Activity Resilience of Urban Residents: Empirical Evidence Based on the Recovery of Rail Transit Ridership During the COVID-19 Pandemic

doi:10.12141/j.issn.1000-565X.220609

Abstract

Abstract:

Urban residents’ activity resilience is a vital component of society resilience. Based on the concept of society resilience, this paper qualitatively verified the existence of urban residents’ activity resilience during the COVID-19 pandemic, and quantitatively analyzed its evolution mechanism. Firstly, by using the daily rail transit ridership data of 28 cities in China in the two years before and after the outbreak of COVID-19, residents’ activity resilience was defined and measured, and a logistic function was introduced to model the ridership recovery process. Secondly, from the perspective of policy adjustment and behavior adaptation, this paper constructed and analyzed the action adjustment and learning adaptation process of plural subjects such as the government and the public. The results show that the larger the scale of rail transit network, the higher the residents’ activity resilience. As compared with the cities with a population of less than 5 million, the time for megacities with a population of more than 10 million to recover to 50%, 80%, 90% and 100% of the weekday ridership is 7 days, 15 days, 47 days and 95 days earlier, respectively. The later the recovery period, the greater the difference, and the weekend also shows a similar pattern. Rigid weekday activities have better resilience than flexible weekend activities, and the logistic function fitting parameters α of ridership recovery on weekdays and weekends are 0.019 and 0.016, respectively; H are 77.07 and 106.82 d, respectively. During the second outbreak after the first round of pandemic, the decline of rail transit ridership decreases, the average daily growth of ridership recovery rate in the second outbreak is 1~3 times that of the first round of pandemic, and residents’ activity resilience improves significantly. It concludes that the credibility created by accurate and effective interventions enhances the public compliance with policies, and that public awareness and learning about risks can significantly improve activity resilience. The research results provide a new perspective on how to estimate the travel demand and activity recovery and evaluate the impact of social interventions.

Key words: urban transport, activity resilience, COVID-19 pandemic, rail transit ridership, recovery ability

CLC Number:

U491

CHEN Xiaohong, TIAN Tiantian, ZHANG Hua. Activity Resilience of Urban Residents: Empirical Evidence Based on the Recovery of Rail Transit Ridership During the COVID-19 Pandemic[J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(7): 109-119.

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恢复率/%	工作日客流恢复时间/d				周末客流恢复时间/d
恢复率/%	所有城市	超大城市	特大城市	其他城市	所有城市	超大城市	特大城市	其他城市
50	60	57	59	64	90	79	92	95
80	155	140	164	155	200	186	206	202
90	207	181	196	228	237	212	219	265
100	210	189	199	284	247	251	254	299

城市类别	α			H/d
城市类别	所有客流	工作日客流	周末客流	所有客流	工作日客流	周末客流
所有城市	0.018	0.019	0.016	87.10	77.07	106.82
超大城市	0.023	0.026	0.019	77.23	65.54	96.62
特大城市	0.018	0.019	0.017	85.22	78.27	107.63
其他城市	0.016	0.017	0.015	93.60	81.85	111.26

恢复率/%	周末较工作日客流恢复的平均滞后时间/d
恢复率/%	所有城市	超大城市	特大城市	其他城市
50	30	22	33	31
80	45	46	42	47
90	30	31	23	37
100	37	62	55	15

城市	α			H/d
城市	所有客流	工作日客流	周末客流	所有客流	工作日客流	周末客流
国内28个城市	0.018	0.019	0.016	87.10	77.07	106.82
纽约	0.007	0.006	0.007	307.97	322.91	277.93
伦敦	0.012	0.011	0.013	236.09	245.23	216.62

城市	疫情阶段	持续时间	累计确诊病例	最低点客流占2019年同期客流的比例/%		平均日增恢复率/%		α	H/d
城市	疫情阶段	持续时间	累计确诊病例	工作日	周末	工作日	周末	α	H/d
北京	首轮疫情	2020-01-19—2020-03-06	419	7	7	0.45	0.43	0.023	100.31
北京	二轮疫情	2020-06-11—2020-07-04	335	40	23	0.48	0.51	0.025	74.61
大连	首轮疫情	2020-01-22—2020-04-02	27	10	7	0.56	0.43	0.024	69.53
大连	二轮疫情	2020-07-22—2020-08-05	92	28	16	1.40	1.44	0.034	36.41
武汉	首轮疫情	2019-12-26—2020-04-03	50 333	0	0	0.41	0.35	0.014	106.53
武汉	二轮疫情	2021-08-02—2021-08-11	40	36	26	2.11	1.91	0.100	24.07
深圳	首轮疫情	2020-01-19—2020-04-28	462	7	6	0.37	0.35	0.024	59.64
深圳	二轮疫情	2022-02-13—2022-04-01	1 177	0	0	10.58	6.72	0.161	6.04
上海	首轮疫情	2020-01-20—2020-03-02	338	9	8	0.34	0.30	0.016	68.79
上海	二轮疫情	2022-03-01—2022-06-26	627 110	0	0	8.43	6.16	0.281	7.98