Walking is the original form of transportation, and pedestrians have always made up a significant share of transportation system users. In contrast to motorized traffic, which has to move on precisely defined lanes and follow strict rules, pedestrian traffic is not heavily regulated. Moreover, pedestrians have specific characteristics—in terms of size and protection—which make them much more vulnerable than drivers. In addition, the difference in speed between pedestrians and motorized vehicles increases their vulnerability. All these characteristics, together with the large number of pedestrians on the road, lead to many safety problems that professionals have to deal with. One way to tackle them is to model pedestrian behavior using microsimulation tools. Of course, modeling also raises questions of reliability, and this is also the focus of this paper. The aim of the present research is to contribute to improving the reliability of microsimulation models for pedestrians by testing the possibility of applying neural networks in the model calibration process. Pedestrian behavior is culturally conditioned and the adaptation of the model to local specifics in the calibration process is a prerequisite for realistic modeling results. A neural network is formulated, trained and validated in order to link not-directly measurable model parameters to pedestrian crossing time, which is given as output by the microsimulation tool. The crossing time of pedestrians passing the road on a roundabout entry leg has been both simulated and calculated by the network, and the results were compared. A correlation of 94% was achieved after both training and validation steps. Finally, tests were performed to identify the main parameters that influence the estimated crossing time.

中文翻译：

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应用于微观模拟的神经网络：行人过街时间的预测模型

步行是最初的交通方式，行人一直占交通系统用户的很大一部分。与必须在精确定义的车道上行驶并遵守严格规则的机动交通相比，行人交通不受严格监管。此外，行人具有特定的特征——在体型和保护方面——这使得他们比司机更容易受到伤害。此外，行人和机动车辆之间的速度差异增加了他们的脆弱性。所有这些特点，再加上道路上的大量行人，导致了许多专业人士必须处理的安全问题。解决这些问题的一种方法是使用微观模拟工具对行人行为进行建模。当然，建模也提出了可靠性问题，而这也是本文的重点。本研究的目的是通过测试在模型校准过程中应用神经网络的可能性来提高行人微观仿真模型的可靠性。行人行为受文化影响，在校准过程中使模型适应当地具体情况是获得真实建模结果的先决条件。制定、训练和验证神经网络，以便将非直接可测量的模型参数与行人过街时间联系起来，该时间由微观模拟工具作为输出给出。行人在环岛入口路段过马路的穿越时间已经通过网络进行了模拟和计算，并对结果进行了比较。在训练和验证步骤之后实现了 94% 的相关性。