Signal Coordination Analysis Between Intersection: Case Study
Abstract
The increase number of vehicles every year has caused a declining road function and performance, which then causes a high volume of vehicles in Intersection I of Endro Suratmin street – Pulau Tegal street / Pulau Legundi street, Intersection II of Urip Sumoharjo street – Soekarno-Hatta street, and Intersection III of Urip Sumoharjo street – Arif Rahman Hakim street. The distance between intersection I to II is 460 m, and Intersection II to III is 700 m. The distance between these intersections become a factor that lead to unsatisfactory level of comfort for road users, which in turn causes problems. The problem occurs because of the absence of signal coordination between the three intersections which causes traffic jams during peak hours. Therefore, this research will provide alternative solutions to overcome these problems. This research uses data from field surveys including traffic volume, intersection geometry, and traffic signal data. VISSIM software is used to analyze queue lengths and delays in existing conditions and signal coordination planning. In addition, reference methods such as the calculation of the Indonesian Road Capacity Manual, the Webster Method, and the Transportation Research Board in Highway Capacity Manual are also used. As the result of the analysis, it was found that the performance of the three intersections had not been coordinated. In the existing condition, the service level value in each arm reached E service level, only a few arms at the three intersections have C service level with a delay time of 29.58 seconds/vehicle. Therefore, three planning solutions were conducted to coordinate signals between the three intersections using a plan with a new cycle time acquired from the Webster method. Plan I coordinates the signals of the three intersections by using the new cycle time of intersection I at each intersection, Plan II coordinates the signals of the three intersections by using a new cycle time of intersection II at each intersection, and Plan III coordinates the signals of the three intersections in each arms using the new cycle time. Out of the three plans, the first plan is obtained as the best solution for giving a significant reduction in delays with an average service level of B.
References
Alshayeb, S., Stevanovic, A., & Effinger, J. R. (2022). Investigating impacts of various operational conditions on fuel consumption and stop penalty at signalized intersections. International Journal of Transportation Science and Technology, 11(4), 690-710. https://doi.org/10.1016/j.ijtst.2021.09.005
An, C., Zhang, W., Wang, Y., Ke, S., & Xia, J. (2024). Robust Traffic Signal Retiming Based on Queue Service Time Estimation Using Low-Penetration Connected Vehicle Data. Systems, 13(1), 15. https://www.mdpi.com/2079-8954/13/1/15
Ardalan, T., Magalotti, M. J., & Stevanovic, A. (2024). Multimodal signal retiming projects: a survey-based exploration of traffic signal professionals’ practices and challenges. Future Transportation, 4(4), 1121-1141. https://doi.org/10.3390/futuretransp4040054
Cesme, B., Hayes, A., Wang, S., Warchol, S., Root, A., Bhagat, K., & Rouphail, N. (2023). Multimodal intersection signal timing considerations framework, performance measures, and case study. Transportation research record, 2677(9), 513-524. https://doi.org/10.1177/03611981231159410
Das, D., Altekar, N. V., & Head, K. L. (2023). Priority-based traffic signal coordination system with multi-modal priority and vehicle actuation in a connected vehicle environment. Transportation research record, 2677(5), 666-681. https://doi.org/10.1177/03611981221134627
Dasgupta, S., Rahman, M., & Jon, S. (2024). Harnessing digital twin technology for adaptive traffic signal control: Improving signalized intersection performance and user satisfaction. IEEE internet of things journal, 11(22), 36596-36618. https://doi.org/10.1109/JIOT.2024.3420439
Deng, Z., Yang, K., Shen, W., & Shi, Y. (2023). Cooperative platoon formation of connected and autonomous vehicles: Toward efficient merging coordination at unsignalized intersections. IEEE Transactions on Intelligent Transportation Systems, 24(5), 5625-5639. https://doi.org/10.1109/TITS.2023.3235774
Fajardo, D., Au, T. C., Waller, S. T., Stone, P., & Yang, D. (2011). Automated intersection control: Performance of future innovation versus current traffic signal control. Transportation Research Record, 2259(1), 223-232.
Haque, M. M. (2025). Systematic Review on The Impact Of AI-Enhanced Traffic Simulation On US Urban Mobility And Safety. ASRC Procedia: Global Perspectives in Science and Scholarship, 1(01), 833-861. https://doi.org/10.63125/jj96yd66
He, Y., & Zeng, A. (2024). Expanding bottlenecks reveals hidden bottlenecks and leads to more congested city centers. Physica A: Statistical Mechanics and its Applications, 640, 129707. https://doi.org/10.1016/j.physa.2024.129707
Jiang, S., Pan, T., Zhong, R., Chen, C., Li, X. A., & Wang, S. (2022). Coordination of mixed platoons and eco-driving strategy for a signal-free intersection. IEEE Transactions on Intelligent Transportation Systems, 24(6), 6597-6613. https://doi.org/10.1109/TITS.2022.3211934
Li, D., Zhu, F., Wu, J., Wong, Y. D., & Chen, T. (2024). Managing mixed traffic at signalized intersections: An adaptive signal control and CAV coordination system based on deep reinforcement learning. Expert Systems with Applications, 238, 121959. https://doi.org/10.1016/j.eswa.2023.121959
Lieberthal, E. B., Serok, N., Duan, J., Zeng, G., & Havlin, S. (2024). Addressing the urban congestion challenge based on traffic bottlenecks. Philosophical Transactions A, 382(2285), 20240095. https://doi.org/10.1098/rsta.2024.0095
Liu, M., Zhao, J., Hoogendoorn, S. P., & Wang, M. (2022). An optimal control approach of integrating traffic signals and cooperative vehicle trajectories at intersections. Transportmetrica B: transport t dynamics, 10(1), 971-987. https://doi.org/10.1080/21680566.2021.1991505
Luitel, S., Subedi, A. K., Nepal, S., & Tiwari, H. (2025). A Multi-Faceted Approach to Urban Congestion in Developing Nations: Theory, Practice, and the Kathmandu Experience. Journal of Advanced College of Engineering and Management, 11, 139-163. https://doi.org/10.3126/jacem.v11i1.84536
Ma, C., Liu, Y., Xu, X., & Zhao, H. (2025). A Review of Research on Coordinated Control of Traffic Signals at Urban Road Intersection Groups. Promet-Traffic&Transportation, 37(6), 1489-1507. https://doi.org/10.7307/ptt.v37i6.892
Mainali, S., Regmi, S., Wagle, K., & Bhele, R. (2024). Unraveling the Traffic Congestion Due to Bottleneck: A Review. International Journal on Engineering Technology, 2(1), 71-79. https://doi.org/10.3126/injet.v2i1.72522
Majstorović, Ž., Tišljarić, L., Ivanjko, E., & Carić, T. (2023). Urban traffic signal control under mixed traffic flows: Literature review. Applied Sciences, 13(7), 4484.
Markony, G. A. U. Z., & Siena, J. N. (2025). Urban congestion and transport inefficiencies: Implications for livability in Dhaka City. Tamalanrea: Journal of Government and Development (JGD), 2(3). https://doi.org/10.69816/jgd.v2i3.46247
Najid, & Marlianny, T. (2022, February). Overview of side friction factors for evaluation of capacity calculation at indonesian highway capacity manual. In Proceedings of the Second International Conference of Construction, Infrastructure, and Materials: ICCIM 2021, 26 July 2021, Jakarta, Indonesia (pp. 373-383). Singapore: Springer Nature Singapore.
Nandhini, M., & Rabik, M. M. (2024). A comprehensive overview of the alignment between platoon control approaches and clustering strategies. Green Energy and Intelligent Transportation, 3(6), 100223. https://doi.org/10.1016/j.geits.2024.100223
Patel, V., & Maltare, N. (2025). From Algorithms to Connectivity: A Comprehensive Review of Traffic Signal Optimization and Communication Based Cooperative Control. Archives of Computational Methods in Engineering, 1-31. https://doi.org/10.1007/s11831-025-10455-w?urlappend=%3Futm_source%3Dresearchgate.net%26utm_medium%3Darticle
Pojani, D., & Stead, D. (2015). Sustainable urban transport in the developing world: beyond megacities. Sustainability, 7(6), 7784-7805. https://doi.org/10.3390/su7067784
Pudasaini, P. (2025). Data-Driven Monitoring of Operations and Safety at Signalized Intersections Using Multi-Source Traffic Data (Doctoral dissertation, The University of Arizona).
Qadri, S. S. S. M., Gökçe, M. A., & Öner, E. (2020). State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review, 12(1), 55. https://doi.org/10.1186/s12544-020-00439-1
Russo, F., Comi, A., & Chilà, G. (2024). Dynamic approach to update utility and choice by emerging technologies to reduce risk in urban road transportation systems. Future Transportation, 4(3), 1078-1099. https://doi.org/10.3390/futuretransp4030052
Shafik, A. K., & Rakha, H. A. (2024). Integrated back of queue estimation and vehicle trajectory optimization considering uncertainty in traffic signal timings. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2024.3460375
Shahbazi, Z., & Nowaczyk, S. (2023). Enhancing energy efficiency in connected vehicles for traffic flow optimization. Smart Cities, 6(5), 2574-2592. https://doi.org/10.3390/smartcities6050116
Tan, C., Cao, Y., Ban, X., & Tang, K. (2024). Connected vehicle data-driven fixed-time traffic signal control considering cyclic time-dependent vehicle arrivals based on cumulative flow diagram. IEEE Transactions on Intelligent Transportation Systems, 25(8), 8881-8897. https://doi.org/10.1109/TITS.2024.3360090
Tan, C., Cao, Y., Ban, X., & Tang, K. (2024). Connected vehicle data-driven fixed-time traffic signal control considering cyclic time-dependent vehicle arrivals based on cumulative flow diagram. IEEE Transactions on Intelligent Transportation Systems, 25(8), 8881-8897. https://doi.org/10.1109/TITS.2024.3360090
Uribe-Chavert, P., Posadas-Yagüe, J. L., & Poza-Lujan, J. L. (2025). Evaluating traffic control parameters: From efficiency to sustainable development. Smart Cities, 8(2), 57. https://doi.org/10.3390/smartcities8020057
Wang, C., Zhao, Y., Li, L., Qu, X., & Ran, B. (2025). Enhancing Vehicle Platoons in Connected and Automated Environments With an Improved Spectral Clustering-Based Pinning Control Strategy. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2025.3531450
Wang, T., Yuan, Z., Zhang, Y., Zhang, J., & Tian, J. (2023). A driving guidance strategy with pre-stop line at signalized intersection: Collaborative optimization of capacity and fuel consumption. Physica A: Statistical Mechanics and its Applications, 626, 129068. https://doi.org/10.1016/j.physa.2023.129068
Wei, H., Zheng, G., Gayah, V., & Li, Z. (2019). A survey on traffic signal control methods. arXiv preprint arXiv:1904.08117. https://doi.org/10.48550/arXiv.1904.08117
Xiong, Y., Qin, G., Zeng, J., Tang, K., Zhu, H., & Chung, E. (2025). Co-Optimization and Interpretability of Intelligent–Traditional Signal Control Based on Spatiotemporal Pressure Perception in Hybrid Control Scenarios. Sustainability, 17(16), 7521. https://doi.org/10.3390/su17167521
Yue, R., Yang, G., Zheng, Y., Tian, Y., & Tian, Z. (2022). Effects of traffic signal coordination on the safety performance of urban arterials. Computational Urban Science, 2(1), 3. https://doi.org/10.1007/s43762-021-00029-4?urlappend=%3Futm_source%3Dresearchgate.net%26utm_medium%3Darticle
Zhu, J., Ma, W., Yu, C., Zhao, Y., & Zhong, Z. (2024). Cycle-by-Cycle Estimation of Queue Length at Signalized Intersections Using Spatially Sparse Connected Vehicle Trajectories. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2024.3498012
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