A Synthetic Ground-Truth Dataset for Online Map Matching
Resumo
Reliable evaluation of online map-matching algorithms is hindered by the scarcity of trajectory datasets with reusable, exact road-segment ground truth. This work presents a parameterizable pipeline that generates synthetic workloads by preserving reference routes while independently controlling GNSS-like spatial noise and temporal sampling. Its demonstration case uses the O’Hare road network, OpenStreetMap (OSM), and the Simulation of Urban MObility (SUMO) to generate trajectories, reconcile simulator edges with OSM identifiers, and derive noisy observations at 1, 5, 10, and 20 s intervals. The resulting dataset contains 1,759 trajectories ranging from 519.33 m to 8.97 km. Validation on 10 routes yielded 3 perfect, 3 minor-deviation, and 4 significant-deviation matches; downstream experiments supported 790 valid executions across four map-matching tools. The pipeline and its O’Hare instantiation provide an auditable workload for reproducible online map-matching evaluation.
Palavras-chave:
Online map matching, Synthetic ground truth, GNSS trajectories
Referências
Alvarez Lopez, P., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., and Wießner, E. (2018). Microscopic traffic simulation using sumo. In Proceedings[...]. IEEE International Conference on Intelligent Transportation Systems, IEEE.
Boeing, G. (2025). Modeling and Analyzing Urban Networks and Amenities With OSMnx. Geographical Analysis, 57(4):567–577.
Chao, P., Xu, Y., Hua, W., and Zhou, X. (2020). A Survey on Map-Matching Algorithms.
de Sousa Pissardini, R., de Oliveira, R. H., Vaz, J. A., de Almeida Filho, F. G. V., and da Fonseca Junior, E. S. (2017). O problema do posicionamento para transporte terrestre no ambiente urbano. Revista Brasileira de Geomática, 5(3):380–403.
Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1):269–271.
Erdmann, J. and Ebendt, R. (2014). Benchmarking Trajectory Matchers with SUMO. In Proceedings[...], pages 226–230, Porto, Portugal. European Simulation and Modelling Conference, Eurosis-ETI.
Goh, C., Dauwels, J., Mitrovic, N., Asif, M. T., Oran, A., and Jaillet, P. (2012). Online map-matching based on Hidden Markov model for real-time traffic sensing applications. In Proceedings [...], pages 776–781. International IEEE Conference on Intelligent Transportation Systems.
Guastella, D. A., Montero-Porras, E., Morales-Hernández, A., and Bontempi, G. (2025). Traffic Modeling with SUMO: a Tutorial.
Li, H., Kulik, L., and Ramamohanarao, K. (2014). Spatio-temporal trajectory simplification for inferring travel paths. In Proceedings[...], SIGSPATIAL ’14, pages 63–72, New York, NY, USA. ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Association for Computing Machinery.
Newson, P. and Krumm, J. (2009). Hidden Markov map matching through noise and sparseness. In Proceedings [...], pages 336–343, New York, NY, USA. ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Association for Computing Machinery.
Quddus, M. A., Ochieng, W. Y., and Noland, R. B. (2007). Current map-matching algorithms for transport applications: State-of-the art and future research directions. Transportation Research Part C: Emerging Technologies, 15(5):312–328.
Rehrl, K., Gröchenig, S., and Wimmer, M. (2018). Optimization and Evaluation of a High-Performance Open-Source Map-Matching Implementation. In Mansourian, A., Pilesjö, P., Harrie, L., and van Lammeren, R., editors, Geospatial Technologies for All, pages 251–270. Springer International Publishing, Cham.
Saki, S. and Hagen, T. (2022). A Practical Guide to an Open-Source Map-Matching Approach for Big GPS Data. SN Computer Science, 3(5):415.
Sandve, G. K., Nekrutenko, A., Taylor, J., and Hovig, E. (2013). Ten Simple Rules for Reproducible Computational Research. PLOS Computational Biology, 9(10):1–4.
Wöltche, A. (2023). Open source map matching with markov decision processes: A new method and a detailed benchmark with existing approaches. Transactions in GIS, 27(7):1959–1991.
Zhang, G. and Hsu, L.-T. (2021). Performance assessment of GNSS diffraction models in urban areas. NAVIGATION, 68(2):369–389.
Boeing, G. (2025). Modeling and Analyzing Urban Networks and Amenities With OSMnx. Geographical Analysis, 57(4):567–577.
Chao, P., Xu, Y., Hua, W., and Zhou, X. (2020). A Survey on Map-Matching Algorithms.
de Sousa Pissardini, R., de Oliveira, R. H., Vaz, J. A., de Almeida Filho, F. G. V., and da Fonseca Junior, E. S. (2017). O problema do posicionamento para transporte terrestre no ambiente urbano. Revista Brasileira de Geomática, 5(3):380–403.
Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1):269–271.
Erdmann, J. and Ebendt, R. (2014). Benchmarking Trajectory Matchers with SUMO. In Proceedings[...], pages 226–230, Porto, Portugal. European Simulation and Modelling Conference, Eurosis-ETI.
Goh, C., Dauwels, J., Mitrovic, N., Asif, M. T., Oran, A., and Jaillet, P. (2012). Online map-matching based on Hidden Markov model for real-time traffic sensing applications. In Proceedings [...], pages 776–781. International IEEE Conference on Intelligent Transportation Systems.
Guastella, D. A., Montero-Porras, E., Morales-Hernández, A., and Bontempi, G. (2025). Traffic Modeling with SUMO: a Tutorial.
Li, H., Kulik, L., and Ramamohanarao, K. (2014). Spatio-temporal trajectory simplification for inferring travel paths. In Proceedings[...], SIGSPATIAL ’14, pages 63–72, New York, NY, USA. ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Association for Computing Machinery.
Newson, P. and Krumm, J. (2009). Hidden Markov map matching through noise and sparseness. In Proceedings [...], pages 336–343, New York, NY, USA. ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Association for Computing Machinery.
Quddus, M. A., Ochieng, W. Y., and Noland, R. B. (2007). Current map-matching algorithms for transport applications: State-of-the art and future research directions. Transportation Research Part C: Emerging Technologies, 15(5):312–328.
Rehrl, K., Gröchenig, S., and Wimmer, M. (2018). Optimization and Evaluation of a High-Performance Open-Source Map-Matching Implementation. In Mansourian, A., Pilesjö, P., Harrie, L., and van Lammeren, R., editors, Geospatial Technologies for All, pages 251–270. Springer International Publishing, Cham.
Saki, S. and Hagen, T. (2022). A Practical Guide to an Open-Source Map-Matching Approach for Big GPS Data. SN Computer Science, 3(5):415.
Sandve, G. K., Nekrutenko, A., Taylor, J., and Hovig, E. (2013). Ten Simple Rules for Reproducible Computational Research. PLOS Computational Biology, 9(10):1–4.
Wöltche, A. (2023). Open source map matching with markov decision processes: A new method and a detailed benchmark with existing approaches. Transactions in GIS, 27(7):1959–1991.
Zhang, G. and Hsu, L.-T. (2021). Performance assessment of GNSS diffraction models in urban areas. NAVIGATION, 68(2):369–389.
Publicado
19/10/2026
Como Citar
SOUZA, José Vinícius Evangelista Dias de; MAGALHÃES, Regis Pires; MACÊDO, José Antônio Fernandes de.
A Synthetic Ground-Truth Dataset for Online Map Matching. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 23. , 2026, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 2101-2112.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2026.31510.
