journal article·2021-01-28Open access

Experiences with Establishing a Simulation Scenario of the City of Duisburg with Real Traffic Volume

Xiaoyi Ma, Xiaowei Hu, Thomas Weber, Dieter Schramm

Applied Sciences· Volume 11· Issue 3

Abstract

This article presents the experience of building a simulation scenario of the whole city of Duisburg using real traffic data. The establishment of the simulation scenario is based on road network and traffic volume. In most cases, it is hard to collect all data sources with high precision. Moreover, it is time-consuming to set up a realistic traffic scenario. Even with available data, conversion, calibration, and validation all take a large effort. With the increase of the respective simulation area, the difficulty and workload rise. In this study, a simulation scenario of the whole city of Duisburg with the road network area of 232 km2 and Origin/Destination (OD) matrix area over 800 km2 was established in the software package SUMO. Four cases with different networks and traffic volumes were built and compared with real traffic data collected from induction loops. The percentage of simulated traffic volume in real traffic volume range can be up to 72.22%.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Traffic control and management · Traffic Prediction and Management Techniques · Transportation Planning and Optimization

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Mechatronics, University of Duisburg-Essen, 47047 Duisburg, Germany

References (17)

Show source-provided references
1. Lopez, P.A., Wiessner, E., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flotterod, Y.-P., Hilbrich, R., Lucken, L., Rummel, J., and Wagner, P. (2018, January 4–7). Microscopic Traffic Simulation using SUMO. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.10.1109/ITSC.2018.8569938 ↗
2. Behrisch, M., and Weber, M. (2015). Traffic simulation for all: A real world traffic scenario from the city of Bologna. Modeling Mobility with Open Data, Springer. Lecture Notes in Mobility.10.1007/978-3-319-15024-6 ↗
3. Recent development and applications of SUMO-Simulation of Urban MObility
4. Codeca, L., Frank, R., and Engel, T. (2015, January 7–8). LuST: A 24-hour Scenario of Luxembourg City for SUMO Traffic simulations. Proceedings of the SUMO User Conference 2015—Intermodal Simulation for Intermodal Transport, Berlin, Germany.
5. Asano, Y., Ito, N., Inaoka, H., Imai, T., and Uchitane, T. (2015). Traffic simulation of Kobe-city. Proceedings of the International Conference on Social Modeling and Simulation, Plus Econophysics Colloquium 2014, Springer.10.1007/978-3-319-20591-5_23 ↗
6. Ma, X., Hu, X., and Schramm, D. (2020). Effects of School Activities on Traffic Flow on Example of Traffic Scenario from the City Wuhan. Neue Dimensionen der Mobilität, Springer Gabler.10.1007/978-3-658-29746-6_14 ↗
7. Wireless sensor networks in intelligent transportation systems10.1002/wcm.616 ↗
8. Traffic flow estimation using acoustic signal10.1016/j.engappai.2017.05.019 ↗
9. Ma, X., Hu, X., Weber, T., and Schramm, D. (2021). Traffic Simulation of Future Intelligent Vehicles in Duisburg City Inner Ring. Appl. Sci., 11.10.3390/app11010029 ↗
10. OpenStreetMap Contributors (2020, August 19). Planet Dump [Data File from 01.10.2017]. Available online: https://planet.openstreetmap.org.
11. (2018, July 08). JOSM. Available online: https://josm.openstreetmap.de/.
12. (2020, August 19). Google Steet View. Available online: https://www.google.com/streetview/.
13. Schmidt, G., and Thomas, B. (1996). Hochrechnungsfaktoren für manuelle und automatische Kurzzeitzählungen im Innerortsbereich. Forschung Straßenbau Straßenverkehrstechnik, Bundesministerium für Verkehr.
14. A note on two problems in connexion with graphs10.1007/BF01386390 ↗
15. Traffic light control using deep policy-gradient and value-function-based reinforcement learning10.1049/iet-its.2017.0153 ↗
16. Westerman, M., Litjens, R., and Linnart, J.P.Z. (1996). Integration of Probe Vehicle and Induction Loop Data: Estimation of Travel Times and Automatic Incident Detection, University of California. PATH Research Report UCB-ITS-PRR-96-13, Institute of Transportation Studies.
17. Treiber, M., and Arne, K. (2014). Traffic flow dynamics: Data, models and simulation. Physics Today, Springer.10.1007/978-3-642-32460-4 ↗