SPATIAL REGRESSION MODEL ANALYSIS OF TRAFFIC VOLUME AND SPEED IN KEDIRI CITY

Authors

  • Mochammad Danara Indra Pradigta Program Studi Teknik Sipil, Fakultas Teknik, Universitas Kadiri
  • Evita Fitrianis Hidiyati Program Studi Teknik Sipil, Fakultas Teknik, Universitas Kadiri
  • Andri Dwi Cahyono Program Studi Teknik Sipil, Fakultas Teknik, Universitas Kadiri
  • Faiz Muhammad Azhari Program Studi Teknik Sipil, Fakultas Teknik, Universitas Kadiri
  • Muhamad Kusaini Program Studi Teknik Sipil, Fakultas Teknik, Universitas Kadiri
  • Tahta Mihrobul Muna Program Studi Teknik Sipil, Fakultas Teknik, Universitas Kadiri

DOI:

https://doi.org/10.21009/jpensil.v14i3.58969

Keywords:

Kediri City Intersections, Spatial Heterogeneity, Vehicle Speed, Vehicle Volume

Abstract

Urban development is often accompanied by traffic challenges, particularly the increasing volume of vehicles that is not commensurate with the capacity of road infrastructure. This study aims to evaluate the effect of spatial heterogeneity of vehicle volume and type on vehicle speed at four main intersections in Kediri City: Alun-Alun, Semampir, Bandar Alim, and Kawi. A quantitative approach was used through direct observation, floating car method, and linear regression analysis and ANOVA. The results show that intersections with high vehicle volume tend to have lower speeds, especially when dominated by heavy vehicles. Alun-Alun intersection has the highest R-Square value for vehicle volume (0.847), while Bandar Alim intersection recorded the highest R-Square for vehicle speed (82%). T-test and ANOVA indicate a significant effect of vehicle direction and type on speed, especially at Semampir and Bandar Alim. These findings demonstrate the importance of integrating volume and speed in traffic management, as well as the need for a data-driven and spatial approach in developing intelligent transportation systems. Therefore, it is necessary to discuss further in the objectives and body of the research regarding the causal relationship between vehicle volume, vehicle type composition, travel direction, and spatial variations between intersections, so that the research results can provide a stronger scientific basis for adaptive traffic management policies to the specific conditions of each intersection.

References

Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019). Applications of artificial intelligence in transport: An overview. Sustainability (Switzerland), 11(1). https://doi.org/10.3390/su11010189

Ahmed Alkaissi, Z. (2024). Traffic congestion evaluation of urban streets based on fuzzy inference system and GIS application. Ain Shams Engineering Journal, 15(6), 102725. https://doi.org/10.1016/j.asej.2024.102725

Allam, Z., Sharifi, A., Bibri, S. E., Jones, D. S., & Krogstie, J. (2022). The Metaverse as a Virtual Form of Smart Cities: Opportunities and Challenges for Environmental, Economic, and Social Sustainability in Urban Futures. Smart Cities, 5(3), 771–801. https://doi.org/10.3390/smartcities5030040

Betkier, I. (2025). Estimating travel time in transport network with a combined multi-attributed graph convolutional neural network and multilayer perceptron model. Engineering Applications of Artificial Intelligence, 142(December 2024), 109898. https://doi.org/10.1016/j.engappai.2024.109898

Betkier, I., & Oszczypała, M. (2024). A novel approach to traffic modelling based on road parameters, weather conditions and GPS data using feedforward neural networks. Expert Systems with Applications, 245(December 2023). https://doi.org/10.1016/j.eswa.2023.123067

Bittencourt, J. C. N., Jesus, T. C., Peixoto, J. P. J., & Costa, D. G. (2025). The Road to Intelligent Cities. Smart Cities, 8(3), 77. https://doi.org/10.3390/smartcities8030077

Cahyono, A. D., Mahardana, Z. B., Hidiyati, E. F., & Rahmawaty, F. (2023). Dampak Arus Lalu Lintas Terhadap Tingkat Layanan Jaringan Jalan Kota Kediri Berdasarkan Ihcm 1997. Wahana Teknik Sipil: Jurnal Pengembangan Teknik Sipil, 28(1), 108–114. https://doi.org/10.32497/wahanats.v28i1.4565

Chatzinikolaou, D. (2025). On Smart Cities and Triple-Helix Intermediaries: A Critical-Realist Perspective. Smart Cities, 8(3), 1–20. https://doi.org/10.3390/smartcities8030074

Cheng, J., Yan, R., & Gao, Y. (2020). Exploring spatial heterogeneity in accessibility and transit mode choice. Transportation Research Part D: Transport and Environment, 87(September), 102521. https://doi.org/10.1016/j.trd.2020.102521

Gao, W., Zhao, C., Zeng, Y., & Tang, J. (2024). Exploring the Spatio-Temporally Heterogeneous Impact of Traffic Network Structure on Ride-Hailing Emissions Using Shenzhen, China, as a Case Study. Sustainability (Switzerland) , 16(11). https://doi.org/10.3390/su16114539

Guo, L., Cheng, W., Liu, C., Zhang, Q., & Yang, S. (2023). Exploring the Spatial Heterogeneity and Influence Factors of Daily Travel Carbon Emissions in Metropolitan Areas: From the Perspective of the 15-min City. Land, 12(2). https://doi.org/10.3390/land12020299

Hairrudin, & Suroso, A. (2025). COMMUNITY RESPONSE (CRM)-BASED PRIORITY SCALE SYSTEM FOR TRANSJAKARTA ROUTE REPAIR in DKI JAKARTA PROVINCE. Jurnal PenSil, 14(1), 30–38. https://doi.org/10.21009/jpensil.v14i1.47880

Haydari, A., & Yilmaz, Y. (2022). Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey. IEEE Transactions on Intelligent Transportation Systems, 23(1), 11–32. https://doi.org/10.1109/TITS.2020.3008612

Herdian Bayu Ash Siddiq, R., Hidayatulloh, H., Alam, M. S., Nur Fadhillah, S., Yuniar, G., Valensia, W., Wulansari, A., Kurniawan, R., & Zulchumairoh Nurhasanti, D. (2024). Evaluation of Public Transport Facilities At Ports, Airports, Stations, and Terminals. Jurnal PenSil, 13(2), 193–205. https://doi.org/10.21009/jpensil.v13i2.43600

Hidayati, F., & Rarasati, A. D. (2023). Factors Affecting the Development of an Integrated Toll Transaction System To Improve Traffic Volume Distribution. Jurnal PenSil, 12(3), 388–399. https://doi.org/10.21009/jpensil.v12i3.36337

Huang, Y., & Xu, W. (Ato). (2021). Spatial and temporal heterogeneity of the impact of high-speed railway on urban economy: Empirical study of Chinese cities. Journal of Transport Geography, 91(September 2019), 102972. https://doi.org/10.1016/j.jtrangeo.2021.102972

Ji, J., Wang, J., Huang, C., Wu, J., Xu, B., Wu, Z., Zhang, J., & Zheng, Y. (2023). Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction. Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, 37, 4356–4364. https://doi.org/10.1609/aaai.v37i4.25555

Kirimtat, A., Krejcar, O., Kertesz, A., & Tasgetiren, M. F. (2020). Future Trends and Current State of Smart City Concepts: A Survey. IEEE Access, 8, 86448–86467. https://doi.org/10.1109/ACCESS.2020.2992441

Li, M., Pan, X., Liu, C., & Li, Z. (2025). Federated deep reinforcement learning-based urban traffic signal optimal control. Scientific Reports, 15(1), 1–23. https://doi.org/10.1038/s41598-025-91966-1

Li, T., Chen, Z., Luo, S., & Delbosc, A. (2025). Assessing the Spatial Heterogeneous Impacts of Urban Heat Island Effects on Active Travel by Leveraging Social Media Data. Multimodal Transportation, 4(4), 100243. https://doi.org/10.1016/j.multra.2025.100243

Lin, P., Hong, Y., He, Y., & Pei, M. (2024). Advancing and lagging effects of weather conditions on intercity traffic volume: A geographically weighted regression analysis in the Guangdong-Hong Kong-Macao Greater Bay Area. International Journal of Transportation Science and Technology, 13, 58–76. https://doi.org/10.1016/j.ijtst.2023.11.003

Lopes, N. M., Aparicio, M., & Neves, F. T. (2025). Challenges and prospects of artificial intelligence in aviation: a bibliometric study. Data Science and Management, 8(2), 207–223. https://doi.org/10.1016/j.dsm.2024.11.001

Lubis, K., Mahda, N., & Irwan. (2023). the Performance Evaluation of Laston Ac-Wc Unit Asphalt Mixing Plant, for Natural Rubber Results Daktalitas & Marshall Test. Jurnal PenSil, 12(1), 65–76. https://doi.org/10.21009/jpensil.v12i1.30867

Ma, Y. W., & Chiu, P. H. (2025). A novel risk-based access control engine in zero trust architecture for IoT network. International Journal of Information Security, 24(3), 1–14. https://doi.org/10.1007/s10207-025-01030-2

Meng, X., Li, Y., Liu, K., Liu, Y., Yang, B., Song, X., Liao, G., Wang, S., Yu, Z., Chen, L., Pan, X., & Lin, Y. (2025). Spatial data intelligence and city metaverse: A review. Fundamental Research, 5(3), 1169–1193. https://doi.org/10.1016/j.fmre.2023.10.014

Mueller, A. G., & Weiler, S. (2023). Spatial Models of Travel Behavior and Land Use Restriction. Journal of Sustainable Real Estate, 15(1). https://doi.org/10.1080/19498276.2023.2174661

Murakami, D., & Seya, H. (2022). Spatial Regression in the Presence of a Hierarchical Transportation Network: Application to Land Price Analysis. Frontiers in Sustainable Cities, 4(May), 1–10. https://doi.org/10.3389/frsc.2022.905967

Nian, G., Sun, J., & Huang, J. (2021). Exploring the Effects of Urban Built Environment on Road Travel Speed Variability with a Spatial Panel Data Model. ISPRS International Journal of Geo-Information, 10(12). https://doi.org/10.3390/ijgi10120829

Prasetya, I. P. G. I. B., Baharuddin, B., & Wibawa, G. N. A. (2024). Pemodelan Regresi Spasial untuk Menentukan Faktor-Faktor yang Berpengaruh terhadap Tingkat Kriminalitas di Provinsi Bali dan Jawa Timur. Jurnal Syntax Admiration, 5(6), 2033–2046. https://doi.org/10.46799/jsa.v5i6.1207

Prasetyo, E. A., Poernomo, Y. C. S., Siswanto, E., & Pradigta, M. D. I. (2022). Desain Overlay Perkerasan Lentur Pada Jalan Joyoboyo Timur Kediri Dengan Metode Analisa Komponen. Jurnal Manajemen Teknologi & Teknik Sipil, 5(1), 74. https://doi.org/10.30737/jurmateks.v5i1.2815

Putra G, A., Tiro, M. A., & Aidid, M. K. (2019). Metode Boostrap dan Jackknife dalam Mengestimasi Parameter Regresi Linear Ganda (Kasus: Data Kemiskinan Kota Makassar Tahun 2017). VARIANSI: Journal of Statistics and Its Application on Teaching and Research, 1(2), 32. https://doi.org/10.35580/variansiunm12895

Qian, Q., Liu, Y., He, M., He, M., Qian, H., & Shi, Z. (2024). Understanding the Spatial Heterogeneity Impact of Determinants on Ridership of Urban Rail Transit Across Different Passenger Groups. Journal of Advanced Transportation, 2024(1). https://doi.org/10.1155/2024/9933244

Reza, S., Ferreira, M. C., Machado, J. J. M., & Tavares, J. M. R. S. (2022). A multi-head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networks. Expert Systems with Applications, 202(February), 117275. https://doi.org/10.1016/j.eswa.2022.117275

Saadi, A., Abghour, N., Chiba, Z., Moussaid, K., & Ali, S. (2025). A survey of reinforcement and deep reinforcement learning for coordination in intelligent traffic light control. Journal of Big Data, 12(1). https://doi.org/10.1186/s40537-025-01104-x

Tang, J., Gao, F., Liu, F., Zhang, W., & Qi, Y. (2019). Understanding spatio-temporal characteristics of urban travel demand based on the Combination of GWR and GLM. Sustainability (Switzerland), 11(19). https://doi.org/10.3390/su11195525

Tio Purnomo, A., Dwi Cahyono, A., Widyatmoko, D., & Al Hasbi, N. (2024). Peningkatan Kualitas Aspal Dalam Konstruksi Jalan. 25(1), 29–36.

Tong, Z., Ye, F., Yan, M., Liu, H., & Basodi, S. (2021). A survey on algorithms for intelligent computing and smart city applications. Big Data Mining and Analytics, 4(3), 155–172. https://doi.org/10.26599/BDMA.2020.9020029

Wang, W., Yuan, Z., Yang, Y., Yang, X., & Liu, Y. (2019). Factors influencing traffic accident frequencies on urban roads: A spatial panel time-fixed effects error model. PLoS ONE, 14(4), 1–18. https://doi.org/10.1371/journal.pone.0214539

Wu, J., Tang, G., Shen, H., & Rasouli, S. (2023). Spatial Heterogeneity in the Nonlinear Impact of Built Environment on Commuting Time of Active Users: A Gradient Boosting Regression Tree Approach. Journal of Advanced Transportation, 2023. https://doi.org/10.1155/2023/6217672

Wu, W., Liu, X., Zhou, Y., & Zhao, K. (2025). Spatial heterogeneity of built environment’s impact on urban vitality using multi-source big data and MGWR. Scientific Reports, 15(1), 1–19. https://doi.org/10.1038/s41598-025-06956-0

Wu, Z., Lai, P. L., Shang, K. C., & Fang, M. (2024). Investigating the impact of spatial dependence and heterogeneity on airport relationships: Empirical evidence from China. Humanities and Social Sciences Communications, 11(1), 1–11. https://doi.org/10.1057/s41599-024-03124-z

Yang, S., & Qian, S. (2019). Understanding and Predicting Travel Time with Spatio-Temporal Features of Network Traffic Flow, Weather and Incidents. IEEE Intelligent Transportation Systems Magazine, 11(3), 12–28. https://doi.org/10.1109/MITS.2019.2919615

Zhang, J., Zhao, S., Peng, C., & Gong, X. (2022). Spatial Heterogeneity of the Recovery of Road Traffic Volume from the Impact of COVID-19: Evidence from China. Sustainability (Switzerland), 14(21). https://doi.org/10.3390/su142114297

Zhong, S., Wang, Z., Wang, Q., Liu, A., & Cui, J. (2021). Exploring the Spatially Heterogeneous Effects of Urban Built Environment on Road Travel Time Variability. Journal of Transportation Engineering, Part A: Systems, 147(1), 1–13. https://doi.org/10.1061/jtepbs.0000469

Downloads

Published

2025-09-30

How to Cite

Pradigta, M. D. I., Hidiyati, E. F., Cahyono, A. D., Azhari, F. M., Kusaini, M., & Muna, T. M. (2025). SPATIAL REGRESSION MODEL ANALYSIS OF TRAFFIC VOLUME AND SPEED IN KEDIRI CITY. Jurnal Pensil : Pendidikan Teknik Sipil, 14(3), 531–546. https://doi.org/10.21009/jpensil.v14i3.58969