| 摘要: |
| 城市街道形态对促进非机动车使用有
重要作用。现有文献多在宏观尺度分析密度对
出行方式选择的影响，或在微观尺度聚焦个体
骑行行为，缺乏中观尺度对空间结构自身作用
的深入实证研究。本研究基于天津13个案例街
区、401个街道断面以及2014年和2018年的实
测交通截面流量对比，在街区和街道两个尺度
分析非机动车道路使用情况受街道形态、轨道
交通、城市密度、道路设计四大类因素的综合影
响。研究发现四年非机动车占比明显增加，但其
增量主要替代了步行而非机动车。在街区尺度，
对非机动车出行影响最大的因素为商业密度，
街道拓扑形态与建筑面积次之。在街道尺度，
街道拓扑形态是影响非机动车流量分布的主导
因素，建筑密度为辅助因素。本研究提出“小街
区、顺路网”的街道拓扑形态更适宜支持骑行，
有助于实现双碳目标。 |
| 关键词: 非机动车 街道拓扑形态 空间句法 出行方式选择 截面流量 |
| DOI：10.13791/j.cnki.hsfwest.20240615 |
| 分类号: |
| 基金项目:国家自然科学基金面上项目（5227081451） |
|
| Street topological structure and cycling uses: A multiyear span data analysis based on gatecount flow data in Tianjin |
|
SHENG Qiang
|
| Abstract: |
| After the booming usage of shared bikes from 2017 in China, the number of cyclists has
increased in many major cities. To promote non-motorized traffic became an important strategy for
urban renewal and redevelopment under the goals of carbon peaking and carbon neutrality. Most
research on non-motorized traffic borrowed the model for vehicles, focusing on the factors at city
scale related to traffic demand and modal split. Other studies analyzed individual behavior choice
at micro scale, focusing on the route choice or the spatial quality. However, there are very few
empirical studies explored on the role of street pattern at neighborhood or street scale, dealing with
the spatial distribution of non-motorized traffic. Especially, the emergence of shared bikes in China
provides a valuable chance to study the changing tendency through a comparative analysis. This
research is based on detail gate count data of 401 streets in 13 neighborhoods in Tianjin in 2014 and
2018. Using space syntax model, it studies the impact of street topological street pattern (measured
by choice and integration values at different radius) at city-, neighborhood- and street scales. Other
than topological street pattern, this research also analyzes factors such as distance to metro station,
density (commercial POIs and building areas), street design (traffic light, railing, greenery, etc.)
on t he modal s plit a nd flow d istribution of non-motorized vehicles. T he fi ndings a re s ummarized
as following. 1) By comparing the amount and share of non-motorized traffic before and after the
emergence of shared bike, data suggest that the increase of non-motorized traffic substitutes the
pedestrian rather than vehicle traffic in all case areas. 2) At neighborhood scale analysis on the share
of cyclist usage, the density of shops (radii=200 m) shows strongest correlation on both 2014 (R=0.88)
and 2018 (R=0.89). Topological street pattern (log choice r1km, R=0.61-0.63) and building density
(Total building area, radii=200 m, R=0.60-0.67) also show clear correlation. However, these factors
are strongly interrelated with each other that they cannot be all included in multi-variant regression
model. All street design factors show very weak correlation, which indicate street design functions
not at neighborhood scale. 3) At street scale, this study group all gate count data into one model (401
gate count data) without average the data by the case areas. It also makes a distinction between the
analysis on major streets as the border of neighborhood (101 gate count data) and minor streets inside
the neighborhood only (300 gate count data). These three data sets also divided by two years (2014
and 2018). Six multi-variant regression model are established to analyze the flow intensity on each
gate. The results show all data models and major streets in 2014 and 2018 can explain the data very
well (adj. R2<0.6). Two minor street model have relatively lower R square value (adj. R2=0.47-0.55).
Among all of these six models, topological street pattern has major impact on the non-motorized flow
distribution. Building density factor (total building area in radii of 1 km) has minor impact. Additionally,
there is a clear difference in the radius of space syntax parameters for major streets (7.5 km) and minor
streets (2 km). These findings do not only demonstrate that the space syntax model could be used
to analysis non-motorized traffic, but also further verify the linkage between individual behavior
and the emergent pattern of movement for this group. The differences of scale factors in topological
street pattern reflect the behavior pattern of short distance cycling by bike and longer distancemovement by mopeds. As a summary, based on detailed gate count data in four years span, this research suggests “small block size” and “street continuity”
are key features of street pattern to support non-motorized traffic, which may help to reach the goals of carbon peaking and carbon neutrality |
| Key words: non-motorized transportation typological street pattern space syntax modal split gate count data |
