A Review of Data Analytic Applications in Road Traffic Safety. Part 1: Descriptive and Predictive Modeling
暂无分享,去创建一个
Fadel M. Megahed | Steven E. Rigdon | Alexander Vinel | Mohammad Ali Alamdar Yazdi | Karen C. Davis | Nasrin Mohabbati-Kalejahi | Miao Cai | Qiong Hu | Amir Mehdizadeh | S. Rigdon | F. Megahed | K. Davis | A. Vinel | Amir Mehdizadeh | Qiong Hu | Miao Cai | Nasrin Mohabbati-Kalejahi | Mohammad Ali Alamdar Yazdi
[1] S. Erdogan. Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey. , 2009, Journal of safety research.
[2] Zahir Tari,et al. A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis , 2014, IEEE Transactions on Emerging Topics in Computing.
[3] Reinier J Jansen. Harsh Braking by Truck Drivers: A Comparison of Thresholds and Driving Contexts Using Naturalistic Driving Data , 2018 .
[4] Wei Wang,et al. Predicting crash likelihood and severity on freeways with real-time loop detector data. , 2013, Accident; analysis and prevention.
[5] Dot Hs,et al. The 100 Car Naturalistic Driving Study , 2002 .
[6] Paula C. Morrow,et al. THE INFLUENCE OF CARRIER SCHEDULING PRACTICES ON TRUCK DRIVER FATIGUE , 2002 .
[7] Cliff T. Ragsdale,et al. The truck driver scheduling problem with fatigue monitoring , 2018, Decis. Support Syst..
[8] Moinul Hossain,et al. A real-time crash prediction model for the ramp vicinities of urban expressways , 2013 .
[9] Paula C. Morrow,et al. Truck Driving Environments and Their Influence on Driver Fatigue and Crash Rates , 2001 .
[10] Mohamed Abdel-Aty,et al. Evaluation of surrogate measures for pedestrian trips at intersections and crash modeling. , 2019, Accident; analysis and prevention.
[11] A. Boyd. The United States department of transportation , 1968 .
[12] Wei Wang,et al. Evaluation of the impacts of traffic states on crash risks on freeways. , 2012, Accident; analysis and prevention.
[13] Wei Zeng,et al. Visualizing Interchange Patterns in Massive Movement Data , 2013, Comput. Graph. Forum.
[14] George Yannis,et al. Impact of real-time traffic characteristics on crash occurrence: Preliminary results of the case of rare events. , 2018, Accident; analysis and prevention.
[15] T. Åkerstedt,et al. Validation of the S and C components of the three-process model of alertness regulation. , 1995, Sleep.
[16] Jun Yan,et al. Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach , 2013 .
[17] Hossam S. Hassanein,et al. Vehicle as a Mobile Sensor , 2014, FNC/MobiSPC.
[18] Isabelle Thomas,et al. Intra-urban location and clustering of road accidents using GIS: a Belgian example , 2004, Int. J. Geogr. Inf. Sci..
[19] Sohail Asghar,et al. A REVIEW OF FEATURE SELECTION TECHNIQUES IN STRUCTURE LEARNING , 2013 .
[20] Heidrun Schumann,et al. Stacking-Based Visualization of Trajectory Attribute Data , 2012, IEEE Transactions on Visualization and Computer Graphics.
[21] Mohamed Abdel-Aty,et al. Safety analytics for integrating crash frequency and real-time risk modeling for expressways. , 2017, Accident; analysis and prevention.
[22] Ramayya Krishnan,et al. VAIT: A Visual Analytics System for Metropolitan Transportation , 2013, IEEE Transactions on Intelligent Transportation Systems.
[23] Hans P A Van Dongen,et al. Predicting performance and safety based on driver fatigue. , 2019, Accident; analysis and prevention.
[24] Ye Zhao,et al. Visualizing Hidden Themes of Taxi Movement with Semantic Transformation , 2014, 2014 IEEE Pacific Visualization Symposium.
[25] Thomas A. Dingus,et al. Estimating Crash Risk , 2011 .
[26] Ling Wang,et al. Analysis of real-time crash risk for expressway ramps using traffic, geometric, trip generation, and socio-demographic predictors. , 2017, Accident; analysis and prevention.
[27] Andy H. Lee,et al. Assessing the driving performance of older adult drivers: on-road versus simulated driving. , 2003, Accident; analysis and prevention.
[28] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[29] Karim Ismail,et al. Traffic conflict techniques for road safety analysis: open questions and some insights , 2014 .
[30] Manuel J. A. Eugster,et al. osmar: OpenStreetMap and R , 2013, R J..
[31] Konstantinos G. Zografos,et al. A bi-objective time-dependent vehicle routing and scheduling problem for hazardous materials distribution , 2012, EURO J. Transp. Logist..
[32] Jian Lu,et al. Identification of Accident Blackspots on Rural Roads Using Grid Clustering and Principal Component Clustering , 2019, Mathematical Problems in Engineering.
[33] Ferat Sahin,et al. A survey on feature selection methods , 2014, Comput. Electr. Eng..
[34] Qi Shi,et al. Big Data applications in real-time traffic operation and safety monitoring and improvement on urban expressways , 2015 .
[35] Thomas A. Dingus,et al. Near Crashes as Crash Surrogate for Naturalistic Driving Studies , 2010 .
[36] Piyushimita Thakuriah,et al. Evaluating pedestrian crashes in areas with high low-income or minority populations. , 2010, Accident; analysis and prevention.
[37] Pavel Berkhin,et al. A Survey of Clustering Data Mining Techniques , 2006, Grouping Multidimensional Data.
[38] Siyuan Liu,et al. T-Watcher: A New Visual Analytic System for Effective Traffic Surveillance , 2013, 2013 IEEE 14th International Conference on Mobile Data Management.
[39] Menno-Jan Kraak,et al. The space - time cube revisited from a geovisualization perspective , 2003 .
[40] S. Washington,et al. Statistical and Econometric Methods for Transportation Data Analysis , 2010 .
[41] Fei-Yue Wang,et al. A Survey of Traffic Data Visualization , 2015, IEEE Transactions on Intelligent Transportation Systems.
[42] Xiaoduan Sun,et al. Investigation on the wrong way driving crash patterns using multiple correspondence analysis. , 2018, Accident; analysis and prevention.
[43] Francisco Bravo,et al. Real-time crash prediction in an urban expressway using disaggregated data , 2018 .
[44] Cláudio T. Silva,et al. Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips , 2013, IEEE Transactions on Visualization and Computer Graphics.
[45] Sherali Zeadally,et al. Sensor Technologies for Intelligent Transportation Systems , 2018, Sensors.
[46] Timothy C. Coburn,et al. Statistical and Econometric Methods for Transportation Data Analysis , 2004, Technometrics.
[47] Ronald R Knipling. Threats to Scientific Validity in Truck Driver Hours-of-Service Studies , 2017 .
[48] Feng Guo,et al. Individual driver risk assessment using naturalistic driving data. , 2013, Accident; analysis and prevention.
[49] Darya Filippova,et al. ICE--visual analytics for transportation incident datasets , 2009, 2009 IEEE International Conference on Information Reuse & Integration.
[50] Xiaoru Yuan,et al. TripVista: Triple Perspective Visual Trajectory Analytics and its application on microscopic traffic data at a road intersection , 2011, 2011 IEEE Pacific Visualization Symposium.
[51] Ori Gudes,et al. Investigating articulated heavy-vehicle crashes in Western Australia using a spatial approach. , 2017, Accident; analysis and prevention.
[52] Mohamed Abdel-Aty,et al. Utilizing support vector machine in real-time crash risk evaluation. , 2013, Accident; analysis and prevention.
[53] Fred L. Mannering,et al. The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives , 2010 .
[54] Fadel M. Megahed,et al. Using visual data mining in highway traffic safety analysis and decision making , 2015 .
[55] Mohamed Abdel-Aty,et al. Estimation of Real-Time Crash Risk , 2011 .
[56] Jingqiu Guo,et al. Real-time crash prediction on freeways using data mining and emerging techniques , 2017, Journal of Modern Transportation.
[57] M Abkowitz,et al. Developing a risk/cost framework for routing truck movements of hazardous materials. , 1988, Accident; analysis and prevention.
[58] Mukesh Khare,et al. Principal component analysis of urban traffic characteristics and meteorological data , 2003 .
[59] Darya Filippova,et al. Visual Analytics for Transportation Incident Data Sets , 2009 .
[60] Simon Folkard,et al. Predictions from the three-process model of alertness. , 2004, Aviation, space, and environmental medicine.
[61] Sharon Newnam,et al. Work-related injury and illness among older truck drivers in Australia: A population based, retrospective cohort study , 2019, Safety Science.
[62] Michael S Griffith. Striving forward with analysis, research, and technology at the United States Federal Motor Carrier Safety Administration , 2007 .
[63] Menno-Jan Kraak. Visualising spatial distributions , 2005 .
[64] Maurizio Guida,et al. A crash-prediction model for multilane roads. , 2007, Accident; analysis and prevention.
[65] Berry Eggen,et al. Measuring driving styles: a validation of the multidimensional driving style inventory , 2015, AutomotiveUI.
[66] George Yannis,et al. A review of the effect of traffic and weather characteristics on road safety. , 2014, Accident; analysis and prevention.
[67] Simon Washington,et al. Impact of real-time traffic characteristics on freeway crash occurrence: systematic review and meta-analysis. , 2015, Accident; analysis and prevention.
[68] D. Dinges,et al. A new mathematical model for the homeostatic effects of sleep loss on neurobehavioral performance. , 2009, Journal of theoretical biology.
[69] Hany M. Hassan,et al. Predicting reduced visibility related crashes on freeways using real-time traffic flow data. , 2013, Journal of safety research.
[70] William Wright,et al. GeoTime Information Visualization , 2004, IEEE Symposium on Information Visualization.
[71] T. Dingus,et al. Distracted driving and risk of road crashes among novice and experienced drivers. , 2014, The New England journal of medicine.
[72] Jun Yan,et al. Kernel Density Estimation of traffic accidents in a network space , 2008, Comput. Environ. Urban Syst..
[73] Pedro Larrañaga,et al. A review of feature selection techniques in bioinformatics , 2007, Bioinform..
[74] Shin Hyoung Park,et al. Identification of Influential Weather Factors on Traffic Safety Using K-means Clustering and Random Forest , 2016 .
[75] Robin Lovelace,et al. Geocomputation with R , 2019 .
[76] Dot Hs,et al. The 100-Car Naturalistic Driving Study Phase II - Results of the 100-Car Field Experiment , 2006 .
[77] Chris Jurewicz,et al. An international review of challenges and opportunities in development and use of crash prediction models , 2018 .
[78] David E. Goldberg,et al. Genetic algorithms and Machine Learning , 1988, Machine Learning.
[79] William E McConnaughey,et al. HAZARDOUS MATERIALS TRANSPORTATION , 1970 .
[80] Michael E. Tipping,et al. Probabilistic Principal Component Analysis , 1999 .
[81] Mohamed Abdel-Aty,et al. Bayesian random effect models incorporating real-time weather and traffic data to investigate mountainous freeway hazardous factors. , 2013, Accident; analysis and prevention.
[82] Jian Sun,et al. A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data , 2015 .
[83] Mohamed M. Ahmed,et al. Assessment of Interaction of Crash Occurrence, Mountainous Freeway Geometry, Real-Time Weather, and Traffic Data , 2012 .
[84] Weiguo Han,et al. Visual Exploratory Data Analysis of Traffic Volume , 2006, MICAI.
[85] Eugene Garfield,et al. KeyWords Plus™—algorithmic derivative indexing , 1993 .
[86] Ning Wu,et al. A new approach for modeling of Fundamental Diagrams , 2002 .
[87] Michael L. Pack,et al. Visualization in Transportation: Challenges and Opportunities for Everyone , 2010, IEEE Computer Graphics and Applications.
[88] Mohamed Abdel-Aty,et al. Comprehensive Analysis of the Relationship between Real-Time Traffic Surveillance Data and Rear-End Crashes on Freeways , 2006 .
[89] Juneyoung Park,et al. Real-time crash prediction for expressway weaving segments , 2015 .
[90] Massimo Aria,et al. bibliometrix: An R-tool for comprehensive science mapping analysis , 2017, J. Informetrics.
[91] Adel W. Sadek,et al. A Novel Variable Selection Method based on Frequent Pattern Tree for Real-time Traffic Accident Risk Prediction , 2015, ArXiv.
[92] T. Golob,et al. A Method for Relating Type of Crash to Traffic Flow Characteristics on Urban Freeways , 2002 .
[93] Ronald R Knipling,et al. Naturalistic Driving Events: No Harm, No Foul, No Validity , 2017 .
[94] Jim P. Stimpson,et al. Trends in fatalities from distracted driving in the United States, 1999 to 2008. , 2010, American journal of public health.
[95] Zhang Yi,et al. A Flow Volumes Data Compression Approach for Traffic Network Based on Principal Component Analysis , 2007, 2007 IEEE Intelligent Transportation Systems Conference.
[96] Lalit Sivanandan Nookala,et al. Weather Impact on Traffic Conditions and Travel Time Prediction , 2006 .
[97] Aliaksei Laureshyn,et al. In search of surrogate safety indicators for vulnerable road users: a review of surrogate safety indicators , 2018 .
[98] Feng Guo,et al. Statistical Methods for Naturalistic Driving Studies , 2019, Annual Review of Statistics and Its Application.
[99] Ramandeep Kaur,et al. A Survey of Clustering Techniques , 2010 .
[100] Alan Penn,et al. Spatial distribution of urban pollution: Civilizing urban traffic , 1996 .
[101] Kerner,et al. Experimental properties of complexity in traffic flow. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.
[102] Tadahiro Taniguchi,et al. Visualization of Driving Behavior Based on Hidden Feature Extraction by Using Deep Learning , 2017, IEEE Transactions on Intelligent Transportation Systems.
[103] Bernhard Schölkopf,et al. Kernel Principal Component Analysis , 1997, ICANN.
[104] Wei Wang,et al. A Genetic Programming Model for Real-Time Crash Prediction on Freeways , 2013, IEEE Transactions on Intelligent Transportation Systems.
[105] Nikola Bogunovic,et al. A review of feature selection methods with applications , 2015, 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).
[106] Tarek Sayed,et al. Traffic accident modeling: some statistical issues , 2006 .
[107] Lucy T. Nowell,et al. ThemeRiver: visualizing theme changes over time , 2000, IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings.
[108] S. M. Sohel Mahmud,et al. Application of proximal surrogate indicators for safety evaluation: A review of recent developments and research needs , 2017 .
[109] Michael A. Regan,et al. Driver distraction: A review of the literature , 2003 .
[110] Chandra R. Bhat,et al. Analytic methods in accident research: Methodological frontier and future directions , 2014 .
[111] K. Kristiansen,et al. [Traffic accidents]. , 1963, Tidsskrift for den Norske laegeforening : tidsskrift for praktisk medicin, ny raekke.
[112] Daniel J. Graham,et al. Use of Accident Prediction Models in Road Safety Management – An International Inquiry , 2016 .
[113] Richard J. Hanowski,et al. Driver Distraction in Commercial Vehicle Operations , 2009 .
[114] Feng Guo,et al. Data and methods for studying commercial motor vehicle driver fatigue, highway safety and long-term driver health. , 2019, Accident; analysis and prevention.
[115] James H Banks,et al. SYNTHESIS OF RECENT WORK ON THE NATURE OF SPEED-FLOW AND FLOW-OCCUPANCY (OR DENSITY) RELATIONSHIPS ON FREEWAYS , 1992 .
[116] R. Dennis Cook,et al. Principal Components, Sufficient Dimension Reduction, and Envelopes , 2018 .
[117] Rosaldo J. F. Rossetti,et al. Pattern mining from historical traffic big data , 2017, 2017 IEEE Region 10 Symposium (TENSYMP).
[118] Riccardo Poli,et al. Particle swarm optimization , 1995, Swarm Intelligence.
[119] C. Chiorri,et al. Sleep Apnea, Sleep Debt and Daytime Sleepiness Are Independently Associated with Road Accidents. A Cross-Sectional Study on Truck Drivers , 2016, PLoS ONE.
[120] Jarke J. van Wijk,et al. Cluster and Calendar Based Visualization of Time Series Data , 1999, INFOVIS.
[121] Samina Khalid,et al. A survey of feature selection and feature extraction techniques in machine learning , 2014, 2014 Science and Information Conference.
[122] D. Dinges,et al. Dynamic circadian modulation in a biomathematical model for the effects of sleep and sleep loss on waking neurobehavioral performance. , 2013, Sleep.
[123] Richard J Hanowski,et al. Multiple Conditions Increase Preventable Crash Risks Among Truck Drivers in a Cohort Study , 2017, Journal of occupational and environmental medicine.
[124] Azad Abdulhafedh,et al. Road Crash Prediction Models: Different Statistical Modeling Approaches , 2017 .
[125] V. Caron,et al. United states. , 2018, Nursing standard (Royal College of Nursing (Great Britain) : 1987).