Extended Desired Safety Margin Car-Following Model That Considers Variation of Historical Perceived Risk and Acceptable Risk
Abstract
In this paper, an extended desired safety margin (DSM) car-following model is proposed by accounting for the effect of the variation of historical perceived risk and acceptable risk in terms of the DSM model. By conducting gray correlation analysis, an investigation is carried out into whether the variation of historical perceived risk and acceptable risk has important effects on the acceleration or deceleration of a target vehicle based on a real-vehicle test platform. Through simulated results, the dynamic performance of an extended DSM model is investigated in comparison with the DSM model. Conclusions show that the extended DSM model can better reflect the characteristics of traffic flow compared with the DSM model, and can improve the performance of the vehicle in the start, stop, and car-following processes. Numerical simulations further demonstrate that the extended DSM model can improve traffic safety via the changes of time-to-collision and safety margin when external disturbance is introduced into the leading vehicle. Thus, historical information about target vehicles should be considered to improve the performance of car-following in adaptive cruise control (ACC) and automotive platoon driving.
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References
1. Chandler R. E., Herman R., Montroll E. W. Traffic Dynamics: Studies in Car Following. Operations Research, Vol. 6, No. 2, 1958, pp. 165–184.
2. Gazis D. C., Herman R., Rothery R. W. Nonlinear Follow-the-Leader Models of Traffic Flow. Operations Research, Vol. 9, No. 4, 1961, pp. 545–567.
3. Bexelius S. An Extended Model for Car-Following. Transportation Research, Vol. 2, No. 1, 1968, pp. 13–21.
4. Lenz H., Wagner C. K., Sollacher R. Multi-Anticipative Car-Following Model. The European Physical Journal B, Vol. 7, No. 2, 1999, pp. 331–335.
5. Addison P. S., Low D. J. A Novel Nonlinear Car-Following Model. Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol. 8, No. 4, 1998, pp. 791–799.
6. Helly W. Simulation of Bottlenecks in Single-Lane Traffic Flow. In Proc., Symposium on Theory of Traffic Flow, Research Laboratories, General Motors. Elsevier, New York, 1959, pp. 207–238.
7. Treiber M., Hennecke A., Helbing D. Congested Traffic States in Empirical Observations and Microscopic Simulations. Physical Review E, Vol. 62, No. 2Pt A, 2000, p. 1805.
8. Lee G. A Generalization of Linear Car-Following Theory. Operations Research, Vol. 14, No. 4, 1966, pp. 595–606.
9. Ahmed K. I. Modeling Drivers’ Acceleration and Lane Changing Behavior. Massachusetts Institute of Technology, 1999.
10. Herman R., Rothery R. W. Car Following and Steady State Flow. In Proc., 2nd International Symposium on the Theory of Traffic Flow (Almond J., ed.), OECD, Paris, 1965.
11. Koshi M., Kuwahara M., Akahane H. Capacity of Sags and Tunnels on Japanese Motorways. ITE Journal, Vol. 62, No. 5, 1992, pp. 17–22.
12. Xing J. A Parameter Identification of a Car-Following Model. In Steps Forward. Intelligent Transport Systems World Congress, Yokohama, Japan, 1995, pp. 1739–1745.
13. Gipps P. G. A Behavioural Car-Following Model for Computer Simulation. Transportation Research Part B Methodological, Vol. 15, No. 2, 1981, pp. 105–111.
14. Newell G. F. Nonlinear Effects in the Dynamics of Car Following. Operations Research, Vol. 9, No. 2, 1961, pp. 209–229.
15. Bando M., Hasebe K., Nakayama A., Shibata A., Sugiyama Y. Dynamical Model of Traffic Congestion and Numerical Simulation. Physical Review E, Vol. 51, No. 2, 1995, p. 1035.
16. Farin G. E., Hansford D. Generalized Force Model of Traffic Dynamics. Physical Review E, Vol. 58, No. 1, 1998, pp. 133–138.
17. Jiang R., Wu Q., Zhu Z. Full Velocity Difference Model for a Car-Following Theory. Physical Review E, Vol. 64, No. 1 Pt 2, 2001, p. 017101.
18. Gong H., Liu H., Wang B. H. An Asymmetric Full Velocity Difference Car-Following Model. Physica A Statistical Mechanics and Its Applications, Vol. 387, No. 11, 2008, pp. 2595–2602.
19. Li Y. Car-Following Model Based on the Information of Multiple Ahead & Velocity Difference. Systems Engineering—Theory & Practice, Vol. 30, No. 7, 2010, pp. 1326–1332.
20. Zhao X., Gao Z. A New Car-Following Model: Full Velocity and Acceleration Difference Model. The European Physical Journal B—Condensed Matter and Complex Systems, Vol. 47, No. 1, 2005, pp. 145–150.
21. Tao W. Multiple Velocity Difference Model and Its Stability Analysis. Acta Physica Sinica, Vol. 55, No. 2, 2006, pp. 634–640.
22. Li Y., Sun D., Liu W., Zhang M., Zhao M., Liao X., Tang L. Modeling and Simulation for Microscopic Traffic Flow Based on Multiple Headway, Velocity and Acceleration Difference. Nonlinear Dynamics, Vol. 66, No. 1–2, 2011, pp. 15–28.
23. Orosz G., Krauskopf B., Wilson R. E. Bifurcations and Multiple Traffic Jams in a Car-Following Model with Reaction-Time Delay. Physica D: Nonlinear Phenomena, Vol. 211, No. 3, 2005, pp. 277–293.
24. Orosz G., Stépán G. Subcritical Hopf Bifurcations in a Car-Following Model with Reaction-Time Delay. Proceedings Mathematical Physical & Engineering Sciences, Vol. 462, No. 2073, 2006, pp. 2643–2670.
25. Low D., Addision P. Chaos in a Car-Following Model with a Desired Headway Time. Proc., 30th International Symposium on Automotive Technology & Automation Conference, Florence, Italy. ATT/ITS Advances for Enhancing Passenger Freight and Intermodal Transportation Systems, Automotive Automation, 1997, pp. 175–182.
26. Tang T., Shi W., Shang H., Wang Y. A New Car-Following Model with Consideration of Inter-Vehicle Communication. Nonlinear Dynamics, Vol. 76, No. 4, 2014, pp. 2017–2023.
27. Tang T., Wang Y., Yang X., Wu Y. A New Car-Following Model Accounting for Varying Road Condition. Nonlinear Dynamics, Vol. 70, No. 2, 2012, pp. 1397–1405.
28. Tang T. Q., Chen L., Yang S. C., Shang H. Y. An Extended Car-Following Model with Consideration of the Electric Vehicle’s Driving Range. Physica A Statistical Mechanics and Its Applications, Vol. 430, 2015, pp. 148–155.
29. Tang T. Q., Huang H. J., Xu G. A New Macro Model with Consideration of the Traffic Interruption Probability. Physica A Statistical Mechanics and Its Applications, Vol. 387, No. 3, 2009, pp. 975–983.
30. Tang T. Q., Shi W. F., Shang H. Y., Wang Y. P. An Extended Car-Following Model with Consideration of the Reliability of Inter-Vehicle Communication. Measurement, Vol. 58, No. 11, 2014, pp. 286–293.
31. Li Y., Zhang L., Peeta S., Pan H., Zheng T., Li Y., He X. Non-Lane-Discipline-Based Car-Following Model Considering the Effects of Two-Sided Lateral Gaps. Nonlinear Dynamics, Vol. 80, No. 1–2, 2015, pp. 227–238.
32. Ge H. X., Zhu H. B., Dai S. Q. Effect of Looking Backward on Traffic Flow in a Cooperative Driving Car Following Model. European Physical Journal B—Condensed Matter and Complex Systems, Vol. 54, No. 4, 2006, pp. 503–507.
33. Yu S., Shi Z. An Improved Car-Following Model Considering Headway Changes with Memory. Physica A Statistical Mechanics and Its Applications, Vol. 421, 2015, pp. 1–14.
34. Peng G., Lu W., He H., Gu Z. Nonlinear Analysis of a New Car-Following Model Accounting for the Optimal Velocity Changes with Memory. Communications in Nonlinear Science and Numerical Simulation, Vol. 40, 2016, pp. 197–205.
35. Winsum W. V. The Human Element in Car Following Models. Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 2, No. 4, 1999, pp. 207–211.
36. Yang H. H., Peng H. Development of an Errorable Car-Following Driver Model. Vehicle System Dynamics, Vol. 48, No. 6, 2010, pp. 751–773.
37. Andersen G. J., Sauer C. W. Optical Information for Car Following: The Driving by Visual Angle (Dva) Model. Human Factors, Vol. 49, No. 5, 2007, p. 878.
38. Jin S., Wang D. H., Huang Z. Y., Tao P. F. Visual Angle Model for Car-Following Theory. Physica A Statistical Mechanics and Its Applications, Vol. 390, No. 11, 2011, pp. 1931–1940.
39. Hamdar S. H., Treiber M., Mahmassani H. S., Kesting A. Modeling Driver Behavior as Sequential Risk-Taking Task. Transportation Research Record: Journal of the Transportation Research Board, 2008. 2088: 208–217.
40. Lu G., Cheng B., Wang Y., Lin Q. A Car-Following Model Based on Quantified Homeostatic Risk Perception. Mathematical Problems in Engineering, Vol. 2013, No. 6, 2013, pp. 165–185.
41. Yu S., Shi Z. An Extended Car-Following Model Considering Vehicular Gap Fluctuation. Measurement, Vol. 70, 2015, pp. 137–147.
42. Ge H. X., Dai S. Q., Dong L. Y., Xue Y. Stabilization Effect of Traffic Flow in an Extended Car-Following Model Based on an Intelligent Transportation System Application. Physical Review E, Vol. 70, No. 6 Pt 2, 2004, p. 066134.
43. Jiang R., Wu Q. S. The Night Driving Behavior in a Car-Following Model. PhysicaA Statistical Mechanics and Its Applications, Vol. 375, No. 1, 2007, pp. 297–306.
44. Tang T. Q., Li C. Y., Huang H. J. A New Car-Following Model with the Consideration of the Driver’s Forecast Effect. Physics Letters A, Vol. 374, No. 38, 2010, pp. 3951–3956.
45. Tang T. Q., Huang H. J., Zhao S. G., Xu G. An Extended Ov Model with Consideration of Driver’s Memory. International Journal of Modern Physics B, Vol. 23, No. 5, 2009, pp. 743–752.
46. Lu G., Cheng B., Lin Q., Wang Y. Quantitative Indicator of Homeostatic Risk Perception in Car Following. Safety Science, Vol. 50, No. 9, 2012, pp. 1898–1905.
47. Gipps P. G. A Behavioural Car-Following Model for Computer Simulation. Transportation Research Part B: Methodological, Vol. 15, No. 2, 1981, pp. 105–111.
48. Vangi D., Virga A. Evaluation of Emergency Braking Deceleration for Accident Reconstruction. Vehicle System Dynamics, Vol. 45, No. 10, 2007, pp. 895–910.
49. Sokolovskij E. Experimental Investigation of the Braking Process of Automobiles. Transport, Vol. 20, No. 3, 2010, pp. 91–95.
50. Treiber M., Kesting A., Thiemann C. Traffic Flow Dynamics: Data, Models and Simulation. Springer, Heidelberg, New York, 2013.
51. Highway Capacity Manual. TRB, National Research Council, Washington, D.C., 2000.
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Article first published online: June 7, 2018
Issue published: December 2018
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The authors confirm contribution to the paper as follows: study conception and design: Guangquan Lu, Junjie Zhang, Yunpeng Wang; data collection: Junjie Zhang; analysis and interpretation of results: Junjie Zhang, Guangquan Lu, Yunpeng Wang; draft manuscript preparation: Junjie Zhang. All authors reviewed the results and approved the final version of the manuscript.
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