Transit Usage in Social Shocks: A Case Study of Station-Level Metro Ridership in Anti-Extradition Protests in Hong Kong

Abstract

As a form of social movement aiming to effect social change, protests could bring about unintended impacts on all walks of life. In other words, the cost of protests can be incurred by those who might not be protesters. The protests triggered by an extradition bill in Hong Kong since 2019 are no exception. This paper focuses on the impacts on the ridership of the metro system on protest days. It synthesizes and hypothesizes factors influencing the distribution of the ridership changes and conducts an empirical study in the context of Hong Kong to study the possible influences and spatial dependence. It is found that, across metro stations, political orientation (percentage of votes to pro-democracy camp in the 2019 Election of District Councils), law enforcement (permission from the police to protest), land use type (especially for commercial and open space), population age and income, as well as transit/road network characteristics and intermodal connectivity, significantly influence the ridership of metro stations during protest days. In addition, the mixed regressive spatial autoregressive model has higher explanatory power than the ordinary least square model, suggesting the need for a spatial lag and error specification. The results could also have significant implications for policy and planning for operating metro services and managing metro stations before, during, and after social shocks.

Keywords

congestion mitigation or reduction planning and analysis policy public transportation rail transit systems ridership stations subway transportation demand management travel demand modeling urban

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References

Brenner

Marcuse

Mayer

Cities for People, Not for Profit. City, Vol. 13, No. 2–3, 2009, pp. 176–184. https://doi.org/10.1080/13604810903020548.

Harvey

The Right to the City. International Journal of Urban and Regional Research, Vol. 27, No. 4, 2003, pp. 939–941. https://doi.org/10.1111/j.0309-1317.2003.00492.x.

Uitermark

Nicholls

How Local Networks Shape a Global Movement: Comparing Occupy in Amsterdam and Los Angeles. Social Movement Studies, Vol. 11, No. 3–4, 2012, pp. 295–301. https://doi.org/10.1080/14742837.2012.704181.

Yam

Hong Kong’s Extradition Bill Protests: Dozens of Questions Remain To Be Answered. South China Morning Post, June29, 2019.

Chan

H.-Y.

Zhou

Research Notes: Social Movement Revealing Opportunities for Grassroots Transport Initiatives: Lessons From Hong Kong. Journal of the Eastern Asia Society for Transportation Studies, Vol. 14, 2022, pp. 50–70. https://doi.org/10.11175/easts.14.50.

Salah Fahmi

Bloggers’ Street Movement and the Right to the City. (Re)Claiming Cairo’s Real and Virtual “Spaces of Freedom.”

Environment and Urbanization, Vol. 21, No. 1, 2009, pp. 89–107. https://doi.org/10.1177/0956247809103006.

Tufekci

Twitter and Tear Gas: The Power and Fragility of Networked Protest. Yale University Press, New Haven CT and London, 2017.

Robles

Long

Wong

100 Days of Protests Rock Hong Kong. South China Morning Post, September17, 2019.

Chan

H.-Y.

Zhou

Public Transportation and Social Movements: Learning From the Hong Kong Anti-Extradition Bill Protests. Transportation Research Record: Journal of the Transportation Research Board, 2022. 2676: 553–566.

10.

Parkes

S. D.

Jopson

Marsden

Understanding Travel Behaviour Change During Mega-Events: Lessons From the London 2012 Games. Transportation Research Part A: Policy and Practice, Vol. 92, 2016, pp. 104–119. https://doi.org/10.1016/j.tra.2016.07.006.

11.

Hensher

D. A.

Brewer

A. M.

Going for Gold at the Sydney Olympics: How Did Transport Perform?

Transport Reviews, Vol. 22, No. 4, 2002, pp. 381–399. https://doi.org/10.1080/01441640110121112.

12.

Mulley

Moutou

C. J.

Not too Late to Learn From the Sydney Olympics Experience: Opportunities Offered by Multimodality in Current Transport Policy. Cities, Vol. 45, 2015, pp. 117–122. https://doi.org/10.1016/j.cities.2014.10.004.

13.

McCarthy

Ravelli

R. J.

Sinclair-Williams

Health Impact Assessment of the 2012 London Olympic Transport Plans. The European Journal of Public Health, Vol. 20, No. 6, 2010, pp. 619–624. https://doi.org/10.1093/eurpub/ckp154.

14.

Zhou

Hristova

Noulas

Mascolo

Evaluating the Impact of the 2012 Olympic Games Policy on the Regeneration of East London Using Spatio-Temporal Big Data. Social and Information Networks, 2018. https://doi.org/10.48550/arXiv.1807.01925

15.

Potoglou

Robinson

Kim

C. W.

Burge

Warnes

Quantifying Individuals’ Trade-Offs Between Privacy, Liberty and Security: The Case of Rail Travel in UK. Transportation Research Part A: Policy and Practice, Vol. 44, No. 3, 2010, pp. 169–181. https://doi.org/10.1016/j.tra.2009.12.006.

16.

Elias

Albert

Shiftan

Travel Behavior in the Face of Surface Transportation Terror Threats. Transport Policy, Vol. 28, 2013, pp. 114–122. https://doi.org/10.1016/j.tranpol.2012.08.005.

17.

Fasolo

Phillips

L. D.

A Study of the Impact of the July Bombings on Londoners’ Travel Behaviour. London, 2008.

18.

López-Rousseau

Avoiding the Death Risk of Avoiding a Dread Risk: The Aftermath of March 11 in Spain. Psychological Science, Vol. 16, No. 6, 2005, pp. 426–428. https://doi.org/10.1111/j.0956-7976.2005.01551.x.

19.

Litman

Terrorism, Transit and Public Safety: Evaluating the Risks. Journal of Public Transportation, Vol. 8, No. 4, 2005, pp. 33–45. https://doi.org/10.5038/2375-0901.8.4.3.

20.

Hirsch

A. R.

Lee Levert

The Katrina Conspiracies: The Problem of Trust in Rebuilding an American City. Journal of Urban History, Vol. 35, No. 2, 2009, pp. 207–219. https://doi.org/10.1177/0096144208327908.

21.

Loo

B. P. Y.

Leung

K. Y. K.

Transport Resilience: The Occupy Central Movement in Hong Kong From Another Perspective. Transportation Research Part A: Policy and Practice, Vol. 106, 2017, pp. 100–115. https://doi.org/10.1016/j.tra.2017.09.003.

22.

Badger

Why Highways Have Become the Center of Civil Rights Protest. The Washington Post, July13, 2016.

23.

Gutiérrez

Cardozo

O. D.

García-Palomares

J. C.

Transit Ridership Forecasting at Station Level: An Approach Based on Distance-Decay Weighted Regression. Journal of Transport Geography, Vol. 19, No. 6, 2011, pp. 1081–1092. https://doi.org/10.1016/j.jtrangeo.2011.05.004.

24.

Jun

M.-J.

Choi

Jeong

J.-E.

Kwon

K.-H.

Kim

H.-J.

Land Use Characteristics of Subway Catchment Areas and Their Influence on Subway Ridership in Seoul. Journal of Transport Geography, Vol. 48, 2015, pp. 30–40. https://doi.org/10.1016/j.jtrangeo.2015.08.002.

25.

Chan

H.-Y.

Chen

Liu

Impacts of the Walking Environment on Mode and Departure Time Shifts in Response to Travel Time Change: Case Study in the Multi-Layered Hong Kong Metropolis. Travel Behaviour and Society, Vol. 28, 2022, pp. 288-299. https://doi.org/10.1016/j.tbs.2022.04.010.

26.

Chan

H.-Y.

Chen

Lam

Evaluating the Value of New Metro Lines Using Route Diversity Measures: The Case of Hong Kong’s Mass Transit Railway System. Journal of Transport Geography, Vol. 91, 2021, p. 102945. https://doi.org/10.1016/j.jtrangeo.2020.102945.

27.

Hsieh

C.-H.

Feng

C.-M.

The Highway Resilience and Vulnerability in Taiwan. Transport Policy, Vol. 87, 2020, pp. 1–9. https://doi.org/10.1016/j.tranpol.2018.08.010.

28.

Zhao

Deng

Song

Zhu

Analysis of Metro Ridership at Station Level and Station-to-Station Level in Nanjing: An Approach Based on Direct Demand Models. Transportation, Vol. 41, No. 1, 2014, pp. 133–155. https://doi.org/10.1007/s11116-013-9492-3.

29.

Zhao

Deng

Song

Zhu

What Influences Metro Station Ridership in China? Insights From Nanjing. Cities, Vol. 35, 2013, pp. 114–124. https://doi.org/10.1016/j.cities.2013.07.002.

30.

Gan

Yang

Feng

Timmermans

H. J. P.

Examining the Relationship Between Built Environment and Metro Ridership at Station-to-Station Level. Transportation Research Part D: Transport and Environment, Vol. 82, 2020, p. 102332. https://doi.org/10.1016/j.trd.2020.102332.

31.

Endres

Senda-Cook

Location Matters: The Rhetoric of Place in Protest. Quarterly Journal of Speech, Vol. 97, No. 3, 2011, pp. 257–282. https://doi.org/10.1080/00335630.2011.585167.

32.

Zhou

Yang

Yin

Zhang

Can TODness Improve (Expected) Performances of TODs? An Exploration Facilitated by Non-Traditional Data. Transportation Research Part D: Transport and Environment, Vol. 74, 2019, pp. 28–47. https://doi.org/10.1016/j.trd.2019.07.008.

33.

Shi

Zhang

Liu

Exploring Spatiotemporal Variation in Hourly Metro Ridership at Station Level: The Influence of Built Environment and Topological Structure. Sustainability, Vol. 10, No. 12, 2018, p. 4564. https://doi.org/10.3390/su10124564.

34.

Zhao

Tsui

K.-L.

Geographically Modeling and Understanding Factors Influencing Transit Ridership: An Empirical Study of Shenzhen Metro. Applied Sciences, Vol. 9, No. 20, 2019, p. 4217. https://doi.org/10.3390/app9204217.

35.

Centre for Communication and Public Opinion Survey. Public Opinion & Political Development in Hong Kong: Survey Results. Chinese University, Hong Kong, 2014.

36.

Lai

F. T. T.

Hall

B. J.

Liang

Galea

Hou

W. K.

Socioeconomic Determinants of Depression Amid the Anti-Extradition Bill Protests in Hong Kong: The Mediating Role of Daily Routine Disruptions. Journal of Epidemiology and Community Health, Vol. 74, No. 12, 2020, pp. 988–994. https://doi.org/10.1136/jech-2019-213693.

37.

Lam

J. T. M.

The 2019 District Council Election in Hong Kong: A Localism Perspective. Asian Affairs: An American Review, Vol. 48, No. 2, 2021, pp. 113–132. https://doi.org/10.1080/00927678.2021.1903779.

38.

Hong Kong Government. Cap. 245 Public Order Ordinance. Hong Kong E-Legislation. https://www.elegislation.gov.hk/hk/cap245.

39.

Zhou

Yang

“Familiar Strangers” in the Big Data Era: An Exploratory Study of Beijing Metro Encounters. Cities, Vol. 97, 2020, p. 102495. https://doi.org/10.1016/j.cities.2019.102495.

40.

Kissling

W. D.

Carl

Spatial Autocorrelation and the Selection of Simultaneous Autoregressive Models. Global Ecology and Biogeography, Vol. 17, No. 1, 2008, pp. 59-71. https://doi.org/10.1111/j.1466-8238.2007.00334.x.

41.

Anselin

Exploring Spatial Data With GeoDaTM: A Workbook. Spatial Analysis Laboratory, Department of Geography, University of Illinois, Urbana-Champaign, 2005.

42.

Transport Department. Monthly Traffic and Transport Digest. Transport Department, 2020. Retrieved from https://www.td.gov.hk/en/transport_in_hong_kong/transport_figures/monthly_traffic_and_transport_digest/index.html

43.

Hong Kong Police Force. Public Order Event Statistics. The Government of Hong Kong Special Adminstrative Region. https://www.police.gov.hk/ppp_en/09_statistics/poes.html. Accessed January 17, 2021.

44.

Duhalde

Huang

History of Hong Kong Protests: Riots, Rallies and Brollies. South China Morning Post, July4, 2019.

45.

HKSARG Planning Department. Hong Kong Outline Zoning Plans Land Use Zonings. Esri China (Hong Kong). https://www.arcgis.com/home/item.html?id=5375a88ec76143ea974d5fb64efbec0d. Accessed April 2, 2021.

46.

Transport Department. Road Network (2nd Generation). DATA.GOV.HK. https://data.gov.hk/en-data/dataset/hk-td-tis_15-road-network-v2. Accessed March 1, 2021.

47.

Transport Department. Headway Information of Public Transport Services. DATA.GOV.HK. https://data.gov.hk/en-data/dataset/hk-td-tis_11-pt-headway-en. Accessed March 1, 2021.

48.

Hagberg

Schult

Swart

NetworkX Reference: Release 2.6rc1.Dev0. NetworkX Developers. https://networkx.org/documentation/latest/_downloads/networkx_reference.pdf. Accessed April 2, 2021.

49.

Census and Statistics Department. 2016 Population By-Census. Financial Services and the Treasury Bureau. https://www.bycensus2016.gov.hk/en/.

50.

HKSARG Electoral Affairs Commission. 2019 District Council Ordinary Election Constituency Boundaries. DATA.GOV.HK. https://www.eac.hk/en/distco/2019dc_elect_map.htm. Accessed April 2, 2021.

51.

HKSARG Electoral Affairs Commission. 2019 District Council Election - Election Results. District Council Election. https://www.elections.gov.hk/dc2019/eng/results.html. Accessed April 2, 2021.

52.

Kuby

Barranda

Upchurch

Factors Influencing Light-Rail Station Boardings in the United States. Transportation Research Part A: Policy and Practice, Vol. 38, No. 3, 2004, pp. 223–247. https://doi.org/10.1016/j.tra.2003.10.006.

53.

Cardozo

O. D.

García-Palomares

J. C.

Gutiérrez

Application of Geographically Weighted Regression to the Direct Forecasting of Transit Ridership at Station-Level. Applied Geography, Vol. 34, 2012, pp. 548–558. https://doi.org/10.1016/j.apgeog.2012.01.005.

54.

Bernick

M. S.

Cervero

R. B.

Transit Villages for the 21st Century. McGraw-Hill, New York, NY, 1997.

55.

Kim

H.-Y.

Statistical Notes for Clinical Researchers: Assessing Normal Distribution (2) Using Skewness and Kurtosis. Restorative Dentistry & Endodontics, Vol. 38, No. 1, 2013, p. 52. https://doi.org/10.5395/rde.2013.38.1.52.

56.

Ghasemi

Zahediasl

Normality Tests for Statistical Analysis: A Guide for Non-Statisticians. International Journal of Endocrinology and Metabolism, Vol. 10, No. 2, 2012, pp. 486–489. https://doi.org/10.5812/ijem.3505.

57.

Bolstad

B. M.

Irizarry

Astrand

Speed

T. P.

A Comparison of Normalization Methods for High Density Oligonucleotide Array Data Based on Variance and Bias. Bioinformatics, Vol. 19, No. 2, 2003, pp. 185–193. https://doi.org/10.1093/bioinformatics/19.2.185.

58.

Brownlee

Data Preparation for Machine Learning: Data Cleaning, Feature Selection, and Data Transforms in Python. Machine Learning Mastery, 2020. Retrieved from https://machinelearningmastery.com/data-preparation-for-machine-learning/

59.

Liu

Huang

Zhang

Chen

B. Y.

Chen

Spatial-Interaction Network Analysis of Built Environmental Influence on Daily Public Transport Demand. Journal of Transport Geography, Vol. 92, 2021, p. 102991. https://doi.org/10.1016/j.jtrangeo.2021.102991.

60.

Xiao

Zhou

Liu

Yang

Predicting Vibrancy of Metro Station Areas Considering Spatial Relationships Through Graph Convolutional Neural Networks: The Case of Shenzhen, China. Environment and Planning B: Urban Analytics and City Science, Vol. 48, No. 8, 2021, pp. 2363–2384. https://doi.org/10.1177/2399808320977866.

61.

Schwanen

Lucas

Akyelken

Solsona

D. C.

Carrasco

J.-A.

Neutens

Rethinking the Links Between Social Exclusion and Transport Disadvantage Through the Lens of Social Capital. Transportation Research Part A: Policy and Practice, Vol. 74, 2015, pp. 123–135. https://doi.org/10.1016/j.tra.2015.02.012.

62.

Chan

H.-Y.

Chen

Sze

N.-N.

Liu

COVID-19, Community Response, Public Policy, and Travel Patterns: A Tale of Hong Kong. Transport Policy, Vol. 106, 2021, pp. 173–184. https://doi.org/10.1016/j.tranpol.2021.04.002.