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All Journal Jurnal Penelitian Sekolah Tinggi Transportasi Darat UNP Journal of Statistics and Data Science
Edi Santosa
Politeknik Transportasi Darat Indonesia - STTD
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Social Sciences

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Comparative Study of Signalized Intersection Performance Analysis Methods Bobby Agung Hermawan; I Made Arka Hermawan; Anasta Wirawan; Edi Santosa; Veronica; Alfath Satria Negara Syaban; Setya Wijayanta
Jurnal Penelitian Sekolah Tinggi Transportasi Darat Vol 16 No 1 (2025): June 2025
Publisher : Politeknik Transportasi Darat Indonesia - STTD Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55511/jpsttd.v16i1.717

Abstract

At intersections, traffic performance indicators consist of saturation degree, queue length, and delay. There are several methods commonly used to calculate performance indicators at intersections, including the PKJI method (2023) and the RJ method. Salter (1981). Each method produces different performance values. Between the PKJI 2023 method and the RJ method, it is not yet known which method is most representative of field conditions, therefore it is necessary to identify which method produces performance that is most in accordance with real conditions in the field. There are several differences and similarities between the analysis of signalized intersections with the PKJI 2023 and RJ methods. Salter, namely the emp value between the PKJI 2023 method and the RJ Salter method, it can be seen that the PKJI 2023 method divides the emp into 2 (two), namely protected and opposed emp, while RJ Salter does not divide it. The magnitude of the emp value is also different for each type of vehicle, this is likely due to the characteristics of vehicle behavior and the geometry of the intersection. In addition, the saturation current formula between the two methods is different, but in the PKJI 2023 and RJ methods. Salter has a So variable (basic saturation current) in the formula. Calculation of cycle time analysis, green time, and approach capacity of the 2nd intersection method with the same approach. Calculation of traffic behavior in this case the queue length and traffic delay from the 2 methods are different. The results of the Mann-Whitney test between the median performance parameters from the survey results and PKJI 2023 show that there is a difference. While the test results between PKJI 2023 and RJ. Salter there is no difference.
Hierarchical Bayesian Modeling with IAR Hexagonal Grids for Reconstructing Incomplete OD Matrices Eko Primadi Hendri; Sarah Fadhlia; Edi Santosa; Rachmat Sadili; Sudirman Anggada
UNP Journal of Statistics and Data Science Vol. 4 No. 3 (2026): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol4-iss3/508

Abstract

Extracting Origin-Destination (OD) matrices in open Bus Rapid Transit systems such as Transjakarta is essential for urban mobility analysis. However, this process is often hindered by incomplete observations, particularly due to missing tap-out data, which leads to extreme sparsity, zero-inflation, and overdispersion in the resulting matrices. This study addresses the problem of probabilistically reconstructing highly sparse OD matrices while accounting for spatial dependencies. To overcome limitations in previous imputation methods—such as ignoring network topology or being affected by the Modifiable Areal Unit Problem (MAUP)—this research proposes a hierarchical Bayesian approach integrating an Intrinsic Autoregressive (IAR) prior within an isotropic hexagonal (H3) tessellation framework. A Negative Binomial distribution is employed to model overdispersed count data, while latent spatial intensities and missing destinations are jointly estimated using Markov Chain Monte Carlo (MCMC). The proposed Spatial IAR model achieves stable convergence with a maximum , whereas the independent non-spatial model fails to converge adequately ( ). Although the independent model produces lower WAIC and LOOIC values (14313.41 and 14313.66) than the Spatial IAR model (14551.88 and 14611.18), the indicates that the apparent predictive superiority is spurious due to inferential instability. Posterior Predictive Checks further confirm that the spatial model successfully reproduces the overdispersion and zero-inflation characteristics of the observed mobility data. Overall, the results demonstrate that spatial regularization is essential for reconstructing high-dimensional sparse urban mobility data and improving the robustness of transportation mobility analysis.
Co-Authors Alfath Satria Negara Syaban Anasta Wirawan Bobby Agung Hermawan, Bobby Agung Hendri, Eko Primadi I Made Arka Hermawan Rachmat Sadili Sarah Fadhlia Setya Wijayanta Sudirman Anggada Veronica