Optimized passage strategies and game-theoretic analysis for unbalanced intersections in mixed CAV and CHV traffic

doi:10.3969/j.issn.1674-8484.2026.03.013

Abstract

Abstract:

An intersection passage optimization strategy based on multi-agent negotiation was proposed to optimize urban intersections under unbalanced traffic flow in a mixed environment of connected and automated vehicles (CAVs) and conventional human-driven vehicles (CHVs). Three strategies were developed: when no pedestrians or non-motorized vehicles were crossing, CAVs were allowed to perform lane-borrowing left turns across the median, while approach vehicles turned left directly; when pedestrians or non-motorized vehicles crossed normally, the left-turn waiting area and the CAV lane-borrowing left-turn area were extended; when a negotiated agreement on delayed crossing with charging compensation was reached, wireless charging was provided to improve the acceptability of waiting, CAVs performed lane-borrowing left turns across the median while approach vehicles turned left directly, and the subsequent release scheme was dynamically adjusted within a single cycle to improve responsiveness to traffic-flow variation. The strategy was built on the outcome of pedestrian-vehicle crossing negotiation and a waiting-compensation mechanism, in which the wireless charging service offered during delayed crossing reduced the waiting cost of pedestrians and non-motorized vehicles. With pedestrians/non-motorized vehicles and motor vehicles taken as the game players, bilateral evolutionary game models were established, and a three-dimensional payoff of “time cost-safety risk-energy compensation” was introduced. The results show that, under mixed and unbalanced traffic conditions, the combination of signal-compliant crossing by pedestrians/non-motorized vehicles, extended left-turn waiting for CHVs, and CAV lane-borrowing left turns into the extended area constitutes an evolutionarily stable strategy, which significantly reduces delay and disorder without altering the existing channelization or signal-control framework.

Key words: connected and automated vehicles (CAVs), intersection passage strategy, evolutionary game theory, unbalanced traffic flow

CLC Number:

U492.8

GUO Shitong, WU Chengcheng, ZHENG Dingwei, HUANG Xingyu. Optimized passage strategies and game-theoretic analysis for unbalanced intersections in mixed CAV and CHV traffic[J]. Journal of Automotive Safety and Energy, 2026, 17(3): 409-419.

Figures/Tables 15

References 21

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决策	机动车通行(S_V1)	机动车不通行(S_V0)
行人过街(S_P1)	(R_P1, R_V1)	(R_P2, R_V2)
行人不过街(S_P0)	(R_P3, R_V3)	(R_P4, R_V4)

决策	机动车通行(S_V1)	机动车不通行(S_V0)
行人过街(S_P1)	(R_P1, R_V1)	(R_P2, R_V2)
行人不过街(S_P0)	(R_P3, R_V3)	(R_P4, R_V4)

主体	t_ST2 / s	t_ST3 / s
行人	69.43	73.11
非机动车	57.50	63.00
机动车	55.32	52.67

主体	t_ST2 / s	t_ST3 / s
行人	69.43	73.11
非机动车	57.50	63.00
机动车	55.32	52.67

策略	t_max / s	t_min / s	归一化求和值T′	交通熵H
策略1	462.74	193.32	13.889 0	3.188
策略2	1 662.11	686.42	13.989 6	3.137
策略3	1 474.10	680.26	17.682 0	3.262