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Implementing innovative traffic simulation models with aerial traffic survey

机译:利用空中交通调查实施创新的交通仿真模型

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Traffic data and human driver behaviours simulation are two of the most important parameters for the proper implementation of a traffic simulation. In our case study, we implemented an innovative method to obtain both a complex set of data of OD matrix and detailed human driver behaviours data in order to set a specific scenario simulation. Firstly, we recorded an aerial video and, subsequently, we conducted an advanced traffic analysis of aerial video data using DataFromSky service. The result is a complex complete set of traffic parameters. Data collection is based not only on classic OD matrix (volume data, vehicles classification, hourly rates, etc.), but also on innovative dynamic vehicle database parameters (speeds, lateral and tangential accelerations, travel times and distances, trajectories, etc.). All of these parameters are supplied to each vehicle detected. For this case study, our team provided as well a set of specific simulation parameters, such as gap time and follow-up time, for each entry/exit analysed. It is clear that most of this data were used to the calibration and improvement of our simulation network. In particular, we inserted specific values for each vehicle class (dimensional and dynamic values) and a detailed calibration of our behavioural models in Krauss settings. The resulting traffic simulation scenario shows the highest correlation value between real and simulated driver behaviours, probably, never obtained before.
机译:交通数据和人机行为仿真是正确实现流量模拟的两个最重要的参数。在我们的案例研究中,我们实施了一种创新方法,以获取OP矩阵的复杂数据和详细的人员驱动程序行为数据,以便设置特定的方案模拟。首先,我们录制了一个航拍视频,随后,我们使用DataFromsky服务进行了航空视频数据的高级流量分析。结果是复杂的完整流量参数集。数据收集不仅基于经典OD矩阵(卷数据,车辆分类,每小时),而且还要在创新动态车辆数据库参数(速度,横向和切线加速,行程时间和距离,轨迹等)上的基于创新动态车辆数据库参数。所有这些参数都被提供给检测到的每辆车。对于这种案例研究,我们的团队也提供了一组特定的模拟参数,例如分析的每个条目/退出,如间隙时间和随访时间。很明显,大多数这些数据用于校准和改进我们的仿真网络。特别是,我们为每个车辆类(维度和动态值)插入了特定值,以及在Krauss设置中的行为模型的详细校准。由此产生的流量仿真方案显示了实际和模拟驱动程序行为之间的最高相关值,可能是之前从未获得过的。

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