A Parameter Calibration Method of Micro Traffic Simulation Based on Index Coupling
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摘要: 为了优化交通仿真模型的参数标定方法，提高仿真模型的精度和还原真实道路环境，研究了同时考虑多个校正指标的仿真标定方法。以仿真结果为导向，通过敏感性分析确定面向应用需求的标定参数。在考虑不同校正指标相互影响的基础上，以不同时间区间下的误差变异性作为影响权重，构建考虑多个校正指标的仿真标定模型，建立了考虑6种速度的目标函数。基于VISSIM仿真软件的二次开发功能，结合MATLAB语言对模型进行了实现，以免疫遗传算法为基本求解方法，通过两阶段的熵权赋值与自适应调整确定143组参数结果。最后采用均匀取值、递归取值、指标耦合取值3种方式比较了不同取值方法之间的优劣性。仿真结果显示不同时间段主线小客车速度的误差平方值大于0.01的频数下降了50%，大型货车下降了60%；在车速方面，主线小客车现有误差5%，下降了7%，大型货车现有误差1.5%，下降了5.2%，小型货车与匝道车速误差均维持在6.5%左右；主线小客车速度与主线大型货车速度具有更小的权重值，维持在0.15~0.2范围，误差变异性更小，在目标函数中的作用更小。结果表明：相较单一指标的标定方法，基于指标耦合的标定方法考虑了多个指标之间的相互影响，同时综合考虑各指标的误差，克服了以往标定1个指标而导致其他指标误差过大的缺点。Abstract: A method is proposed by considering multiple calibration indexes to optimize the parameter calibration of a traffic simulation model, improve the accuracy of the simulation model, and restore real road environments. Guided by simulation results, the calibration parameters for application-specific requirements are determined by sensitivity analysis. Considering the mutual influence of different calibration indexes, a simulation model is calibrated by simultaneously considering multiple calibration metrics which takes the variability of errors across distinct time intervals as the weights, and the objective function of the model calibration is developed based on six velocities. The model is implemented through secondary development of VISSIM software with MATLAB language. 143 groups of parameters are determined by two-stage entropy weight assignment and adaptive adjustment based on immune genetic algorithm. The effectiveness of the proposed method is validated by comparing with several baseline methods in three ways: uniform value, recursive value, and index coupling value approaches. The simulation results indicate a 50% decrease of squared errors exceeding 0.01 for the main line speed, and a 60% reduction for large trucks across various time periods. Regarding speed, the existing errors are 5% and 1.5% for main passenger cars and large trucks, reduced by 7% and 5.2%, respectively. The errors of estimated speeds for small trucks and ramps remains at approximately 6.5%; The speeds of main passenger cars and main trucks have smaller weights, ranging from 0.15 to 0.2, which indicates smaller variabilities of errors and smaller effects on the objective function. The results show that the proposed calibration method based on index coupling effectively takes into account both the in teraction of multiple indexes and the error of individual index, which mitigates the shortcoming of single-index calibration methods that leads to excessive errors of other indexes.
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表 1 待标定参数及范围
Table 1. Calibration parameters and range
驾驶行为 名称 范围 跟驰行为 CC1（车头时距/s） [0.7,2] CC2（跟车变量/m） [2,8] CC3（进入跟车状态的阈值） [-10, 2] CC7（加速度波动幅度/（m/s2）） [0,2] 换道行为 最大减速度/（m/s2） [-8,-2] -1m/s2距离/m [100,300] 安全距离折减系数 [0.3,0.9] 
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表 2 参数标定结果的分散度值
Table 2. Dispersion values of parameter calibration results
参数 分散度 CC1（车头时距/s） 0.29 CC2（跟车变量/m） 0.33 CC3（进入跟车状态的阈值） 0.32 CC7（加速度波动幅度/（m/s2）） 0.32
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表 3 不同取值方法的标定结果
Table 3. Calibration results of different value methods
驾驶行为 直接均值 递归取值 指标耦合取值 CC1（车头时距/s） 1.46 1.43 1.45 CC2（跟车变量/m） 4.78 4.58 7.13 CC3（进入跟车状态的阈值） －0.99 －2.86 －2.38 CC7（加速度波动幅度/（m/s2）） 0.97 1.59 0.21 最大减速度/（m/s2） －2 －7 －3 －1m/s2距离/m 300 200 200 安全距离折减系数 0.9 0.3 0.6
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表 4 指标误差平方和与目标函数值
Table 4. Index error sum of squares and objective function value
单位: % 指标类别 标定前 均匀取值 递归取值 权重取值 主线小客车 29.67 17.56 17.56 17.56 主线小型货车 27.73 29.1 28.9 24.47 主线大型货车 15.85 7.1 7.1 7.1 匝道小客车 14.27 17.76 15.86 13.89 目标函数值 0.215 0.199 0.190 0.166
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表 5 权重结果统计
Table 5. Weight result statistics
取值方法 主线小客车 主线小型货车 主线大型货车 匝道小客车 均匀取值 0.186 1 0.341 6 0.154 0 0.318 4 递归取值 0.173 4 0.327 0 0.143 5 0.356 2 指标耦合取值 0.191 2 0.291 0 0.158 2 0.359 5
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