using social media data, Transp. Res. Part C, 67, 321, 10.1016/j.trc.2016.02.011
Hamilton, 2019, An eXtreme gradient boosting method for identifying the factors contributing to crash/near-crash events: a naturalistic driving study, Can. J. Civ. Eng., 1
Han, 2005, Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning, Int. Conf. Intell. Comput., 878
Kashani, 2019, An agent-based simulation model to evaluate the response to seismic retrofit promotion policies, International Journal of Disaster Risk Reduction, 33, 181, 10.1016/j.ijdrr.2018.10.004
Kaur, 2018, Comparing the behavior of oversampling and undersampling approach of class imbalance learning by combining class imbalance problem with noise, ICT Based Innov., 23, 10.1007/978-981-10-6602-3_3
Kwak, 2016, Predicting crash risk and identifying crash precursors on Korean expressways using loop detector data, Accid. Anal. Prev., 88, 9, 10.1016/j.aap.2015.12.004
Lachapelle, 2015, Walk, bicycle and transit trips of transit dependent and choice riders in the NHTS 2009, J. Phys. Act. Health, 1139, 10.1123/jpah.2014-0052
Lundberg, 2017, A unified approach to interpreting model predictions, Adv. Neural Inf. Process. Syst., 4765
Maaloul, 2017, Adaptive video-based algorithm for accident detection on highways, 12th IEEE Int. Symp. Ind. Embed. Syst., 1
Mansourkhaki, 2016, Non-stationary concept of accident prediction, Proceedings of the Institution of Civil Engineers-Transport, 170, 140, 10.1680/jtran.15.00053
Mansourkhaki, 2017, Introducing prior knowledge for a hybrid accident prediction model, KSCE Journal of Civil Engineering, 1912, 10.1007/s12205-016-0495-4
Meng, 2018, Expressway crash prediction based on traffic big data, 2018 Int. Conf. Signal Process. Mach. Learn., 1
Mihaita, 2019
Mokhtarimousavi, 2019, Improved support vector machine models for work zone crash injury severity prediction and analysis, Transp. Res. Rec., 10.1177/0361198119845899
Mokoatle, 2019, Predicting road traffic accident severity using accident report data in South Africa, 20th Annu. Int. Conf. Digit. Gov. Res., 11, 10.1145/3325112.3325211
Movahedi, 2020, Interrelated Patterns of Electricity, Gas, and Water Consumption in Large-Scale Buildings. (under review), Sustainable Cities and Society
Nasr Esfahani, 2019, Prevalence of cell phone use while driving and its impact on driving performance, focusing on near-crash risk: A survey study in tehran, Journal of Transportation Safety & Security, 10.1080/19439962.2019.1701166
Ozbayoglu, 2017, 1807
Parsa, 2019, Applying Deep Learning to Detect Traffic Accidents in Real Time Using Spatiotemporal Sequential Data, arXiv Preprint
Parsa, 2019, Does security of neighborhoods affect non-mandatory trips? A copula-based joint multinomial-ordinal model of mode and trip distance choices, Transportation Research Board 98th Annual Meeting
Parsa, 2019, Real-time accident detection: coping with imbalanced data, Accid. Anal. Prev., 129, 202, 10.1016/j.aap.2019.05.014
Razi-Ardakani, 2018, A Nested Logit analysis of the influence of distraction on types of vehicle crashes, European Transport Research Review, 44, 10.1186/s12544-018-0316-6
Ribeiro, 2016, Why should i trust you?: Explaining the predictions of any classifier, Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min, 10.1145/2939672.2939778
Schlögl, 2019, A comparison of statistical learning methods for deriving determining factors of accident occurrence from an imbalanced high resolution dataset, Accid. Anal. Prev., 127, 134, 10.1016/j.aap.2019.02.008
Schulz, 2013, I see a car crash: real-time detection of small scale incidents in microblogs, Ext. Semant. Web Conf
Shan, 2018, Predicting duration of traffic accidents based on ensemble learning, Int. Conf. Collab. Comput. Networking, Appl. Work, 252
Shapley, 1953, A value for n-person games, 307
Sharifi, 2019, Exploring heterogeneous pedestrian stream characteristics at walking facilities with different angle intersections, Physica A: Statistical Mechanics and its Applications
Soleimani, 2019, A comprehensive railroad-highway grade crossing consolidation model: a machine learning approach, Accid. Anal. Prev., 128, 65, 10.1016/j.aap.2019.04.002
Štrumbelj, 2014, Explaining prediction models and individual predictions with feature contributions, Knowl. Inf. Syst., 647, 10.1007/s10115-013-0679-x
Sun, 2015, A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data, Transp. Res. Part C, 54, 176, 10.1016/j.trc.2015.03.006
Thomas, 2017, Toward detecting accidents with already available passive traffic information, IEEE 7th Annu. Comput. Commun. Work. Conf., 1
Traffic Incident Management, 2013
Traffic Safety Facts FARS, 2013
Vanhoeyveld, 2018, Imbalanced classification in sparse and large behaviour datasets, Data Min. Knowl. Discov., 32, 25, 10.1007/s10618-017-0517-y
Vishnu, 2018, Intelligent traffic video surveillance and accident detection system with dynamic traffic signal control, Cluster Comput., 135
Wang, 2016, Identification of freeway secondary accidents with traffic shock wave detected by loop detectors, Saf. Sci., 87, 195, 10.1016/j.ssci.2016.04.015
White, 2011, WreckWatch: automatic traffic accident detection and notification with smartphones, Mob. Netw. Appl., 285, 10.1007/s11036-011-0304-8
Xia, 2015, Vision-based traffic accident detection using matrix approximation, 10th Asian Control Conf, 1
Xu, 2016, Real-time detection and classification of traffic jams from probe data, Proc. 24th ACM SIGSPATIAL Int. Conf. Adv. Geogr. Inf. Syst., 10.1145/2996913.2996988
Xu, 2016, Real-time estimation of secondary crash likelihood on freeways using high-resolution loop detector data, Transp. Res. Part C, 71, 406, 10.1016/j.trc.2016.08.015
Yishui, 2015, Research of highway bottlenecks based on catastrophe theory, 2015 Int. Conf. Transp. Inf. Saf., 138
Zaldivar, 2011, Providing accident detection in vehicular networks through OBD-II devices and Android-based Smartphones, 813
Zhang, 2016, On-site traffic accident detection with both social media and traffic data, Proc. 9th Trienn. Symp. Transp. Anal. (TRISTAN), 3