Performance evaluation of microscopic traffic flow models with test track data
Performance evaluation of microscopic traffic flow models with test track data
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DOI：
10.3141/1876-10
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发表时间：
2004-01-01
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影响因子：
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通讯作者：
Asano, M
中科院分区：
文献类型：
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作者：
Ranjitkar, P;Nakatsuji, T;Asano, M
研究了六种微观交通流模型的性能，分析了这些模型与实时运动全球定位系统（GPS）测量结果的拟合程度。配备GPS接收器的10辆乘用车参加了在测试轨道上进行的汽车跟踪实验。采用基于遗传算法的方法对车速和车头时距两种不同情况下的模型参数进行优化。针对前车车速受到不同程度干扰所带来的半波、一波、两波、三波、随机和匀速模式等驾驶工况，分析了各模型的优化性能。对于速度数据，5个模型表现良好，平均百分位数误差在3.87% ~ 4.71%之间，标准差在1.09% ~ 1.64%之间。在后一种情况下，只有3种模型表现良好，平均百分位数误差在12.04% ~ 12.91%之间，标准差在4.53% ~ 5.13%之间。所有模型在前一种情况下的表现都优于后一种情况。与模型间差异相比，个体间差异显著，表明驾驶员个体对车辆跟随现象的影响。
The performance of six microscopic traffic flow models was investigated on the basis of how well these models fit with the real-time kinematic Global Positioning System (GPS) measurements. Ten passenger cars equipped with the GPS receivers participated in the car-following experiments, conducted at a test track. The genetic algorithm-based approach is adopted to optimize the model parameters for two different cases: using speed and headway data. The optimized performance of each model is analyzed for various driving conditions introduced by the different level of disturbances to the lead vehicle's speed, which include half-wave, one-wave, two-wave, three-wave, random, and constant speed patterns. In the former case with speed data, five models performed well with the average percentile error ranging from 3.87% to 4.71% and standard deviation ranging from 1.09% to 1.64%. In the latter case with headway data, only three models performed well with the average percentile error ranging from 12.04% to 12.91% and standard deviation ranging from 4.53% to 5.13%. All models performed better in the former case than in the latter case. The interpersonal variations are significant compared with the intermodel variations and indicate individual drivers' influence on the car-following phenomena.