Intelligent CCTV for Urban Design: AI-Based Analysis of Soft Infrastructure at Intersections
Intelligent CCTV for Urban Design: AI-Based Analysis of Soft Infrastructure at Intersections
用于城市设计的智能闭路电视：基于人工智能的交叉路口软基础设施分析
Abstract: Artificial intelligence (AI) and computer vision are transforming transportation data collection. This study introduces an AI-enabled analytics framework leveraging existing CCTV infrastructure to evaluate the impact of soft interventions, such as temporary pedestrian refuges and curb extensions, on vehicle speed and safety.
摘要： 人工智能（AI）和计算机视觉正在改变交通数据的收集方式。本研究引入了一个人工智能驱动的分析框架，利用现有的闭路电视（CCTV）基础设施，评估临时行人避难岛和路缘延伸等“软干预”措施对车辆速度和安全的影响。
Using deep learning and perspective-based speed estimation, we evaluated driver behavior before and after interventions, with repeated post-installation monitoring in Week 1 and Week 2, in Minneapolis.
通过深度学习和基于透视的速度估算技术，我们评估了明尼阿波利斯市在干预措施实施前后的驾驶员行为，并在安装后的第一周和第二周进行了重复监测。
Findings reveal that at unsignalized intersections, mean and 85th-percentile speeds fell by up to 18.75% and 16.56%, respectively, while pass-through traffic decreased by as much as 12.2%. Signalized intersections showed comparable reductions except one location, with mean and 85th-percentile speeds dropping by up to 20.0% and 17.19%.
研究结果显示，在无信号灯交叉路口，平均速度和第85百分位速度分别下降了高达18.75%和16.56%，过境交通流量减少了多达12.2%。除一个地点外，有信号灯的交叉路口也出现了类似的降幅，平均速度和第85百分位速度分别下降了高达20.0%和17.19%。
These results demonstrate the traffic-calming effectiveness of soft infrastructure and underscore the utility of AI-powered methods for rapid, low-cost, and evidence-based transport policy evaluation.
这些结果证明了软基础设施在交通减速方面的有效性，并强调了人工智能驱动的方法在实现快速、低成本且基于证据的交通政策评估方面的实用价值。
Paper Details:
- Authors: Vinit Katariya, Seungjin Kim, Curtis Craig, Nichole Morris, Hamed Tabkhi
- arXiv ID: 2605.05402
- Submission Date: 6 May 2026
论文详情：
- 作者： Vinit Katariya, Seungjin Kim, Curtis Craig, Nichole Morris, Hamed Tabkhi
- arXiv 编号： 2605.05402
- 提交日期： 2026年5月6日