Two-stage Calibration and Optimization Method for Microscopic Traffic Simulation Model Parameters Based on Neural Network Surrogate Models

doi:10.16182/j.issn1004731x.joss.25-FZ0635

Abstract

Abstract:

A two-stage calibration and optimization method is proposed to address the problem that parameter calibration methods for microscopic traffic simulation models are time-consuming. In the first stage, a surrogate model based on neural networks is trained to establish the mapping relationship between model parameters and evaluation indicators, and a genetic algorithm (GA) is combined to screen candidate parameters. In the second stage, after obtaining the approximate optimal parameters, by employing this set of parameters as initial values, a genetic algorithm is re-executed by combining the real simulation model for optimization to further improve calibration accuracy. Experimental results show that the basic optimal parameter combination obtained in the first stage can achieve substantial reductions in calibration time while ensuring simulation accuracy, while the second stage enables quick convergence of GA based on the basic optimal parameter combination. The proposed method improves parameter optimization efficiency by 79.7% without compromising prediction accuracy, providing a foundation for the automation of microscopic traffic simulation model parameters.

Key words: microscopic traffic simulation, model parameter calibration, neural network, efficiency improvement, automated calibration

CLC Number:

TP391.9

Liu Yijia, Zhou Chenjing, Pan Dong, Rong Jian, Xiao Yang. Two-stage Calibration and Optimization Method for Microscopic Traffic Simulation Model Parameters Based on Neural Network Surrogate Models[J]. Journal of System Simulation, 2026, 38(6): 1711-1721.

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参数名称	参数解释	数值范围
OB_d/m	车辆在纵向能感知到的前车距离范围(影响响应延迟与跟驰起点)	[150, 350]
N_pr	驾驶员同时感知与判断的前车数量，影响反应保守程度	[1, 5]
AXE_average/s	停车状态下车辆之间的平均期望车头间距	[1, 4]
BX_add	安全距离的加法部分，反映最小跟驰容忍程度	[1, 5]
BX_mult	安全距离乘法项，调整随速度增长的安全距离幅度	[1, 6]
ACC_max/(m/s²)	车辆能实现的最大加速度上限	[-6, -2]
ACC_ac	加速过程中的平滑性调节因子	[-2, -0.5]
GAP_min/m	变道所需的最小安全车间距	[0.5, 5]
ABX	驾驶员对邻车横向距离变化的敏感程度	[0.1, 1]
CO_ac	当前车对前车强行变道时的减速容忍度，反映交通流中的合作性水平	[-5, -1]
D/m	换道过程中保持目标车道稳定间距的下限	[50, 250]
T_dis/s	驾驶员在决定换道后可容忍的最大等待时长	[10,60]

网络结构 (隐藏层节点)	网络层数	测试集 MAPE/%	备注
64	1	10.40	简单结构，拟合精度不足
64-32	2	9.08	最终选择，结构紧凑、精度较优
128-64-32	3	8.60	精度最优，但结构复杂、训练耗时
128-64	2	9.10	相比最终方案结构更复杂

参数名称	取值
OB_d/m	251.25
N_pr	3
AXE_average/s	2.77
BX_add	3.54
BX_mult	4.03
ACC_max/(m/s²)	-2.8
ACC_ac	-1.4
GAP_min/m	3.3
ABX	0.64
CO_ac	-2.5
D/m	150.69
T_dis/s	60

参数名称	取值
OB_d/m	250.25
N_pr	3
AXE_average/s	2.59
BX_add	3.57
BX_mult	4.43
ACC_max/(m/s²)	-2.7
ACC_ac	-1.36
GAP_min/m	3.05
ABX	0.60
CO_ac	-2.5
D/m	160.69
T_dis/s	60

车辆类型	一阶段	两阶段优化
汽车	8.61	6.6
货车	10.01	7.9