Lane Change Behavior Patterns and Risk Analysis in Expressway Weaving Areas: Unsupervised Data-Mining Method
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 11
Abstract
The occurrence of accidents in expressway weaving areas is significantly influenced by frequent lane change maneuvers. Acquiring the lane change behavior pattern characteristics of vehicles in this area can provide prior knowledge for autonomous vehicles when performing lane change maneuvers, which helps ensure the safety of autonomous vehicles. This study aims to extract lane change behavior patterns of vehicles in weaving areas, to analyze the distribution differences of patterns across different lane change maneuvers, and to explore risk characteristics during the lane change process. First, a lane-changing sequence segmentation method was designed based on the hierarchical Dirichlet process hidden semi-Markov model (HDP-HSMM) algorithm, taking into account the interaction with surrounding vehicles and risk factors. Second, the Gaussian mixture model latent Dirichlet allocation (GMM-LDA) algorithm was employed to cluster the segments and derive patterns of lane-changing behavior that include risk attributes. The trajectory data from the UCF SST dataset were used to validate the method framework and make an in-depth analysis. The results show that the behavior patterns obtained by this method are able to better describe the operational and risk states of the vehicle. Variations exist in the behavioral patterns of different types of lane change maneuvers throughout the entire process. Spatial distribution disparities exist in the behavior patterns of lane change maneuvers across various sections of weaving areas. The findings of this study provide behavioral characteristics of different types of lane change maneuvers in weaving areas, which might contribute to enhancing the accurate recognition of lane change behaviors by autonomous vehicles.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was funded by the Beijing Municipal Science and Technology Commission Project “Key Technology Research and Application of Driver Abnormal Behavior Recognition” (Z221100005222021) and Beijing Key Laboratory of Traffic Data Analysis and Mining.
Author contributions: The authors confirm contribution to the paper as follows: study conception and design: Yinjia Guo, Xin Gu, Yanyan Chen, Jifu Guo; data collection: Yinjia Guo, Xin Gu; analysis and interpretation of results: Yinjia Guo, Xin Gu; draft manuscript preparation: Yinjia Guo, Xin Gu. All authors reviewed the results and approved the final version of the manuscript.
References
Aboah, A., Y. Adu-Gyamfi, and S. V. Gursoy. 2023. “Driver maneuver detection and analysis using time series segmentation and classification.” J. Transp. Eng. Part A: Syst. 149 (3): 04022157. https://doi.org/10.1061/JTEPBS.TEENG-7312.
Agamennoni, G., S. Worrall, J. R. Ward, and E. M. Neboty. 2014. “Automated extraction of driver behaviour primitives using Bayesian agglomerative sequence segmentation.” In Proc., 17th Int. IEEE Conf. on Intelligent Transportation Systems (ITSC). New York: IEEE.
Ahmed, K. I. 1999. “Modeling drivers’ acceleration and lane changing behavior.” Ph.D. thesis, Dept. of Civil and Environmental Engineering, Massachusetts Institute of Technology.
Ahmed, K. I., M. E. Ben-Akiva, H. N. Koutsopoulos, and R. G. Mishalani. 1996. “Models of freeway lane changing and gap acceptance behavior.” In Proc., 13th Int. Symp. on Transportation and Traffic Theory. Washington, DC: National Highway Traffic Safety Administration.
Aria, E., J. Olstam, and C. Schwietering. 2016. “Investigation of automated vehicle effects on driver’s behavior and traffic performance.” Transp. Res. Proc. 15: 761–770. https://doi.org/10.1016/j.trpro.2016.06.063.
Bai, Y., Y. Zhang, X. Li, and J. Hu. 2022. “Cooperative weaving for connected and automated vehicles to reduce traffic oscillation.” Transp. A: Transp. Sci. 18 (Jan): 125–143. https://doi.org/10.1080/23249935.2019.1645758.
Beal, M., Z. Ghahramani, and C. Rasmussen. 2001. “The infinite hidden Markov Model.” In Advances in neural information processing systems, 14. Cambridge, MA: MIT Press.
Bezerra, R., K. Ohno, S. Kojima, and S. Tadokoro. 2021. “Region recognition based on HMM using primitive motion transitions.” In Proc., IEEE Intelligent Transportation Systems Conf., 441–448. New York: IEEE.
Chandra, R., U. Bhattacharya, T. Mittal, X. Li, A. Bera, and D. Manocha. 2020. “GraphRQI: Classifying driver behaviors using graph spectrums.” In Proc., 2020 IEEE Int. Conf. on Robotics and Automation, 4350–4357. New York: IEEE.
Chen, Y., G. Li, S. Li, W. Wang, S. E. Li, and B. Cheng. 2021. “Exploring behavioral patterns of lane change maneuvers for human-like autonomous driving.” In Proc., IEEE Transactions on Intelligent Transportation Systems, 1–14. New York: IEEE.
Chen, Z., Y. Zhang, C. Wu, and B. Ran. 2019. “Understanding individualization driving states via latent Dirichlet allocation model.” IEEE Intell. Trans. Syst. Magazine 11 (Jun): 41–53. https://doi.org/10.1109/MITS.2019.2903525.
Cheung, E., A. Bera, E. Kubin, K. Gray, and D. Manocha. 2018. “Identifying driver behaviors using trajectory features for vehicle navigation.” In Proc., Int. Conf. on Intelligent Robots and Systems (IROS), 3445–3452. New York: IEEE.
Chovan, J. D., L. Tijerina, G. Alexander, and D. L. Hendricks. 1994. Examination of lane change crashes and potential IVHS countermeasures. Washington, DC: National Highway Traffic Safety Administration.
Daamen, W., M. Loot, and S. P. Hoogendoorn. 2010. “Empirical analysis of merging behavior at freeway on-ramp.” Transp. Res. Rec. 2188 (1): 108–118. https://doi.org/10.3141/2188-12.
Deligianni, S. P., M. Quddus, A. Morris, A. Anvuur, and S. Reed. 2017. “Analyzing and modeling drivers’ deceleration behavior from normal driving.” Transp. Res. Rec. 2663 (1): 134–141. https://doi.org/10.3141/2663-17.
Di, X., and R. Shi. 2021. “A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning.” Transp. Res. Part C: Emerg. Technol. 125 (Apr): 103008. https://doi.org/10.1016/j.trc.2021.103008.
Ding, H., X. ShangGuan, and W. Yang. 2022. “Lane change style identification for natural driving environments.” In Proc., 2022 6th CAA Int. Conf. on Vehicular Control and Intelligence, 1–7. New York: IEEE.
Eftekhari, H. R., and M. Ghatee. 2018. “Hybrid of discrete wavelet transform and adaptive neuro fuzzy inference system for overall driving behavior recognition.” Transp. Res. Part F: Traffic Psychol. Behav. 58 (Oct): 782–796. https://doi.org/10.1016/j.trf.2018.06.044.
Ferguson, T. S. 1973. “A Bayesian analysis of some nonparametric problems.” Ann. Statis. 1 (Mar): 209–230. https://doi.org/10.1214/aos/1176342360.
Fox, E. B., E. B. Sudderth, M. I. Jordan, and A. S. Willsky. 2011. “A sticky HDP-HMM with application to speaker diarization.” Ann. Appl. Statis. 5 (Jun): 1020–1056. https://doi.org/10.1214/10-AOAS395.
Golob, T. F., W. W. Recker, and V. M. Alvarez. 2004. “Safety aspects of freeway weaving sections.” Transp. Res. Part A: Policy Pract. 38 (1): 35–51. https://doi.org/10.1016/j.tra.2003.08.001.
Hao, W., Z. Zhang, Z. Gao, K. Yi, L. Liu, and J. Wang. 2020. “Research on mandatory lane-changing behavior in highway weaving sections.” J. Adv. Transp. 2020 (1): 1–9. https://doi.org/10.1155/2020/3754062.
He, Y., P. Wang, and C.-Y. Chan. 2019. “Understanding lane change behavior under dynamic driving environment based on real-world traffic dataset.” In Proc., 2019 5th Int. Conf. on Transportation Information and Safety, 1092–1097. New York: IEEE.
Higgs, B., and M. Abbas. 2015. “Segmentation and clustering of car-following behavior: Recognition of driving patterns.” IEEE Trans. Intell. Transp. Syst. 16 (Jun): 81–90. https://doi.org/10.1109/TITS.2014.2326082.
Hossny, M., S. Mohamed, and S. Nahavandi. 2015. “Driver behaviour prediction for motion simulators using changepoint segmentation.” In Proc., 2015 IEEE Int. Conf. on Systems, Man, and Cybernetics, 457–462. New York: IEEE.
Hou, X., W. Li, B. Zou, L. Tang, K. Wang, and W. Huang. 2023. “A novel lane-changing recognition method using frequency analysis.” J. Transp. Eng. Part A: Syst. 149 (2): 04022146. https://doi.org/10.1061/JTEPBS.TEENG-7043.
Johnson, M. J., and A. S. Willsky. 2012. “Bayesian nonparametric hidden semi-Markov models.” Preprint, submitted September 7, 2012. https://doi.org/10.48550/arXiv.1203.1365.
Khattak, Z. H., B. L. Smith, M. D. Fontaine, J. Ma, and A. J. Khattak. 2022. “Active lane management and control using connected and automated vehicles in a mixed traffic environment.” Transp. Res. Part C: Emerg. Technol. 139 (Jun): 103648. https://doi.org/10.1016/j.trc.2022.103648.
Klitzke, L., C. Koch, and F. Köster. 2020. “Identification of lane-change maneuvers in real-world drivings with hidden Markov model and dynamic time warping.” In Proc., 2020 IEEE 23rd Int. Conf. on Intelligent Transportation Systems (ITSC), 1–7. New York: IEEE. https://doi.org/10.1109/ITSC45102.2020.9294481.
Knoop, V. L., S. P. Hoogendoorn, Y. Shiomi, and C. Buisson. 2012. “Quantifying the number of lane changes in traffic: Empirical analysis.” Transp. Res. Rec. 2278 (1): 31–41. https://doi.org/10.3141/2278-04.
Kusuma, A., R. Liu, and C. Choudhury. 2020. “Modelling lane-changing mechanisms on motorway weaving sections.” Transp. B: Transp. Dyn. 8 (Jun): 1–21. https://doi.org/10.1080/21680566.2019.1703840.
Kusuma, A., R. Liu, C. Choudhury, and F. Montgomery. 2014. “Analysis of the driving behaviour at weaving section using multiple traffic surveillance data.” Transp. Res. Procedia 3 (Aug): 51–59. https://doi.org/10.1016/j.trpro.2014.10.090.
Li, Y., D. Wu, J. Lee, M. Yang, and Y. Shi. 2020. “Analysis of the transition condition of rear-end collisions using time-to-collision index and vehicle trajectory data.” Accid. Anal. Preven. 144 (Sep): 105676. https://doi.org/10.1016/j.aap.2020.105676.
Liao, Y., Y. Yang, Z. Ding, K. Tong, and Y. Zeng. 2021. “Risk distribution characteristics and optimization of short weaving area for complex municipal interchanges.” J. Adv. Transp. 2021 (1): 1–10. https://doi.org/10.1155/2021/5573335.
Lin, N., C. Zong, M. Tomizuka, P. Song, Z. Zhang, and G. Li. 2014. “An overview on study of identification of driver behavior characteristics for automotive control.” Math. Probl. Eng. 2014 (1): 1–15. https://doi.org/10.1155/2014/569109.
Ma, Y., P. Zhang, and B. Hu. 2019. “Active lane-changing model of vehicle in B-type weaving region based on potential energy field theory.” Phys. A: Statis. Mech. Appl. 535 (Dec): 122291. https://doi.org/10.1016/j.physa.2019.122291.
Mahboubi, Z., and M. J. Kochenderfer. 2017. “Learning traffic patterns at small airports from flight tracks.” IEEE Trans. Intell. Transp. Syst. 18 (Jul): 917–926. https://doi.org/10.1109/TITS.2016.2598064.
Mallipaddi, V., and M. Anderson. 2020. “Analysis of crashes on freeway weaving sections.” In Proc., Int. Conf. on Transportation and Development 2020, 157–168. Reston, VA: ASCE.
Minderhoud, M. M., and P. H. L. Bovy. 2001. “Extended time-to-collision measures for road traffic safety assessment.” Accid. Anal. Prev. 33 (Apr): 89–97. https://doi.org/10.1016/S0001-4575(00)00019-1.
Monot, N., X. Moreau, A. Benine-Neto, A. Rizzo, and F. Aioun. 2018. “Comparison of rule-based and machine learning methods for lane change detection.” In Proc., 2018 21st International Conference on Intelligent Transportation Systems, 198–203. New York: IEEE.
Nakamura, T., T. Nagai, and N. Iwahashi. 2009. “Grounding of word meanings in multimodal concepts using LDA.” In Proc., 2009 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 3943–3948. New York: IEEE.
Peng, J., Y. Guo, R. Fu, W. Yuan, and C. Wang. 2015. “Multi-parameter prediction of drivers’ lane-changing behaviour with neural network model.” Appl. Ergon. 50 (Jul): 207–217. https://doi.org/10.1016/j.apergo.2015.03.017.
Rabiner, L. R. 1989. “A tutorial on hidden Markov models and selected applications in speech recognition.” In Vol. 77 of Proc., IEEE, 257–286. New York: IEEE.
Schomakers, E.-M., V. Lotz, F. Glawe, and M. Ziefle. 2023. “The effect of design and behaviour of automated micro-vehicles for urban delivery on other road users’ perceptions.” Multimodal Transp. 2 (4): 100079. https://doi.org/10.1016/j.multra.2023.100079.
Sekizawa, S., S. Inagaki, T. Suzuki, S. Hayakawa, N. Tsuchida, T. Tsuda, and H. Fujinami. 2007. “Modeling and recognition of driving behavior based on stochastic switched ARX model.” IEEE Trans. Intell. Transp. Syst. 8 (4): 593–606. https://doi.org/10.1109/TITS.2007.903441.
Siebinga, O. 2021. “TraViA: A traffic data visualization and annotation tool in Python.” J. Open Source Software 6 (65): 3607. https://doi.org/10.21105/joss.03607.
Tang, H., and X. Mao. 2020. “Analysis on characteristics and causes of traffic accidents in interweaving areas of freeways.” In Proc., 20th COTA Int. Conf. of Transportation Professionals, 4101–4110. Reston, VA: ASCE.
Toledo, T., and D. Zohar. 2007. “Modeling duration of lane changes.” Transp. Res. Rec. 1999 (1): 71–78. https://doi.org/10.3141/1999-08.
Transportation Research Board. 2010. Highway capacity manual. Cambridge, MA: MIT Press.
UCF-SST Lab. 2023. “UCF-SST-CitySim1-Dataset.” Accessed August 11, 2023. https://github.com/UCF-SST-Lab/UCF-SST-CitySim1-Dataset.
Wang, C., Y. Xie, H. Huang, and P. Liu. 2021. “A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling.” Accid. Anal. Prev. 157 (Aug): 106157. https://doi.org/10.1016/j.aap.2021.106157.
Wang, H., W. Wang, S. Yuan, X. Li, and L. Sun. 2022. “On social interactions of merging behaviors at highway on-ramps in congested traffic.” IEEE Trans. Intell. Transport. Syst. 23 (2): 11237–11248. https://doi.org/10.1109/TITS.2021.3102407.
Wang, M., M. J. Cassidy, P. Chan, and A. D. May. 1993. “Evaluating the capacity of freeway weaving sections.” J. Transp. Eng. 119 (3): 360–384. https://doi.org/10.1061/(ASCE)0733-947X(1993)119:3(360).
Wang, X., and L. Xu. 2021. “Factors influencing young drivers’ willingness to engage in risky driving behavior: Continuous lane-changing.” Sustainability 13 (5): 6459. https://doi.org/10.3390/su13116459.
Xue, Q., K. Wang, J. J. Lu, and Y. Liu. 2019. “Rapid driving style recognition in car-following using machine learning and vehicle trajectory data.” J. Adv. Transp. 2019 (1): 9085238. https://doi.org/10.1155/2019/9085238.
Yarlagadda, J., P. Jain, and D. S. Pawar. 2021. “Assessing safety critical driving patterns of heavy passenger vehicle drivers using instrumented vehicle data—An unsupervised approach.” Accid. Anal. Prev. 163 (Dec): 106464. https://doi.org/10.1016/j.aap.2021.106464.
Yuan, J., M. Abdel-Aty, Q. Cai, and J. Lee. 2019. “Investigating drivers’ mandatory lane change behavior on the weaving section of freeway with managed lanes: A driving simulator study.” Transp. Res. Part F: Traffic Psychol. Behav. 62 (Dec): 11–32. https://doi.org/10.1016/j.trf.2018.12.007.
Zhang, R., S. Masoud, and N. Masoud. 2023a. “Impact of autonomous vehicles on the car-following behavior of human drivers.” J. Transp. Eng. Part A: Syst. 149 (3): 04022152. https://doi.org/10.1061/JTEPBS.TEENG-7385.
Zhang, W., and W. Wang. 2019. “Learning V2V interactive driving patterns at signalized intersections.” Transp. Res. Part C: Emerg. Technol. 108 (4): 151–166. https://doi.org/10.1016/j.trc.2019.09.009.
Zhang, Y., Y. Chen, X. Gu, N. N. Sze, and J. Huang. 2023b. “A proactive crash risk prediction framework for lane-changing behavior incorporating individual driving styles.” Accid. Anal. Prev. 188 (Aug): 107072. https://doi.org/10.1016/j.aap.2023.107072.
Zhang, Y., Y. Zou, and L. W. W. Han. 2021a. “Understanding the merging behavior patterns and evolutionary mechanism at freeway on-ramps.” J. Intell. Transp. Syst. 27 (Aug): 573–586.
Zhang, Y., Y. Zou, and L. Wu. 2021b. “V2V spatiotemporal interactive pattern recognition and risk analysis in lane.” Preprint, submitted May 22, 2021. https://arxiv.org/abs/2105.10688v1.
Zheng, L., and T. Sayed. 2019. “From univariate to bivariate extreme value models: Approaches to integrate traffic conflict indicators for crash estimation.” Transp. Res. Part C: Emerg. Technol. 103 (5): 211–225. https://doi.org/10.1016/j.trc.2019.04.015.
Zheng, L., T. Sayed, M. Essa, and Y. Guo. 2019. “Do simulated traffic conflicts predict crashes? An investigation using the extreme value approach.” In Proc., 2019 IEEE Intelligent Transportation Systems Conf., 631–636. New York: IEEE.
Zheng, O., M. Abdel-Aty, L. Yue, A. Abdelraouf, Z. Wang, and N. Mahmoud. 2024. “CitySim: A drone-based vehicle trajectory dataset for safety oriented research and digital twins.” Transp. Res. Rec. 2678 (4): 606–621. https://doi.org/10.1177/03611981231185768.
Zheng, Z., S. Ahn, and C. M. Monsere. 2010. “Impact of traffic oscillations on freeway crash occurrences.” Accid. Anal. Prev. 42 (4): 626–636. https://doi.org/10.1016/j.aap.2009.10.009.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 11 • November 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Dec 21, 2023
Accepted: Apr 3, 2024
Published online: Aug 28, 2024
Published in print: Nov 1, 2024
Discussion open until: Jan 28, 2025
ASCE Technical Topics:
- Accidents
- Analysis (by type)
- Business management
- Data analysis
- Disaster risk management
- Driver behavior
- Engineering fundamentals
- Equipment and machinery
- Highway and road management
- Highway transportation
- Highways and roads
- Infrastructure
- Methodology (by type)
- Practice and Profession
- Public administration
- Public health and safety
- Research methods (by type)
- Risk management
- Traffic accidents
- Traffic engineering
- Traffic management
- Transportation engineering
- Uncrewed vehicles
- Vehicles
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.