A Capacity Modelling Study of Diverging Areas in Urban Tunnels Under Intelligent Connected Environments
Downloads
With the advancement of connected automated vehicles (CAVs), their increasing presence in traffic flows is expected to reshape road capacity. In urban tunnels, diverging areas are essential for traffic efficiency and safety. However, existing research has mainly focused on merging and diverging areas in expressways and arterial roads, with limited attention to urban tunnel environments. To address this gap, this study proposes a capacity estimation method for the diverging areas in urban tunnels under an intelligent connected environment. A simulation framework was developed to analyse key influencing factors under different CAV penetration rates. Then, a capacity estimation model was established using a multivariate nonlinear fitting approach and was validated through SUMO simulation experiments. The results indicate that: (1) penetration rate, diversion ratio and deceleration lane length are key parameters influencing capacity; (2) a CAV penetration rate of 0.5 marks a threshold beyond which deceleration lane length has little impact; and (3) a case study using the Nanping Tunnel in Chongqing shows that the model’s prediction error is within 5.29%. This study offers a targeted capacity estimation method for diverging areas in urban tunnels, aiding adaptive traffic control design and CAV deployment planning.
Downloads
Althoff M, Maierhofer S, Pek C. Provably-correct and comfortable adaptive cruise control. IEEE Transactions on Intelligent Vehicles. 2020;6(1):159-74. DOI: 10.1109/TIV.2020.2991953.
Shi X, Li X. Constructing a fundamental diagram for traffic flow with automated vehicles: Methodology and demonstration. Transportation Research Part B: Methodological. 2021;150:279-92. DOI: 10.1016/j.trb.2021.06.011.
Dai Z, Liu XC, Chen X, Ma X. Joint optimization of scheduling and capacity for mixed traffic with autonomous and human-driven buses: A dynamic programming approach. Transportation Research Part C: Emerging Technologies. 2020;114:598-619. DOI: 10.1016/j.trc.2020.03.001.
Calvi A, Bella F, D'Amico F. Evaluating the effects of the number of exit lanes on the diverging driver performance. Journal of Transportation Safety & Security. 2018;10(1-2):105-23. DOI: 10.1080/19439962.2016.1208313.
Chen D, Ahn S. Capacity-drop at extended bottlenecks: Merge, diverge, and weave. Transportation Research Part B: Methodological. 2018;108:1-20. DOI: 10.1016/j.trb.2017.12.006.
Patkar M, Dhamaniya A. Influence of nonmotorized vehicles on speed characteristics and capacity of mixed motorized traffic of urban arterial midblock sections. Journal of transportation engineering, Part A: Systems. 2020;146(4):04020013. DOI: 10.1061/JTEPBS.0000325.
Ghosh T, Roy SK, Gangopadhyay S. Assessment of multilane highway capacity through simulation process by considering the effect of behavior of driver of a vehicle. Journal of The Institution of Engineers (India): Series A. 2020;101(4):589-96. DOI: 10.1007/s40030-020-00475-z.
Yao Z, Xu T, Jiang Y, Hu R. Linear stability analysis of heterogeneous traffic flow considering degradations of connected automated vehicles and reaction time. Physica A: Statistical Mechanics and Its Applications. 2021;561:125218. DOI: 10.1016/j.physa.2020.125218.
Yao Z, et al. Analysis of the impact of maximum platoon size of CAVs on mixed traffic flow: An analytical and simulation method. Transportation Research Part C: Emerging Technologies. 2023;147:103989. DOI: 10.1016/j.trc.2022.103989.
Yao Z, Ma Y, Ren T, Jiang Y. Impact of the heterogeneity and platoon size of connected vehicles on the capacity of mixed traffic flow. Applied Mathematical Modelling. 2024;125:367-89. DOI: 10.1016/j.apm.2023.09.001.
Chen B, et al. A future intelligent traffic system with mixed autonomous vehicles and human-driven vehicles. Information Sciences. 2020;529:59-72. DOI: 10.1016/j.ins.2020.02.009.
Peng Y, et al. Enhancing mixed traffic flow with platoon control and lane management for connected and autonomous vehicles. Sensors. 2025;25(3):644. DOI: 10.3390/s25030644.
Miqdady T, de Oña R, Casas J, de Oña J. Studying traffic safety during the transition period between manual driving and autonomous driving: A simulation-based approach. IEEE Transactions on Intelligent Transportation Systems. 2023;24(6):6690-710. DOI: 10.1109/TITS.2023.3241970.
Sala M, Soriguera F. Capacity of a freeway lane with platoons of autonomous vehicles mixed with regular traffic. Transportation research part B: methodological. 2021;147:116-31. DOI: 10.1016/j.trb.2021.03.010.
Guan H, Wang H, Meng Q, Mak CL. Markov chain-based traffic analysis on platooning effect among mixed semi-and fully-autonomous vehicles in a freeway lane. Transportation Research Part B: Methodological. 2023;173:176-202. DOI: 10.1016/j.trb.2023.04.006.
Ghiasi A, Hussain O, Qian ZS, Li X. A mixed traffic capacity analysis and lane management model for connected automated vehicles: A Markov chain method. Transportation Research Part B: Methodological. 2017;106:266-92. DOI: 10.1016/j.trb.2017.09.022.
Qin Y, Luo Q, Wang H. Markov chain-based capacity modeling for mixed traffic flow with bi-class connected vehicle platoons on minor road at priority intersections. Physica A: Statistical Mechanics and its Applications. 2025;658:130301. DOI: 10.1016/j.physa.2024.130301.
Dhamaniya A, Bari CS, Patkar M. Capacity analysis of urban arterial midblock sections under mixed traffic conditions. International Journal of Intelligent Transportation Systems Research. 2022;20(2):409-21. DOI: 10.1007/s13177-022-00298-1.
Wu N, Lemke K. A new model for level of service of freeway merge, diverge, and weaving segments. Procedia-social and behavioral sciences. 2011;16:151-61. DOI: 10.1016/j.sbspro.2011.04.438.
Mohan R, Eldhose S, Manoharan G. Network-level heterogeneous traffic flow modelling in VISSIM. Transportation in developing economies. 2021;7:1-17. DOI: 10.1007/s40890-021-00117-4.
Liu Z, et al. A Gaussian-process-based data-driven traffic flow model and its application in road capacity analysis. IEEE Transactions on Intelligent Transportation Systems. 2023;24(2):1544-63. DOI: 10.1109/TITS.2022.3223982.
Yu Q, et al. The impact of automated vehicles on road and intersection capacity. Applied Sciences. 2023;13(8):5073. DOI: 10.3390/app13085073.
Qin Y, Luo Q, Xiao T, He Z. Modeling the mixed traffic capacity of minor roads at a priority intersection. Physica A: Statistical Mechanics and its Applications. 2024;636:129541. DOI: 10.1016/j.physa.2024.129541.
Fu C, et al. Capacity evaluation and application in urban expressway tunnel section under lane-reduction condition. Journal of Chongqing Jiaotong University (Natural Sciences). 2022;41(02):52-7. DOI: 10.3969/j.issn.1674-0696.2022.02.08.
Lu L, Lu J, Xing Y. Statistical analysis of traffic accidents in Shanghai river crossing tunnels and safety countermeasures. Discrete dynamics in nature and society. 2014;2014(1): 824360. DOI: 10.1155/2014/824360.
Yeung JS, Wong YD. Road traffic accidents in Singapore expressway tunnels. Tunnelling and Underground Space Technology. 2013;38: 534-541. DOI: 10.1016/j.tust.2013.09.002.
Llopis Serrano G. Traffic accidents in Spanish road tunnels. Proceedings of the Institution of Civil Engineers-Transport. 2022;175(1): 43-49. DOI: 10.1680/jtran.18.00043.
Zhang X, Wang J, Li T. Safety factors of exit slip roads on China's urban motorways. Proceedings of the Institution of Civil Engineers-Transport. 2017;170(5): 276-286. DOI: 10.1680/jtran.15.00057.
Manual HC. Highway capacity manual. Washington, DC. 2000;2(1):1.
Lipp LE, Corcoran LJ, Hickman GA. Benefits of central computer control for Denver ramp-metering system1991.
Erdmann J. SUMO’s lane-changing model. Modeling Mobility with Open Data: 2nd SUMO Conference. 2015: 105-123. DOI: 10.1007/978-3-319-15024-6_7.
Jiang C, et al. Safety evaluation of mixed traffic flow with truck platoons equipped with (cooperative) adaptive cruise control, stochastic human-driven cars and trucks on port freeways. Physica A: Statistical Mechanics and its Applications. 2024;643:129802. DOI: 10.1016/j.physa.2024.129802.
Mahdinia I, Arvin R, Khattak AJ, Ghiasi A. Safety, energy, and emissions impacts of adaptive cruise control and cooperative adaptive cruise control. Transportation Research Record. 2020;2674(6):253-67. DOI: 10.1177/0361198120918572.
Zhou Z, Li L, Qu X, Ran B. PACC: A platoon-based adaptive cruise control strategy based on leader-following information topology to mitigate traffic oscillations under CAV environment. Physica A: Statistical Mechanics and its Applications. 2024;654:130117. DOI: 10.1016/j.physa.2024.130117.
Yao Z, Hu R, Jiang Y, Xu T. Stability and safety evaluation of mixed traffic flow with connected automated vehicles on expressways. Journal of safety research. 2020;75:262-74. DOI: 10.1016/j.jsr.2020.09.012.
Ye L, Yamamoto T. Evaluating the impact of connected and autonomous vehicles on traffic safety. Physica A: Statistical Mechanics and its Applications. 2019;526:121009. DOI: 10.1016/j.physa.2019.04.245.
Zheng F, et al. Analyzing the impact of automated vehicles on uncertainty and stability of the mixed traffic flow. Transportation Research Part C: Emerging Technologies. 2020;112:203-19. DOI: 10.1016/j.trc.2020.01.017.
Cai X, et al. Capacity analysis of short-distance continuous diversion areas on mountainous urban expressways. PloS one. 2024;19(10):e0306881. DOI: 10.1371/journal.pone.0306881.
Wang Y, et al. Ego-efficient lane changes of connected and automated vehicles with impacts on traffic flow. Transportation Research Part C: Emerging Technologies. 2022;138:103478. DOI: 10.1016/j.trc.2021.103478.
Copyright (c) 2026 Xiaoyu CAI, Yudong BAI, Bo PENG, Cailin LEI, Yanping WANG
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
