Integrating Topographical Map Information in SUMO to Simulate Realistic Micromobility Trips in Hilly and Steep Terrains

Authors

DOI:

https://doi.org/10.52825/scp.v5i.1131

Keywords:

SUMO, Simulation, Elevation Data, Micromobility, Shared Mobility, GBFS, Traction-Battery

Abstract

Nowadays, shared micromobility has become a trend in cities as an alternative to conventional automotive vehicles, especially for short-distance travel. It also plays an important role in the reduction of the number of automotive vehicles which results in a decrease of air pollution and traffic congestion. Shared micromobility is, however, influenced by the terrain characteristics. Varying elevation within a fleet operational area can cause imbalances in the use of micromobility stations if a steep terrain lies between stations. It also impacts the energy consumption of electric micromobility vehicles such as e-bicycles and e-scooters. Therefore, to simulate the state of charge (SOC) of traction batteries for micromobility close to reality, it is essential to include elevation data into the simulation model. This paper proposes a workflow for Simulation of Urban MObility (SUMO) comprising several steps with concrete implementation and validation in order to prepare and define the simulation model with micromobility stations and the integration of elevation data using a REST API. The integration of elevation and bike station data is validated with a defined vehicle type following a route in the hilly part of Stuttgart, Germany. A comparison of micromobility trips, with and without elevation data, was performed through a simulation by recording changes in energy consumption and driven altitude differences. The proposed workflow provides a basis for more complex use cases such as analysing micromobility business areas, improving vehicle.

Downloads

Download data is not yet available.

References

V. Verbavatz and M. Barthelem, “Critical factors for mitigating car traffic in cities,” PLOS ONE, vol. 14, pp. 1–16, 2019. DOI: https://doi.org/10.1371/journal.pone.0219559.

G. P. Rocha Filho, R. I. Meneguette, J. R. Torres Neto, et al., “Enhancing intelligence in traffic management systems to aid in vehicle traffic congestion problems in smart cities,” Ad Hoc Networks, vol. 107, 2020, ISSN: 1570-8705. DOI: https://doi.org/10.1016/j.adhoc.2020.102265.

R. L. Abduljabbar, S. Liyanage, and H. Dia, “The role of micro-mobility in shaping sustainable cities: A systematic literature review,” Transportation Research Part D: Transport and Environment, vol. 92, 2021. DOI: https://doi.org/10.1016/j.trd.2021.102734.

R. Zhu, X. Zhang, D. Kondor, P. Santi, and C. Ratti, “Understanding spatio-temporal heterogeneity of bike-sharing and scooter-sharing mobility,” Computers, Environment and Urban Systems, vol. 81, p. 101 483, 2020, ISSN: 01989715. DOI: https://doi.org/10.1016/j.compenvurbsys.2020.101483.

J.Wang and Y.Wang, “A two-stage incentive mechanism for rebalancing free-floating bike sharing systems: Considering user preference,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 82, pp. 54–69, 2021, ISSN: 13698478. DOI: https://doi.org/10.1016/j. trf.2021.08.003.

D. Chen and K. Sakai, “A user-based bike return algorithm for docked bike sharing systems,” in Workshop Proceedings of the 51st International Conference on Parallel Processing, New York, NY, USA: ACM, 2022, pp. 1–8, ISBN: 9781450394451. DOI: https://doi.org/10.1145/3547276.3548443.

P. Yi, F. Huang, and J. Peng, “A rebalancing strategy for the imbalance problem in bikesharing systems,” Energies, vol. 12, no. 13, p. 2578, 2019. DOI: https://doi.org/10.3390/en12132578.

M. Drwal, E. Gerding, S. Stein, K. Hayakawa, and H. Kitaoka, “Adaptive pricing mechanisms for on-demand mobility,” in Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, ser. AAMAS ’17, Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems, 2017, pp. 1017–1025. [Online]. Available: http://eprints.soton.ac.uk/id/eprint/405176 (visited on 02/23/2024).

P. A. Lopez, E. Wiessner, M. Behrisch, et al., “Microscopic Traffic Simulation using SUMO,” en, in 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI: IEEE, Nov. 2018, pp. 2575–2582, ISBN: 978-1-72810-321-1 978- 1-72810-323-5. DOI: https://doi.org/10.1109/ITSC.2018.8569938. [Online]. Available: https://ieeexplore.ieee.org/document/8569938/ (visited on 12/20/2023).

L. Codeca and J. Härri, “Monaco SUMO Traffic (MoST) Scenario: A 3D Mobility Scenario for Cooperative ITS,” en, 2018, pp. 43–29. DOI: 10.29007/1zt5. [Online]. Available: https://easychair.org/publications/paper/x3nd.

opentopodata.org, “Open Topo Data,” 2024. [Online]. Available: https://www.opentopodata.org/ (visited on 02/06/2024).

L. Codeca, J. Erdmann, V. CAHILL, and J. Haerri, “Saga: An activity-based multi-modal mobility scenariogenerator for sumo,” SUMO Conference Proceedings, vol. 1, pp. 39–58, 2022. DOI: https://doi.org/10.52825/scp.v1i.99.

A. Roosta, H. Kaths, M. Barthauer, J. Erdmann, Y.-P. Flötteröd, and M. Behrisch, “State of bicycle modeling in sumo,” SUMO Conference Proceedings, vol. 4, pp. 55–64, 2023. DOI: https://doi.org/10.52825/scp.v4i.215.

H. Kaths and A. Roosta, “Framework for simulating cyclists in sumo,” SUMO Conference Proceedings, vol. 4, pp. 105–113, 2023. DOI: https://doi.org/10.52825/scp.v4i.219.

topographic-map.com, “Stuttgart topographic map,” 2024. [Online]. Available: https://en-gb.topographic-map.com/map-vpm5k/Stuttgart/?center=48.73966%2C9.12123&zoom=12&popup=48.85076%2C9.10767&base=2 (visited on 02/13/2024).

stadtklima-stuttgart.de, “2. Topographic conditions,” 2024. [Online]. Available: https://www.stadtklima-stuttgart.de/index.php?climate_s21_basics_chapt_2 (visited on 02/13/2024).

sumo.dlr.de, “OSMWebWizard,” 2023. [Online]. Available: https://sumo.dlr.de/docs/Tutorials/OSMWebWizard.html (visited on 02/14/2024).

wiki.openstreetmap.org, “Key:ele,” 2023. [Online]. Available: https://wiki.openstreetmap.org/wiki/Key:ele (visited on 02/13/2024).

arcgis.com, “Databases and ArcGIS,” 2023. [Online]. Available: https://desktop.arcgis.com/en/arcmap/latest/manage-data/databases/databases-and-arcgis.htm (visited on 02/06/2024).

A. Freymann. “SUMO3D4MicroMobility.” (2024), [Online]. Available: https://github.com/keim-hs-esslingen/SUMO3D4MicroMobility (visited on 02/23/2024).

sumo.dlr.de, “Plainxml,” 2024. [Online]. Available: https://sumo.dlr.de/docs/Networks/PlainXML.html (visited on 02/12/2024).

Y. Xu, X. Yan, V. P. Sisiopiku, L. A. Merlin, F. Xing, and X. Zhao, Micromobility Trip Origin and Destination Inference Using General Bikeshare Feed Specification (GBFS) Data, en, arXiv:2010.12006 [cs], Oct. 2020. [Online]. Available: http://arxiv.org/abs/2010.12006 (visited on 02/13/2024).

Deutsche Bahn Connect GmbH. “RegioRadStuttgart.” (2024), [Online]. Available: https://www.regioradstuttgart.de/ (visited on 02/23/2024).

Deutsche Bahn AG. “API- Deutsche Bahn AG.” (2024), [Online]. Available: https://apis.deutschebahn.com (visited on 02/23/2024).

Deutsche Bahn AG. “Developers- Deutsche Bahn AG.” (2024), [Online]. Available: https://developers.deutschebahn.com (visited on 02/23/2024).

Downloads

Published

2024-07-17

How to Cite

Freymann, A., Reichsöllner, E., Ravlija, D., Trautwein, I., & Sonntag, M. (2024). Integrating Topographical Map Information in SUMO to Simulate Realistic Micromobility Trips in Hilly and Steep Terrains. SUMO Conference Proceedings, 5, 235–246. https://doi.org/10.52825/scp.v5i.1131

Conference Proceedings Volume

Section

Conference papers
Received 2024-02-23
Accepted 2024-04-03
Published 2024-07-17