生命周期视角下建成环境对居民出行强度的影响机制

doi:10.16097/j.cnki.1009-6744.2025.06.010

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (6): 109-117.DOI: 10.16097/j.cnki.1009-6744.2025.06.010

生命周期视角下建成环境对居民出行强度的影响机制

张涛¹ ，宋彤彤¹ ，程龙^*2 ，贾庆林³

1. 太原科技大学，车辆与交通工程学院，太原030024；2.东南大学，江苏省城市智能交通重点实验室，南京211189； 3. 大连海事大学，综合交通运输协同创新中心，辽宁大连116026

收稿日期:2025-09-17 修回日期:2025-10-10 接受日期:2025-10-16 出版日期:2025-12-25 发布日期:2025-12-24
作者简介:张涛(1986—)，男，山西临汾人，副教授。
基金资助:
国家自然科学基金(52372301)；山西省基础研究计划(202403021221150)。

Impact Mechanism of Built Environment on Travel Intensity of Residents from a Life Cycle Perspective

ZHANG Tao¹, SONG Tongtong¹, CHENG Long^*2, JIA Qinglin³

1. School of Vehicle and Traffic Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China; 2. Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, China; 3. Collaborative Innovation Centre for Integrated Transport, Dalian Maritime University, Dalian 116026, Liaoning, China

Received:2025-09-17 Revised:2025-10-10 Accepted:2025-10-16 Online:2025-12-25 Published:2025-12-24
Supported by:
National Natural Science Foundation of China (52372301)；Fundamental Research Program of Shanxi Province (202403021221150)。

摘要/Abstract

摘要： 为探究生命周期视角下建成环境对居民出行强度的影响机制，本文将居民划分为无子、满巢I、满巢II和空巢4个阶段，以温岭市居民出行量、地理空间和建成环境数据为基础，设置居民出行强度为被解释变量，结合建成环境“5D”要素衍生出7种解释变量，基于多尺度地理加权回归模型分析建成环境对不同阶段居民出行强度的影响机制。研究发现，多尺度地理加权回归模型能够更有效地揭示变量在不同空间尺度上对居民出行强度的影响差异性。总体来看，居住密度、公交站点密度和商业设施密度对4个阶段居民出行强度均呈现正向显著影响，无子与空巢阶段的影响差异性在空间分布格局上较为相似，满巢I和满巢II阶段则表现出高度一致性；医疗设施密度在4个阶段均存在正向显著影响，集中在医疗资源丰富地区，但在满巢I、满巢II和空巢阶段该变量同时还表现出负向显著影响，主要分布于医疗设施相对欠发达地区。从不同阶段来看，无子阶段私家车拥有率低，对时间出行成本敏感度较高，受公交站点密度的影响差异性较大；空巢阶段短距离出行频次高、就医出行需求较多，受居住和医疗设施密度的影响差异性均最大；满巢I和满巢II阶段均需面临工作和家庭双重压力，不同的是，满巢I阶段受上下班通勤制约较大，满巢II阶段的出行偏重于家庭生活，分别受公交站点和商业设施密度的影响差异性最大。

关键词: 城市交通, 影响机制, 多尺度地理加权回归模型, 出行强度, 生命周期

Abstract: To investigate the impact mechanism of the built environment on the travel intensity of residents from a life-cycle perspective, this study divides resident life into four stages: childless, full nest I, full nest II, and empty nest. The study sets the travel intensity of residents as the explained variable, and combines with the seven explanatory variables derived from the "5D" elements of the built environment. The impact mechanism of the built environment on the travel intensity residents in different life stages is analyzed by using a multi-scale geographically weighted regression model, based on the data of the trip volume, geospatial space, and built environment in Wenling City. The study found that a multi-scale geographically weighted regression model can better reveal the differential impact of built environment variables on the travel intensity of residents across different spatial scales. Overall, the density of residence, bus stop and commercial facility all have a significant positive impact on the travel intensity of residents across the four life stages. The differences of impact between the childless and empty nest stages are similar in spatial distribution pattern, whereas those between the full nest I and full nest II stages are highly consistent. The density of medical facilities has a significant positive impact across all four life stages, and it is concentrated in the areas with abundant medical resources. However, in the full nest I, full nest II, and empty nest stages, the density of medical facilities has a significant negative impact, which is primarily distributed in areas with relatively underdeveloped medical facilities. From the perspective of different life stages, the childless stage has a low rate of private car ownership, a high sensitivity to travel time cost, and relatively significant differences in the impact of bus stop density. In the empty nest stage, short-distance travel is frequent and medical travel is in high demand. The impacts of residence and medical facility densities have a largest difference. Both the residents over the stages of full nest I and full nest II endure the dual pressures from work and family. The distinction is that the full nest I stage is heavily restricted by commuting to and from work, while the full nest II stage focuses more on family life. These results in the stages of full nest I and full nest II show the most significant differences in the impact from the density of bus stop and commercial facility, respectively.

Key words: urban traffic, impact mechanism, multi-scale geographically weighted regression model, travel intensity, life cycle

中图分类号:

U121

张涛, 宋彤彤, 程龙, 贾庆林. 生命周期视角下建成环境对居民出行强度的影响机制[J]. 交通运输系统工程与信息, 2025, 25(6): 109-117.

ZHANG Tao, SONG Tongtong, CHENG Long, JIA Qinglin. Impact Mechanism of Built Environment on Travel Intensity of Residents from a Life Cycle Perspective[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(6): 109-117.

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生命周期视角下建成环境对居民出行强度的影响机制

Impact Mechanism of Built Environment on Travel Intensity of Residents from a Life Cycle Perspective

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