IMPACT OF SIDE BARRIER ON ROAD SERVICE LEVELS CASE STUDY: INTERSECTION UNSIGNALIZED RAJAGALUH
DOI:
https://doi.org/10.37253/leader.v2i1.9531Keywords:
Intersection, Traffic Volume, Side BarriesAbstract
Majalengka is one of the cities in West Java impacted by the rapid growth of private vehicle numbers. One place in Majalengka that often experiences traffic congestion is the intersection of three markets and the terminal in Rajagaluh. This road connects to Cirebon and tourist areas in the Sindangwangi District. This happens for several reasons, including the significant number of side obstacles along the road around the intersection. Side obstacles at this intersection include street vendors, parked/stopped vehicles, and pedestrians. This research aims to determine the road service level through field surveys at the junction of the three markets and the Rajagaluh terminal. The research methodology employed in this study is qualitative. The use of this qualitative method is interrelated, involving data collection, data grouping, data validation, and finally, data analysis, allowing the emergence of new theories and insights. This research was conducted at the unsignalized intersection near Rajagaluh Market and Terminal in Majalengka Regency. This unsignalized intersection is a junction for inter-district routes located near the terminal and market. Based on the table above, it is found that in the northbound direction, the level of road service is average during the morning and afternoon, while it is classified as high during the evening. In the westbound direction, the level of road service is mild during the morning and afternoon, and during the evening, it is classified as high. The road service level in the eastbound direction is similar, average during the morning and afternoon, and increased during the evening. Therefore, according to the author, the government must improve to anticipate the high traffic volume that will lead to congestion in the future. Based on the study, side obstacles are still relatively low to average. However, this does not mean it will be a benchmark in the future. It is possible that in the coming years, these side obstacles will increase, leading to congestion.
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References
Abiansyah, L., & Rifai, A. (2020). Analysis Traffic Volume of Rigid Pavement Damage on Roads Badami Karawang. Journal of World Conference, 2 (2). pp, 190-199.
Baffoe-Twum, E., Asa, E., & Awuku, B. (2022). Estimation of annual average daily traffic (AADT) data for low-volume roads: a systematic literature review and meta-analysis. , 13. Emerald Open Research, 4, 13.
Chen, C., Wang, J., Xu, Q., Wang, J., & Li, K. (2021). Mixed platoon control of automated and human-driven vehicles at a signalized intersection: dynamical analysis and optimal control. Transportation Research Part C: Emerging Technologies, 127, 103138.
Dong, H., Zhuang, W., Chen, B., Lu, Y., Liu, S., Xu, L., & Yin, G. (2022). Predictive energy-efficient driving strategy design of connected electric vehicle among multiple signalized intersections. Transportation Research Part C: Emerging Technologies, 1.
Fajar, A., & Yasruddin, Y. (2021). PERFORMANCE ANALYSIS OF UNSIGNALIZED INTERSECTION ON PERJUANGAN AND VETERAN STREET MARTAPURA BANJAR DISTRICT. CERUCUK, 4(1), 33-60.
Haryati, S., & Najid, N. (2021). ANALISIS KAPASITAS DAN KINERJA LALU LINTAS PADA RUAS JALAN JENDERAL SUDIRMAN JAKARTA. JMTS: Jurnal Mitra Teknik Sipil, 4(1), 95-108.
Immanuel, Y., Rifai, A. I., & Prasetijo, J. (2022). The Road Performance Analysis of the Tuah Madani Roundabout, Batam-Indonesia. Indonesian Journal of Multidisciplinary Science, 1(1), 27-36.
Jaysingpure, K. P., & Bijwe, A. R. (2022). Highway Safety Using Rolling Barrier. International Journal of Research in Engineering and Science (IJRES), 10(3), 04-09.
Johanes, A., Dermawan, W. B., Isradi, M., & Rifai, A. I. (2022). Analysis of the Satisfaction Level of Sidewalk Users:(Case Study on Jl Jendral Ahmad Yani Bekasi). ADRI International Journal of Engineering and Natural Science, 7(1), 74-82.
Kashyap, A. A., Devarakonda, A., Nayak K, S. R., KV, S., & Bhat, S. J. (2022). Traffic flow prediction models–A review of deep learning techniques. Cogent Engineering, 9(1), 2010510.
Ma'aruf, M., Eprilianto, D., & Megawati, S. (2021). Collaborative Governance in Handling Traffic Problems in the City of Surabaya. Proceedings of the 1st Tidar International Conference onAdvancing Local Wisdom Towards Global Megatrends, TIC 2020, 21-22 October 2020, Magelang, Jawa Tengah, Indonesia, 3.
Marzoug, R., Lakouari, N., Pérez Cruz, J., & Vega Gómez, C. (2022). Model Cellular Automata untuk Analisis dan Optimalisasi Emisi Lalu Lintas di Simpang Bersinyal. Keberlanjutan , 14 (21), 14048.
Pathivada, B., & Perumal, V. (2019). Analyzing dilemma driver behavior at signalized intersection under mixed traffic conditions. Transportation research part F: traffic psychology and behaviour, 60, 111-120.
Pitlova, A. K. (2019). CRITICAL GAPS AT UNSIGNALIZED INTERSECTIONS WITH BENDING RIGHT-OF-WAY. COMMUNICATIONS, 20.
Rifai, A. I., Surgiarti, Y. A., Isradi, M., & Mufhidin, A. (2021). Analysis of Road Performance and the impact of Development in Pasar Minggu, Jakarta: Case Study of Jalan Lenteng Agung-Tanjung Barat. ADRI International Journal of Civil Engineering, 6(1), 68-74.
Wahyudi, M. A., Rifai, A. I., & Prasetijo, J. (2022). Analysis of the Effectiveness of Traffic Flow Diversion on Road Performance: A Case of Jalan Gajah Mada Development Project, Batam. Indonesian Journal of Multidisciplinary Science, 1(1), 92-102.
Winaya, A. (2020). On-Street Parking and Traffic Flow Performance at Kapasan Shopping Area Surabaya. . JACEE (Journal of Advanced Civil and Environmental Engineering), 3(1), 9-16.
Wu, F., & Ma, W. (2022). Clustering Analysis of the Spatio-Temporal On-Street Parking Occupancy Data: A Case Study in Hong Kong. Sustainability, 14(13), 7957.
Yuan, J., & Abdel-Aty, M. (2019). Approach-level real-time crash risk analysis for signalized intersections. Accident Analysis & Prevention, 119, 2.
Zhou, Q., Chen, N., & Lin, S. (2022). FASTNN: A Deep Learning Approach for Traffic Flow Prediction Considering Spatiotemporal Features. Sensors, 22(18), 6921.