Use of a model-based gradient boosting framework to assess spatial and non-linear effects of variables on pedestrian crash frequency at macro-level
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[1] Robert J. Schneider,et al. Analyzing Pedestrian and Bicyclist Crashes at the Corridor Level: Structural Equation Modeling Approach , 2019 .
[2] Mohamed Abdel-Aty,et al. A classification tree based modeling approach for segment related crashes on multilane highways. , 2010, Journal of safety research.
[3] Chandra R. Bhat,et al. On Accommodating Spatial Dependence in Bicycle and Pedestrian Injury Counts by Severity Level , 2013 .
[4] Mohamed Abdel-Aty,et al. Joint Modeling of Pedestrian and Bicycle Crashes: Copula-Based Approach , 2016 .
[5] Yu Zhang,et al. Insights from Integrated Geo-Location Data for Pedestrian Crashes, Demographics, and Land Uses , 2020 .
[6] Jörg Müller,et al. Spatial smoothing techniques for the assessment of habitat suitability , 2008, Environmental and Ecological Statistics.
[7] Matthias Schmid,et al. A review of spline function procedures in R , 2019, BMC Medical Research Methodology.
[8] Pengpeng Xu,et al. Towards activity-based exposure measures in spatial analysis of pedestrian-motor vehicle crashesThis article was handled by Associate Editor Chris Lee. , 2020, Accident; analysis and prevention.
[9] Gerhard Tutz,et al. Variable Selection and Model Choice in Geoadditive Regression Models , 2009, Biometrics.
[10] Lu Bai,et al. A joint probability model for pedestrian crashes at macroscopic level: Roles of environment, traffic, and population characteristics. , 2020, Accident; analysis and prevention.
[11] Benjamin Hofner,et al. Model-based boosting in R: a hands-on tutorial using the R package mboost , 2012, Computational Statistics.
[12] Patrick A. Brady,et al. Analysis of Types of Crashes at Signalized Intersections by Using Complete Crash Data and Tree-Based Regression , 2005 .
[13] Muhammad Ahsanul Habib,et al. Pedestrian Injury Severity Levels in the Halifax Regional Municipality, Nova Scotia, Canada , 2015 .
[14] Dibakar Saha,et al. A conceptual framework to understand the role of built environment on traffic safety. , 2020, Journal of safety research.
[15] Piyushimita Thakuriah,et al. Evaluating pedestrian crashes in areas with high low-income or minority populations. , 2010, Accident; analysis and prevention.
[16] David W. S. Wong,et al. Comparing implementations of global and local indicators of spatial association , 2018, TEST.
[17] Mohamed Abdel-Aty,et al. Nature of Modeling Boundary Pedestrian Crashes at Zones , 2012 .
[18] Rajiv Bhatia,et al. An area-level model of vehicle-pedestrian injury collisions with implications for land use and transportation planning. , 2009, Accident; analysis and prevention.
[19] Thomas G. Dietterich,et al. Training conditional random fields via gradient tree boosting , 2004, ICML.
[20] Mohamed Abdel-Aty,et al. Macro-level pedestrian and bicycle crash analysis: Incorporating spatial spillover effects in dual state count models. , 2016, Accident; analysis and prevention.
[21] Samiul Hasan,et al. Applying machine learning approaches to analyze the vulnerable road-users' crashes at statewide traffic analysis zones. , 2019, Journal of safety research.
[22] Zongni Gu,et al. Investigation into the built environment impacts on pedestrian crash frequencies during morning, noon/afternoon, night, and during peak hours: a case study in Miami County, Florida , 2019 .
[23] Mohamed Abdel-Aty,et al. Macrolevel Model Development for Safety Assessment of Road Network Structures , 2012 .
[24] Torsten Hothorn,et al. Model-based boosting in high dimensions , 2006, Bioinform..
[25] Richard Silberglitt,et al. The Road to Zero: A Vision for Achieving Zero Roadway Deaths by 2050. , 2018, Rand health quarterly.
[26] Kara M Kockelman,et al. A Poisson-lognormal conditional-autoregressive model for multivariate spatial analysis of pedestrian crash counts across neighborhoods. , 2013, Accident; analysis and prevention.
[27] Alois Knoll,et al. Gradient boosting machines, a tutorial , 2013, Front. Neurorobot..
[28] Yi-Shih Chung,et al. Factor complexity of crash occurrence: An empirical demonstration using boosted regression trees. , 2013, Accident; analysis and prevention.
[29] Chuan Ding,et al. Non-linear effects of the built environment on automobile-involved pedestrian crash frequency: A machine learning approach. , 2018, Accident; analysis and prevention.
[30] Mohamed Abdel-Aty,et al. Geographical unit based analysis in the context of transportation safety planning , 2013 .
[31] Xiaoqi Zhai,et al. Boundary crash data assignment in zonal safety analysis: An iterative approach based on data augmentation and Bayesian spatial model. , 2018, Accident; analysis and prevention.
[32] Tarek Sayed,et al. Evaluating the impact of connectivity, continuity, and topography of sidewalk network on pedestrian safety. , 2017, Accident; analysis and prevention.
[33] Pei-Sung Lin,et al. The Effects of Neighbourhood Characteristics and the Built Environment on Pedestrian Injury Severity: A Random Parameters Generalized Ordered Probability Model with Heterogeneity in Means and Variances , 2017 .
[34] Lu Bai,et al. Exposure to pedestrian crash based on household survey data: Effect of trip purpose. , 2019, Accident; analysis and prevention.
[35] Liping Fu,et al. Using a flexible multivariate latent class approach to model correlated outcomes: A joint analysis of pedestrian and cyclist injuries , 2017 .
[36] Xuesong Wang,et al. Macro-level safety analysis of pedestrian crashes in Shanghai, China. , 2016, Accident; analysis and prevention.
[37] P. Bühlmann,et al. Boosting With the L2 Loss , 2003 .
[38] Mohamed Abdel-Aty,et al. Multivariate crash modeling for motor vehicle and non-motorized modes at the macroscopic level. , 2015, Accident; analysis and prevention.
[39] Majid Sarvi,et al. Macroscopic modeling of pedestrian and bicycle crashes: A cross-comparison of estimation methods. , 2016, Accident; analysis and prevention.
[40] Dibakar Saha,et al. Prioritizing Highway Safety Manual's crash prediction variables using boosted regression trees. , 2015, Accident; analysis and prevention.
[41] S. Wood. Generalized Additive Models: An Introduction with R, Second Edition , 2017 .
[42] Peng Chen,et al. Effects of the Built Environment on Automobile-Involved Pedestrian Crash Frequency and Risk , 2016 .
[43] Matthias Schmid,et al. Applying additive modelling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages , 2012 .
[44] Chandra R. Bhat,et al. A new spatial and flexible multivariate random-coefficients model for the analysis of pedestrian injury counts by severity level , 2017 .
[45] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[46] Wenhao Li,et al. The built environment and the incidence of pedestrian and cyclist crashes , 2013 .
[47] Satish V. Ukkusuri,et al. The role of built environment on pedestrian crash frequency , 2012 .
[48] Carolina Burnier,et al. Severity of injury resulting from pedestrian-vehicle crashes: What can we learn from examining the built environment? , 2009 .
[49] Yunlong Zhang,et al. Crash Frequency Analysis with Generalized Additive Models , 2008 .
[50] G. Tutz,et al. Generalized Additive Modeling with Implicit Variable Selection by Likelihood‐Based Boosting , 2006, Biometrics.
[51] Xuedong Yan,et al. Exploring precrash maneuvers using classification trees and random forests. , 2009, Accident; analysis and prevention.
[52] Joan G. Hudson,et al. The challenge of safe and active transportation: Macrolevel examination of pedestrian and bicycle crashes in the Austin District , 2019, Journal of Transportation Safety & Security.
[53] Mohamed Abdel-Aty,et al. Macroscopic spatial analysis of pedestrian and bicycle crashes. , 2012, Accident; analysis and prevention.
[54] Torsten Hothorn,et al. Estimation and regularization techniques for regression models with multidimensional prediction functions , 2010, Stat. Comput..
[55] Haojie Li,et al. Comparison of exposure in pedestrian crash analyses: A study based on zonal origin-destination survey data , 2020 .