The structure of road networks plays a pivotal role in shaping transportation dynamics. It also provides insights into how drivers experience city streets and helps uncover each urban environment's unique characteristics and challenges. Consequently, characterizing cities based on their road network patterns can facilitate the identification of similarities and differences, informing collaborative traffic management strategies, particularly at a regional scale. While previous studies have investigated global network patterns for cities, they have often overlooked detailed characterizations within a single large urban region. Additionally, most existing research uses metrics like degree, centrality, orientation, etc., and misses the nuances of street networks at the intersection level, specifically the geometric angles formed by links at intersections, which could offer a more refined feature for characterization. To address these gaps, this study examines over 100 cities in the San Francisco Bay Area. We introduce a novel metric for classifying intersections, distinguishing between different types of 3-way and 4-way intersections based on the angles formed at the intersections. Through the application of clustering algorithms in machine learning, we have identified three distinct typologies - grid, orthogonal, and organic cities - within the San Francisco Bay Area. We demonstrate the effectiveness of the metric in capturing the differences between cities based on street and intersection patterns. The typologies generated in this study could offer valuable support for city planners and policymakers in crafting a range of practical strategies tailored to the complexities of each city's road network, covering aspects such as evacuation plans, traffic signage placements, and traffic signal control.

翻译：道路网络结构在塑造交通动态方面起着关键作用。它也为理解驾驶员如何体验城市街道提供了洞见，并有助于揭示每个城市环境的独特特征与挑战。因此，基于道路网络模式对城市进行表征，可以促进识别相似性与差异性，为协作交通管理策略（特别是在区域尺度上）提供信息。尽管先前的研究已探讨了城市的全局网络模式，但往往忽略了单一大型城市区域内的详细表征。此外，现有研究大多使用度、中心性、方向性等指标，未能捕捉街道网络在交叉口层面的细微特征，特别是交叉口处连接形成的几何角度，这可能为表征提供更精细的特征。为填补这些空白，本研究考察了旧金山湾区的100多个城市。我们引入了一种新的交叉口分类指标，根据交叉口处形成的角度区分不同类型的3路和4路交叉口。通过应用机器学习中的聚类算法，我们在旧金山湾区识别出三种不同的类型——网格型、正交型和有机型城市。我们证明了该指标在基于街道和交叉口模式捕捉城市差异方面的有效性。本研究生成的类型学可为城市规划者和政策制定者提供宝贵支持，帮助他们针对每个城市道路网络的复杂性制定一系列实用策略，涵盖疏散计划、交通标志布设和交通信号控制等方面。