Dynamic optimal lane management strategy for multi-lane urban expressway with bi-class connected vehicles

Abstract

Traffic flow mobility on expressway plays an important role in urban development. With the emergent technologies, connected vehicles, including both connected automated vehicles (CAVs) and connected regular vehicles (CRVs), are equipped with connectivity features to improve efficiency of urban expressway in a mixed traffic scenario. Existing research indicates that without targeted management, the integration of CAVs and CRVs into regular vehicles (RVs) traffic can lead to a series of congestion issues. A potential solution lies in the implementation of dedicated lanes, each designated for specific vehicle types, which could alleviate traffic flow complications. Therefore, this paper proposes a dynamic optimal lane management strategy for multi-lane mixed traffic urban expressway, aimed at maximizing the whole discharge flow. To begin with, we present an analytical method to mathematically derive discharge flow of each lane type including mixed traffic lane, CAV/CRV dedicated lane, and RV dedicated lane, from the perspective of both traffic demand and traffic capacity. Then, taking a three-lane mixed traffic urban expressway as an application, we conduct a numerical analysis of the dynamic optimal lane management strategy, based on comparisons among eight distinct strategies, under varying factors of traffic demands, penetration rates of CAVs and CRVs, and allocation proportions of vehicles upstream assigned to mixed traffic lanes. The results validate the effectiveness of the analytical method proposed for lane management strategies. It also indicates that the aforementioned system factors have significant impacts on the dynamic optimal lane management strategy.

Keywords

Lane management strategy / Mixed traffic discharge flow / Connected automated vehicles / Connected regular vehicles / Urban expressway

References

[1] MilanésV, ShladoverS, SpringJ, NowakowskiC, KawazoeH, NakamuraM. Cooperative adaptive cruise control in real traffic situations. IEEE Trans Intell Transp Syst, 2014, 15: 296-305.

[2] WangH, QinY, WangW, ChenJ. Stability of CACC-manual heterogeneous vehicular flow with partial CACC performance degrading. Transportmetrica B: Transport Dynamics, 2019, 7: 788-813

[3] QinY, XiaoT, WangH. Optimization strategy for connected automated vehicles to reduce energy consumption on freeway in rainy weather. Energy, 2024, 296. 131204

[4] QinY, WangH, NiD. Lighthill-Whitham-Richards model for traffic flow mixed with cooperative adaptive cruise control vehicles. Transp Sci, 2021, 554883-907.

[5] QinY, WangH. Stabilizing mixed cooperative adaptive cruise control traffic flow to balance capacity using car-following model. Journal of Intelligent Transportation Systems, 2023, 27157-79.

[6] GeJI, AvedisovSS, HeCR, et al. . Experimental validation of connected automated vehicle design among human-driven vehicles. Transportation Research Part C: Emerging Technologies, 2018, 91: 335-352.

[7] LiT, NgoduyD, HuiF, ZhaoX. A car-following model to assess the impact of V2V messages on traffic dynamics. Transportmetrica B: Transport Dynamics, 2020, 81150-165

[8] RuanT, ZhouL, WangH. Stability of heterogeneous traffic considering impacts of platoon management with multiple time delays. Physica A, 2021, 583. 126294

[9] ZhuJ, TasicI, QuX. Flow-level coordination of connected and autonomous vehicles in multilane freeway ramp merging areas. Multimodal Transportation, 2022, 11. 100005

[10] SalaM, SorigueraF. Macroscopic modeling of connected autonomous vehicle platoons under mixed traffic conditions. Transportation Research Procedia, 2020, 47: 163-170.

[11] Fu X, Lv X (2022) Multi-lane’s control performance differentiation on traffic efficiency under the lane-level dynamic coordination strategy. Journal of Intelligent Transportation Systems 1–18 https://doi.org/10.1080/15472450.2022.2157213.

[12] PanT, LamWH, SumaleeA, ZhongR. Multiclass multilane model for freeway traffic mixed with connected automated vehicles and regular human-piloted vehicles. Transportmetrica A: Transport Science, 2021, 1715-33.

[13] QinY, LuoQ, WangH. Stability analysis and connected vehicles management for mixed traffic flow with platoons of connected automated vehicles. Transportation Research Part C: Emerging Technologies, 2023, 157. 104370

[14] CaiM, XuQ, ChenC, WangJ, LiK, WangJ, WuX. Formation control with lane preference for connected and automated vehicles in multi-lane scenarios. Transportation Research Part C: Emerging Technologies, 2022, 136. 105313

[15] XuH, XuR, XiaX, SathyanA, GuoY, BujanovicP, LeslieE, GoliM, MaJ. Strategic and tactical decision-making for cooperative vehicle platooning with organized behavior on multi-lane highways. Transportation Research Part C: Emerging Technologies, 2022, 145. 103952

[16] RoncoliC, PapageorgiouM, PapamichailI. Traffic flow optimisation in presence of vehicle automation and communication systems–Part II: optimal control for multi-lane motorways. Transportation Research Part C: Emerging Technologies, 2015, 57: 260-275.

[17] SamaranayakeS, ChandS, SinhaA, DixitV. Impact of connected and automated vehicles on the travel time reliability of an urban network. International Journal of Transportation Science and Technology, 2023, 13: 171-185.

[18] LiangQ, LiX, ChenZ, PanT, ZhongR. Day-to-day traffic control for networks mixed with regular human-piloted and connected autonomous vehicles. Transportation Research Part B: Methodological, 2023, 178. 102847

[19] LiY, QinZ, ZhuH, PeetaS, GaoX. Platoon control of connected vehicles with heterogeneous model structures considering external disturbances. Green Energy and Intelligent Transportation, 2022, 1. 100038

[20] GulerSI, MenendezM, MeierL. Using connected vehicle technology to improve the efficiency of intersections. Transportation Research Part C: Emerging Technologies, 2014, 46: 121-131.

[21] Heshami S, Kattan L (2023) A stochastic microscopic based freeway traffic state and spatial-temporal pattern prediction in a connected vehicle environment. Journal of Intelligent Transportation Systems 1–27 https://doi.org/10.1080/15472450.2022.2130291.

[22] MaY, LiuQ, FuJ, LiuK, LiQ. Collision-avoidance lane change control method for enhancing safety for connected vehicle platoon in mixed traffic environment. Accid Anal Prev, 2023, 184. 106999

[23] LiuH, YuanT, ZengX, GuoK, WangY, MoY, XuH. Eco-driving strategy for connected automated vehicles in mixed traffic flow. Physica A, 2024, 633. 129388

[24] Yang H, Ucar S, Oguchi K (2023) Connected vehicle enabled hierarchical anomaly behavior management system for city-level networks. International Journal of Transportation Science and Technology 2046–4030

[25] GhiasiA, HussainO, QianZ, LiX. A mixed traffic capacity analysis and lane management model for connected automated vehicles: a Markov chain method. Transportation Research Part B: Methodological, 2017, 106: 266-292.

[26] LiuH, KanX, ShladoverSE, LuX, FerlisRE. Modeling impacts of cooperative adaptive cruise control on mixed traffic flow in multi-lane freeway facilities. Transportation Research Part C: Emerging Technologies, 2018, 95: 261-279.

[27] RadSR, FarahH, TaaleH, AremB. The impact of a dedicated lane for connected and automated vehicles on the behaviour of drivers of manual vehicles. Transport Res F: Traffic Psychol Behav, 2021, 82: 141-153.

[28] MaK, WangH. How connected and automated vehicle–exclusive lanes affect on-ramp junctions. Journal of Transportation Engineering, Part A: Systems, 2021, 147204020157.

[29] YaoZ, LiL, LiaoW, WangY, WuY. Optimal lane management policy for connected automated vehicles in mixed traffic flow. Physica A, 2024, 637: 0378-4371.

[30] SunY, JeneliusE, BurghoutW, XieB. Evaluation of motorway lane control strategies for mixed flow of autonomous and human-driven vehicles. Journal of Transportation Engineering, Part A: Systems, 2023, 1491104023109.

[31] Zheng Y, Xu M, Wu S, Wang S (2023) Lane management for asymmetric mixed traffic flow on bidirectional multi-lane roadways. Transportmetrica A: Transport Science 1–24 https://doi.org/10.1080/23249935.2023.2294492.

[32] BujanovicP, LochraneT. Capacity predictions and capacity passenger car equivalents of platooning vehicles on basic segments. Journal of Transportation Engineering, Part A: Systems, 2018, 1441004018063.

[33] QinY, LuoQ, XiaoT, HeZ. Modeling the mixed traffic capacity of minor roads at a priority intersection. Physica A, 2024, 636. 129541

[34] ChenD, AhnS, ChitturiM, NoyceDA. Towards vehicle automation: roadway capacity formulation for traffic mixed with regular and automated vehicles. Transportation Research Part B: Methodological, 2017, 100: 196-221.

[35] QinY, XieL, GongS, DingF, TangH. An optimal lane configuration management scheme for a mixed traffic freeway with connected vehicle platoons. Physica A, 2023, 634. 129444

[36] TreiberM, HenneckeA, HelbingD. Congested traffic states in empirical observations and microscopic simulations. Phys Rev E, 2000, 62: 1805-1824.

[37] QinY, WangH, RanB. Impacts of cooperative adaptive cruise control platoons on emissions under traffic oscillation. Journal of Intelligent Transportation Systems, 2021, 254376-383.

[38] LiY, WangW, XingL, FanQ, WangH. Longitudinal safety evaluation of electric vehicles with the partial wireless charging lane on freeways. Accid Anal Prev, 2018, 111: 133-141.

[39] MilanesV, ShladoverSE. Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data. Transportation Research Part C: Emerging Technologies, 2014, 48: 285-300.

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