街道设计与驾驶行为：来自米兰、阿姆斯特丹和迪拜大规模研究的证据
Street design and driving behavior: evidence from a large-scale study in Milan, Amsterdam, and Dubai
AI总结：
本研究利用街景图像语义分割与机器学习，分析米兰、阿姆斯特丹和迪拜的街道设计对30 km/h限速遵守的影响，发现窄街和密集建成环境有助于降速，并开发了可预测车速、支持规划干预的工具。
摘要：
近年来，城市越来越多地将限速从50 km/h降低到30 km/h，以提高道路安全、减少噪声污染并促进可持续交通方式。然而，实现对这些新限速的遵守仍然是城市规划者面临的一个关键挑战。本研究调查了米兰驾驶员对30 km/h限速的遵守情况，并考察街道特征如何影响驾驶行为。我们的研究结果表明，仅仅引入更低的限速并不足以有效降低行驶速度，这凸显了理解街道设计如何提高限速遵守度的必要性。为了理解这一关系，我们将基于计算机视觉的语义分割模型应用于Google Street View图像。大规模分析表明，较窄的街道和密集的建筑环境与较低的速度相关，而可见度更高、天空视野更大的道路则会促使更快的驾驶。为了评估当地环境对超速行为的影响，我们将所开发的方法框架应用于另外两个城市：阿姆斯特丹——与米兰类似，是一个最初并非为汽车而建的历史悠久的欧洲城市；以及迪拜——近几十年来以更以汽车为中心的设计发展起来的城市。分析结果在很大程度上证实了在米兰获得的发现，这表明本文所确定的用于驾驶员速度遵守的道路设计指南具有广泛的适用性。最后，我们开发了一个机器学习模型，基于街道特征来预测行驶速度。我们通过估算如果米兰在全市范围内采用30 km/h限速时的限速遵守情况，展示了该模型的预测能力。该工具为城市规划者提供了可操作的见解，支持设计干预措施以提高限速遵守度。
英文摘要：
In recent years, cities have increasingly reduced speed limits from 50 km/h to 30 km/h to enhance road safety, reduce noise pollution, and promote sustainable modes of transportation. However, achieving compliance with these new limits remains a key challenge for urban planners. This study investigates drivers' compliance with the 30 km/h speed limit in Milan and examines how street characteristics influence driving behavior. Our findings suggest that the mere introduction of lower speed limits is not sufficient to reduce driving speeds effectively, highlighting the need to understand how street design can improve speed limit adherence. To comprehend this relationship, we apply computer vision-based semantic segmentation models to Google Street View images. A large-scale analysis reveals that narrower streets and densely built environments are associated with lower speeds, whereas roads with greater visibility and larger sky views encourage faster driving. To evaluate the influence of the local context on speeding behaviour, we apply the developed methodological framework to two additional cities: Amsterdam, which, similar to Milan, is a historic European city not originally developed for cars, and Dubai, which instead has developed in recent decades with a more car-centric design. The results of the analyses largely confirm the findings obtained in Milan, which demonstrates the broad applicability of the road design guidelines for driver speed compliance identified in this paper. Finally, we develop a machine learning model to predict driving speeds based on street characteristics. We showcase the model's predictive power by estimating the compliance with speed limits in Milan if the city were to adopt a 30 km/h speed limit city-wide. The tool provides actionable insights for urban planners, supporting the design of interventions to improve speed limit compliance.
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