基于GIS的道路热区鉴别方法

卢佩莹; 姚申君; 吴健平; 余柏蒗; 钟海东

doi:10.19818/j.cnki.1671-1637.2011.04.015

基于GIS的道路热区鉴别方法

doi: 10.19818/j.cnki.1671-1637.2011.04.015

1.
香港大学地理系, 香港
2.
华东师范大学地理信息科学教育部重点实验室, 上海 200062

基金项目:

香港研究资助局GRF项目 HKU747207H

详细信息

作者简介:
卢佩莹（1970-）, 女, 香港人, 香港大学教授, 工学博士, 从事交通地理与安全研究

中图分类号: U491.1
计量
- 文章访问数: 693
- HTML全文浏览量: 61
- PDF下载量: 910
- 被引次数: 0
出版历程
- 收稿日期: 2011-03-18
- 刊出日期: 2011-08-25

Identification method of road hot zone based on GIS

1.
Department of Geography, The University of Hong Kong, Hong Kong, China
2.
Key Laboratory of Geographic Information Science of Ministry of Education, East China Normal University, Shanghai 200062, China

More Information

Author Bio:
LOO B P Y(1970-), female, professor, PhD, +852-28597024, bpyloo@hku.hk

摘要

摘要: 基于地理信息系统以及热区基本模型, 研究了道路热区的鉴别方法。该方法对道路网依据一定优先权进行合并以获取道路基本单元, 模拟了交通事故的空间分布, 并采用Monte Carlo法定义各道路基本单元交通事故数阈值, 通过检验道路基本单元的空间邻近性得到热区, 并对上海世博园周边道路热区进行了鉴别。分析结果表明: 道路网经合并后, 不规则道路基本单元的百分比由41.5%下降到14.8%;世博园周边共有84个仅涉及车辆、33个涉及行人的热区, 与实际相符。可见, 该方法能有效鉴别道路危险区域。
- 交通安全 /
- 道路危险区域 /
- 地理信息系统 /
- 热区 /
- 交通事故
Abstract: Based on GIS and the basic model of hot zone, the identification method of road hot zone was studied. Road networks were merged by following a priority sequence, and the basic spatial units (BSU) of road were obtained. The spatial distribution of traffic accidents was simulated, and the threshold value of traffic accident for each BSU was defined by using Monte Carlo method. The spatial contiguities of BSUs were examined to obtain hot zones, and the hot zones around Shanghai Expo Venue were identified. Analysis result shows that the share of irregular BSUs decreases from 41.5% to 14.8% after merging road networks. There are 84 hot zones involving only vehicles and 33 hot zones involving pedestrians, which accords with the real situation. So the method can effectively identify road hazardous locations.
- traffic safety /
- hazardous road location /
- geographical information system /
- hot zone /
- road crash

HTML全文

图 1 交通事故分布对比

Figure 1. Comparison of traffic accident distributions

下载: 全尺寸图片幻灯片

图 2 路段合并流程

Figure 2. Flow of merging road links

下载: 全尺寸图片幻灯片

图 3 道路基本单元空间结构

Figure 3. Spatial structure of raod basic units

下载: 全尺寸图片幻灯片

图 4 仅涉及车辆的热区

Figure 4. Hot zones involving only vehicles

下载: 全尺寸图片幻灯片

图 5 涉及行人的热区

Figure 5. Hot zones involving pedestrians

下载: 全尺寸图片幻灯片

图 6 热区分布

Figure 6. Distribution of hot zones

下载: 全尺寸图片幻灯片

表 1 原始道路网各路段的长度统计

Table 1. Statistics of original road link lengths

长度区间/m	(0, 25)	[25, 50)	[50, 75)	[75, 100)	[100, +∞)
路段数	120	205	218	197	980

下载: 导出CSV

表 2 分割后各道路基本单元的长度统计

Table 2. Statistics of basic road unit lengths after segmenting

长度区间/m	(0, 25)	[25, 50)	[50, 75)	[75, 100)	100
道路基本单元数(占总数百分比/%)	397 (9.6)	493 (12.0)	422 (10.2)	397 (9.6)	2 410 (58.5)

下载: 导出CSV

表 3 合并后各路段的长度统计

Table 3. Statistics of road link lengths after merging

长度区间/m	(0, 25)	[25, 50)	[50, 75)	[75, 100)	[100, +∞)
路段数	7	20	23	35	418

下载: 导出CSV

表 4 合并后的道路网经分割后各道路基本单元的长度统计

Table 4. Statistics of basic spatial unit lengths based on merging road links after segmenting

长度区间/m	(0, 25)	[25, 50)	[50, 75)	[75, 100)	100
道路基本单元数(占总数百分比/%)	130 (3.6)	147 (4.1)	127 (3.6)	124 (3.5)	3 036 (85.2)

下载: 导出CSV

表 5 热区统计

Table 5. Statistics of hot zones

热区类型		仅涉及车辆	涉及行人
热区总数		84	33
热区中道路基本单元数	最小值	2	2
热区中道路基本单元数	最大值	11	5
道路基本单元上发生的交通事故数	最小值	18	3
道路基本单元上发生的交通事故数	最大值	511	26

下载: 导出CSV

参考文献(14)

[1]	ELVIK R. State-of-the-art approaches to road accident black spot management and safety analysis of road networks[R]. Oslo: Institute of Transport Economics, 2007.
[2]	LOO B P Y. The identification of hazardous road locations: a comparison of the black site and hot zone methodologies in Hong Kong[J]. International Journal of Sustainable Transportation, 2009, 3 (3): 187-202. doi: 10.1080/15568310801915583
[3]	BLACK W R. Network autocorrelation in transport network and flow systems[J]. Geographical Analysis, 1992, 24 (3): 207-222.
[4]	BLACK W R, THOMAS I. Accidents on Belgium's motorways: a network autocorrelation analysis[J]. Journal of Transport Geography, 1998, 6 (1): 23-31. doi: 10.1016/S0966-6923(97)00037-9
[5]	FLAHAUT B, MOUCHART M, MARTIN E S, et al. The local spatial autocorrelation and the kernel method for identifying black zones: a comparative approach[J]. Accident Analysis and Prevention, 2003, 35 (6): 991-1004. doi: 10.1016/S0001-4575(02)00107-0
[6]	FLAHAUT B. Impact of infrastructure and local environment on road unsafety: logistic modeling with spatial autocorrelation[J]. Accident Analysis and Prevention, 2004, 36 (6): 1055-1066. doi: 10.1016/j.aap.2003.12.003
[7]	MORANP A P. The interpretation of statistical maps[J]. Journal of the Royal Statistical Society, Series B: Methodological, 1948, 10 (2): 243-251.
[8]	ANSELIN L. Local indicators of spatial association—LISA[J]. Geographical Analysis, 1995, 27 (2): 93-115.
[9]	YAMADAI, THILL J C. Local indicators of network-constrained clusters in spatial patterns represented by a link attribute[J]. Annals of the Association of American Geographers, 2010, 100 (2): 269-285. doi: 10.1080/00045600903550337
[10]	GETIS A, ORD J K. The analysis of spatial association by use of distance statistics[J]. Geographical Analysis, 1992, 24 (3): 189-206.
[11]	MOONS E, BRIJS T, WETS G. Identifying hazardous road locations: hot spots versus hot zones[C]//GAVRILOVA M L, TAN C J K. Transactions on Computational Science VI. Heidelberg: Springer-Verlag Berlin Heidelberg, 2009: 288-300.
[12]	YAMADA I, THILL J C. Local indicators of network-constrained clusters in spatial point patterns[J]. Geographical Analysis, 2007, 39 (3): 268-292.
[13]	LOO B P Y. Validating crash locations for quantitative spatial analysis: a GIS-based approach[J]. Accident Analysis and Prevention, 2006, 38 (5): 879-886.
[14]	OPENSHAW S, CHARLTON M E, WYMER C, et al. A mark 1 geographical analysis machine for the automated analysis of point data sets[J]. International Journal of Geographical Information Systems, 1987, 1 (4): 335-358.