基于网络口碑大数据的城市步行与自行车交通廊道选线规划--以北京市海淀区为例

通过大数据分析城市公共服务空间对公众出行的影响是步行和自行车交通廊道选线规划的重要步骤。已有研究主要聚焦空间分布规律展开分析,对公共设施服务能力内在差异性考虑较少。引入网络口碑大数据,通过口碑分值表征城市公共服务空间的吸引力,在此基础上构建基于网络口碑大数据的城市步行和自行车交通廊道选线规划思路,并以北京市海淀区为例展开实证分析。结合最小累积阻力模型和网络分析法等方法,最终构建由商业综合、休闲娱乐、生活服务3类廊道组成的步行和自行车交通廊道网络。网络口碑大数据兼具空间分布和口碑质量的双重属性,能够帮助规划者和决策者立足区域视角识别与量化部分类型公共服务设施的内在差异性,其支撑下的步行和自行车交通廊道规划更加强调公众使用满意度对选线过程的影响,在未来城市交通规划领域有较好的应用前景。

Route Selection Planning for Urban Pedestrian and Bicycle Transport Corridor Based on Internet Word-of-Mouth Big Data: A Case Study of Haidian District in Beijing

Analyzing the impact of urban public service spaces on public travel through big data is a critical step in the route selection planning of pedestrian and bicycle transport corridors (PBTC). Existing researches mainly focus on the spatial distribution laws of urban public service spaces, while paying less attention to their inherent differences. In view of this, the research introduces the internet word-of-mouth (IWOM) big data to characterize the attractiveness of urban public service spaces through word-of-mouth scores, based on which establishes the framework for PBTC route selection planning based on IWOM big data, and carries out empirical analysis with Haidian District in Beijing as an example. In combination with such methods as minimum cumulative resistance (MCR) model and network analysis, the research finally builds a PBTC network composed of three types of PBTCs respectively for commercial complexes, leisure and entertainment, and life services. Research results show that IWOM big data has the dual attributes of spatial distribution and word-of-mouth quality, which can help planners and decision-makers identify and quantify the inherent differences of some types of public service facilities from a regional perspective. At the same time, the PBTC planning supported by IWOM big data puts more emphasis on the impact of public satisfaction on route selection process, which has a good application prospect in the field of future urban transport planning.