天气因素对城市地铁客流时空分布的影响——基于智能交通卡数据的实证研究

doi:10.18306/dlkxjz.2020.01.005

Abstract

Abstract:

Urban geographic space, climate, and transportation system are interrelated, and recently available traffic and spatial big data bring new opportunities for understanding the relationship among them. Urban rail transit is an important transport mode for residents to travel green and relieve traffic congestion in big cities in China. In-depth study of factors that affect the changes in the spatial and temporal distributions of metro passenger flows is conducive to the formulation of reasonable land use and traffic demand management policies, and can also provide a theoretical basis for real-time response to the changes in travel demand under specific weather conditions and optimization of transit service operation. To study the impact of weather conditions on metro usage in densely populated areas, in this research the influence of local weather factors (including temperature, humidity, rainfall and so on) on hourly metro passenger flows was investigated based on metro smart card data and weather data from Nanjing City, China. A time series model—seasonal autoregressive integrated moving average with explanatory variables (SARIMAX)—was developed to investigate the impact of weather conditions on metro passenger flows. It is found that some weather factors such as rainfall have significant influence on metro passenger flows. Except for some special sites (large residential areas and large transportation hubs), the influence of weather factors on metro passenger flows reduces gradually from the city center to suburban areas. The effects of weather conditions on regular metro passengers and irregular metro passengers were explicitly compared in this study. Irregular metro passengers are found more vulnerable to adverse weather conditions than regular metro passengers.

Key words: urban metro system, smart card data, passenger flow distribution, weather factors, Nanjing City

XU Manling, FU Xiao, TANG Junyou, LIU Zhiyuan. Effects of weather factors on the spatial and temporal distributions of metro passenger flows: An empirical study based on smart card data[J].PROGRESS IN GEOGRAPHY, 2020, 39(1): 45-55.

Figures/Tables 13

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时间	气温差/℃	相对湿度差/%	气压差/hPa	风速差/（km/h）	降雨天数占比/%
1—2月	10.00	53.0	32.51	13	33.3
3—4月	18.34	50.0	27.77	25	29.5
5—6月	14.44	44.0	19.64	18	47.5
7—8月	11.11	44.5	12.19	11	27.4
9—10月	17.77	41.0	30.82	24	42.6
11—12月	16.67	35.5	22.01	21	36.1
研究时段(3月9日—4月30日)	18.34	50.0	27.77	25	28.3

日期	卡号	卡种	设备编号	进站时间	进站站点编号	出站时间	出站站点编号
2016-03-09	990772894357	102	22079702	15:58:08	98	16:07:42	97
2016-03-09	990772838262	101	22022805	15:23:43	25	15:38:29	28
2016-03-09	993172771712	3	22046202	13:09:36	2	13:38:18	62

日期	时间	气温/℃	相对湿度/%	气压/hPa	风向	风速/(km/h)	天气状况
2016-03-09	5:00	3	75	1027	东北偏北	10.8	晴间多云
2016-03-09	6:00	3	70	1027	东北	14.4	晴间多云
2016-03-09	8:00	3	70	1029	东北	21.6	晴

时段	变量	系数	P值	AIC	MAPE
早高峰	气温	-47	0.81	1236.27	1.68%
	相对湿度	-31	0.55
	气压	-96	0.59
	风速	190	0.01^**
	降雨	-2827	0.09^*
晚高峰	气温	315	0.59	1467.68	5.94%
	相对湿度	-77	0.60
	气压	262	0.62
	风速	-473	0.04^**
	降雨	-3533	0.57

时间	变量	系数	P值	AIC	MAPE
周六	气温	-57	0.31	2797.00	7.79%
	相对湿度	-2	0.88
	气压	-51	0.30
	风速	-31	0.34
	降雨(小雨)	-469	0.78
	降雨(中雨到大雨)	-672	0.46
	降雨(雷阵雨)	-1329	0.24
周日	气温	98	0.40	2399.27	8.67%
	相对湿度	12	0.52
	气压	65	0.28
	风速	-8	0.79
	降雨(小雨)	-1429	0.04^**
	降雨(中到大雨)	-3968	0.02^**

Effects of weather factors on the spatial and temporal distributions of metro passenger flows: An empirical study based on smart card data

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代表点	O1	O2	O3	O4	O5	D1	D2	D3	D4	D5
实际地铁站点	柳洲东路	油坊桥	双龙大道	马群	迈皋桥	新街口	集庆门大街	元通	仙林中心	百家湖
站点性质	西北向居住区	西向居住区	南向居住区	东北向居住区	北向居住区	CBD	西向商住混合区	西南向商住混合区	东北向商住混合区	南向商住混合区

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