Pre-study and insights to a sequential MATSim-SUMO tool-coupling to deduce 24h driving profiles for SAEVs




New mobility concepts such as shared, autonomous, electric vehicle (SAEV) fleets raise questions to the vehicles’ technical design. Compared to privately owned human driven cars, SAEVs are expected to exhibit different load profiles that entail the need for newly dimensioned powertrain and battery components. Since vehicle architecture is very sensitive to operating characteristics, detailed SAEV driving cycles are crucial for requirement engineering. As real world measurements reach their limit with new mobility concepts, this contribution seeks to evaluate three different traffic simulation approaches in their ability to model detailed SAEV driving profiles. (i) The mesoscopic traffic simulation framework MATSim is analyzed as it is predestined for large-scale fleet simulation and allows the tracking of individual vehicles. (ii) To improve driving dynamics, MATSim’s simplified velocity profiles are enhanced with real-world driving cycles. (iii) A sequential tool-coupling of MATSim with the microscopic traffic simulation tool SUMO is pursued. All three approaches are compared and evaluated by means of a comprehensive test case study. The simulation results are compared in terms of driving dynamics and energy related key performance indicators (KPI) and then benchmarked against real driving cycles. The sequential tool-coupling approach shows the greatest potential to generate reliable SAEV driving profiles.


G. Amirjamshidi and M. J. Roorda. Development of simulated driving cycles for light, medium, and heavy duty trucks: Case of the toronto waterfront area. Transportation Research Part D: Transport and Environment, 34:255 – 266, 2015.

A. Anastassov, D. Jang, and G. Giurgiu. Driving speed profiles for autonomous vehicles. In 2017 IEEE Intelligent Vehicles Symposium (IV). IEEE, jun 2017.

K. W. Axhausen, A. Horni, and H. J. Herrmann. Final report: The risk for a gridlock and the macroscopic fundamental diagram. Technical report, 2015.

J. Bischoff and M. Maciejewski. Simulation of city-wide replacement of private cars with autonomous taxis in berlin. Procedia Computer Science, 83:237–244, 12 2016.

J. Bischoff, F. J. Marquez-Fernandez, G. Domingues-Olavarria, M. Maciejewski, and K. Nagel. Impacts of vehicle fleet electrification in Sweden - a simulation-based assessment of long-distance trips. Technical report, MT.ITS, 2019.

L. Briem, N. Mallig, and P. Vortisch. Creating an integrated agent-based travel demand model by combining mobiTopp and MATSim. Procedia Computer Science, 151:776–781, Jan. 2019.

R. Chen and M. W. Levin. Dynamic user equilibrium of mobility-on-demand system with linear programming rebalancing strategy. Transportation Research Record: Journal of the Transportation Research Board, 2673(1):447–459, jan 2019.

T. D. Chen, K. M. Kockelman, and J. P. Hanna. Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions. Transportation Research Part A: Policy and Practice, 94:243 – 254, 2016.

S. Chugh, P. Kumar, M. Muralidharan, M. K. B, M. Sithananthan, A. Gupta, B. Basu, and R. K. Malhotra. Development of delhi driving cycle: A tool for realistic assessment of exhaust emissions from passenger cars in delhi. In SAE Technical Paper Series. SAE International, apr 2012.

T. V. da Rocha, A. Can, C. Parzani, B. Jeanneret, R. Trigui, and L. Leclercq. Are vehicle trajectories simulated by dynamic traffic models relevant for estimating fuel consumption? Transportation Research Part D: Transport and Environment, 24:17–26, oct 2013.

C. F. Daganzo. The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory. Transportation Research Part B, Vol. 28B(No. 4):pp. 269–287, 1994.

K. P. Divakarla, A. Emadi, and S. N. Razavi. Journey mapping—a new approach for defining automotive drive cycles. IEEE Transactions on Industry Applications, 52(6):5121–5129, nov 2016.

J. Erdmann. SUMO’s lane-changing model. In Modeling Mobility with Open Data, pages 105–123. Springer International Publishing, 2015.

D. J. Fagnant and K. M. Kockelman. Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in austin, texas. Transportation, 45(1):143–158, Jan 2018.

A. Fotouhi and M. Montazeri-Gh. Tehran driving cycle development using the k-means clustering method. Scientia Iranica, 20(2):286 – 293, 2013.

Fraunhofer Institute for Industrial Mathematics (ITWM). Virtual measurement campaign. product flyer, accessed: april 2020.

C. Gawron. An iterative algorithm to determine the dynamic user equilibrium in a traffic simulation model. International Journal of Modern Physics C, Vol. 09(No. 03):pp. 393–407, 1998.

Q. Gong, S. Midlam-Mohler, V. Marano, and G. Rizzoni. An iterative markov chain approach for generating vehicle driving cycles. SAE International Journal of Engines, 4(1):1035–1045, apr 2011.

T. Holdstock and M. Bryant. Electric drivetrain architecture optimisation for autonomous vehicles based on representative cycles. In 17th Int. CTI Symp. Automotive Transmissions, Berlin, 2018.

A. Horni, K. Nagel, and K. W. Axhausen, editors. The multi-agent transport simulation MATSim. Ubiquity Press, London, 2016.

Y. Hou, E. Wood, E. Burton, and J. Gonder. Suitability of synthetic driving profiles from traffic micro-simulation for real-world energy analysis: Preprint. National Renewable Energy Laboratory (NREL), 10 2015.

S. Hörl, F. Becker, T. J. P. Dubernet, and K. W. Axhausen. Induzierter Verkehr durch autonome Fahrzeuge. Eine Abschätzung. Technical report, SNF and ETH Zürich, 2019.

I. Kaddoura, G. Leich, and K. Nagel. The impact of pricing and service area design on the modal shift towards demand responsive transit. Submitted to the 9th Int. Workshop on Agent-based Mobility, Traffic and Transportation Models (ABMTRANS), Warsaw, Poland, Apr. 2020.

S. H. Kamble, T. V. Mathew, and G. Sharma. Development of real-world driving cycle: Case study of pune, india. Transportation Research Part D: Transport and Environment, 14(2):132–140, 2009.

S. Krauss. Microscopic modeling of traffic flow: Investigation of collision free vehicle dynamics. Technical report, 1998. Dissertation. Mathematisch-Naturwissenschaftliche Fakultät, Universität Köln and German Aerospace Center (DLR). LIDO-Berichtsjahr 1999.

S. Krauss, P. Wagner, and C. Gawron. Metastable states in a microscopic model of traffic flow. Physical Review E, 5:5597–5602, 1997. LIDO-Berichtsjahr 1997,.

J. Liu, K. Kockelman, P. Bösch, and F. Ciari. Tracking a system of shared autonomous vehicles across the austin, texas network using agent-based simulation. Transportation, 08 2017.

J. Liu, K. Kockelman, and A. Nichols. Anticipating the emissions impacts of smoother driving by connected and autonomous vehicles, using the moves model. 01 2017.

B. Loeb, K. M. Kockelman, and J. Liu. Shared autonomous electric vehicle (SAEV) operations across the Austin, Texas network with charging infrastructure decisions. Transportation Research Part C: Emerging Technologies, 89:222 – 233, 2018.

P. A. Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y.-P. Flötteröd, R. Hilbrich, L. Lücken, J. Rummel, P. Wagner, and E. Wießner. Microscopic traffic simulation using SUMO. In The 21st IEEE IC on Intelligent Transportation Systems, pages 2575–2582. IEEE, Nov. 2018.

M. Maciejewski. Benchmarking minimum passenger waiting time in online taxi dispatching with exact offline optimization methods, 2014.

M. Maciejewski, J. Bischoff, and K. Nagel. An assignment-based approach to efficient real-time city-scale taxi dispatching. IEEE Intelligent Systems, 31(1):68–77, jan 2016.

N. Mallig, M. Kagerbauer, and P. Vortisch. mobiTopp – a modular agent-based travel demand modelling framework. Procedia Computer Science, 19:854 – 859, 2013. 4th IC on Ambient Systems, Networks and Technologies (ANT) , 3rd IC on Sustainable Energy Information Technology (SEIT).

A. Moreno, A. Michalski, C. Llorca, and R. Moeckel. Shared autonomous vehicles effect on vehicle-km traveled and average trip duration. Journal of Advanced Transportation, 2018, 2018.

P. Nyberg, E. Frisk, and L. Nielsen. Using real-world driving databases to generate driving cycles with equivalence properties. IEEE Transactions on Vehicular Technology, 65(6):4095–4105, 2016.

S. Shi, N. Lin, Y. Zhang, J. Cheng, C. Huang, L. Liu, and B. Lu. Research on Markov property analysis of driving cycles and its application. Transportation Research Part D: Transport and Environment, 47:171–181, aug 2016.

G. Song, L. Yu, and Y. Zhang. Applicability of traffic microsimulation models in vehicle emissions estimates. Transportation Research Record: Journal of the Transportation Research Board, 2270(1):132–141, jan 2012.

I. H. Tchappi, V. C. Kamla, S. Galland, and J. C. Kamgang. Towards an multilevel agent-based model for traffic simulation. Procedia Computer Science, 109:887 – 892, 2017. 8th IC on Ambient Systems, Networks and Technologies (ANT), 7th IC on Sustainable Energy Information Technology (SEIT).

M. Treiber, A. Hennecke, and D. Helbing. Congested traffic states in empirical observations and microscopic simulations. Physical Review E, 62:1805–1824, 02 2000.

Z. Xiao, Z. Dui-Jia, and S. Jun-Min. A synthesis of methodologies and practices for developing driving cycles. Energy Procedia, 16:1868–1873, 2012.

P. Yuhui, Z. Yuan, and Y. Huibao. Development of a representative driving cycle for urban buses based on the k-means cluster method. Cluster Computing, 22(S3):6871–6880, jan 2018.

H. Zhang, C. J. R. Sheppard, T. E. Lipman, and S. J. Moura. Joint fleet sizing and charging system planning for autonomous electric vehicles. 2018.

X. Zhao, Q. Yu, J. Ma, Y. Wu, M. Yu, and Y. Ye. Development of a representative EV urban driving cycle based on a k-means and SVM hybrid clustering algorithm. Journal of Advanced Transportation, 2018:1–18, nov 2018.




How to Cite

Triebke, H., Kromer, M., & Vortisch, P. (2022). Pre-study and insights to a sequential MATSim-SUMO tool-coupling to deduce 24h driving profiles for SAEVs. SUMO Conference Proceedings, 1, 93–112.



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