Research Article
BibTex RIS Cite

Year 2025, Volume: 12 Issue: 1, 332 - 346, 26.03.2025

Emmanuel Okechukwu Nwafor , Folake Olubunmi Akintayo

https://doi.org/10.54287/gujsa.1588040

Abstract

References

  • Adeke, P. T., Atoo, A. A., & Zava, A. E. (2018). Traffic signal design and performance assessment of 4-leg intersections using Webster’s model: A case of ‘SRS’and ‘B-division’Intersections in Makurdi Town. Traffic, 5(05).
  • Alghamdi, M. (2024). Smart city urban planning using an evolutionary deep learning model. Soft Comput 28, 447–459. https://doi.org/10.1007/s00500-023-08219-4
  • Akintayo, F.O., & Adibeli, S.A., (2022). Safety performance of selected bus stops in Ibadan Metropolis, Nigeria, Journal of Public Transportation, Volume 24, https://doi.org/10.1016/j.jpubtr.2022.100003
  • An, R., Tong, Z., Ding, Y., Tan, B., Wu, Z., Xiong, Q., & Liu, Y. (2022). Examining non-linear built environment effects on injurious traffic collisions: A gradient boosting decision tree analysis. Journal of Transport & Health, 24, 101296.
  • Barri, E. Y., Farber, S., Jahanshahi, H., & Beyazit, E. (2022). Understanding transit ridership in an equity context through a comparison of statistical and machine learning algorithms. Journal of Transport Geography, 105, 103482.
  • Bauriedl, S., & Strüver, A. (2020). Platform Urbanism: Technocapitalist Production of Private and Public Spaces. Urban Planning, 5(4), 267-276. https://doi.org/10.17645/up.v5i4.3414
  • Bharadiya, J. (2023). Artificial Intelligence in Transportation Systems: a Critical Review. American Journal of Computing and Engineering, 6(1), 34 - 45. https://doi.org/10.47672/ajce.1487
  • Biala, O. T., Aderinlewo O. O., Asonja, C. O., Titiloye O. D. Ojekunle, M. O., & Nwafor E. O. (2024): Comparative Analysis of Linear Regression and Multilayer Perceptron Neural Network (MLPNN) Models for Trip Generation Modelling of Ilorin City, Nigeria . Journal of Engineering and Engineering Technology /18(1),65-72.
  • Cheng, Y. D., Yang, L. K. & Deng, S. (2022). Nonprofit density and distributional equity in public service provision: Exploring racial/ethnic disparities in public park access across U.S. cities. Public Administration Review, 82(3): 473–486. https://doi.org/10.1111/puar.13465
  • Choudhary, A., Agrawal, A. P., Logeswaran, R. & Unhelkar, B. (Eds.). (2021). Applications of Artificial Intelligence and Machine Learning. Lecture Notes in Electrical Engineering. https://doi.org/10.1007/978-981-16-3067-5
  • Cruz, C. O., & Sarmento, J. M. (2020). “Mobility as a service” platforms: A critical path towards increasing the sustainability of transportation systems. Sustainability, 12(16), 6368.
  • Díaz, G., Macià, H., Valero, V., Boubeta-Puig, J. & Cuartero, F. (2018). An Intelligent Transportation System to control air pollution and road traffic in cities integrating CEP and Colored Petri Nets. Neural Computing and Applications. https://doi.org/10.1007/s00521-018-3850-1
  • Gallo, M., & Marinelli, M. (2020). Sustainable mobility: A review of possible actions and policies. Sustainability, 12(18), 7499.
  • Kashifi, M. T., Jamal, A., Kashefi, M. S., Almoshaogeh, M., & Rahman, S. M. (2022). Predicting the travel mode choice with interpretable machine learning techniques: A comparative study. Travel Behaviour and Society, 29, 279-296.
  • Khan, M. A., Etminani-Ghasrodashti, R., Shahmoradi, A., Kermanshachi, S., Rosenberger, J. M., & Foss, A. (2022). Integrating shared autonomous vehicles into existing transportation services: evidence from a paratransit service in Arlington, Texas. International Journal of Civil Engineering, 20(6), 601-618.
  • Klaus, S. & Wegener M. (2004). Evaluating Urban Sustainability Using Land-Use Transport Interaction Models, Spiekermann & Wegener Urban and Regional Research (S&W), EJTIR, 4, no. 3, (2004), pp.251-272
  • Koumetio Tekouabou, S. C., Diop, E. B., Azmi, R. et al. (2023). Artificial Intelligence Based Methods for Smart and Sustainable Urban Planning: A Systematic Survey. Arch Computat Methods Eng 30, 1421–1438. https://doi.org/10.1007/s11831-022-09844-2
  • Koushik, A. N., Manoj, M., & Nezamuddin, N. (2020). Machine learning applications in activity-travel behaviour research: a review. Transport reviews, 40(3), 288-311.
  • Lee, S., Lee, J., Hiemstra-van Mastrigt, S., & Kim, E. (2022). What cities have is how people travel: Conceptualizing a data-mining-driven modal split framework. Cities, 131, 103902. https://doi.org/10.1016/j.cities.2022.103902
  • Lovelace, R. (2021). Open source tools for geographic analysis in transport planning. J Geogr Syst 23, 547–578. https://doi.org/10.1007/s10109-020-00342-2
  • Lyons, G., Mokhtarian, P., Dijst, M., & Böcker, L. (2018). The dynamics of urban metabolism in the face of digitalization and changing lifestyles: Understanding and influencing our cities. Resources, Conservation and Recycling, 132, 246-257.
  • Nair, R. (2023). Unraveling the Decision-making Process Interpretable Deep Learning IDS for Transportation Network Security. Journal of Cybersecurity & Information Management, 12(2).
  • NPC, National Population Commission. (2006). National Population Census of Federal Republic of Nigeria Official Gazette, 96 (2).
  • Olugbade, S., Ojo, S., Imoize, A. L., Isabona, J., & Alaba, M. O. (2022). A review of artificial intelligence and machine learning for incident detectors in road transport systems. Mathematical and Computational Applications, 27(5), 77.
  • Otuoze, S. H., Hunt, D. V., & Jefferson, I. (2021). Neural network approach to modelling transport system resilience for major cities: case studies of lagos and kano (Nigeria). Sustainability, 13(3), 1371.
  • Pradhan, R. P., Arvin, M. B., & Nair, M. (2021). Urbanization, transportation infrastructure, ICT, and economic growth: A temporal causal analysis. Cities, 115, 103213.
  • Pulugurta, S., Arun, A. & Errampalli, M. (2013). Use of Artificial Intelligence for Mode Choice Analysis and Comparison with Traditional Multinomial Logit Model. Procedia - Social and Behavioral Sciences, 104, 583–592. https://doi.org/10.1016/j.sbspro.2013.11.152
  • Richards, M. J. & Zill, J. C., (2019). Modelling Mode Choice with Machine Learning Algorithms, Australasian Transport Research Forum 2019 Proceedings 30 September 2, October, Canberra, Australia.
  • Sun, H., Chen, Y., Wang, Y. & Liu, X. (2021). Correction to: Trip purpose inference for tourists by machine learning approaches based on mobile signaling data. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-03443-y
  • Zhang, X., & Zhao, X. (2022). Machine learning approach for spatial modeling of ridesourcing demand. Journal of Transport Geography, 100(C).

Application of Decision Tree Algorithms for Predicting Trip Purposes in Makurdi, Nigeria

Year 2025, Volume: 12 Issue: 1, 332 - 346, 26.03.2025

Emmanuel Okechukwu Nwafor , Folake Olubunmi Akintayo

https://doi.org/10.54287/gujsa.1588040

Abstract

Decision tree models are versatile and interpretable machine learning algorithms widely used for both classification and regression tasks in transportation planning. This research focuses on analysing the suitability of decision tree algorithms in predicting trip purposes in Makurdi, Nigeria. The methodology involves formalizing household demographic and trip information datasets obtained through an extensive survey process. Modelling and prediction were conducted using Python programming language, and evaluation metrics such as R-squared and Mean Absolute Error (MAE) were used to assess the model’s performance. The results indicate that the model performed well, achieving accuracies of 84% and 68% and low MAE values of 0.188 and 0.314 on training and validation data, respectively. These findings suggest the model's reliability for future predictions. The study concludes that the decision tree-based model provides actionable insights for urban planners, transportation engineers, and policymakers to make informed decisions for improving transportation planning and management in Makurdi, Nigeria.

Keywords

Decision Tree Algorithm Trip Purpose Intelligent Transport Sustainable Transport

References

  • Adeke, P. T., Atoo, A. A., & Zava, A. E. (2018). Traffic signal design and performance assessment of 4-leg intersections using Webster’s model: A case of ‘SRS’and ‘B-division’Intersections in Makurdi Town. Traffic, 5(05).
  • Alghamdi, M. (2024). Smart city urban planning using an evolutionary deep learning model. Soft Comput 28, 447–459. https://doi.org/10.1007/s00500-023-08219-4
  • Akintayo, F.O., & Adibeli, S.A., (2022). Safety performance of selected bus stops in Ibadan Metropolis, Nigeria, Journal of Public Transportation, Volume 24, https://doi.org/10.1016/j.jpubtr.2022.100003
  • An, R., Tong, Z., Ding, Y., Tan, B., Wu, Z., Xiong, Q., & Liu, Y. (2022). Examining non-linear built environment effects on injurious traffic collisions: A gradient boosting decision tree analysis. Journal of Transport & Health, 24, 101296.
  • Barri, E. Y., Farber, S., Jahanshahi, H., & Beyazit, E. (2022). Understanding transit ridership in an equity context through a comparison of statistical and machine learning algorithms. Journal of Transport Geography, 105, 103482.
  • Bauriedl, S., & Strüver, A. (2020). Platform Urbanism: Technocapitalist Production of Private and Public Spaces. Urban Planning, 5(4), 267-276. https://doi.org/10.17645/up.v5i4.3414
  • Bharadiya, J. (2023). Artificial Intelligence in Transportation Systems: a Critical Review. American Journal of Computing and Engineering, 6(1), 34 - 45. https://doi.org/10.47672/ajce.1487
  • Biala, O. T., Aderinlewo O. O., Asonja, C. O., Titiloye O. D. Ojekunle, M. O., & Nwafor E. O. (2024): Comparative Analysis of Linear Regression and Multilayer Perceptron Neural Network (MLPNN) Models for Trip Generation Modelling of Ilorin City, Nigeria . Journal of Engineering and Engineering Technology /18(1),65-72.
  • Cheng, Y. D., Yang, L. K. & Deng, S. (2022). Nonprofit density and distributional equity in public service provision: Exploring racial/ethnic disparities in public park access across U.S. cities. Public Administration Review, 82(3): 473–486. https://doi.org/10.1111/puar.13465
  • Choudhary, A., Agrawal, A. P., Logeswaran, R. & Unhelkar, B. (Eds.). (2021). Applications of Artificial Intelligence and Machine Learning. Lecture Notes in Electrical Engineering. https://doi.org/10.1007/978-981-16-3067-5
  • Cruz, C. O., & Sarmento, J. M. (2020). “Mobility as a service” platforms: A critical path towards increasing the sustainability of transportation systems. Sustainability, 12(16), 6368.
  • Díaz, G., Macià, H., Valero, V., Boubeta-Puig, J. & Cuartero, F. (2018). An Intelligent Transportation System to control air pollution and road traffic in cities integrating CEP and Colored Petri Nets. Neural Computing and Applications. https://doi.org/10.1007/s00521-018-3850-1
  • Gallo, M., & Marinelli, M. (2020). Sustainable mobility: A review of possible actions and policies. Sustainability, 12(18), 7499.
  • Kashifi, M. T., Jamal, A., Kashefi, M. S., Almoshaogeh, M., & Rahman, S. M. (2022). Predicting the travel mode choice with interpretable machine learning techniques: A comparative study. Travel Behaviour and Society, 29, 279-296.
  • Khan, M. A., Etminani-Ghasrodashti, R., Shahmoradi, A., Kermanshachi, S., Rosenberger, J. M., & Foss, A. (2022). Integrating shared autonomous vehicles into existing transportation services: evidence from a paratransit service in Arlington, Texas. International Journal of Civil Engineering, 20(6), 601-618.
  • Klaus, S. & Wegener M. (2004). Evaluating Urban Sustainability Using Land-Use Transport Interaction Models, Spiekermann & Wegener Urban and Regional Research (S&W), EJTIR, 4, no. 3, (2004), pp.251-272
  • Koumetio Tekouabou, S. C., Diop, E. B., Azmi, R. et al. (2023). Artificial Intelligence Based Methods for Smart and Sustainable Urban Planning: A Systematic Survey. Arch Computat Methods Eng 30, 1421–1438. https://doi.org/10.1007/s11831-022-09844-2
  • Koushik, A. N., Manoj, M., & Nezamuddin, N. (2020). Machine learning applications in activity-travel behaviour research: a review. Transport reviews, 40(3), 288-311.
  • Lee, S., Lee, J., Hiemstra-van Mastrigt, S., & Kim, E. (2022). What cities have is how people travel: Conceptualizing a data-mining-driven modal split framework. Cities, 131, 103902. https://doi.org/10.1016/j.cities.2022.103902
  • Lovelace, R. (2021). Open source tools for geographic analysis in transport planning. J Geogr Syst 23, 547–578. https://doi.org/10.1007/s10109-020-00342-2
  • Lyons, G., Mokhtarian, P., Dijst, M., & Böcker, L. (2018). The dynamics of urban metabolism in the face of digitalization and changing lifestyles: Understanding and influencing our cities. Resources, Conservation and Recycling, 132, 246-257.
  • Nair, R. (2023). Unraveling the Decision-making Process Interpretable Deep Learning IDS for Transportation Network Security. Journal of Cybersecurity & Information Management, 12(2).
  • NPC, National Population Commission. (2006). National Population Census of Federal Republic of Nigeria Official Gazette, 96 (2).
  • Olugbade, S., Ojo, S., Imoize, A. L., Isabona, J., & Alaba, M. O. (2022). A review of artificial intelligence and machine learning for incident detectors in road transport systems. Mathematical and Computational Applications, 27(5), 77.
  • Otuoze, S. H., Hunt, D. V., & Jefferson, I. (2021). Neural network approach to modelling transport system resilience for major cities: case studies of lagos and kano (Nigeria). Sustainability, 13(3), 1371.
  • Pradhan, R. P., Arvin, M. B., & Nair, M. (2021). Urbanization, transportation infrastructure, ICT, and economic growth: A temporal causal analysis. Cities, 115, 103213.
  • Pulugurta, S., Arun, A. & Errampalli, M. (2013). Use of Artificial Intelligence for Mode Choice Analysis and Comparison with Traditional Multinomial Logit Model. Procedia - Social and Behavioral Sciences, 104, 583–592. https://doi.org/10.1016/j.sbspro.2013.11.152
  • Richards, M. J. & Zill, J. C., (2019). Modelling Mode Choice with Machine Learning Algorithms, Australasian Transport Research Forum 2019 Proceedings 30 September 2, October, Canberra, Australia.
  • Sun, H., Chen, Y., Wang, Y. & Liu, X. (2021). Correction to: Trip purpose inference for tourists by machine learning approaches based on mobile signaling data. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-03443-y
  • Zhang, X., & Zhao, X. (2022). Machine learning approach for spatial modeling of ridesourcing demand. Journal of Transport Geography, 100(C).
There are 30 citations in total.

Details

Primary Language English
Subjects Transportation Engineering
Journal Section Civil Engineering
Authors

Emmanuel Okechukwu Nwafor 0000-0002-5177-4941

Folake Olubunmi Akintayo 0000-0001-6494-9811

Publication Date March 26, 2025
Submission Date November 19, 2024
Acceptance Date February 13, 2025
Published in Issue Year 2025 Volume: 12 Issue: 1

Cite

APA Nwafor, E. O., & Akintayo, F. O. (2025). Application of Decision Tree Algorithms for Predicting Trip Purposes in Makurdi, Nigeria. Gazi University Journal of Science Part A: Engineering and Innovation, 12(1), 332-346. https://doi.org/10.54287/gujsa.1588040
 Download Cover Image