Objective and Subjective Analysis to Quantify Influence Factors of Driving Risk*

This paper proposes a method to quantify the driving risks of different traffic elements. We incorporate the objective analysis using naturalistic driving study (NDS) and the subjective analysis with driver attitude questionnaire (DAQ). The objective driving risks are investigated by a large-scale NDS experiment with multiple sources. Meanwhile, typical driver behavior parameters, such as velocity, time headway, and acceleration, are selected and analyzed. A self-reported survey of 364 drivers is conducted to subjectively evaluate the potential risks which drivers may suffer in various situations. NDS and DAQ are then combined together using the multinomial logit model to obtain the relative risks. Results demonstrate that the proposed method can provide an effective measure to quantify the influence factors of driving risks in dynamic environment. It is interesting to note that the risk value from subjective evaluation tends to be higher, implying that the subjective evaluation might be emotional and multi-factor coupling.

[1]  Mark Asbridge,et al.  A meta-analysis of the effects of texting on driving. , 2014, Accident; analysis and prevention.

[2]  Feng Guo,et al.  Driver crash risk factors and prevalence evaluation using naturalistic driving data , 2016, Proceedings of the National Academy of Sciences.

[3]  A. Çelik,et al.  A multinomial logit analysis of risk factors influencing road traffic injury severities in the Erzurum and Kars Provinces of Turkey. , 2014, Accident; analysis and prevention.

[4]  Mohamed M Ahmed,et al.  Real-time assessment of fog-related crashes using airport weather data: a feasibility analysis. , 2014, Accident; analysis and prevention.

[5]  F. Mannering,et al.  The effects of road-surface conditions, age, and gender on driver-injury severities. , 2011, Accident; analysis and prevention.

[6]  Rajesh Paleti,et al.  A Modified Rank Ordered Logit model to analyze injury severity of occupants in multivehicle crashes , 2017 .

[7]  Keqiang Li,et al.  Driving safety field theory modeling and its application in pre-collision warning system , 2016 .

[8]  R. Peterson A Meta-analysis of Cronbach's Coefficient Alpha , 1994 .

[9]  Heike Martensen,et al.  Comparing single vehicle and multivehicle fatal road crashes: a joint analysis of road conditions, time variables and driver characteristics. , 2013, Accident; analysis and prevention.

[10]  Carlo G. Prato,et al.  Assessing the relationship between the Driver Behavior Questionnaire and the Driver Skill Inventory: Revealing sub-groups of drivers , 2014 .

[11]  Zuduo Zheng,et al.  Incorporating human-factors in car-following models : a review of recent developments and research needs , 2014 .

[12]  Mehdi Hosseinpour,et al.  Exploring the effects of roadway characteristics on the frequency and severity of head-on crashes: case studies from Malaysian federal roads. , 2014, Accident; analysis and prevention.

[13]  Fang Zhang,et al.  Analysis of Chinese driver's lane change characteristic based on real vehicle tests in highway , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[14]  Mike McDonald,et al.  Determinants of following headway in congested traffic , 2009 .

[15]  O. Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[16]  James K. Archibald,et al.  A Satisficing Approach to Aircraft Conflict Resolution , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[17]  Rune Elvik,et al.  An exploratory analysis of models for estimating the combined effects of road safety measures. , 2009, Accident; analysis and prevention.

[18]  B. Reimer,et al.  Behavior differences in drivers with attention deficit hyperactivity disorder: the driving behavior questionnaire. , 2005, Accident; analysis and prevention.

[19]  Amaury Nègre,et al.  Probabilistic Analysis of Dynamic Scenes and Collision Risks Assessment to Improve Driving Safety , 2011, IEEE Intelligent Transportation Systems Magazine.

[20]  Bryan Reimer,et al.  An investigation of the relationship between the driving behavior questionnaire and objective measures of highway driving behavior , 2012 .

[21]  Shahriar Keshmiri,et al.  Multichannel sense-and-avoid radar for small UAVs , 2013 .

[22]  Ramsey Michael Faragher,et al.  Understanding the Basis of the Kalman Filter Via a Simple and Intuitive Derivation [Lecture Notes] , 2012, IEEE Signal Processing Magazine.

[23]  Mao-Bin Hu,et al.  Traffic Experiment Reveals the Nature of Car-Following , 2014, PloS one.

[24]  Lei Zhang,et al.  An Adaptive Longitudinal Driving Assistance System Based on Driver Characteristics , 2013, IEEE Transactions on Intelligent Transportation Systems.

[25]  Salissou Moutari,et al.  What are the factors that contribute to road accidents? An assessment of law enforcement views, ordinary drivers' opinions, and road accident records. , 2018, Accident; analysis and prevention.