A Scoring Method for Driving Safety Credit Using Trajectory Data

Urban traffic systems worldwide are suffering from severe traffic safety problems. Traffic safety is affected by many complex factors, and heavily related to all drivers' behaviors involved in traffic system. Drivers with aggressive driving behaviors increase the risk of traffic accidents. In order to manage the safety level of traffic system, we propose Driving Safety Credit inspired by credit score in financial security field, and design a scoring method using driver's trajectory data and violation records. First, we extract driving habits, aggressive driving behaviors and traffic violation behaviors from driver's trajectories and traffic violation records. Next, we train a classification model to filtered out irrelevant features. And at last, we score each driver with selected features. We verify our proposed scoring method using 40 days of traffic simulation, and proves the effectiveness of our scoring method.

[1]  D. Shinar,et al.  Aggressive driving: an observational study of driver, vehicle, and situational variables. , 2004, Accident; analysis and prevention.

[2]  Tamitza Toroyan,et al.  Global status report on road safety , 2009, Injury Prevention.

[3]  Deron Liang,et al.  The effect of feature selection on financial distress prediction , 2015, Knowl. Based Syst..

[4]  L. Evans,et al.  The dominant role of driver behavior in traffic safety. , 1996, American journal of public health.

[5]  Jin-Hyuk Hong,et al.  A smartphone-based sensing platform to model aggressive driving behaviors , 2014, CHI.

[6]  Erhan Akin,et al.  Estimating driving behavior by a smartphone , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[7]  T. Dingus,et al.  Crash and risky driving involvement among novice adolescent drivers and their parents. , 2011, American journal of public health.

[8]  James H Hedlund,et al.  Countermeasures That Work: A Highway Safety Countermeasure Guide For State Highway Safety Offices , 2007 .

[9]  Yu Zheng,et al.  Travel time estimation of a path using sparse trajectories , 2014, KDD.

[10]  Florian Michahelles,et al.  Driving behavior analysis with smartphones: insights from a controlled field study , 2012, MUM.

[11]  E. Petridou,et al.  Human factors in the causation of road traffic crashes , 2004, European Journal of Epidemiology.

[12]  Penousal Machado,et al.  Simulating the Impact of Drivers ’ Personality on City Transit , 2013 .

[13]  Daniel Krajzewicz,et al.  SUMO - Simulation of Urban MObility An Overview , 2011 .

[14]  Gregory D. Abowd,et al.  Driver Classification Based on Driving Behaviors , 2016, IUI.

[15]  Soushan Wu,et al.  Credit rating analysis with support vector machines and neural networks: a market comparative study , 2004, Decis. Support Syst..

[16]  Qinghua Huang,et al.  Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches , 2014, Knowl. Based Syst..

[17]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[18]  Ram Dantu,et al.  Safe Driving Using Mobile Phones , 2012, IEEE Transactions on Intelligent Transportation Systems.

[19]  Yong Shi,et al.  Credit card churn forecasting by logistic regression and decision tree , 2011, Expert Syst. Appl..

[20]  Thierry Derrmann,et al.  Driver Behavior Profiling Using Smartphones: A Low-Cost Platform for Driver Monitoring , 2015, IEEE Intelligent Transportation Systems Magazine.

[21]  Brian O'Neill,et al.  ON-THE-ROAD DRIVING RECORDS OF LICENSED RACE DRIVERS , 1974 .

[22]  Wei-Yang Lin,et al.  Machine Learning in Financial Crisis Prediction: A Survey , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[23]  Yu Zheng,et al.  Trajectory Data Mining , 2015, ACM Trans. Intell. Syst. Technol..