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AI Computer Vision for Pedestrian Oases: How to Create Safe, Comfortable, Attractive Public Spaces

Cities around the world are facing a plethora of challenges as they strive to create more liveable, sustainable, and equitable urban environments. One of the most pressing issues is the need to provide pedestrians with safe, comfortable, and attractive public spaces where they can move around, rest, and interact with others. However, designing and managing pedestrian oases in dense urban areas is a complex and demanding task that requires a multifaceted approach, including the use of advanced technologies.

This blog discusses the benefits of using AI computer vision to support the planning and implementation of pedestrian oases in the heart of the city. AI computer vision can enhance the effectiveness and efficiency of urban planning and management by providing real-time data and insights on pedestrian behaviour, preferences, and needs. Specifically, through three key benefits of leveraging AI computer vision for pedestrian oases: improving safety, enhancing user experience, and optimising resource allocation.

The urban landscape is constantly evolving, and so are the challenges facing city planners and policymakers. One of the most important challenges is the need to create public spaces that are safe, comfortable, and accessible for pedestrians, who are the lifeblood of urban areas. Pedestrian oases, defined as well-designed, well-managed, and well-maintained public spaces that prioritise pedestrians' needs and preferences, can help address this challenge. However, pedestrian oases are not easy to create and maintain, especially in dense urban areas, where space is limited, and the demand for various uses is high.

Fortunately, recent advances in AI computer vision technology offer new opportunities for urban planners and managers to address some of the most pressing issues related to pedestrian oases. AI computer vision can provide real-time data and insights on pedestrian behaviour, preferences, and needs, which can help inform decision-making and improve the efficiency and effectiveness of urban planning and management.

Improving Pedestrian Safety:

One of the most critical benefits of AI computer vision for pedestrian oases is improving safety. Pedestrian accidents and collisions are a significant concern in many urban areas, especially in areas with high pedestrian traffic, such as parks, plazas, and shopping districts. AI computer vision can help detect potential safety hazards, such as pedestrian congestion, speeding vehicles, or obstacles, and alert pedestrians, drivers, and authorities in real-time. A good example, is smart cameras equipped with AI computer vision can monitor pedestrian crossings and detect near-miss incidents, which can help identify areas with high risk and inform targeted interventions.

Enhancing User Experience:

Another significant benefit of AI computer vision for pedestrian oases is enhancing user experience. Pedestrian oases should not only be safe but also attractive and comfortable for pedestrians to use. AI computer vision can help gather data on pedestrian behaviour and preferences, such as preferred walking paths, resting areas, or lighting conditions, which can help inform the design and management of pedestrian oases. Using smart sensors and AI algorithms, planners can analyse the patterns of pedestrian traffic and identify areas where benches, water fountains, or public art installations could enhance the user experience.

Aerial view of a park
Paths can be determined by Ai analysis of common pedestrian movement

Ai Computer Vision Models Suited to Pedestrian Monitoring

An Ai model is an algorithm that is trained with large amounts of data to recognise certain patterns. Segmentation models are effective for identifying and classifying pixel groups, while object detection is better at identifying and classifying object types in an image or video. Both these models can work hand in hand in identifying pedestrian patterns and activities in a city landscape.

Semantic segmentation and Object detection models can be used to analyse and identify different objects and regions in an image or video. In the context of pedestrian movement, these models can be used to identify pedestrian areas, walkways, and crossings in town centres. This can provide valuable data to town planners, allowing them to better understand pedestrian movement patterns and usage of public spaces.

The Ai models can be used by analysing street-level images or video footage of town centres. By applying semantic segmentation or object detection models to these images, urban planners can identify pedestrian areas and walkways, and quantify their usage over time. By tracking pedestrian movement across multiple images or video frames, planners can determine the most frequently used paths and areas in town centres. This information can then be used to optimise the placement of street furniture, pedestrian crossings, and other urban design elements, making the town centre more accessible and user-friendly.

An area least thought of is identifying areas where pedestrians are not currently utilising, providing opportunities for new pedestrian areas. Urban planners can identify underutilised areas and determine how they can be repurposed for pedestrian use. If a parking lot is underutilised, for example, it can be transformed into a pedestrian plaza or green space, providing a new pedestrian area for residents and visitors to enjoy.

Urban planners have multiple street cameras at their fingertips nowadays, which means they have access to large amounts of image and video data that can be very useful in managing city landscapes. Ai computer vision has become a powerful tool for analysing large amounts of data, which is making the job of the urban planner far richer in insights.

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