SPATIALLY SIGNIFICANT ANALYSIS OF THE VOLUME WITH SPEED RELATIONSHIP AT SIGNALIZED INTERSECTIONS

Evita Fitrianis Hidiyati; Andri Dwi Cahyono; Mochammad Danara Indra Pradigta; Faiz Muhammad Azhari; Nandana Faizal Bahtiar; Moh Ali Maftuh

doi:10.21009/jpensil.v15i2.66212

Authors

Evita Fitrianis Hidiyati Department of Civil Engineering, Faculty of Engineering, University of Kadiri Selomangleng Street, No. 01, Pojok, Mojoroto, Kediri City, 64115, Indonesia
Andri Dwi Cahyono Department of Civil Engineering, Faculty of Engineering, University of Kadiri Selomangleng Street, No. 01, Pojok, Mojoroto, Kediri City, 64115, Indonesia
Mochammad Danara Indra Pradigta Department of Civil Engineering, Faculty of Engineering, University of Kadiri Selomangleng Street, No. 01, Pojok, Mojoroto, Kediri City, 64115, Indonesia
Faiz Muhammad Azhari Department of Civil Engineering, Faculty of Engineering, University of Kadiri Selomangleng Street, No. 01, Pojok, Mojoroto, Kediri City, 64115, Indonesia
Nandana Faizal Bahtiar Department of Civil Engineering, Faculty of Engineering, University of Kadiri Selomangleng Street, No. 01, Pojok, Mojoroto, Kediri City, 64115, Indonesia
Moh Ali Maftuh Department of Civil Engineering, Faculty of Engineering, University of Kadiri Selomangleng Street, No. 01, Pojok, Mojoroto, Kediri City, 64115, Indonesia

DOI:

https://doi.org/10.21009/jpensil.v15i2.66212

Keywords:

Geographically Weighted Regression (GWR), Signalized Intersection, Spatial Analysis, Traffic Volume, Vehicle Speed

Abstract

In Kediri City, Indonesia this study examines the spatial variations in the correlation between vehicle speed and traffic volume at signalized junctions. Field surveys were conducted during peak-hour periods at eight signalized intersections, with 30 directional approaches used as observation units. The analysis considered four vehicle categories: motorcycles, passenger cars, trucks, and buses. Geographically Weighted Regression (GWR) was utilized to investigate local variation in volume-speed sensiticity, whereas Ordinary Least Squares (OLS) regression served as a global reference model. The results show that higher traffic volume is generally associated with lower vehicle speed, but the strength of this relationship differs by location and vehicle type. Motorcycles have the weakest sensitivity because of their greater maneuverability in mixed traffic. In contrast, trucks and buses show stronger speed reductions due to larger vehicle dimensions, lower acceleration capability, and greater maneuvering-space requirements. Spatially, Alun-Alun and Kemuning intersections show stronger local volume–speed relationships, as indicated by more negative local coefficients and higher Local R² values. The ANOVA-based comparison indicates that GWR provides different levels of improvement over the global model, particularly for heavy vehicles. These findings support the need for signalized intersection management that considers both local traffic conditions and vehicle-type characteristics.

References

Abdel-aty, M., Wang, Z., Zheng, O., & Abdelraouf, A. (2023). Advances and applications of computer vision techniques in vehicle trajectory generation and surrogate traffic safety indicators. Accident Analysis and Prevention, 191(March), 107191. https://doi.org/10.1016/j.aap.2023.107191

Adibah, A. N., Abadi, K., Amal, A. S., & Darmawan, A. A. (2024). Fenomena Keterkaitan Kecepatan Faktual Kendaraan dengan Tingkat Pelayanan Jalan ( Studi Kasus : Jalan Asrikaton , Kabupaten Malang ). 24(3), 2516–2522. https://doi.org/10.33087/jiubj.v24i3.5558

Alisan, O. (2024). An Analysis of the Spatial Variations in the Relationship Between Built Environment and Severe Crashes. International Journal of Geo-Information, 13(465).

Aljoufie, M. (2021). Modeling Road Safety in Car-Dependent Cities : Case of Jeddah City , Saudi Arabia. Sustainability, 13(1816).

Almasi, S. A. (2025). Evaluating the efficiency of spatial-geographical models for vehicle crash frequency estimation : A case study on the urban road network of hamadan province. Transportation Engineering, 21(April).

Almasi, S. A., Reza, A., Lomer, B., & Khaksar, H. (2023). Using Spatial Modeling for Identifying Determinants Influencing Crash Estimate : Case Study of Hamedan , Iran. Transportation Research Record. https://doi.org/10.1177/03611981231197647

Apriyani, N. F., Yuniarti, D., Memi, D., & Hayati, N. (2020). The Model of Mixed Geographically Weighted Regression (MGWR) (Case Study: The Number of Diarrhea Sufferers in East Kalimantan Province on 2015). Jurnal EKSPONENSIAL, 9(1), 59–66.

Carrillo-González, J. G., & Perez-Martinez, F. (2022). Explaining the Mean and Variability of Extra Travel Time in Traffic with Interacting Fast and Slow Vehicles. Applied Sciences (Switzerland), 12(14). https://doi.org/10.3390/app12147176

Chen, Bokui; Chen, Yaohui; Wu, Y. (2023). The Effects of Autonomous Vechicles on Traffic Efficiency and Energy Consumption. MDPI Systems, 11(347), 1–23.

Cui, R., & Zhang, J. (2025). Exploring the Relationship Between the Built Environment and Spatiotemporal Heterogeneity of Urban Traffic Congestion During Tourism Peaks : A Case Study of Harbin , China. International Journal of Geo-Information, 14(470).

Guerrieri, M., & Parla, G. (2021). Deep learning and yolov3 systems for automatic traffic data measurement by moving car observer technique. Infrastructures, 6(9). https://doi.org/10.3390/infrastructures6090134

He, J., Wei, Y., & Yu, B. (2023). Geographically weighted regression based on a network weight matrix : a case study using urbanization driving force data in China. International Journal of Geographical Information Science, 0(0), 1–27. https://doi.org/10.1080/13658816.2023.2192122

Hidiyati, E. F., Nugroho, L. D., & Haris Muhammadun. (2023). Improving Mobility in Kediri City With Mkji Analysis 1997 and Time Slice At Simpang Empat Dhoho Plaza. International Journal Science and Technology, 2(3), 92–101. https://doi.org/10.56127/ijst.v2i3.1110

Huang, Y., Wang, X., & Patton, D. (2018). Examining spatial relationships between crashes and the built environment : A geographically weighted regression approach. Journal of Transport Geography, 69(April), 221–233. https://doi.org/10.1016/j.jtrangeo.2018.04.027

Jawarneh, R., & Abulibdeh, A. (2024). Geospatial modelling of seasonal water and electricity consumption in Doha ’ s residential buildings using multiscale geographically weighted regression ( MGWR ) and Bootstrap analysis. Sustainable Cities and Society, 113(June).

Klar, R., & Rubensson, I. (2024). Spatio-Temporal Investigation of Public Transport Demand Using Smart Card Data. 241–268. https://doi.org/10.1007/s12061-023-09542-x

Konadu, E., Nimako, E., Kodzo, R., & Kojo, S. (2024). Geographically weighted logistic regression model for identifying risk factors for malaria infection among under-5 children in Ghana. Scientific African, 26(September), e02398. https://doi.org/10.1016/j.sciaf.2024.e02398

Lessani, M. N., & Li, Z. (2024). SGWR : similarity and geographically weighted regression. International Journal of Geographical Information Science, 38(7), 1232–1255. https://doi.org/10.1080/13658816.2024.2342319

Li, X., Yu, S., Huang, X., Dadashova, B., & Cui, W. (n.d.). Do underserved and socially vulnerable communities observe more crashes ? A spatial examination of social vulnerability and crash risks in Texas A spatial examination of social vulnerability and crash risks in Texas. 1–47.

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

Lin, P., Liu, X., Pei, M., & Wu, P. (2022). Revealing the spatial variation in vehicle travel time with weather and driver travel frequency impacts: Findings from the Guangdong–Hong Kong–Macao Greater Bay Area, China. Electronic Research Archive, 30(10), 3711–3734. https://doi.org/10.3934/era.2022190

Mahmud, N., & Khan, A. (2022). Geography and Sustainability A spatial regression modeling framework for examining relationships between the built environment and pedestrian crash occurrences at macroscopic level : A study in a developing country context. Geography and Sustainability, 3(4), 312–324. https://doi.org/10.1016/j.geosus.2022.09.005

Mühlematter, D. J., Wiedemann, N., Xin, Y., & Raubal, M. (2024). Spatially-aware station based car-sharing demand prediction. Journal of Transport Geography, 114(December 2023), 103765. https://doi.org/10.1016/j.jtrangeo.2023.103765

Mulat, T., Alemu, K., & Moges, Y. (2024). Heliyon Geographically weighted regression analysis to assess hotspots of early sexual initiation and associated factors in Ethiopia. Heliyon, 10(9), e30535. https://doi.org/10.1016/j.heliyon.2024.e30535

Nugroho, U., & Dwiatmaja, G. C. (2020). Analisis Kinerja Simpang Bersinyal menggunakan Bantuan Perangkat Lunak Vissim Student Version. (Studi Kasus : Simpang Sompok , Candisari , Semarang). Jurnal Teknik Sipil, 16(1), 1–21.

Omrani, F., Shad, R., & Ziaee, S. A. (2025). Atmospheric Environment : X A multiscale geographically weighted regression approach to emphasize the effects of traffic characteristics on vehicular emissions. Atmospheric Environment: X, 25(December 2024), 100315. https://doi.org/10.1016/j.aeaoa.2025.100315

Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., & Stewart Fotheringham, A. (2019). MGWR: A python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS International Journal of Geo-Information, 8(6). https://doi.org/10.3390/ijgi8060269

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, 14(3), 531–546.

Primasari, R. B., Rusgiyono, A., & Ispriyanti, D. (2023). Pemodelan Mixed Geographically Weighted Regression (Mgwr) Dengan Jarak Euclidean Dan Jarak Manhattan (Studi Kasus : Kematian Bayi Neonatal Di Jawa Tengah Tahun 2018-2020). Jurnal Gaussian, 11(4), 478–487. https://doi.org/10.14710/j.gauss.11.4.478-487

Qu, X., Zhu, X., Xiao, X., Wu, H., Guo, B., & Li, D. (2021). Exploring the Influences of Point-of-Interest on Traffic Crashes during Weekdays and Weekends via Multi-Scale Geographically Weighted Regression.

Raden Herdian Bayu, Ash Siddiq; Hamzah, Hidayatulloh; M. Saepul, Alam; Shafira Nur, F. (2024). Evaluation of Public Transport Facilities at Ports, Airports, Station, and Terminals. Jurnal Pensil, 13(2), 193–205. https://doi.org/10.21009/jpensil.v13i2.43600

Ramadhani, E., Salwa, N., & Mazaya, M. S. (2020). Identifikasi Faktor-Faktor yang Memengaruhi Angka Harapan Hidup di Sumatera Tahun 2018 Menggunakan Analisis Regresi Spasial Pendekatan Area. Journal of Data Analysis, 3(2), 62–75.

Reda, A. K., Tavasszy, L., Gebresenbet, G., & Ljungberg, D. (2023). Modelling the effect of spatial determinants on freight (trip) attraction: A spatially autoregressive geographically weighted regression approach. Research in Transportation Economics, 99(May). https://doi.org/10.1016/j.retrec.2023.101296

Rhee, K., Kim, J., Lee, Y., & Ulfarsson, G. F. (2016). Spatial regression analysis of traffic crashes in Seoul. Accident Analysis and Prevention, 91, 190–199. https://doi.org/10.1016/j.aap.2016.02.023

Shabrina, Z., Buyuklieva, B., & Ng, M. K. M. (2021). Short-Term Rental Platform in the Urban Tourism Context: A Geographically Weighted Regression (GWR) and a Multiscale GWR (MGWR) Approaches. Geographical Analysis, 53(4), 686–707. https://doi.org/10.1111/gean.12259

Tang, J., Gao, F., Liu, F., Han, C., & Lee, J. (2020). Spatial heterogeneity analysis of macro-level crashes using geographically weighted Poisson quantile regression. Accident Analysis and Prevention, 148(April), 105833. https://doi.org/10.1016/j.aap.2020.105833

Tesfamichael, S. G., Shiferaw, Y. A., & Phiri, M. (2022). Monthly geographically weighted regression between climate and vegetation in the Eastern Cape Province of South Africa : Clustering pattern shifts and biome-dependent accuracies. Scientific African, 18, e01423. https://doi.org/10.1016/j.sciaf.2022.e01423

Wang, S., Gao, K., Zhang, L., Yu, B., & Easa, S. M. (2024). Geographically weighted machine learning for modeling spatial heterogeneity in traffic crash frequency and determinants in US. Accident Analysis and Prevention, 199(February).

Wang, Z., Gong, X., Zhang, Y., Liu, S., & Chen, N. (2023). Multi-Scale Geographically Weighted Elasticity Regression Model to Explore the Elastic Effects of the Built Environment on Ride-Hailing Ridership.

Yakob Alfandy, E. A., Handayani, A. T., Astutik, H. P., Studi, P., & Sipil, T. (2024). Analisis kinerja simpang bersinyal menggunakan software ptv vissim (studi kasus : simpang proliman, prambanan) tamanmartani, kabupaten sleman, daerah istimewa yogyakarta. Spektrum Sipil, 11(2), 97–108.

Yang, X., Yang, Y., Xu, S., Han, J., Chai, Z., & Yang, G. (2023). A New Algorithm for Large-Scale Geographically Weighted Regression with K-Nearest Neighbors.

Zafri, N. M., & Khan, A. (2023). Using geographically weighted logistic regression ( GWLR ) for pedestrian crash severity modeling : Exploring spatially varying relationships with natural and built environment factors. IATSS Research, 47(3), 325–334. https://doi.org/10.1016/j.iatssr.2023.07.004