Development, Calibration, and Validation of a Large-Scale Traffic Simulation Model: Belgium Road Network




Travel demand modelling, Belgium road network, Mesoscopic traffic simulation, SUMO


Development of large-scale traffic simulation models have always been challenging for transportation researchers. One of the essential steps in developing traffic simulation models, which needs lots of resources, is travel demand modeling. Therefore, proposing travel demand models that require less data than classical travel demand models is highly important, especially in large-scale networks. This paper first presents a travel demand model named as probabilistic travel demand model, then it reports the process of development, calibration and validation of Belgium traffic simulation model. The probabilistic travel demand model takes cities' population, distances between the cities, yearly vehicle-kilometer traveled, and yearly truck trips as inputs. The extracted origin-destination matrices are imported into the SUMO traffic simulator. Mesoscopic traffic simulation and the dynamic user equilibrium traffic assignment are used to build the base case model. This base case model is calibrated using the traffic count data. Al-so, the validation of the model is performed by comparing the real (extracted from Google Map API) and simulated travel times between the cities. The validation results ensure that the model is a superior representation of reality with a high level of accuracy. The model will be helpful for road authorities, planners, and decision-makers to test different scenarios, such as the im-pact of abnormal conditions or the impact of connected and autonomous vehicles on the Belgium road network.


Download data is not yet available.


E. J. Miller, “Travel demand models, the next generation: Boldly going where no-one has gone before,” in Mapping the Travel Behavior Genome, Elsevier, 2020, pp. 29–46. DOI:

OECD, “Population (indicator),” 2022. .

Statista, “Total length of the road network in Belgium,” 2009. (accessed Nov. 21, 2022).

Statista, “Total length of the railway lines in Italy from 2011 to 2018.,” 2020. (accessed Nov. 21, 2022).

W. Decoster, L. Van Elsen, P. De Splenter, and A. Van Snick, “BELGIAN TRANSPORT & LOGISTICS,” 2020.

SPF Mobilité et Transport, “Premiers résultats de l’enquête Monitor sur la mobilité des Belges,” 2019. Bruxelles, pp. 1–6, 2018.

STATBEL, “Structure of the Population | Statbel,” Structure of the Population - Statbel - Belgium in figures, 2022. (accessed Nov. 21, 2022).

FPB, “Base de données transport,” 2017.[]=EU15_BE1&D1[]=EU15_BE2&D1[]=EU15_BE3&D2[]=W50PRIVATE&D3[]=WW10SNEL&D3[]=WW20GEN&D3[]=WW30GEM&lang=fr&DB=TRANSP (accessed Dec. 20, 2022).

STATBEL, “Road freight transport,” Road freight transport, 2022. (accessed Nov. 26, 2022).

L. Sgambi, T. Jacquin, N. Basso, and E. Garavaglia, “The robustness of infrastructure network assessed through a probabilistic flow model and a static traffic assignment algorithm–the case of the Belgian road network,” in IABMAS2020 10th Int. Conf. on Bridge Maintenance, Safety and Management, 2021, pp. 1–6.

T. Jacquin, “Modélisation temporelle du trafic pour des études de résilience sur le réseau routier belge,” Université catholique de Louvain, 2019.

J. de D. Ortúzar and L. G. Willumsen, Modelling Transport. John wiley & sons, 2011. DOI:

NCHRP, Traffic Data Collection, Analysis, and Forecasting for Mechanistic Pavement Design, vol. 538. Transportation Research Board, 2004.

B. Bamdad Mehrabani, J. Erdmann, L. Sgambi, and M. Snelder, “Proposing a Simulation-Based Dynamic System Optimal Traffic Assignment Algorithm for SUMO: An Approximation of Marginal Travel Time,” in SUMO Conference Proceedings, 2022, vol. 3, pp. 121–143, doi: 10.52825/scp.v3i.119. DOI:

DLR, “SUMO User Documentation,” 2021.

M. Aghababaei, S. Costello, and P. Ranjitkar, South Island Model: development and calibration. 2019.

J. Barceló, Fundamentals of traffic simulation, vol. 145. Springer, 2010. DOI:

J. Casas, J. L. Ferrer, D. Garcia, J. Perarnau, and A. Torday, “Traffic simulation with aimsun,” in Fundamentals of traffic simulation, Springer, 2010, pp. 173–232. DOI:

N. G. Eissfeldt, “Vehicle-based modelling of traffic. Theory and application to environmental impact modelling,” University of Cologne. Universität zu Köln, p. 199, 2004, [Online]. Available:

D.-I. C. Presinger, “Calibration and Validation of Mesoscopic Traffic Flow Simulation.” Graz University of Technology, 2021.

Vlaanderen, “Verkeersindicatoren,” 2022.

S. Shafiei, Z. Gu, and M. Saberi, “Calibration and validation of a simulation-based dynamic traffic assignment model for a large-scale congested network,” Simul. Model. Pract. Theory, vol. 86, pp. 169–186, 2018, doi: 10.1016/j.simpat.2018.04.006. DOI:

Google LLC, “Distance Matrix API: Developer Guide,” Google Maps Platform, 2017.




How to Cite

Bamdad Mehrabani, B., Sgambi, L., Maerivoet, S., & Snelder, M. (2023). Development, Calibration, and Validation of a Large-Scale Traffic Simulation Model: Belgium Road Network . SUMO Conference Proceedings, 4, 15–27.

Conference Proceedings Volume


Conference papers

Funding data