Space headway calculation and analysis at turn movement trajectories using hybrid model

Document Type : Article

Authors

1 Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing, China.;Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, China

2 Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing, China .; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, China

3 Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing, China. ;Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, China.

4 Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing, China.; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, China.

Abstract

Space headway calculation and analysis play an important role in identifying surrounding obstacles and understanding traffic scene. However, the performance of existing methods is limited by the complexity of computer processing. In addition, it is quite difficult to obtain space headway at turn movement trajectories, mainly owing to the limitation of rectilinear propagation. Therefore, a hybrid model based on spline curve and numerical integration was proposed to estimate distance of the front vehicle and vehicle trajectory in this study. The space headway at turn movement trajectories was analogous to the track of a vehicle, which could be fitted by a quadratic spline curve. Newton-Cotes numerical integration was employed to calculate distance due to its meshing flexibility and ease of implementation. Data collected from Lankershim Boulevard in the city of Los Angeles, California (USA) were used to evaluate performance of the hybrid model. Compared with another algorithm based on computer vision and trilinear method, the results showed that the proposed model worked successfully and outperformed the competing method in terms of accuracy and reliability. Finally, the proposed method was applied to investigate the effects of vehicle speed, relative speed of vehicles, and time period on the spacing of vehicles during car-following.

Keywords

Main Subjects

References

Refrences;
1. Al-Kaisy, A. and Durbin, C. Platooning on two-lane two-way highways: an empirical investigation", J. Adv. Transport., 43(1), pp. 71{88 (2009). 
2. Zhao, L. and Chien, S.I. Investigating the impact of stochastic vehicle arrivals to optimal stop spacing and headway for a feeder bus route", J. Adv. Transport., 49, pp. 341{357 (2015). 
3. Behnood, A., Modiri-Gharehveran, M., and Roshandeh, A.M. The effects of drivers' behavior on driverinjury severities in Iran: An application of the mixedlogit model", Sci. Iran., 23(6), pp. 2429{2440 (2016). 
4. Steux, B., Laurgeau, C., Salesse, L., et al. Fade: A vehicle detection and tracking system featuring monocular color vision and radar data fusion", Proc. IEEE Intell. Vehicles Symp., 2, pp. 632{639 (2002). 
5. Soudbakhsh, D., Eskandarian, A., and Chichka, D. Vehicle collision avoidance maneuvers with limited lateral acceleration using optimal trajectory control", J. Dyn. Syst. Meas. Contr., 135(4), p. 041006 (2013). 
6. Toledo-Moreo, R. and Zamora-Izquierdo, M.A. Collision avoidance support in roads with lateral and longitudinal maneuver prediction by fusing GPS/IMU and digital maps", Transport. Res. C-Emerg. Technol., 18(4), pp. 611{625 (2010). 
7. Wang, J., Zhang, L., Zhang, D., et al. An adaptive longitudinal driving assistance system based on driver characteristics", IEEE Trans. Intell. Transp. Syst., 14(1), pp. 1{12 (2013). 
8. McCall, J.C. and Trivedi, M.M. Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation", IEEE Trans. Intell. Transp. Syst., 7(1), pp. 20{37 (2006). 
9. Huang, A. and Teller, S. Probabilistic lane estimation for autonomous driving using basis curves", Auton. Robots, 31, pp. 269{283 (2011). 
10. Gunay, B. Using automatic number plate recognition technology to observe drivers' headway preferences", J. Adv. Transport., 46, pp. 305{317 (2012). 
11. Aghabayk, K., Sarvi, M., Forouzideh, N., et al. Modelling heavy vehicle car-following behavior in congested tra_c conditions", J. Adv. Transport., 48, pp. 1017{ 1029 (2014). 
12. Li, K. and Gao, Z. An improved car-following model for railway tra_c", J. Adv. Transport., 47, pp. 475{482 (2013). 
13. Sarvi, M. Heavy commercial vehicles-following behavior and interactions with different vehicle classes", J. Adv. Transport., 47, pp. 572{580 (2013). 
14. Beliveau, Y.J., Fithian, J.E., and Deisenroth, M.P. Autonomous vehicle navigation with real-time 3D laser based positioning for construction", Autom. Construct., 5(4), pp. 261{272 (1996). 
15. Streller, D., Furstenberg, K., and Dietmayer, K. Vehicle and object models for robust tracking in traffic scenes using laser range images", Proc. IEEE 5th Int. Conf. Intell. Transport. Syst., pp. 118{123, Singapore (2002). 
16. Carullo, A. and Parvis, M. An ultrasonic sensor for distance measurement in automotive applications", IEEE Sensors J., 1(2), pp. 143{147 (2001). 
17. Clark, S. and Durrant-Whyte, H. Autonomous land vehicle navigaton using millimeter wave radar", Int. Conf. Robot. Automat., 4, pp. 3697{3702 (1998). 
18. Truong, Q.B. and Lee, B.R. New lane detection algorithm for autonomous vehicles using computer vision", Proc. Int. Conf. Control Autom. Syst., pp. 1208{1213 (2008). C. Wang et al./Scientia Iranica, Transactions A: Civil Engineering 27 (2020) 2669{2679 2679 
19. Stau_er, C. and Grimson, W.E.L. Adaptive background mixture models for real-time tracking", Proc. Computer Vision and Pattern Recognition, 2, pp. 246{ 252 (1999). 
20. Kilger, M. A shadow handle in a video-based real-time traffic monitoring system", Proc. IEEE Workshop on Applications of Computer Vision, pp. 11{18 (1992). 
21. Zhao, H., Liu, Z., and Zhang, H. Segmentation of multiple moving targets based on modi_ed C-V model", J. Sci. Instrum., 31(5), pp. 1082{1089 (2010). 
22. Galic, S. and Loncaric, S. Spatio-temporal image segmentation using optical ow and clustering algorithm", First International Workshop on Image and Signal Processing and Analysis, pp. 63{68 (2000). 
23. Masoud, O., Papanikolopoulos, N.P., and Kwon, E. The use of computer vision in monitoring weaving sections", IEEE Trans. Intell. Transp. Syst., 2(1), pp. 18{25 (2001). 
24. Wu, B., Chen, W., Chang, C., et al. A new vehicle detection with distance estimation for lane change warning systems", Proc. IEEE Intell. Vehicles Symp., pp. 698{703 (2007). 
25. Wang, W., Lu, M., Kao, H., et al. Nighttime vehicle distance measuring systems", IEEE Trans. Circuits Syst. II, 54(1), pp. 81{85 (2007). 
26. Chen, Y., Das, M., and Bajpai, D. Vehicle tracking and distance estimation based on multiple image features", Proc. 4th Can. Conf. Comput. Robot Vis., pp. 371{378 (2007). 
27. Zhang, Q. and Xie, M. Study on the method of measuring the preceding vehicle distance based on trilinear method", Proc. 2nd Int. Conf. Comput. Modeling Simul., 2, pp. 475{479 (2010). 
28. Wu, C., Lin, C., and Lee, C. Applying a functional neurofuzzy network to real-time lane detection and front-vehicle distance measurement", IEEE Trans. Syst. Man Cybern. C Appl. Rev., 42(4), pp. 577{589 (2012). 
29. Zhao, X., Zhang, C., Yang, B., et al. Adaptive knot placement using a GMM-based continuous optimization algorithm in B-spline curve approximation", Comput.-Aided Des., 43(6), pp. 598{604 (2011). 
30. Park, H. and Lee, J.H. B-spline curve fitting based on adaptive curve re_nement using dominant points", Comput.-Aided Des., 39, pp. 439{451 (2007). 
31. Kouibia, A. and Pasadas, M. Approximation by smoothing variational vector splines for noisy data", J. Comput. Appl. Math., 211, pp. 213{222 (2008). 
32. Jahanpour, J. High speed contouring enhanced with C-2 PH quintic spline curves", Sci. Iran., 19(2), pp. 311{319 (2012). 33. Rostami, S., Shojaee, S., and Sa_ari, H. An explicit time integration method for structural dynamics using cubic B-spline polynomial functions", Sci. Iran., 20(1), pp. 23{33 (2013). 
34. Leader, J., Numerical Analysis and Scientific Computation, MA, Addison-Wesley (2004). 
35. Hearn, D., Baker, M.P., and Carithers, W.R., Computer Graphics with OpenGL, Fourth Edition, Pearson Education (2011). 36. Aczel, J. On weighted synthesis of judgments", Aequationes Math., 27(1), pp. 288{307 (1984). 
37. Thiemann, C., Treiber, M., and Kesting, A. Estimating acceleration and lane-changing dynamics from next generation simulation trajectory data", Transp. Res. Rec., 2088, pp. 90{101 (2008). 
38. Dahlquist, G. and Bjork, A. Equidistant interpolation and the Runge phenomenon", Numer. Meth., 1, pp. 101{103 (1974). 
39. Kesting, A., Traffic Flow Dynamics: Data, Models and Simulation, Springer (2013). 
40. Chakroborty, P. and Kikuchi, S. Evaluation of the general motors based car-following models and a proposed fuzzy inference model", Transport. Res. CEmerg. Technol., 7(4), pp. 209{235 (1999).
Scientia Iranica
Volume 27, Issue 6 - Serial Number 6
Transactions on Civil Engineering (A)
November and December 2020
Pages 2669-2679

Files

History

  • Receive Date: 29 July 2016
  • Revise Date: 17 October 2018
  • Accept Date: 03 March 2019

Share

How to cite

Statistics