Probability of Roadside Accidents for Curved Sections on Highways

To predict the probability of roadside accidents for curved sections on highways, we chose eight risk factors that may contribute to the probability of roadside accidents to conduct simulation tests and collected a total of 12,800 data obtained from the PC-crash software. The chi-squared automatic interaction detection (CHAID) decision tree technique was employed to identify significant risk factors and explore the influence of different combinations of significant risk factors on roadside accidents according to the generated decision rules, so as to propose specific improved countermeasures as the reference for the revision of the Design Specification for Highway Alignment (JTG D20-2017) of China. Considering the effects of related interactions among different risk factors on roadside accidents, path analysis was applied to investigate the importance of the significant risk factors. The results showed that the significant risk factors were in decreasing order of importance, vehicle speed, horizontal curve radius, vehicle type, adhesion coefficient, hard shoulder width, and longitudinal slope. The first five important factors were chosen as predictors of the probability of roadside accidents in the Bayesian network analysis to establish the probability prediction model of roadside accidents. Eventually, the thresholds of the various factors for roadside accident blackspot identification were given according to probabilistic prediction results.

[1]  Thomas A. Dingus,et al.  Contributing Factors to Run-off-road Crashes and Near-crashes , 2009 .

[2]  Xiaoduan Sun,et al.  Risk evaluation method for highway roadside accidents , 2019 .

[3]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[4]  Jin Xu,et al.  Vehicle trajectory at curved sections of two-lane mountain roads: a field study under natural driving conditions , 2018 .

[5]  Federico Fraboni,et al.  Using data mining techniques to predict the severity of bicycle crashes. , 2017, Accident; analysis and prevention.

[6]  Hsin-Li Chang,et al.  MODELING THE RELATIONSHIP OF ACCIDENTS TO MILES TRAVELED , 1986 .

[7]  Chinmoy Pal,et al.  Effective Numerical Simulation Tool for Real-World Rollover Accidents by Combining PC-Crash and FEA , 2007 .

[8]  João Lourenço Cardoso,et al.  SAFESIDE: A computer-aided procedure for integrating benefits and costs in roadside safety intervention decision making , 2015 .

[9]  Fazil Celik,et al.  Statistical Analysis of Truck Accidents for Divided Multilane Interurban Roads in Turkey , 2018 .

[10]  Gray Beauchamp,et al.  Analysis of a Dolly Rollover with PC-Crash , 2009 .

[11]  C Zegeer,et al.  EFFECT OF LANE WIDTH, SHOULDER WIDTH, AND SHOULDER TYPE ON HIGHWAY SAFETY , 1987 .

[12]  H Steffan,et al.  HOW TO USE PC-CRASH TO SIMULATE ROLLOVER CRASHES. IN: OCCUPANT AND VEHICLE RESPONSES IN ROLLOVERS , 2004 .

[13]  S. Arabia,et al.  Systems of Navier-Stokes equations on Cantor sets , 2013 .

[14]  Yifei Yuan,et al.  A Bayesian Network model for contextual versus non-contextual driving behavior assessment , 2017 .

[15]  Marcus A Brewer,et al.  Analysis of roadway departure crashes on two-lane rural roads in Texas. , 2011 .

[16]  Jian Lu,et al.  Causation Analysis of Hazardous Material Road Transportation Accidents by Bayesian Network Using Genie , 2018, Journal of Advanced Transportation.

[17]  Filipe Moura,et al.  Detecting unforgiving roadside contributors through the severity analysis of ran-off-road crashes. , 2015, Accident; analysis and prevention.

[18]  Karim El-Basyouny,et al.  Exploring the association between speed and safety: A path analysis approach. , 2016, Accident; analysis and prevention.

[19]  Adnan Darwiche,et al.  Modeling and Reasoning with Bayesian Networks , 2009 .

[20]  Shaw-Pin Miaou.  Estimating Vehicle Roadside Encroachment Frequencies by Using Accident Prediction Models , 1996 .

[21]  W. S. Voon,et al.  Single-vehicle crashes along rural mountainous highways in Malaysia: An application of random parameters negative binomial model. , 2017, Accident; analysis and prevention.

[22]  G. Tutz,et al.  An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. , 2009, Psychological methods.

[23]  G. V. Kass.  An Exploratory Technique for Investigating Large Quantities of Categorical Data , 1980 .

[24]  Eric T. Donnell,et al.  Models of vehicle operating speeds along two-lane rural highway transition zones: panel and multilevel modeling approaches , 2011 .

[25]  Robert B Noland,et al.  Traffic fatalities and injuries: the effect of changes in infrastructure and other trends. , 2003, Accident; analysis and prevention.

[26]  María Jesús López Boada,et al.  Study of van roadworthiness considering their maintenance and periodic inspection. The Spanish case , 2014 .

[27]  Haichao Li,et al.  A semi-analytical approach to analyze vibration characteristics of uniform and stepped annular-spherical shells with general boundary conditions , 2019, European Journal of Mechanics - A/Solids.

[28]  Jinbao Zhao,et al.  The use of Bayesian network in analysis of urban intersection crashes in China , 2013 .

[29]  Serena H. Chen,et al.  Good practice in Bayesian network modelling , 2012, Environ. Model. Softw..

[30]  Dot Hs.  Factors Related to Fatal Single-Vehicle Run-Off-Road Crashes , 2009 .

[31]  Sharaf Alkheder.  Learning from the past: traffic safety in the eyes of affected local community in Abu Dhabi City, United Arab Emirates , 2017 .

[32]  C Zegeer,et al.  SAFETY RELATIONSHIPS ASSOCIATED WITH CROSS-SECTIONAL ROADWAY ELEMENTS , 1995 .

[33]  Hsiao-Hwa Chen,et al.  A Rear-End Collision Risk Evaluation and Control Scheme Using a Bayesian Network Model , 2019, IEEE Transactions on Intelligent Transportation Systems.

[34]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[35]  Gilbert Ritschard,et al.  CHAID and Earlier Supervised Tree Methods , 2010 .

[36]  Fred L. Mannering,et al.  The relationship among highway geometrics, traffic-related elements and motor-vehicle accident frequencies , 1998 .

[37]  Fred Mannering,et al.  Impact of roadside features on the frequency and severity of run-off-roadway accidents: an empirical analysis. , 2002, Accident; analysis and prevention.

[38]  Helai Huang,et al.  A multivariate random-parameters Tobit model for analyzing highway crash rates by injury severity. , 2017, Accident; analysis and prevention.

[39]  Bryan T. Adey,et al.  Prediction of road accidents: comparison of two Bayesian methods , 2014 .

[40]  Cejun Liu,et al.  Run-Off-Road Crashes: An On-Scene Perspective , 2011 .

[41]  Baoshan Huang,et al.  Estimating Factors Contributing to Frequency and Severity of Large Truck–Involved Crashes , 2017 .

[42]  Gudmundur F. Ulfarsson,et al.  The crash severity impacts of fixed roadside objects. , 2005, Journal of safety research.

[43]  A. Hayes.  Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach , 2013 .

[44]  Yong Peng,et al.  Method for investigation of child occupant impact dynamics based on real-world accident , 2015 .

[45]  Stergios Mavromatis,et al.  Investigation of vehicle motion on sharp horizontal curves combined with steep longitudinal grades , 2016 .

[46]  William W. Hunter,et al.  Cost-effective geometric improvements for safety upgrading of horizontal curves , 1991 .

[47]  Cole D Fitzpatrick,et al.  The influence of clear zone size and roadside vegetation on driver behavior. , 2014, Journal of safety research.

[48]  Stephen H Richards,et al.  Influence of Curbs on Traffic Crash Frequency on High-Speed Roadways , 2011, Traffic injury prevention.

[49]  Fang Zong,et al.  Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models , 2013 .

[50]  Joseph E. Hummer,et al.  Curve Collisions: Road and Collision Characteristics and Countermeasures , 2010 .

[51]  Sarath C. Joshua,et al.  Estimating truck accident rate and involvements using linear and poisson regression models , 1990 .

[52]  Haichao Li,et al.  Vibration analysis of functionally graded porous cylindrical shell with arbitrary boundary restraints by using a semi analytical method , 2019, Composites Part B: Engineering.

[53]  F Mannering,et al.  Modeling accident frequencies as zero-altered probability processes: an empirical inquiry. , 1997, Accident; analysis and prevention.

[54]  Karen L Stephan,et al.  Characteristics of the Road and Surrounding Environment in Metropolitan Shopping Strips: Association with the Frequency and Severity of Single-Vehicle Crashes , 2014, Traffic injury prevention.

[55]  Sang Hyuk Lee,et al.  Analysis on safety impact of red light cameras using the Empirical Bayesian approach , 2016 .

[56]  Robert Thomson,et al.  Single-vehicle collisions in Europe: analysis using real-world and crash-test data , 2008 .

[57]  Madana Gopal,et al.  Simulation and Testing of a Suite of Field Relevant Rollovers , 2004 .

[58]  Laura Uusitalo,et al.  Advantages and challenges of Bayesian networks in environmental modelling , 2007 .

[59]  Haneen Farah,et al.  Empirical Speed Behavior on Horizontal Ramp Curves in Interchanges in the Netherlands , 2017 .

[60]  Bhagwant Persaud,et al.  Development of Planning-Level Transportation Safety Models using Full Bayesian Semiparametric Additive Techniques , 2010 .

[61]  Tarek Sayed,et al.  Calibrating Road Design Guides Using Risk-Based Reliability Analysis , 2013 .

[62]  Mohammad Jalayer,et al.  Evaluating the safety risk of roadside features for rural two-lane roads using reliability analysis. , 2016, Accident; analysis and prevention.

[63]  Mohammad Jalayer,et al.  Improving roadside design policies for safety enhancement using hazard-based duration modeling. , 2018, Accident; analysis and prevention.

[64]  Marek Bundzel,et al.  Application of neural network in order to recognise individuality of course of vehicle and pedestrian body contacts during accidents , 2019 .

[65]  Mehdi Hosseinpour,et al.  Evaluating the effects of road geometry, environment, and traffic volume on rollover crashes , 2016 .