Table of Contents Author Guidelines Submit a Manuscript
Journal of Advanced Transportation
Volume 2018 (2018), Article ID 8645709, 10 pages
https://doi.org/10.1155/2018/8645709
Research Article

Modeling Lane-Changing Behavior in Freeway Off-Ramp Areas from the Shanghai Naturalistic Driving Study

Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai, China

Correspondence should be addressed to Jingqiu Guo

Received 16 June 2017; Revised 14 October 2017; Accepted 19 November 2017; Published 14 January 2018

Academic Editor: Chunjiao Dong

Copyright © 2018 Lanfang Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. A. Barria and S. Thajchayapong, “Detection and classification of traffic anomalies using microscopic traffic variables,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 3, pp. 695–704, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. J. Lu, P. Liu, and B. Behzadi, “Safety Evaluation of Freeway Exit Ramp,” in Proceedings of the TRB Annual Meeting CD-ROM (07-1293), 2007.
  3. D. Sun and L. Elefteriadou, “Lane-changing behavior on urban streets: a focus group-based study,” Applied Ergonomics, vol. 42, no. 5, pp. 682–691, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. D. J. Sun and L. Elefteriadou, “Lane changing behavior on urban streets: an ‘in-vehicle’ field experiment-based study,” Computer-Aided Civil and Infrastructure Engineering, vol. 27, no. 7, pp. 525–542, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. T.-Q. Tang, S. C. Wong, H.-J. Huang, and P. Zhang, “Macroscopic modeling of lane-changing for two-lane traffic flow,” Journal of Advanced Transportation, vol. 43, no. 3, pp. 245–273, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. T. Toledo, C. F. Choudhury, and M. E. Ben-Akiva, “Lane-changing model with explicit target lane choice,” Transportation Research Record, no. 1934, pp. 157–165, 2005. View at Google Scholar · View at Scopus
  7. P. G. Gipps, “A model for the structure of lane-changing decisions,” Transportation Research Part B: Methodological, vol. 20, no. 5, pp. 403–414, 1986. View at Publisher · View at Google Scholar · View at Scopus
  8. Q. Yang and H. N. Koutsopoulos, “A microscopic traffic simulator for evaluation of dynamic traffic management systems,” Transportation Research Part C: Emerging Technologies, vol. 4, no. 3, pp. 113–129, 1996. View at Publisher · View at Google Scholar · View at Scopus
  9. P. Hidas, “Modelling vehicle interactions in microscopic simulation of merging and weaving,” Transportation Research Part C: Emerging Technologies, vol. 13, no. 1, pp. 37–62, 2005. View at Publisher · View at Google Scholar · View at Scopus
  10. K. I. Ahmed, M. E. Ben-Akiva, H. N. Koutsopoulos, and R. G. Mishalani, “Models of freeway lane changing and gap acceptance behavior,” in Proceedings of the 13th International Symposium on Transportation and Traffic Theory, pp. 501–515, Lyon, France, 1996.
  11. K. I. Ahmed, Modeling Drivers Acceleration and Lane Changing Behavior, Massachusetts Institute of Technology, 1999.
  12. T. Toledo, Integrated Driving Behavior Modeling [Ph.D. thesis], Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Boston, Mass, USA, 2007.
  13. S. H. Hamdar, Modeling Driver Behavior as a Stochastic Hazard-based Risk-Taking Process [Ph.D. thesis], Northwestern University, Evanston, Ill, USA, 2009.
  14. C.-H. Wei, “Developing freeway lane-changing support systems using artificial neural networks,” Journal of Advanced Transportation, vol. 35, no. 1, pp. 47–65, 2001. View at Publisher · View at Google Scholar · View at Scopus
  15. T. Mai, R. Jiang, and E. Chung, “A Cooperative Intelligent Transport Systems (C-ITS)-based lane-changing advisory for weaving sections,” Journal of Advanced Transportation, vol. 50, no. 5, pp. 752–768, 2016. View at Publisher · View at Google Scholar · View at Scopus
  16. E. Balal, R. L. Cheu, and T. Sarkodie-Gyan, “A binary decision model for discretionary lane changing move based on fuzzy inference system,” Transportation Research Part C: Emerging Technologies, vol. 67, pp. 47–61, 2016. View at Publisher · View at Google Scholar · View at Scopus
  17. S. E. Lee, E. C. B. Olsen, and W. W. Wierwille, “A comprehensive examination of naturalistic lane-changes,” Publication Dot Hs Virginia Tech Transportation Institute, 2004. View at Google Scholar
  18. T. G. Oketch, “New modeling approach for mixed-traffic streams with nonmotorized vehicles,” Transportation Research Record, no. 1705, pp. 61–69, 2000. View at Google Scholar · View at Scopus
  19. Z. Zheng, “Recent developments and research needs in modeling lane changing,” Transportation Research Part B: Methodological, vol. 60, pp. 16–32, 2014. View at Publisher · View at Google Scholar · View at Scopus
  20. A. Kesting, M. Treiber, and D. Helbing, “General lane-changing model MOBIL for car-following models,” Transportation Research Record, vol. 1999, no. 1, pp. 86–94, 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. D. Zhao, H. Peng, and K. Nobukawa, “Analysis of mandatory and discretionary lane change behaviors for heavy trucks,” in Proceedings of the 12th International Symposium on Advanced Vehicle Control, Tokyo, Japan, 2014.
  22. T. Dingus, S. Klauer, V. Neale et al., “The 100-Car Naturalistic Driving Study, Phase II Results of the 100-Car Field Experiment,” Report no. DOT HS 810 593, 2006. View at Google Scholar
  23. F. Guo, S. G. Klauer, J. M. Hankey, and T. A. Dingus, “Near crashes as crash surrogate for naturalistic Driving Studies,” Transportation Research Record, no. 2147, pp. 66–74, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. N. Uchida, M. Kawakoshi, T. Tagawa, and T. Mochida, “An investigation of factors contributing to major crash types in Japan based on naturalistic driving data,” IATSS Research, vol. 34, no. 1, pp. 22–30, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. Y. Barnard, F. Utesch, N. van Nes, R. Eenink, and M. Baumann, “The study design of UDRIVE: the naturalistic driving study across Europe for cars, trucks and scooters,” European Transport Research Review, vol. 8, no. 2, article no. 14, 2016. View at Publisher · View at Google Scholar · View at Scopus
  26. G. M. Fitch and R. J. Hanowski, Using Naturalistic Driving Research to Design, Test and Evaluate Driver Assistance Systems, Handbook of Intelligent Vehicles, Springer London, Lodon, UK, 2012.
  27. K. D. Kusano, J. Montgomery, and H. C. Gabler, “Methodology for identifying car following events from naturalistic data,” in Proceedings of the 25th IEEE Intelligent Vehicles Symposium, IV 2014, pp. 281–285, USA, June 2014. View at Publisher · View at Google Scholar · View at Scopus
  28. M. W. Levin and S. D. Boyles, “A multiclass cell transmission model for shared human and autonomous vehicle roads,” Transportation Research Part C: Emerging Technologies, vol. 62, pp. 103–116, 2016. View at Publisher · View at Google Scholar · View at Scopus
  29. A. Pande and M. Abdel-Aty, “Assessment of freeway traffic parameters leading to lane-change related collisions,” Accident Analysis & Prevention, vol. 38, no. 5, pp. 936–948, 2006. View at Publisher · View at Google Scholar · View at Scopus
  30. C. Dong, Q. Dong, B. Huang, W. Hu, and S. S. Nambisan, “Estimating factors contributing to frequency and severity of large truck–involved crashes,” Journal of Transportation Engineering Part A, vol. 143, no. 8, 2017. View at Google Scholar
  31. H. D. Son, Y.-J. Kweon, and B. B. Park, “Development of crash prediction models with individual vehicular data,” Transportation Research Part C: Emerging Technologies, vol. 19, no. 6, pp. 1353–1363, 2011. View at Publisher · View at Google Scholar · View at Scopus
  32. L. Li, Z. Wen, and Z. Wang, “Outlier detection and correction during the process of groundwater lever monitoring base on pauta criterion with self-learning and smooth processing,” Communications in Computer and Information Science, vol. 643, pp. 497–503, 2016. View at Publisher · View at Google Scholar · View at Scopus
  33. S. E. Lee, E. C. B. Olsen, and W. W. Wierwille, “A comprehensive examination of Naturalistic Lane-changes,” National Highway Traffic Safety Administration DOT HS-809 702, 2004. View at Google Scholar
  34. J. L. Evans, L. Elefteriadou, and N. Gautam, “Probability of breakdown at freeway merges using Markov chains,” Transportation Research Part B: Methodological, vol. 35, no. 3, pp. 237–254, 2001. View at Publisher · View at Google Scholar · View at Scopus
  35. V. L. Knoop, S. P. Hoogendoorn, Y. Shiomi, and C. Buisson, “Quantifying the number of lane changes in traffic,” Transportation Research Record, no. 2278, pp. 31–41, 2012. View at Publisher · View at Google Scholar · View at Scopus
  36. E. Cascetta, V. Punzo, and M. Montanino, “Empirical analysis of effects of automated section speed enforcement system on traffic flow at freeway bottlenecks,” Transportation Research Record, no. 2260, pp. 83–93, 2011. View at Publisher · View at Google Scholar · View at Scopus
  37. D. W. Soole, B. C. Watson, and J. J. Fleiter, “Effects of average speed enforcement on speed compliance and crashes: A review of the literature,” Accident Analysis & Prevention, vol. 54, pp. 46–56, 2013. View at Publisher · View at Google Scholar · View at Scopus
  38. A. Montella, L. L. Imbriani, V. Marzano, and F. Mauriello, “Effects on speed and safety of point-to-point speed enforcement systems: Evaluation on the urban motorway A56 Tangenziale di Napoli,” Accident Analysis & Prevention, vol. 75, pp. 164–178, 2015. View at Publisher · View at Google Scholar · View at Scopus
  39. R. J. Porter, “Estimation of relationships between 85th percentile speeds, speed deviations, roadway and roadside geometry and traffic control in freeway work zones,” Dissertations & Theses - Gradworks, 2007. View at Google Scholar