Street design and driving behavior: evidence from a large-scale study in Milan, Amsterdam, and Dubai

2025-07-06

AI Summary

Low driver compliance undermines the effectiveness of 30 km/h speed limit policies. Method: This study develops a computer vision–based street scene analysis framework leveraging semantic segmentation to quantify physical street characteristics—including street width, building density, and sky visibility—and constructs a cross-city machine learning model to predict vehicle speeds. Using Google Street View imagery and ground-truth speed measurements from Milan, Amsterdam, and Dubai, the model identifies how narrow streets and high-density built environments significantly suppress speeding, whereas open sky views promote acceleration. Results: The model demonstrates strong generalizability across all three cities (R² > 0.78). Findings reveal that merely lowering speed limits is insufficient; effective implementation requires integrated spatial design interventions. The proposed methodology enables citywide simulation of speed limit compliance and supports data-driven, fine-grained traffic calming planning.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Investigates driver compliance with 30 km/h speed limits in urban areas
• Examines how street design influences driving behavior and speed adherence
• Develops a predictive model for speed compliance based on street characteristics

Innovation

• Computer vision analyzes street view images
• Machine learning predicts driving speeds
• Comparative study across diverse cities