Regarding the role of time in human life, the driving time can be a tangible criterion to represent the quality of flow in every traffic facility such as freeway interchanges. There are a lot of appreciable attempts which focused on determining travel time in long-length paths in the networks. But, there are not enough considerable studies on predicting the driving time in short-length paths such as the parts of an interchange. Besides, when the study area has to be changed, or when the purpose is to design a nonexistent interchange, predicting the driving time in diverse parts of an interchange cannot be carried out by direct measurement, and when there is information shortage on some hardly achievable traffic-based characteristics of interchanges, predicting the driving time by common procedures will be so hard or even impossible. Using simulation software usually needs considerable time and budget, too. In this paper, focus was on studying the short-length fragments of freeway interchanges to predict driving time by investigating ten real-world interchanges and more than 13,600 different simulated parts of interchanges. An artificial neural network (ANN)-based model and a particle swarm optimization (PSO)-based model were developed, and therefore, predicting driving time was possible just based on basic simple traffic and geometrical properties of the interchange. The results of testing, validating, and statistical analysis depicted a proper and precise development of the models. Although the PSO-based models have higher RMSE values than ANN-based models, the models were reliable enough to use for predicting the driving time in four parts of interchanges.

中文翻译：

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使用人工神经网络和粒子群优化的高速公路交汇处行驶时间预测

就时间在人类生活中的作用而言，行驶时间可以作为衡量高速公路立交等每个交通设施的流量质量的有形标准。有许多可观的尝试专注于确定网络中长路径中的旅行时间。但是，关于预测短距离路径（例如立交桥部分）的行驶时间的研究还不够充分。此外，当需要改变研究区域，或者当目的是设计一个不存在的立交时，不能通过直接测量来预测立交不同部分的行车时间，当一些难以实现的信息短缺时基于交汇处的交通特征，通过普通程序预测行驶时间将非常困难甚至不可能。使用仿真软件通常也需要大量的时间和预算。在本文中，重点是通过调查十个真实世界的立交和 13,600 多个不同的立交模拟部分来研究高速公路立交的短长度片段以预测行驶时间。开发了基于人工神经网络 (ANN) 的模型和基于粒子群优化 (PSO) 的模型，因此，仅基于基本的简单交通和立交的几何特性就可以预测行驶时间。测试、验证和统计分析的结果描述了模型的正确和精确开发。尽管基于 PSO 的模型比基于 ANN 的模型具有更高的 RMSE 值，但这些模型足够可靠，可用于预测四个立交桥的行驶时间。