边界时刻一致性判别方法在北京市出租车运量生成机制中的实证应用

doi:10.13249/j.cnki.sgs.2022.08.009

地理科学 ›› 2022, Vol. 42 ›› Issue (8): 1413-1420.doi: 10.13249/j.cnki.sgs.2022.08.009

边界时刻一致性判别方法在北京市出租车运量生成机制中的实证应用

戴特奇¹^,²(), 黄薪豫¹^,^*(), 卢文清¹^,²

1.北京师范大学地理科学学部，北京 100875
2.北京师范大学地理科学学部环境遥感与数字城市北京市重点实验室，北京 100875

收稿日期:2020-05-06 修回日期:2021-08-21 出版日期:2022-08-25 发布日期:2022-10-11
通讯作者: 黄薪豫 E-mail:daiteqi@bnu.edu.cn;huangxinyu2021@126.com
作者简介:戴特奇（1980-），男，重庆人，副教授，博士，主要研究方向为交通地理、城市与区域发展。E-mail: daiteqi@bnu.edu.cn
基金资助:
国家自然科学基金项目资助(42071152)

The Approach of Boundary Consistency of Time Division and its Application on Taxi Ridership Generation Mechanism in Beijing

Dai Teqi¹^,²(), Huang Xinyu¹^,^*(), Lu Wenqing¹^,²

1. Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2. Beijing Key Laboratory for Remote Sensing of Environment and Digital City, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Received:2020-05-06 Revised:2021-08-21 Online:2022-08-25 Published:2022-10-11
Contact: Huang Xinyu E-mail:daiteqi@bnu.edu.cn;huangxinyu2021@126.com
Supported by:
National Natural Science Foundation of China(42071152)

摘要/Abstract

摘要：

以北京市出租车运量生成机制研究为例，探索时间基本单元大小对交通时段划分及运量生成机制的影响。基于北京市出租车轨迹数据，按10 min步长，采用10 min至60 min共6种时间基本单元对全天数据在交通小区尺度进行划分得到切片数据，应用系统聚类法将切片聚合得到时段划分，得到6种时段划分结果，进而对具有交集的时段采用边界时刻进行一致性判别。在时段划分基础上，应用地理加权回归模型比较分析了不同时间基本单元在上班时段的出租车运量影响因子的差异。结果显示，时间基本单元小于40 min时，上班时段的起始时刻不具有一致性；如果考虑所有时段的起始和结束时刻，则小于50 min时不具有一致性。上班时段的运量生成机制模型结果与时刻一致性判别类似，当时间基本单元在50 min及以上时，回归得到的解释变量具有一致性，小于50 min后则会有所变化。这些结果说明，基本时间单元的大小会影响交通运量时段划分和生成机制研究的结果；考虑到结果一致性和整点划分习惯，推荐采用60 min为基本划分单元。提出的边界时刻一致性判别方法也可以用到其它交通模式和其他相似的大数据研究。

关键词: 时间基本单元, 出租车运量, 系统聚类法, 边界时刻一致性

Abstract:

To explore the effect of the sizes of temporal basic units on period division and generation mechanism, this study takes taxi ridership and its generation mechanism in Beijing as an example, dividing a day into 6 temporal basic units from 10 minutes to 60 minutes according to the step length of 10 minutes. Hierarchical Clustering method is used to group temporal units into period on similar spatial distribution, and then Geographically Weighted Regression model is also applied to compare differences of factors for the ridership generation mechanism. The results show that when the size of temporal basic unit is below 40 minutes, the start time of going-to-work period doesn’t show the consistency with the longer. Taking all periods in a day into consideration, there are no consistency below 50 minutes. The results of generation mechanism in going-to-work period also show that the factors are the same when the temporal unit is equal or greater than 50 minutes. The results above reveal that the size of time slice will affect the results of time division and generation mechanism. Considering the consistency of the results and time using habit, the paper recommends using 60 minutes as the basic temporal unit. The method proposed in this paper on boundary consistency of time division could be used into time division in other transport models or big data researches.

Key words: basic time unit, taxi ridership, Hierarchical Clustering, discriminating boundary moments consistency

中图分类号:

戴特奇, 黄薪豫, 卢文清. 边界时刻一致性判别方法在北京市出租车运量生成机制中的实证应用[J]. 地理科学, 2022, 42(8): 1413-1420.

Dai Teqi, Huang Xinyu, Lu Wenqing. The Approach of Boundary Consistency of Time Division and its Application on Taxi Ridership Generation Mechanism in Beijing[J]. SCIENTIA GEOGRAPHICA SINICA, 2022, 42(8): 1413-1420.

图/表 6

图1

图2

表1

表2

图3

表3

上班时段上车点GWR模型计算结果

时间基本单元大小	自变量	系数最小值	系数最大值	系数均值	系数标准差	模型评价参数
60 min	常数项	–0.467	0.809	0.011	0.244	AICc: 1333.7 R²: 0.529 Adjusted R²: 0.461
	公共服务POI密度	–0.008	0.513	0.226	0.135
	人口密度	–0.013	0.559	0.301	0.142
	本科及以上学历比率	–0.129	0.250	0.051	0.089
	公交站密度	–0.050	0.587	0.136	0.117
50 min	常数项	–0.469	0.843	0.002	0.261	AICc: 1308.6 R²: 0.549 Adjusted R²: 0.484
	公共服务POI密度	–0.004	0.484	0.239	0.124
	人口密度	–0.040	0.527	0.272	0.139
	本科及以上学历比率	–0.110	0.277	0.069	0.094
	公交站密度	–0.042	0.602	0.142	0.118
40 min	常数项	–0.468	0.760	0.013	0.238	AICc: 1351.1 R²: 0.536 Adjusted R²: 0.454
	公共服务POI密度	0.000	0.510	0.230	0.131
	人口密度	–0.070	0.550	0.265	0.138
	中年人口比率	–0.077	0.409	0.078	0.094
	本科及以上学历比率	–0.162	0.381	0.087	0.120
	公交站密度	–0.048	0.563	0.137	0.112
30 min	常数项	–0.495	0.830	0.001	0.256	AICc: 1308.9 R²: 0.549 Adjusted R²: 0.484
	公共服务POI密度	–0.006	0.493	0.249	0.121
	人口密度	–0.073	0.503	0.256	0.132
	本科及以上学历比率	–0.096	0.310	0.079	0.098
	公交站密度	–0.038	0.578	0.154	0.118
20 min	常数项	–0.517	0.834	-0.001	0.260	AICc: 1302.2 R²: 0.554 Adjusted R²: 0.489
	公共服务POI密度	–0.002	0.500	0.254	0.118
	人口密度	–0.085	0.501	0.241	0.129
	本科及以上学历比率	–0.087	0.333	0.090	0.100
	公交站密度	–0.043	0.594	0.160	0.120
10 min	常数项	–0.460	0.836	0.006	0.255	AICc: 1318.8 R²: 0.541 Adjusted R²: 0.475
	公共服务POI密度	–0.006	0.470	0.232	0.127
	人口密度	–0.035	0.542	0.288	0.139
	本科及以上学历比率	–0.116	0.258	0.059	0.092
	公交站密度	–0.041	0.595	0.136	0.117

表3

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类别	变量	取值范围	文献来源
土地功能特征	POI密度	0~2952.175个/km²	[18,28]
土地功能特征	POI混合熵	0.215~0.885	[17,32,34]
人口社会经济属性	人口密度	816.257~55481.068人/km²	[17,26,28,34,35]
	中年人口比率	0.075~0.282	[28,32,35]
	本科及以上学历比率	0.038~0.233	[17,18,32,35]
	二手房房价	4.287~15.020万元/m²	[27]
交通设施	公交站密度	0~254.229个/km²	[32,34]
	地铁站密度	0~3.783个/km²	[32,34]
	道路密度	0~23.124 km/km²	[17~19,32,34]

时段名称		时间基本单元
时段名称		10 min	20 min	30 min	40 min	50 min	60 min
注：按20 min切片无法聚类为4类，按聚类为3类的结果展示
上车点	清晨时段	23:50~6:20	23:40~7:00	0:00~6:30	0:00~6:40	0:00~6:40	0:00~6:00
	上班时段	6:20~8:10	7:00~9:00	6:30~9:00	6:40~8:00	6:40~8:20	6:00~8:00
	工作时段	8:10~17:30	9:00~23:40	9:00~17:30	8:00~17:20	8:20~17:30	8:00~18:00
	夜间时段	17:30~23:50	9:00~23:40	17:30~24:00	17:20~24:00	17:30~24:00	18:00~24:00
下车点	清晨时段	0:00~6:50	0:00~6:40	0:00~5:30	0:00~6:40	0:00~5:50	0:00~6:00
	上班时段	6:50~8:40	6:40~8:00	5:30~7:30	6:40~8:00	5:50~8:20	6:00~8:00
	工作时段	8:40~16:00	8:00~16:00	7:30~17:00	8:00~16:00	8:20~16:40	8:00~17:00
	夜间时段	16:00~24:00	16:00~24:00	17:00~24:00	16:00~24:00	16:40~24:00	17:00~24:00

边界时刻一致性判别方法在北京市出租车运量生成机制中的实证应用

The Approach of Boundary Consistency of Time Division and its Application on Taxi Ridership Generation Mechanism in Beijing

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