City-scale simulation modelling of active modes of transportation (i.e., walking and cycling) is becoming increasingly popular in recent years. The heterogeneous and complex behaviour of these transportation modes, however, indicates the need for a shift from the traditional car and public transport centred modelling approaches towards incorporating the requirements for walking and cycling behaviour, while maintaining the run-time efficiency of the models. In this paper, we introduce and test our algorithm to create road network representations, designed and optimised to be used in city-scale active transportation modelling. The algorithm relies on open and universal data. In addition to the major roads and attributes typically used in transport modelling (e.g., speed limit, number of lanes, permitted travel modes), the algorithm also captures minor roads usually favoured by pedestrians and cyclists, along with road attributes such as bicycle-specific infrastructure, traffic signals, road gradient and road surface type. Furthermore, it simplifies the complex geometries of the network and merges parallel roads, if applicable, to make it suitable for large-scale simulations.

To examine the utility and performance of the algorithm, we used it to create a network representation for Greater Melbourne, Australia, and compared the output with a network created using an existing simulation toolkit along with another network from an existing city-scale transport model from the Victorian government. Through simulation experiments with these networks, we illustrated that for routed trips on our network for walking and cycling, it is of comparable accuracy to the common network conversion tools in terms of travel distance of the shortest paths while being more than two times faster when used for simulating different sample sizes. Therefore, our algorithm offers a flexible and adjustable solution for users to create road networks for city-scale active transport modelling while balancing between their desired simulation accuracy and run-time.

近年来，主动交通方式（即步行和骑自行车）的城市规模模拟建模越来越流行。然而，这些交通方式的异构和复杂行为表明，需要从传统的以汽车和公共交通为中心的建模方法转变为结合步行和骑自行车行为的要求，同时保持模型的运行时效率。在本文中，我们介绍并测试我们的算法来创建道路网络表示，设计和优化用于城市规模的主动交通建模。该算法依赖于开放和通用的数据。除了交通建模中通常使用的主要道路和属性（例如，速度限制、车道数量、允许的出行模式），该算法还捕获通常受行人和骑自行车者青睐的次要道路，以及道路属性，例如自行车专用基础设施、交通信号、道路坡度和路面类型。此外，它还简化了网络的复杂几何形状并合并平行道路（如果适用），使其适用于大规模模拟。

为了检查算法的效用和性能，我们使用它为澳大利亚大墨尔本地区创建网络表示，并将输出与使用现有模拟工具包创建的网络以及来自现有城市规模交通模型的另一个网络进行比较维多利亚州政府。通过对这些网络的模拟实验，我们表明对于我们网络上的步行和骑车路线旅行，它在最短路径的行驶距离方面与常见的网络转换工具具有可比性，而使用时速度要快两倍以上用于模拟不同的样本量。所以，