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Mikrosimulasi Junction Metering dalam Meningkatkan Kinerja Simpang Menggunakan Software Vissim Budiharjo, Anton; Rusmandani, Pipit; Inggriani, Keke; Maulyda, Mohammad Archi
MEDIA KOMUNIKASI TEKNIK SIPIL Volume 27, Nomor 2 (2021)
Publisher : Department of Civil Engineering, Diponegoro University

Jarak pandang yang tidak sesuai standar, waktu antrian dan rendahnya kesadaran pengguna kendaraan bermotor untuk memberikan prioritas kepada pengguna jalan lain merupakan suatu penyebab terhambatnya pergerakan lalu lintas dan mengurangi kinerja persimpangan. Mayoritas kendaraan ketika menuju ataupun pada jalan minor menggunakan lajur lawan karena tidak terdapat marka pembagi, hal tersebut menyebabkan terjadinya konflik berupa crossing dan merging. Berdasarkan hal tersebut maka peneliti melakukan penelitian mengenai Junction Metering yang terdiri dari pengaturan kontrol dan sensor untuk meningkatkan keselamatan bagi pengguna jalan. Metode yang digunakan dalam analisis kinerja simpang adalah dengan menggunakan bantuan software PTV Vissim Full Version dan perhitungan Gap Raff and Hunt. Data yang dikumpulkan dalam peneitian ini terdiri dari empat aspek, (1) inventarisasi simpang; (2) volume lalu lintas; (3) kecepatan lalu lintas; dan (4) selang waktu. Penanganan dari permasalahan berupa junction metering yang kemudian dilakukan perbandingan dengan kondisi eksisting. Variabel pembanding berupa tingkat pelayanan, panjang antrian dan waktu tundaan. Efektifitas penanganan simpang menggunakan junction metering dapat mengurangi panjang antrian menjadi 75% dan waktu tundaan 17% dengan klasifikasi tingkat pelayanan rata-rata simpang adalah A.

Kepuasan Pengguna dan Tantangan Pelayanan Angkutan Umum Perkotaan di Indonesia Nurul Fitriani; Dani F. Brilianti; Reza Yoga Anindita; Pipit Rusmandani; Destria Rachmita
Jurnal Keselamatan Transportasi Jalan (Indonesian Journal of Road Safety) Vol. 13 No. 1 (2026): JURNAL KESELAMATAN TRANSPORTASI JALAN (INDONESIAN JOURNAL OF ROAD SAFETY)
Publisher : Pusat Penelitian dan Pengabdian Masyarakat (P3M)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46447/ktj.v13i1.776

The quality of public transport services in Tegal City from an operational perspective is still considered sub-optimal, as reflected in the low levels of comfort, punctuality, and a load factor of only around 13%. This study aims to analyze user characteristics, measure the level of satisfaction with public transport services, and formulate strategies for service improvement based on field data. A quantitative approach was employed through a survey of public transport users, using SERVQUAL analysis and the Customer Satisfaction Index (CSI). Assessment of 19 indicators shows that all service aspects are considered very important (average MIS 4.32), while satisfaction levels fall within the satisfied–fairly satisfied category (average MSS 3.65) with a CSI value of around 73%, indicating a remaining gap between expectations and reality. The main weaknesses lie in the availability of safety facilities (X4), waiting time (X6), service equality for vulnerable groups (X16), the roadworthiness of vehicles (X1), and driver attitude (X19). Therefore, service improvements need to focus on providing safety facilities and conducting regular inspections, reducing waiting times through more reliable scheduling and headways, developing inclusive services for the elderly, persons with disabilities, children, and pregnant women, undertaking fleet renewal and maintenance, as well as implementing training and enforcing a code of ethics to improve driver attitude and professionalism.

Kepuasan Pengguna dan Tantangan Pelayanan Angkutan Umum Perkotaan di Indonesia Nurul Fitriani; Dani F. Brilianti; Reza Yoga Anindita; Pipit Rusmandani; Destria Rachmita
Jurnal Keselamatan Transportasi Jalan (Indonesian Journal of Road Safety) Vol. 13 No. 1 (2026): JURNAL KESELAMATAN TRANSPORTASI JALAN (INDONESIAN JOURNAL OF ROAD SAFETY)
Publisher : Pusat Penelitian dan Pengabdian Masyarakat (P3M)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46447/ktj.v13i1.776

The quality of public transport services in Tegal City from an operational perspective is still considered sub-optimal, as reflected in the low levels of comfort, punctuality, and a load factor of only around 13%. This study aims to analyze user characteristics, measure the level of satisfaction with public transport services, and formulate strategies for service improvement based on field data. A quantitative approach was employed through a survey of public transport users, using SERVQUAL analysis and the Customer Satisfaction Index (CSI). Assessment of 19 indicators shows that all service aspects are considered very important (average MIS 4.32), while satisfaction levels fall within the satisfied–fairly satisfied category (average MSS 3.65) with a CSI value of around 73%, indicating a remaining gap between expectations and reality. The main weaknesses lie in the availability of safety facilities (X4), waiting time (X6), service equality for vulnerable groups (X16), the roadworthiness of vehicles (X1), and driver attitude (X19). Therefore, service improvements need to focus on providing safety facilities and conducting regular inspections, reducing waiting times through more reliable scheduling and headways, developing inclusive services for the elderly, persons with disabilities, children, and pregnant women, undertaking fleet renewal and maintenance, as well as implementing training and enforcing a code of ethics to improve driver attitude and professionalism.

YOLO-Based Real-Time Artificial Intelligence Traffic Counting for Urban Transportation Monitoring in Surakarta: Implications for SDG 11 Bambang Istiyanto; Yan El Rizal Unzilatirrizqi D; Alfan Baharuddin; Pipit Rusmandani
Journal of Current Studies in SDGs Vol. 2 No. 1 (2026): March
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.2.1.152

Objective: To develop and evaluate an artificial intelligence (AI)-based traffic counting system using the YOLO (You Only Look Once) deep learning algorithm to provide accurate and real-time traffic volume data for urban transportation management. Method: Employing a deep learning approach by implementing the YOLO algorithm for vehicle detection and traffic counting. Traffic video data from road objects in Surakarta City were processed to identify and classify various vehicle types. The AI-generated traffic counting results were then compared with manual traffic survey data to assess the system’s accuracy and effectiveness. Results: The findings indicate that the proposed AI-based traffic counting system can accurately detect and classify multiple vehicle categories, including cars, motorcycles, trucks, buses, bicycles, and Bajaj. The traffic-counting data produced by the system were highly readable and reliable. Comparison with manual traffic surveys showed that the AI-generated results were very similar while requiring significantly less time and human resources. The system achieved nearly 100% consistency with the available secondary traffic volume data, demonstrating its effectiveness in monitoring urban traffic conditions. Novelty: Application of the YOLO deep learning algorithm for automated traffic counting in the urban road environment of Surakarta City. The proposed system provides a practical and efficient alternative to conventional manual traffic surveys by delivering accurate, real-time traffic data with minimal human intervention, thereby supporting more effective urban transportation planning and management. These contributions are also relevant to SDG 11 (Sustainable Cities and Communities) by enabling data-driven traffic monitoring and facilitating smarter, more sustainable urban mobility management.

YOLO-Based Real-Time Artificial Intelligence Traffic Counting for Urban Transportation Monitoring in Surakarta: Implications for SDG 11 Bambang Istiyanto; Yan El Rizal Unzilatirrizqi D; Alfan Baharuddin; Pipit Rusmandani
Journal of Current Studies in SDGs Vol. 2 No. 1 (2026): March
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.2.1.152

Objective: To develop and evaluate an artificial intelligence (AI)-based traffic counting system using the YOLO (You Only Look Once) deep learning algorithm to provide accurate and real-time traffic volume data for urban transportation management. Method: Employing a deep learning approach by implementing the YOLO algorithm for vehicle detection and traffic counting. Traffic video data from road objects in Surakarta City were processed to identify and classify various vehicle types. The AI-generated traffic counting results were then compared with manual traffic survey data to assess the system’s accuracy and effectiveness. Results: The findings indicate that the proposed AI-based traffic counting system can accurately detect and classify multiple vehicle categories, including cars, motorcycles, trucks, buses, bicycles, and Bajaj. The traffic-counting data produced by the system were highly readable and reliable. Comparison with manual traffic surveys showed that the AI-generated results were very similar while requiring significantly less time and human resources. The system achieved nearly 100% consistency with the available secondary traffic volume data, demonstrating its effectiveness in monitoring urban traffic conditions. Novelty: Application of the YOLO deep learning algorithm for automated traffic counting in the urban road environment of Surakarta City. The proposed system provides a practical and efficient alternative to conventional manual traffic surveys by delivering accurate, real-time traffic data with minimal human intervention, thereby supporting more effective urban transportation planning and management. These contributions are also relevant to SDG 11 (Sustainable Cities and Communities) by enabling data-driven traffic monitoring and facilitating smarter, more sustainable urban mobility management.

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