Comparing Measured Driver Behavior Distributions to Results from Car-Following Models using SUMO and Real-World Vehicle Trajectories from Radar
SUMO Default vs. Radar-Measured CF model Parameters
Keywords:traffic micro-simulation, car-following models, car-following calibration, intelligent driver model, roadside radar data
In this study, the physical principles governing car-following (CF) behavior and their impact on traffic flow at signalized intersections are investigated. High temporal-resolution radar data is used to provide valuable insights into actual CF behavior, including acceleration, deceleration, and time headway distribution. Demand-calibrated SUMO simulations are run using empirical CF parameter distributions, and three CF models are evaluated: IDM, EIDM, and Krauss. By emulating radar data in SUMO and processing simulated vehicle traces, discrepancies between empirical and simulated parameter distributions are identified. Further analysis includes comparisons with default SUMO CF model parameters. The findings reveal that measured accelerations differ from CF model parameter accelerations and using the empirical value ($\mu = 0.89m/s^2$) leads to unrealistic simulations that fail volume-based calibration. Default parameters for all three models reasonably approximate the mean and median of measured parameters, but fail to capture the true distribution shape, partly due to homogeneity when using default parameters. The results show that it is more effective to simulate with the default parameters provided by SUMO rather than using measurements of real-world distributions without additional calibration. Future work will investigate closing the loop between the measured real-world and SUMO distributions using traditional calibration tactics, as well as assess the impact of calibrated vs. default CF parameters on simulation outputs like fuel consumption.
A. Reuschel, “Fahrzeugbewegungen in der kolonne,” Osterreichisches Ingenieur Archiv, vol. 4, pp. 193–215, 1950.
L. A. Pipes, “An operational analysis of traffic dynamics,” Journal of applied physics, vol. 24, no. 3, pp. 274–281, 1953. DOI: https://www.doi.org/10.1063/1.1721265. DOI: https://doi.org/10.1063/1.1721265
Y. Zhang, X. Chen, J. Wang, Z. Zheng, and K. Wu, “A generative car-following model conditioned on driving styles,” Transportation research part C: emerging technologies, vol. 145, p. 103 926, 2022. DOI: https://www.doi.org/10.1016/j.trc.2022.103926. DOI: https://doi.org/10.1016/j.trc.2022.103926
M. Treiber, A. Hennecke, and D. Helbing, “Congested traffic states in empirical observations and microscopic simulations,” Physical review E, vol. 62, no. 2, p. 1805, 2000. DOI: https://www.doi.org/10.1103/PhysRevE.62.1805. DOI: https://doi.org/10.1103/PhysRevE.62.1805
P. A. Lopez, M. Behrisch, L. Bieker-Walz, et al., “Microscopic traffic simulation usingsumo,” in The 21st IEEE International Conference on Intelligent Transportation Systems, IEEE, 2018. [Online]. Available: https://elib.dlr.de/124092/. DOI: https://doi.org/10.1109/ITSC.2018.8569938
J. Barcel´o et al., Fundamentals of traffic simulation. Springer, 2010, vol. 145. DOI: https://www.doi.org/https://www.doi.org/10.1007/978-1-4419-6142-6.
K. Wunderlich, M. Vasudevan, P. Wang, R. Dowling, A. Skabardonis, and V. Alexiadis, Tat volume iii: Guidelines for applying traffic microsimulation modeling software 2019 update to the 2004 version (fhwa-hop-18-036), English, USDOT & Noblis,Washington, DC, 2019, 130 pp. [Online]. Available: https://trid.trb.org/view/1607216, released.
T. V. da Rocha, L. Leclercq, M. Montanino, et al., “Does traffic-related calibration of carfollowing models provide accurate estimations of vehicle emissions?” Transportation research part D: Transport and Environment, vol. 34, pp. 267–280, 2015. DOI: https://www.doi.org/10.1016/j.trd.2014.11.006. DOI: https://doi.org/10.1016/j.trd.2014.11.006
V. Punzo and F. Simonelli, “Analysis and comparison of microscopic traffic flow models with real traffic microscopic data,” Transportation Research Record, vol. 1934, no. 1, pp. 53–63, 2005. DOI: https://www.doi.org/10.3141/1934-06. DOI: https://doi.org/10.1177/0361198105193400106
L. Jie, H. Van Zuylen, Y. Chen, F. Viti, and I. Wilmink, “Calibration of a microscopic simulation model for emission calculation,” Transportation Research Part C: Emerging Technologies, vol. 31, pp. 172–184, 2013. DOI: https://www.doi.org/10.1016/j.trc.2012.04.008. DOI: https://doi.org/10.1016/j.trc.2012.04.008
J. Asamer, H. J. van Zuylen, and B. Heilmann, “Calibrating vissim to adverse weather conditions,” in 2nd International Conference on Models and Technologies for Intelligent Transportation Systems, 2011, pp. 22–24. [Online]. Available: https://research.tudelft.nl/en/publications/calibrating-vissim-to-adverse-weather-conditions.
B. Notter, M. Keller, and B. Cox, “Handbook emission factors for road transport 4.2,”INFRAS, Bern, 2022. [Online]. Available: https://www.hbefa.net/e/help/HBEFA42_help_en.pdf.
A. Kesting and M. Treiber, “Calibrating car-following models by using trajectory data: Methodological study,” Transportation Research Record, vol. 2088, no. 1, pp. 148–156, 2008. DOI: https://www.doi.org/10.3141/2088-16. DOI: https://doi.org/10.3141/2088-16
V. G. Kovvali, V. Alexiadis, and L. Zhang PE, “Video-based vehicle trajectory data collection,” Tech. Rep., 2007. [Online]. Available: https://trid.trb.org/view/801154.
M. Treiber and A. Kesting, “Microscopic calibration and validation of car-following models – a systematic approach,” Procedia-Social and Behavioral Sciences, vol. 80, pp. 922–939, 2013. DOI: 1https://www.doi.org/0.1016/j.sbspro.2013.05.050. DOI: https://doi.org/10.1016/j.sbspro.2013.05.050
L. Li, X. M. Chen, and L. Zhang, “A global optimization algorithm for trajectory data based car-following model calibration,” Transportation Research Part C: Emerging Technologies, vol. 68, pp. 311–332, 2016. DOI: https://www.doi.org/10.1016/j.trc.2016.04.011. DOI: https://doi.org/10.1016/j.trc.2016.04.011
A. Sharma, Z. Zheng, and A. Bhaskar, “Is more always better? The impact of vehicular trajectory completeness on car-following model calibration and validation,” Transportation research part B: methodological, vol. 120, pp. 49–75, 2019. DOI: https://www.doi.org/10.1016/j.trb.2018.12.016. DOI: https://doi.org/10.1016/j.trb.2018.12.016
S. Ossen and S. P. Hoogendoorn, “Validity of Trajectory-Based Calibration Approach of Car-Following Models in Presence of Measurement Errors,” Transportation Research Record, vol. 2088, no. 1, pp. 117–125, 2008. DOI: https://www.doi.org/10.3141/2088-13. DOI: https://doi.org/10.3141/2088-13
S. Krauß, “Microscopic modeling of traffic flow: Investigation of collision free vehicle dynamics,” 1998. [Online]. Available: https://www.osti.gov/etdeweb/biblio/627062.
S. Krauß, P. Wagner, and C. Gawron, “Metastable states in a microscopic model of traffic flow,” Physical Review E, vol. 55, no. 5, p. 5597, 1997. DOI: https://www.doi.org/10.1103/PhysRevE.55.5597. DOI: https://doi.org/10.1103/PhysRevE.55.5597
L. Bieker-Walz, M. Behrisch, M. Junghans, and K. Gimm, “Evaluation of car-followingmodels at controlled intersections,” Tech. Rep., 2017. [Online]. Available: https://elib.dlr.de/115720.
D. Salles, S. Kaufmann, and H.-C. Reuss, “Extending the intelligent driver model in sumo and verifying the drive off trajectories with aerial measurements,” SUMO Conference Proceedings,vol. 1, pp. 1–25, Jul. 2022. DOI: https://www.doi.org/10.52825/scp.v1i.95. DOI: https://doi.org/10.52825/scp.v1i.95
M. Treiber and A. Kesting, “The intelligent driver model with stochasticity-new insights into traffic flow oscillations,” Transportation research procedia, vol. 23, pp. 174–187, 2017.DOI: https://www.doi.org/10.1016/j.trpro.2017.05.011. DOI: https://doi.org/10.1016/j.trpro.2017.05.011
M. Treiber, A. Kesting, and D. Helbing, “Delays, inaccuracies and anticipation in microscopic traffic models,” Physica A: Statistical Mechanics and its Applications, vol. 360, no. 1, pp. 71–88, 2006. DOI: https://www.doi.org/10.1016/j.physa.2005.05.001. DOI: https://doi.org/10.1016/j.physa.2005.05.001
M. Schrader, Q. Wang, and J. Bittle, “Extension and Validation of NEMA-Style Dual-Ring Controller in SUMO,” in SUMO User Conference, 2022. DOI: https://www.doi.org/10.52825/scp.v3i.115. DOI: https://doi.org/10.52825/scp.v3i.115
Deutsches Zentrum für Luft-und-Raumfahrt (DLR), SUMO RouteSampler, Aug. 2020. [Online]. Available: https://sumo.dlr.de/docs/Tools/Turns.html#routesamplerpy.
H. Agency, Design Manual for Roads and Bridges, Volume 12, Traffic Appraisal of Road Schemes, Section 2, Part I, Traffic Appraisal in Urban Areas, The Stationery Office, London, 1996.
D. R. King, A. Gold, K. B. Phillips, and D. A. Krauss, “Headway times on urban, multiple lane freeways,” in Proceedings of the Human Factors and Ergonomics Society Annual Meeting, SAGE Publications Sage CA: Los Angeles, CA, vol. 66, 2022, pp. 1225–1229. DOI: https://www.doi.org/10.1177/1071181322661413. DOI: https://doi.org/10.1177/1071181322661413
O. C. Puan, “Driver’s car following headway on single carriageway roads,” Malaysian Journal of Civil Engineering, vol. 16, no. 2, pp. 15–27, 2004. [Online]. Available: https://core.ac.uk/download/pdf/11784144.pdf.
Q. Ge and M. Menendez, “Exploring the variance contributions of correlated model parameters: A sampling-based approach and its application in traffic simulation models,” Applied Mathematical Modelling, vol. 97, pp. 438–462, 2021. DOI: https://www.doi.org/10.1016/j.apm.2021.04.012. DOI: https://doi.org/10.1016/j.apm.2021.04.012
J. Kim and H. S. Mahmassani, “Correlated Parameters in Driving Behavior Models: Car-Following Example and Implications for Traffic Microsimulation,” Transportation Research Record, vol. 2249, no. 1, pp. 62–77, 2011. DOI: https://www.doi.org/10.3141/2249-09. DOI: https://doi.org/10.3141/2249-09
S. Hausberger and D. Krajzewicz, “COLOMBO Deliverable 4.2: Extended Simulation Tool PHEM coupled to SUMO with User Guide,” Center (DLR), Tech. Rep., Feb. 2014. [Online]. Available: https://elib.dlr.de/98047/.
How to Cite
Conference Proceedings Volume
Copyright (c) 2023 Max Schrader, Mahdi Al Abdraboh, Joshua Bittle
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Office of Energy Efficiency and Renewable Energy
Grant numbers DE-EE0009210