基于交通大数据的南昌市中心城区等时圈划分及特征分析

doi:10.12082/dqxxkx.2022.210372

摘要/Abstract

摘要：

在当前国土空间规划的背景下,自然资源部提出了基于等时圈的中心城区可达性评价方法。本文以南昌市为研究对象,利用静态交通数据和从开放地图平台获取的工作日15:00（平峰）、18:00（晚高峰）和周末的15:00、18:00的动态交通数据分别生成中心城区等时圈,随后使用混淆矩阵及Kappa系数对两种数据的结果进行一致性检验。研究发现：南昌市中心城区大部分区域都位于以八一广场或绿地中央广场为起点的60 min等时圈内,南昌市域大部分区域则位于120 min等时圈内;静态数据生成的等时圈与对应的工作日晚高峰的动态数据生成的等时圈相比仅具有一般一致性,但前者在中心城区尺度与工作日平峰的动态数据生成的等时圈达到了高度的一致性,更适合在中心城区层面评价工作日平峰的可达性;4个时段的动态数据的等时圈结果表明工作日15:00的中心城区可达性明显优于其他3个时段,但各个时段的等时圈覆盖面积占市域面积的比例随车程的增加都呈现出Logistic曲线特征,各曲线增长的关键时间节点能够为等时圈划分提供更有针对性的分级阈值。

关键词: 可达性, 静态交通数据, 动态交通数据, 等时圈, ArcGIS, Kappa系数, Logistic曲线, 中心城区

Abstract:

For the current territory development planning in China, the Ministry of Natural Resources has put forward a method to evaluate the accessibility of urban centers based on isochrone maps. The use of dynamic traffic data in isochrone maps studies is becoming more and more recurrent, but comparative analyses between dynamic and static data are still rare. In this paper, Nanchang city is taken as a case study to generate the urban center isochrone maps using static and dynamic traffic data. The city is divided into 500 m×500 m grids, with each grid center point representing a given destination while Bayi Square and Greenland Central Square are set as origins. Using the above origins and destinations, the dynamic data were obtained daily from the Baidu open map platform at 15:00 and at 18:00 over nine days-time (Saturday-next Sunday). Subsequently, the confusion matrix and Kappa coefficient are used to test the consistency between the isochrone maps generated by the two datasets. The results suggest that most of Nanchang urban central areas are within a 60 min-circle and most of Nanchang's urban areas are within a 120 min-circle, when taking Bayi Square or Greenland Central Square as the origin. The isochrone maps generated by the static data has just a fair consistency with those generated by the dynamic data at evening peak time on workdays. Within the urban central areas, the isochrone maps generated by the static data have reached a substantial consistency with those generated by the dynamic data at off-peak time on workdays, indicating that the static data is more suitable for evaluating the urban center accessibility at off-peak time on workdays. Besides, the dynamic data can display the temporal characteristics of the isochrone maps. The isochrone maps of the dynamic data at 4 time-points show that the urban center accessibility at 15:00 on workdays is significantly better than others. But the proportions of isochrone surfaces to the total urban areas are found to increase with the drivetime, and their growth curves are in accordance with the trend of the Logistic curve. The key time nodes of each growth curve can provide more targeted division thresholds for isochrone maps. This research highlights the accuracy of the isochrone maps generated by the dynamic data and explores the applicability of the static data. The research also shows that using the key time nodes of the Logistic curve contributes to a more reasonable subdivision of the isochrone map.

Key words: accessibility, static traffic data, dynamic traffic data, isochrone map, ArcGIS, Kappa coefficient, Logistic curve, urban center

刘琳琳, 郑伯红, 骆晨. 基于交通大数据的南昌市中心城区等时圈划分及特征分析[J]. 地球信息科学学报, 2022, 24(2): 220-234.DOI:10.12082/dqxxkx.2022.210372

LIU Linlin, ZHENG Bohong, LUO Chen. Division and Feature Analysis of Nanchang Urban Center Isochrone Maps based on Traffic Big Data[J]. Journal of Geo-information Science, 2022, 24(2): 220-234.DOI:10.12082/dqxxkx.2022.210372

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评价指标	分级参考阈值	赋值
中心城区可达性	车程≤30 min	5
	30 min<车程≤60 min	4
	60 min<车程≤90 min	3
	90 min<车程≤120 min	2
	车程>120min	1

分级	以八一广场为起点		以绿地中央广场为起点		两点同时作为起点
分级	静态数据	动态数据	静态数据	动态数据	静态数据	动态数据
车程≤30 min	6.52	2.44	10.10	3.96	11.76	5.16
车程≤60 min	45.56	30.65	51.20	39.97	53.44	43.78
车程≤90 min	81.24	84.77	80.61	80.23	81.84	86.69
车程≤120 min	92.70	99.17	92.64	97.19	92.72	99.17

动态数据	静态数据
动态数据	≤30 min	30~60 min	60~90 min	90~120 min	>120 min
≤30 min	597	15	0	0	0
30~60 min	853	5501	427	41	240
60~90 min	1	3728	6518	679	2628
90~120 min	0	48	1082	1602	873
>120 min	0	0	0	145	63

动态数据	静态数据
动态数据	≤30 min	30~60 min	60~90 min	90~120 min	>120 min
≤30 min	971	20	0	0	0
30~60 min	1378	6630	481	93	435
60~90 min	0	3011	4104	442	2525
90~120 min	0	144	1890	1510	704
>120 min	0	0	29	519	155

动态数据	静态数据
动态数据	≤30 min	30~60 min	60~90 min	90~120 min	>120 min
≤30 min	1266	26	0	0	0
30~60 min	1466	7128	449	30	145
60~90 min	1	2745	4856	437	2510
90~120 min	0	24	966	1311	1008
>120 min	0	0	0	538	135