标题： 城市评分：一个实时个性化宜居性分析平台 显示英文标题

标题： UrbanScore: A Real-Time Personalised Liveability Analytics Platform

摘要： 本文介绍了UrbanScore——一个实时网络平台，该平台为任何城市地址计算个性化的宜居性评分。 系统融合了五个数据流：(i) 通过Nominatim进行地址地理编码，(ii) 通过Overpass QL从OpenStreetMap提取设施，(iii) TomTom Flow v10的路段级交通指标，(iv) OpenWeatherMap的每小时空气质量读数，以及(v) 用户声明的偏好配置文件，所有数据都存储在Oracle 19c关系型数据库中。 六个子评分（空气、交通、生活方式、教育、地铁可达性、地面交通）被生成，自适应加权并组合；然后使用OpenAI大型语言模型将数值结果转换为简洁、用户友好的解释。 一次覆盖布加勒斯特226平方公里都会区的试点部署，在四周内评估了3450个独特地址。 端到端中位延迟为2.1秒（p95 = 2.9秒），满足<3的非功能性要求。 综合评分范围为34至92（平均值68，标准差11），高分区域沿地铁走廊分布，这些区域拥有丰富的绿地且PM2.5水平低于35 ug m-3。 对Tineretului地区的详细案例研究得出总体评分为91/100，并展示了叙事层如何引导用户选择相似的社区。 局限性包括对第三方API运行时间的依赖、对Well-mapped OSM区域的空间偏差，以及缺乏噪声和犯罪层，18%的调查参与者将其列为期望的改进点。 总体而言，结果表明开放地理数据、商业移动数据流和对话式人工智能可以整合成一个高效、可解释的决策支持工具，将“宜居性分析”置于每个购房者、通勤者和城市规划者的手中。 显示英文摘要

摘要： This paper introduces UrbanScore - a real-time web platform that computes a personalised liveability score for any urban address. The system fuses five data streams: (i) address geocoding via Nominatim, (ii) facility extraction from OpenStreetMap through Overpass QL, (iii) segment-level traffic metrics from TomTom Flow v10, (iv) hourly air-quality readings from OpenWeatherMap, and (v) user-declared preference profiles, all persisted in an Oracle 19c relational store. Six sub-scores (air, traffic, lifestyle, education, metro access, surface transport) are derived, adaptively weighted and combined; an OpenAI large-language model then converts the numeric results into concise, user-friendly explanations. A pilot deployment covering the 226 km2 metropolitan area of Bucharest evaluated 3,450 unique addresses over four weeks. Median end-to-end latency was 2.1 s (p95 = 2.9s), meeting the <3 non-functional requirement. Aggregate scores ranged from 34 to 92 (mean 68, SD 11), with high-scoring clusters along metro corridors that pair abundant green space with PM2.5 levels below 35 ug m-3. A detailed case study of the Tineretului district produced an overall score of 91/100 and demonstrated how the narrative layer guides users toward comparable neighbourhoods. Limitations include dependence on third-party API uptime, spatial bias toward well-mapped OSM regions and the absence of noise and crime layers, cited by 18% of survey participants as a desired enhancement. Overall, the results show that open geodata, commercial mobility feeds and conversational AI can be integrated into a performant, explainable decision-support tool that places "liveability analytics" in the hands of every house-hunter, commuter and city planner.