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Clustering-Based Urban Driving Cycle Generation: A Data-Driven Approach for Traffic Analysis and Sustainable Mobility Applications in Ecuador

Author

Listed:
  • Juan Carlos Almachi

    (Departamento de Ingeniería Mecánica (DIM), Escuela Politécnica Nacional (EPN), Quito 170517, Ecuador)

  • Jonathan Saguay

    (Departamento de Ingeniería Mecánica (DIM), Escuela Politécnica Nacional (EPN), Quito 170517, Ecuador)

  • Edwin Anrango

    (Departamento de Ingeniería Mecánica (DIM), Escuela Politécnica Nacional (EPN), Quito 170517, Ecuador)

  • Edgar Cando

    (Departamento de Ingeniería Mecánica (DIM), Escuela Politécnica Nacional (EPN), Quito 170517, Ecuador)

  • Salvatore Reina

    (Departamento de Ingeniería Mecánica (DIM), Escuela Politécnica Nacional (EPN), Quito 170517, Ecuador)

Abstract

A representative urban driving cycle was developed for Quito, Ecuador, using the K-Means clustering method. From 64 samples and 188,713 geospatial and speed data points, a 2870 s driving cycle was constructed to capture real-world traffic characteristics. Key parameters include an average speed of 22.68 km/h, acceleration and deceleration rates of 0.55 m/s 2 and −0.57 m/s 2 , and a dwell time of 9.66%. Due to Quito’s linear urban development, where mobility is limited to north–south/south–north corridors, the driving cycle reflects frequent accelerations and decelerations along congested arterial roads. A comparative analysis with international driving cycles revealed that Quito’s traffic follows a unique pattern shaped by its geographic constraints. The HK cycle in China showed the greatest similarities, although differences in instantaneous speeds highlight the need for localized models. While this study primarily focuses on methodological robustness, the developed driving cycle provides a foundational dataset for future research on traffic flow optimization, emissions estimation, and sustainable urban mobility strategies. These insights contribute to data-driven decision-making for improving transportation efficiency and environmental impact assessment in cities with similar urban structures.

Suggested Citation

  • Juan Carlos Almachi & Jonathan Saguay & Edwin Anrango & Edgar Cando & Salvatore Reina, 2025. "Clustering-Based Urban Driving Cycle Generation: A Data-Driven Approach for Traffic Analysis and Sustainable Mobility Applications in Ecuador," Sustainability, MDPI, vol. 17(8), pages 1-19, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:8:p:3353-:d:1631379
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    References listed on IDEAS

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    1. Zhipeng Ma & Bo Nørregaard Jørgensen & Zheng Ma, 2024. "A Scoping Review of Energy-Efficient Driving Behaviors and Applied State-of-the-Art AI Methods," Energies, MDPI, vol. 17(2), pages 1-19, January.
    2. Sugar, Catherine A. & James, Gareth M., 2003. "Finding the Number of Clusters in a Dataset: An Information-Theoretic Approach," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 750-763, January.
    3. Bishop, Justin D.K. & Stettler, Marc E.J. & Molden, N. & Boies, Adam M., 2016. "Engine maps of fuel use and emissions from transient driving cycles," Applied Energy, Elsevier, vol. 183(C), pages 202-217.
    4. Padam, Sudarsanam & Singh, Sanjay Kumar, 2004. "Urbanization and urban transport in India: the search for a policy," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 27, pages 26-44.
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