Distance-Time Conversion Factor

This study proposes a novel approach to smart regional planning to address challenges in public service accessibility. We advocate for a tailored strategy based on regional characteristics, focusing on a city-level conversion factor for intuitive travel time and distance estimation in public service planning. Our analysis considers road network distance, Euclidean straight-line distance, and minimum travel time factors based on speed limits and real-time speeds. The introduced distance-time conversion factor (DCF), calculated from the circuity factor (CF) and delay factor (DF). This calculation identifies areas with a high DCF, particularly around major metropolitan centers. The findings serve as crucial indicators for densely populated regions, marking a significant step towards a realistic public infrastructure plan that surpasses traditional uniform location planning limitations. By comprehensively diagnosing issues related to underdevelopment and population overcrowding, our study emphasizes the need for dynamic planning strategies. The DCF provides valuable insights that can guide effective public infrastructure development of regions with heightened demand for improved accessibility. This research contributes a dynamic distance-time conversion factor, refining the landscape of smart public service planning by leveraging real-time traffic big data. The findings underscore the significance of tailoring planning strategies to regional nuances and ultimately paving the way for more effective and responsive public infrastructure development.

本研究针对公共服务可达性所面临的各类挑战，提出了一种全新的智能区域规划方法。我们倡导基于区域特征制定定制化规划策略，重点聚焦城市级转换系数，以实现公共服务规划场景下出行时间与距离的直观估算。本分析综合考量了三类影响因子：道路网络距离、欧几里得直线距离，以及基于限速与实时车速测算的最小出行时间。本文提出的距离时间转换系数（distance-time conversion factor, DCF）由绕行系数（circuity factor, CF）与延误系数（delay factor, DF）计算得到，该系数可精准识别出高DCF区域，尤其是各大都市中心周边地带。研究结果可作为人口稠密地区的关键规划指标，为突破传统统一选址规划的局限、制定更贴合实际的公共基础设施规划迈出了重要一步。本研究通过全面诊断公共服务设施欠发达与人口过度集聚等问题，强调了动态规划策略的必要性。DCF能够提供极具价值的分析视角，可为公共服务可达性需求较高的区域开展高效公共基础设施建设提供指导。本研究提出了动态距离时间转换系数，借助实时交通大数据优化了智能公共服务规划的研究范式。研究结果凸显了针对区域特质定制规划策略的重要意义，最终为更具实效性与响应性的公共基础设施发展铺平了道路。