Basic evaluation for simulation-based crash-risk-models - multiscale modelling regarding dynamic traffic flow states

碰撞风险评估是基础设施规划领域的一项重要任务，目前主要通过统计建模来实现。然而，到目前为止，这组模型并没有根据交通流的动态状态进行可靠的预测。实际研究提案的目的是为即将到来的碰撞风险模拟辅助估计的数学模型和分析考虑奠定基础。在方法上，这将通过扩展现有的和验证的交通流模型的非均匀的动态状态的交通流。因此，负责任的驾驶员的行为被分析为一个不均匀的交通状态，因此纳入模型。在动态交通状态下的驾驶行为识别在真实的交通和实验室驾驶模拟器实验被纳入微观交通模型。从这个小规模的系统严格的数学推导出一个大规模的模型。基于模型的预测可能的碰撞风险，然后推导出大规模的宏观交通流模型的基础上。基于所获得的数据对导出的模型进行了校准和验证。通过研究项目，预计将对数学交通建模产生重大影响。从这一发现，基于风险的决策模型，个人车辆的指导或集体交通影响，可以在未来的发展。

Crash risk estimation is an important task in the area of infrastructure planning, which nowadays is realized by statistical modeling. The group of models however until now does not result in reliable predictions depending on dynamic states of traffic flow. The aim of the actual research proposal is, to create the basis for a mathematical model and analytic consideration for a coming simulation-assisted estimate of crash risk. Methodically, this will be achieved by an extension of the existing and validated traffic flow models by inhomogeneous dynamic state of traffic flow. Therefore the responsible drivers behavior is analyzed as an inhomogeneous state of traffic and so integrated into the model. The driving behavior in dynamic traffic states identified in real traffic and in laboratory driving simulator experiments is incorporated into a microscopic traffic model. From this small-scale system rigorously mathematically a large scale model is derived. Model-based predictions of possible crash risks are then derived on the basis of large-scale macroscopic traffic flow model. The derived models are calibrated and validated based on the obtained data. By the research project a substantial input to mathematical traffic modeling is expected. From this findings risk-based decision models for individual vehicle guidance or collective traffic influencing can be developed in future.