Characterizing Heterogeneity among Merging Positions: Comparison Study between Random Parameter and Latent Class Accelerated Hazard Model
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 6
Abstract
This study aims to build an accurate merging position model by incorporating heterogeneity into the accelerated hazard model based on random parameter and latent class models. Three kinds of distribution (Weibull, log-normal, and log-logistic) are compared. The Weibull distribution is found to outperform the others. The latent class accelerated hazard model better captures the unobserved heterogeneity among drivers. The data used in this study can be naturally segmented into two classes. The large class consists of about 80% of drivers and the estimation results are similar to fixed parameter and random parameter models. The smaller one contains drivers that only consider the subject vehicle’s speed; these drivers merge rather late. Some of these drivers even merge into the adjacent main lane using the shoulder lane when they cannot find suitable gaps at the end of the auxiliary lane. Considering its good performance, the proposed latent class accelerated hazard model could also bring new insights into road design, such as the determination of the length of the acceleration lane or auxiliary lane.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available in a repository online in accordance with funder data retention policies. The NGISM data used to support the findings of this study have been deposited at https://catalog.data.gov/dataset/next-generation-simulation-ngsim-vehicle-trajectories.
Acknowledgments
This work was supported by the Science and Technology Innovation Fund for Youth Scientists of Nanjing Forestry University under Grant No. CX2019021, Scientific Research Start-up Funds of Nanjing Forestry University under Grant No. GXL2020012, and Basic Research Program of the Science and Technology Commission Foundation of Jiangsu Province under Grant No. BK20170932.
References
Ahammed, M. A., Y. Hassan, and T. A. Sayed. 2008. “Modeling driver behavior and safety on freeway merging areas.” J. Transp. Eng. 134 (9): 370–377. https://doi.org/10.1061/(ASCE)0733-947X(2008)134:9(370).
Alexiadis, V., J. Colyar, J. Halkias, R. Hranac, and G. McHale. 2004. “The next generation simulation program.” Inst. Transp. Eng. J. 74 (8): 22.
Anastasopoulos, P. C., M. B. Islam, D. Perperidou, and M. G. Karlaftis. 2012a. “Hazard-based analysis of travel distance in urban environments: Longitudinal data approach.” J. Urban Plann. Dev. 138 (1): 53–61. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000102.
Anastasopoulos, P. C., S. Labi, A. Bhargava, and F. L. Mannering. 2012b. “Empirical assessment of the likelihood and duration of highway project time delays.” J. Constr. Eng. Manage. 138 (3): 390–398. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000437.
Balal, E., R. L. Cheu, T. Gyan-Sarkodie, and J. Miramontes. 2014. “Analysis of discretionary lane changing parameters on freeways.” Int. J. Transp. Sci. Technol. 3 (3): 277–296. https://doi.org/10.1260/2046-0430.3.3.277.
Calvi, A., and M. De Blasiis. 2011. “Driver behavior on acceleration lanes: Driving simulator study.” Transp. Res. Rec. 2248 (1): 96–103. https://doi.org/10.3141/2248-13.
Cambridge Systematics. 2005. Prepared for federal highway administration. Cambridge, UK: Cambridge Systematics.
Chiabaut, N., L. Leclercq, and C. Buisson. 2010. “From heterogeneous drivers to macroscopic patterns in congestion.” Transp. Res. Part B: Methodol. 44 (2): 299–308. https://doi.org/10.1016/j.trb.2009.07.009.
Choudhury, C., V. Ramanujam, and M. Ben-Akiva. 2009. “Modeling acceleration decisions for freeway merges.” Transp. Res. Rec. 2124 (1): 45–57. https://doi.org/10.3141/2124-05.
Choudhury, C. F., M. E. Ben-Akiva, T. Toledo, G. Lee, and A. Rao. 2007. “Modeling cooperative lane changing and forced merging behavior.” In Proc., 86th Annual Meeting of the Transportation Research Board. Washington, DC: Transportation Research Board.
Chu, T. D. 2014. “A study on merging behavior at urban expressway merging sections.” Ph.D. dissertation, Dept. of Civil Engineering, Nagoya Univ.
Chu, T. D., T. Miwa, and T. Morikawa. 2017. “Discrete choice models for gap acceptance at urban expressway merge sections considering safety, road geometry, and traffic conditions.” J. Transp. Eng. 143 (7): 04017025. https://doi.org/10.1061/JTEPBS.0000053.
Dempster, A. P., N. M. Laird, and D. B. Rubin. 1977. “Maximum likelihood from incomplete data via the EM algorithm.” J. R. Stat. Soc. 39 (1): 1–22. https://doi.org/10.1111/j.2517-6161.1977.tb01600.x.
Ettema, D., A. Borgers, and H. Timmermans. 1995. “Competing risk hazard model of activity choice, timing, sequencing, and duration.” Transp. Res. Rec. 1493 (1): 101.
Fraley, C., and A. E. Raftery. 2002. “Model-based clustering, discriminant analysis, and density estimation.” J. Am. Stat. Assoc. 97 (458): 611–631. https://doi.org/10.1198/016214502760047131.
Greene, W. 2007. “Limdep 9.0 econometric modeling guide.” Accessed January 14, 2018. http://www.Limdep.com.
Greene, W. H. 2003. Econometric analysis. London: Pearson.
Greene, W. H., and D. A. Hensher. 2003. “A latent class model for discrete choice analysis: Contrasts with mixed logit.” Transp. Res. 37 (8): 681–698. https://doi.org/10.1016/S0191-2615(02)00046-2.
Gurupackiam, S., and S. L. Jones. 2011. “Empirical study of lane changing in urban streets under varying traffic conditions.” Procedia-Soc. Behav. Sci. 16 (Jan): 259–269. https://doi.org/10.1016/j.sbspro.2011.04.448.
Hamdar, S. H., and H. S. Mahmassani. 2009. “Life in the fast lane: Duration-based investigation of driver behavior differences across freeway lanes.” Transp. Res. Rec. 2124 (1): 89–102. https://doi.org/10.3141/2124-09.
Hidas, P. 2002. “Modelling lane changing and merging in microscopic traffic simulation.” Transp. Res. 10 (5–6): 351–371. https://doi.org/10.1016/S0968-090X(02)00026-8.
Hidas, P. 2005. “Modelling vehicle interactions in microscopic simulation of merging and weaving.” Transp. Res. 13 (1): 37–62. https://doi.org/10.1016/j.trc.2004.12.003.
Hoogendoorn, S. P., and R. Hoogendoorn. 2010. “Generic calibration framework for joint estimation of car-following models by using microscopic data.” Transp. Res. Rec. 2188 (1): 37–45. https://doi.org/10.3141/2188-05.
Hou, Y., P. Edara, and C. Sun. 2014. “Modeling mandatory lane changing using Bayes classifier and decision trees.” IEEE Trans. Intell. Transp. Syst. 15 (2): 647–655. https://doi.org/10.1109/TITS.2013.2285337.
Kim, I., T. Kim, and K. Sohn. 2013. “Identifying driver heterogeneity in car-following based on a random coefficient model.” Transp. Res. 36 (3): 35–44. https://doi.org/10.1016/j.trc.2013.08.003.
Kondyli, A., and L. Elefteriadou. 2011. “Modeling driver behavior at freeway–ramp merges.” Transp. Res. Rec. 2249 (1): 29–37. https://doi.org/10.3141/2249-05.
Kovali, V., V. Alexiadis, and L. Zhang. 2007. “Video-based vehicle trajectory data collection.” In Proc., 86th Annual Meeting of the Transportation Research Board. Washington, DC: Transportation Research Board.
Lee, B., and H. J. P. Timmermans. 2007. “A latent class accelerated hazard model of activity episode durations.” Transp. Res. 41 (4): 426–447. https://doi.org/10.1016/j.trb.2006.06.004.
Li, G. 2018. “Application of finite mixture of logistic regression for heterogeneous merging behavior analysis.” J. Adv. Transp. 2018 (Nov): 21. https://doi.org/10.1155/2018/1436521.
Li, G., S. Fang, J. Ma, and J. Cheng. 2020. “Modeling merging acceleration and deceleration behavior based on gradient-boosting decision tree.” J. Transp. Eng. 146 (7): 05020005. https://doi.org/10.1061/JTEPBS.0000386.
Li, G., Y. Pan, Z. Yang, and J. Ma. 2019. “Modeling vehicle merging position selection behaviors based on a finite mixture of linear regression models.” IEEE Access 7 (Oct): 158445–158458. https://doi.org/10.1109/ACCESS.2019.2950444.
Li, G., and L. Sun. 2018. “Characterizing heterogeneity in drivers’ merging maneuvers using two-step cluster analysis.” J. Adv. Transp. 2018 (May): 17. https://doi.org/10.1155/2018/5604375.
Li, Y., Z. Li, H. Wang, W. Wang, and L. Xing. 2017. “Evaluating the safety impact of adaptive cruise control in traffic oscillations on freeways.” Accid. Anal. Prev. 104 (Jul): 137–145. https://doi.org/10.1016/j.aap.2017.04.025.
Marczak, F., W. Daamen, and C. Buisson. 2013. “Merging behaviour: Empirical comparison between two sites and new theory development.” Transp. Res. 36 (Nov): 530–546. https://doi.org/10.1016/j.trc.2013.07.007.
McLachlan, G., and T. Krishnan. 2007. The EM algorithm and extensions. New York: Wiley.
McLachlan, G., and D. Peel. 2000. Finite mixture models. New York: Wiley.
Montanino, M., and V. Punzo. 2013. “Making NGSIM data usable for studies on traffic flow theory: Multistep method for vehicle trajectory reconstruction.” Transp. Res. Rec. 2390 (1): 99–111. https://doi.org/10.3141/2390-11.
Moylan, E. K., and T. H. Rashidi. 2017. “Latent-segmentation, hazard-based models of travel time.” IEEE Trans. Intell. Transp. Syst. 18 (8): 2174–2180. https://doi.org/10.1109/TITS.2016.2636321.
Ossen, S., and S. P. Hoogendoorn. 2005. “Car-following behavior analysis from microscopic trajectory data.” Transp. Res. Rec. 1934 (1): 13–21. https://doi.org/10.1177/0361198105193400102.
Ossen, S., and S. P. Hoogendoorn. 2011. “Heterogeneity in car-following behavior: Theory and empirics.” Transp. Res. 19 (2): 182–195. https://doi.org/10.1016/j.trc.2010.05.006.
Ossen, S., S. P. Hoogendoorn, and B. G. Gorte. 2006. “Interdriver differences in car-following: A vehicle trajectory–based study.” Transp. Res. Rec. 1965 (1): 121–129. https://doi.org/10.1177/0361198106196500113.
Patel, K., R. Kay, and L. Rowell. 2006. “Comparing proportional hazards and accelerated failure time models: An application in influenza.” J. Appl. Stat. Pharm. Ind. 5 (3): 213–224. https://doi.org/10.1002/pst.213.
Polus, A., M. Livneh, and J. Factor. 1985. “Vehicle flow characteristics on acceleration lanes.” J. Transp. Eng. 111 (6): 595–606. https://doi.org/10.1061/(ASCE)0733-947X(1985)111:6(595).
Punzo, V., M. T. Borzacchiello, and B. Ciuffo. 2011. “On the assessment of vehicle trajectory data accuracy and application to the next generation simulation (NGSIM) program data.” Transp. Res. 19 (6): 1243–1262. https://doi.org/10.1016/j.trc.2010.12.007.
Rahman, M., M. Chowdhury, Y. Xie, and Y. He. 2013. “Review of microscopic lane-changing models and future research opportunities.” IEEE Trans. Intell. Transp. Syst. 14 (4): 1942–1956. https://doi.org/10.1109/TITS.2013.2272074.
Sun, L., H. Zhang, R. Gao, W. Gu, B. Xu, and L. Chen. 2011. “Gaussian mixture models for clustering and classifying traffic flow in real-time for traffic operation and management.” J. Southeast Univ. 27 (2): 174–179. https://doi.org/10.3969/j.issn.1003-7985.2011.02.012.
Thiemann, C., M. Treiber, and A. Kesting. 2008. “Estimating acceleration and lane-changing dynamics from next generation simulation trajectory data.” Transp. Res. Rec. 2088 (1): 90–101. https://doi.org/10.3141/2088-10.
Tilahun, N., and D. Levinson. 2017. “Contacts and meetings: Location, duration and distance traveled.” Travel Behav. Soc. 6 (Aug): 64–74. https://doi.org/10.1016/j.tbs.2016.06.002.
DOT. 2016. “Next generation simulation (NGSIM) vehicle trajectories and supporting data.” Transportation.gov. Accessed March 14, 2021. https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Vehicle-Trajector/8ect-6jqj.
van den Berg, P., T. Arentze, and H. Timmermans. 2012. “A latent class accelerated hazard model of social activity duration.” Transp. Res. 46 (1): 12–21. https://doi.org/10.1016/j.tra.2011.09.015.
Wan, X., P. J. Jin, H. Gu, X. Chen, and B. Ran. 2017. “Modeling freeway merging in a weaving section as a sequential decision-making process.” J. Transp. Eng. 143 (5): 05017002. https://doi.org/10.1061/JTEPBS.0000048.
Wan, X., P. J. Jin, and B. Ran. 2014. “How merging vehicles choose and approach their desired merge position in congested merge area.” In Proc., 93rd Transportation Research Board Annual Meeting. Washington, DC: Transportation Research Board.
Wan, X., P. J. Jin, F. Yang, and B. Ran. 2016. “Merging preparation behavior of drivers: How they choose and approach their merge positions at a congested weaving area.” J. Transp. Eng. 142 (9): 05016005. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000864.
Wang, H., W. Wang, J. Chen, and M. Jing. 2010. “Using trajectory data to analyze intradriver heterogeneity in car-following.” Transp. Res. Rec. 2188 (1): 85–95. https://doi.org/10.3141/2188-10.
Wang, Q., Z. Li, and L. Li. 2014. “Investigation of discretionary lane-change characteristics using next-generation simulation data sets.” J. Intell. Transp. Syst. 18 (3): 246–253. https://doi.org/10.1080/15472450.2013.810994.
Weng, J., X. Gan, and G. Du. 2019. “Random coefficient models for work zone headway distribution.” J. Transp. Eng. 145 (10): 04019042. https://doi.org/10.1061/JTEPBS.0000268.
Weng, J., and Q. Meng. 2011. “Modeling speed-flow relationship and merging behavior in work zone merging areas.” Transp. Res. 19 (6): 985–996. https://doi.org/10.1016/j.trc.2011.05.001.
Yao, Z., L. Shen, R. Liu, Y. Jiang, and X. Yang. 2019. “A dynamic predictive traffic signal control framework in a cross-sectional vehicle infrastructure integration environment.” IEEE Trans. Intell. Transp. Syst. 21 (4): 1455–1466. https://doi.org/10.1109/TITS.2019.2909390.
Yao, Z., T. Xu, Y. Jiang, and R. Hu. 2021. “Linear stability analysis of heterogeneous traffic flow considering degradations of connected automated vehicles and reaction time.” Physica A 561 (Jan): 125218. https://doi.org/10.1016/j.physa.2020.125218.
Yin, C., J. Zhang, and C. J. T. Shao. 2020. “Relationships of the multi-scale built environment with active commuting, body mass index, and life satisfaction in China: A GSEM-based analysis.” Travel Behav. Soc. 21 (Oct): 69–78. https://doi.org/10.1016/j.tbs.2020.05.010.
Zhang, W., T. V. Le, S. V. Ukkusuri, and R. Li. 2018. “Influencing factors and heterogeneity in ridership of traditional and app-based taxi systems.” Transportation 47 (2): 1–26. https://doi.org/10.1007/s11116-018-9931-2.
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Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 6 • June 2021
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© 2021 American Society of Civil Engineers.
History
Received: Aug 26, 2020
Accepted: Jan 28, 2021
Published online: Mar 30, 2021
Published in print: Jun 1, 2021
Discussion open until: Aug 30, 2021
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