Comparing the Travel Behavior of Older Users Between Station-Based and Dockless Bike Sharing Systems

Abstract

As the population worldwide continues to age, providing improved mobility services for older people becomes increasingly crucial. In recent years, the maturation of bike sharing has led older people to progressively incorporate it into their everyday lives. In this study, station-based bike sharing (SBBS) and dockless bike sharing (DBS) are investigated in relation to older population mobility. This research relies on the fusion of multiple data sources, including SBBS and DBS trip records, point of interest (POI), and meteorological conditions in Nanjing, China. A zero-inflated negative binomial regression model is used to compare the travel behaviors among various age groups with the exposure variable. SBBS outperforms DBS in relation to age equity, while diverse travel behavior patterns are exhibited between the age groups. Although younger users are the majority of users, older users travel more frequently than younger users in bike sharing services. Also, older users are more likely to use shared bikes near the religious and health POIs, as well as in higher population density areas. This study provides beneficial insights into the transport equity and service improvement of bike sharing systems.

Keywords

pedestrians bicycles human factors bicycle transportation bike sharing

Get full access to this article

View all access options for this article.

References

Banister

Bowling

Quality of Life for the Elderly: The Transport Dimension. Transport Policy, Vol. 11, No. 2, 2004, pp. 105–115. https://doi.org/10.1016/S0967-070X(03)00052-0.

Cirella

G. T.

Bąk

Kozlak

Pawłowska

Borkowski

Transport Innovations for Elderly People. Research in Transportation Business and Management, Vol. 30, 2019, p. 100381. https://doi.org/10.1016/j.rtbm.2019.100381.

Taylor

Physical Activity Is Medicine for Older Adults. Postgraduate Medical Journal, Vol. 90, 2014, pp. 26–32. https://doi.org/10.1136/postgradmedj-2012-131366.

Callow

D. D.

Arnold-Nedimala

N. A.

Jordan

L. S.

Pena

G. S.

Won

Woodard

J. L.

Smith

J. C.

The Mental Health Benefits of Physical Activity in Older Adults Survive the COVID-19 Pandemic. American Journal of Geriatric Psychiatry, Vol. 28, No. 10, 2020, pp. 1046–1057. https://doi.org/10.1016/j.jagp.2020.06.024.

Winters

Buehler

Götschi

Policies to Promote Active Travel: Evidence from Reviews of the Literature. Current Environmental Health Reports, Vol. 4, 2017, pp. 278–285. https://doi.org/10.1007/s40572-017-0148-x.

Hua

Chen

Zheng

Cheng

Chen

Estimating the Parking Demand of Free-Floating Bike Sharing: A Journey-Data-Based Study of Nanjing, China. Journal of Cleaner Production, Vol. 244, 2020, p. 118764. https://doi.org/10.1016/j.jclepro.2019.118764.

Hanson

V. L.

Influencing Technology Adoption by Older Adults. Interacting with Computers, Vol. 22, No. 6, 2010, pp. 502–509. https://doi.org/10.1016/j.intcom.2010.09.001.

Friemel

T. N.

The Digital Divide Has Grown Old: Determinants of a Digital Divide Among Seniors. New Media & Society, Vol. 18, No. 2, 2016, pp. 313–331. https://doi.org/10.1177/1461444814538648.

Cheng

Chen

Yang

Cao

De Vos

Witlox

Active Travel for Active Ageing in China: The Role of Built Environment. Journal of Transport Geography, Vol. 76, 2019, pp. 142–152. https://doi.org/10.1016/j.jtrangeo.2019.03.010.

10.

Zhang

Yang

Social Factors Influencing the Choice of Bicycle: Difference Analysis Among Private Bike, Public Bike Sharing and Free-Floating Bike Sharing in Kunming, China. KSCE Journal of Civil Engineering, Vol. 23, 2019, pp. 2339–2348. https://doi.org/10.1007/s12205-019-2078-7.

11.

Yuan

van Oort

Jin

Hoogendoorn

A Comparison in Travel Patterns and Determinants of User Demand Between Docked and Dockless Bike-Sharing Systems Using Multi-Sourced Data. Transportation Research Part A: Policy and Practice, Vol. 139, 2020, pp. 148–173. https://doi.org/10.1016/j.tra.2020.06.022.

12.

Raux

Zoubir

Geyik

Who Are Bike Sharing Schemes Members and Do They Travel Differently? The Case of Lyon’s “Velo’v” Scheme. Transportation Research Part A: Policy and Practice, Vol. 106, 2017, pp. 350–363. https://doi.org/10.1016/j.tra.2017.10.010.

13.

Zhang

Brussel

M. J. G.

Thomas

Van Maarseveen

M. F. A. M.

Mining Bike-Sharing Travel Behavior Data: An Investigation into Trip Chains and Transition Activities. Computers, Environment and Urban Systems, Vol. 69, 2018, pp. 39–50. https://doi.org/10.1016/j.compenvurbsys.2017.12.004.

14.

Bakogiannis

Siti

Tsigdinos

Vassi

Nikitas

Monitoring the First Dockless Bike Sharing System in Greece: Understanding User Perceptions, Usage Patterns and Adoption Barriers. Research in Transportation Business and Management, Vol. 33, 2019, p. 100432. https://doi.org/10.1016/j.rtbm.2020.100432.

15.

Xing

Wang

J. J.

Exploring Travel Patterns and Trip Purposes of Dockless Bike-Sharing by Analyzing Massive Bike-Sharing Data in Shanghai, China. Journal of Transport Geography, Vol. 87, 2020, p. 102787. https://doi.org/10.1016/j.jtrangeo.2020.102787.

16.

Mattson

Godavarthy

Bike Share in Fargo, North Dakota: Keys to Success and Factors Affecting Ridership. Sustainable Cities and Society, Vol. 34, 2017, pp. 174–182. https://doi.org/10.1016/j.scs.2017.07.001.

17.

Wang

Akar

Chen

Y. J.

Bike Sharing Differences Among Millennials, Gen Xers, and Baby Boomers: Lessons Learnt from New York City’s Bike Share. Transportation Research Part A: Policy and Practice, Vol. 116, 2018, pp. 1–14. https://doi.org/10.1016/j.tra.2018.06.001.

18.

Scott

D. M.

Ciuro

What Factors Influence Bike Share Ridership? An Investigation of Hamilton, Ontario’s Bike Share Hubs. Travel Behaviour and Society, Vol. 16, 2019, pp. 50–58. https://doi.org/10.1016/j.tbs.2019.04.003.

19.

Dey

B. K.

Anowar

Eluru

A Framework for Estimating Bikeshare Origin Destination Flows Using a Multiple Discrete Continuous System. Transportation Research Part A: Policy and Practice, Vol. 144, 2021, pp. 119–133. https://doi.org/10.1016/j.tra.2020.12.014.

20.

Eren

V. E.

. A Review on Bike-Sharing: The Factors Affecting Bike-Sharing Demand. Sustainable Cities and Society, Vol. 54, 2020, p. 101882.

21.

Yang

Zhang

Zhong

Zhang

Ling

Exploring Spatial Variation of Bike Sharing Trip Production and Attraction: A Study Based on Chicago’s Divvy System. Applied Geography, Vol. 115, 2020, p. 102130. https://doi.org/10.1016/j.apgeog.2019.102130.

22.

Shen

Zhang

Zhao

Understanding the Usage of Dockless Bike Sharing in Singapore. International Journal of Sustainable Transportation, Vol. 12, No. 9, 2018, pp. 686–700. https://doi.org/10.1080/15568318.2018.1429696.

23.

Guo

Yang

Zhao

Dockless Bike-Sharing as a Feeder Mode of Metro Commute? The Role of the Feeder-Related Built Environment: Analytical Framework and Empirical Evidence. Sustainable Cities and Society, Vol. 65, 2021, p. 102594. https://doi.org/10.1016/j.scs.2020.102594.

24.

Hua

Chen

Gan

Wang

Zhao

Forecasting Usage and Bike Distribution of Dockless Bike-Sharing Using Journey Data. IET Intelligent Transport Systems, Vol. 14, No. 12, 2020, pp. 1647–1656. https://doi.org/10.1049/iet-its.2020.0305.

25.

Cheng

De Vos

Chen

Shi

Yang

Witlox

Do Residential Location Effects on Travel Behavior Differ Between the Elderly and Younger Adults?

Transportation Research Part D: Transport and Environment, Vol. 73, 2019, pp. 367–380. https://doi.org/10.1016/j.trd.2019.07.015.

26.

Ding

Sze

N. N.

Guo

Roles of Infrastructure and Land Use in Bicycle Crash Exposure and Frequency: A Case Study Using Greater London Bike Sharing Data. Accident Analysis & Prevention, Vol. 144, 2020, p. 105652. https://doi.org/10.1016/j.aap.2020.105652.

27.

Zhang

Sun

Liu

Free-Floating Bike Sharing in Jiangsu: Users’ Behaviors and Influencing Factors. Energies, Vol. 11, No. 7, 2018, p. 1664. https://doi.org/10.3390/en11071664.

28.

Hua

Chen

Cheng

Lei

How Does Dockless Bike Sharing Serve Users in Nanjing, China? User Surveys vs. Trip Records. Research in Transportation Business & Management, Vol. 43, 2022, p. 100701. https://doi.org/10.1016/j.rtbm.2021.100701.

29.

Hua

Chen

Cheng

Chen

Should Bike-Sharing Continue Operating During the COVID-19 Pandemic? Empirical Findings from Nanjing, China. Journal of Transport & Health, Vol. 23, 2021, p. 101264. https://doi.org/10.1016/j.jth.2021.101264.

30.

Faghih-Imani

Eluru

Incorporating the Impact of Spatio-Temporal Interactions on Bicycle Sharing System Demand: A Case Study of New York CitiBike System. Journal of Transport Geography, Vol. 54, 2016, pp. 218–227. https://doi.org/10.1016/j.jtrangeo.2016.06.008.

31.

Bao

Liu

Wang

Exploring Bikesharing Travel Patterns and Trip Purposes Using Smart Card Data and Online Point of Interests. Networks and Spatial Economics, Vol. 17, No. 4, 2017, pp. 1231–1253. https://doi.org/10.1007/s11067-017-9366-x.

32.

Böcker

Anderson

Uteng

T. P.

Throndsen

Bike Sharing Use in Conjunction to Public Transport: Exploring Spatiotemporal, Age and Gender Dimensions in Oslo, Norway. Transportation Research Part A: Policy and Practice, Vol. 138, 2020, pp. 389–401. https://doi.org/10.1016/j.tra.2020.06.009.

33.

Liu

Zhao

Sharma

Multivariate Random Parameters Zero-Inflated Negative Binomial Regression for Analyzing Urban Midblock Crashes. Analytic Methods in Accident Research, Vol. 17, 2018, pp. 32–46. https://doi.org/10.1016/j.amar.2018.03.001.

34.

Kumagai

Tajika

Hasegawa

Kawanishi

Fujita

Tsujino

Jinnin

, et al. Assessing Recurrence of Depression Using a Zero-Inflated Negative Binomial Model: A Secondary Analysis of Lifelog Data. Psychiatry Research, Vol. 300, 2021, p. 113919. https://doi.org/10.1016/j.psychres.2021.113919.

35.

Mathew

Benekohal

R. F.

Highway-Rail Grade Crossings Accident Prediction Using Zero Inflated Negative Binomial and Empirical Bayes Method. Journal of Safety Research, Vol. 79, 2021, pp. 211–236. https://doi.org/10.1016/j.jsr.2021.09.003.

36.

Middela

M. S.

Ramadurai

Incorporating Spatial Interactions in Zero-Inflated Negative Binomial Models for Freight Trip Generation. Transportation, Vol. 48, No. 5, 2021, pp. 2335–2356. https://doi.org/10.1007/s11116-020-10132-w.

37.

Yan

Liu

Modeling the Service-Route-Based Crash Frequency by a Spatiotemporal-Random-Effect Zero-Inflated Negative Binomial Model: An Empirical Analysis for Bus-Involved Crashes. Accident Analysis & Prevention, Vol. 144, 2020, p. 105674. https://doi.org/10.1016/j.aap.2020.105674.

38.

Briz-Redón

Á.

Mateu

Montes

Modeling Accident Risk at the Road Level Through Zero-Inflated Negative Binomial Models: A Case Study of Multiple Road Networks. Spatial Statistics, Vol. 43, 2021, p. 100503. https://doi.org/10.1016/j.spasta.2021.100503.

39.

Dong

Linking TNC with Passengers: Investigating TNC Use Among Lower-Income Residents with Limited Access to Cars. Travel Behaviour and Society, Vol. 27, 2022, pp. 184–191. https://doi.org/10.1016/j.tbs.2022.01.007.

40.

Dai

Tao

Lin

Visual Analytics of Urban Transportation from a Bike-Sharing and Taxi Perspective. Journal of Visualization, Vol. 23, No. 6, 2020, pp. 1053–1070. https://doi.org/10.1007/s12650-020-00673-8.

41.

Schimohr

Scheiner

Spatial and Temporal Analysis of Bike-Sharing Use in Cologne Taking into Account a Public Transit Disruption. Journal of Transport Geography, Vol. 92, 2021, p. 103017. https://doi.org/10.1016/j.jtrangeo.2021.103017.

42.

Boschmann

E. E.

Brady

S. A.

Travel Behaviors, Sustainable Mobility, and Transit-Oriented Developments: A Travel Counts Analysis of Older Adults in the Denver, Colorado Metropolitan Area. Journal of Transport Geography, Vol. 33, 2013, pp. 1–11. https://doi.org/10.1016/j.jtrangeo.2013.09.001.

43.

De Vos

Alemi

Are Young Adults Car-Loving Urbanites? Comparing Young and Older Adults’ Residential Location Choice, Travel Behavior and Attitudes. Transportation Research Part A: Policy and Practice, Vol. 132, 2020, pp. 986–998. https://doi.org/10.1016/j.tra.2020.01.004.

44.

Craney

T. A.

Surles

J. G.

Model-Dependent Variance Inflation Factor Cutoff Values. Quality Engineering, Vol. 14, No. 3, 2002, pp. 391–403. https://doi.org/10.1081/QEN-120001878.

45.

Choo

Sohn

Park

Mobility Characteristics of the Elderly: A Case for Seoul Metropolitan Area. KSCE Journal of Civil Engineering, Vol. 20, No. 3, 2016, pp. 1023–1031. https://doi.org/10.1007/s12205-016-0651-x.

46.

Feng

The Influence of Built Environment on Travel Behavior of the Elderly in Urban China. Transportation Research Part D: Transport and Environment, Vol. 52, 2017, pp. 619–633. https://doi.org/10.1016/j.trd.2016.11.003.

47.

Shao

Sui

Sun

Spatio-Temporal Travel Patterns of Elderly People – A Comparative Study Based on Buses Usage in Qingdao, China. Journal of Transport Geography, Vol. 76, 2019, pp. 178–190. https://doi.org/10.1016/j.jtrangeo.2019.04.001.

48.

Yang

Jin

Tan

Understanding Bikeshare Mode as a Feeder to Metro by Isolating Metro-Bikeshare Transfers from Smart Card Data. Transport Policy, Vol. 71, 2018, pp. 57–69. https://doi.org/10.1016/j.tranpol.2018.07.008.

49.

Hsu

S.-C.

Chen

P.-C.

Lee

W.-Y.

Improving the Sustainability of Integrated Transportation System with Bike-Sharing: A Spatial Agent-Based Approach. Sustainable Cities and Society, Vol. 41, 2018, pp. 44–51. https://doi.org/10.1016/j.scs.2018.05.023.

50.

Cheng

Wang

de Vos

Huang

Witlox

Exploring Non-Linear Built Environment Effects on the Integration of Free-Floating Bike-Share and Urban Rail Transport: A Quantile Regression Approach. Transportation Research Part A: Policy and Practice, Vol. 162, 2022, pp. 175–187. https://doi.org/10.1016/J.TRA.2022.05.022.

51.

El-Assi

Salah Mahmoud

Nurul Habib

Effects of Built Environment and Weather on Bike Sharing Demand: A Station Level Analysis of Commercial Bike Sharing in Toronto. Transportation, Vol. 44, No. 3, 2017, pp. 589–613. https://doi.org/10.1007/s11116-015-9669-z.

52.

Hyland

Hong

de Farias Pinto

H. K. R.

Chen

Hybrid Cluster-Regression Approach to Model Bikeshare Station Usage. Transportation Research Part A: Policy and Practice, Vol. 115, 2018, pp. 71–89. https://doi.org/10.1016/j.tra.2017.11.009.

53.

Böcker

Dijst

Faber

Weather, Transport Mode Choices and Emotional Travel Experiences. Transportation Research Part A: Policy and Practice, Vol. 94, 2016, pp. 360–373. https://doi.org/10.1016/j.tra.2016.09.021.

54.

Aoyagi

Shephard

R. J.

Sex Differences in Relationships Between Habitual Physical Activity and Health in the Elderly: Practical Implications for Epidemiologists Based on Pedometer/Accelerometer Data from the Nakanojo Study. Archives of Gerontology and Geriatrics, Vol. 56, No. 2, 2013, pp. 327–338.

55.

Hjorthol

Winter Weather – An Obstacle to Older People’s Activities?

Journal of Transport Geography, Vol. 28, 2013, pp. 186–191. https://doi.org/10.1016/j.jtrangeo.2012.09.003.

56.

Ahmed

M. A.

Sadri

A. M.

Hadi

Modeling Social Network Influence on Hurricane Evacuation Decision Consistency and Sharing Capacity. Transportation Research Interdisciplinary Perspectives, Vol. 7, 2020, p. 100180. https://doi.org/10.1016/J.TRIP.2020.100180.

57.

Gren

I. M.

Brutemark

Jägerbrand

Effects of Shipping on Non-Indigenous Species in the Baltic Sea. Science of the Total Environment, Vol. 821, 2022, p. 153465. https://doi.org/10.1016/J.SCITOTENV.2022.153465.

58.

Kim

Wang

X. (Cara)

. To Be Online or In-Store: Analysis of Retail, Grocery, and Food Shopping in New York City. Transportation Research Part C: Emerging Technologies, Vol. 126, 2021, p. 103052. https://doi.org/10.1016/J.TRC.2021.103052.

59.

Kalahasthi

L. K.

Sánchez-Díaz

Pablo Castrellon

Gil

Browne

Hayes

Sentís Ros

Joint Modeling of Arrivals and Parking Durations for Freight Loading Zones: Potential Applications to Improving Urban Logistics. Transportation Research Part A: Policy and Practice, Vol. 166, 2022, pp. 307–329. https://doi.org/10.1016/J.TRA.2022.11.003.

60.

Donnell

Gayah

Regionalized Safety Performance Functions. The Pennsylvania Department of Transportation, Bureau of Planning and Research, Harrisburg, 2016.

61.

Abdul Manan

M. M.

Várhelyi

Çelik

A. K.

Hashim

H. H.

Road Characteristics and Environment Factors Associated with Motorcycle Fatal Crashes in Malaysia. IATSS Research, Vol. 42, No. 4, 2018, pp. 207–220. https://doi.org/10.1016/J.IATSSR.2017.11.001.

62.

Mohammadi

Shafabakhsh

Naderan

Effects of Modal Shares on Crash Frequencies at Aggregate Level. Accident Analysis & Prevention, Vol. 120, 2018, pp. 295–303. https://doi.org/10.1016/J.AAP.2018.08.019.

63.

Sun

Rahman

M. A.

Akter

Das

Modeling Two-Way Stop-Controlled Intersection Crashes with Zero-Inflated Models on Louisiana Rural Two-Lane Highways. IATSS Research, Vol. 45, No. 3, 2021, pp. 303–309. https://doi.org/10.1016/J.IATSSR.2020.12.007.

64.

Mahmoudi

Xiong

Yang

Luo

Modeling the Frequency of Pedestrian and Bicyclist Crashes at Intersections: Big Data-Driven Evidence From Maryland. Transportation Research Record: Journal of the Transportation Research Board, 2022: 03611981221122776.

65.

Ploszaj

Yan

Börner

The Impact of Air Transport Availability on Research Collaboration: A Case Study of Four Universities. PLoS One, Vol. 15, No. 9, 2020, p. e0238360. https://doi.org/10.1371/JOURNAL.PONE.0238360.

66.

Sung

Impact of Pre-Pandemic Travel Mobility Patterns on the Spatial Diffusion of COVID-19 in South Korea. Journal of Transport & Health, Vol. 26, 2022, p. 101479. https://doi.org/10.1016/J.JTH.2022.101479.

67.

Goodspeed

Xie

Dillahunt

T. R.

Lustig

An Alternative to Slow Transit, Drunk Driving, and Walking in Bad Weather: An Exploratory Study of Ridesourcing Mode Choice and Demand. Journal of Transport Geography, Vol. 79, 2019, p. 102481. https://doi.org/10.1016/J.JTRANGEO.2019.102481.

68.

Phun

V. K.

Kato

Chalermpong

Paratransit as a Connective Mode for Mass Transit Systems in Asian Developing Cities: Case of Bangkok in the Era of Ride-Hailing Services. Transport Policy, Vol. 75, 2019, pp. 27–35. https://doi.org/10.1016/J.TRANPOL.2019.01.002.

69.

Deka

Brown

C. T.

Does the Pathway to Transportation Walking for Older Adults Run Through Recreational Walking?

Travel Behaviour and Society, Vol. 20, 2020, pp. 51–61. https://doi.org/10.1016/J.TBS.2020.02.005.

70.

Hensher

D. A.

C. Q.

Reck

D. J.

Mobility as a Service and Private Car Use: Evidence from the Sydney MaaS Trial. Transportation Research Part A: Policy and Practice, Vol. 145, 2021, pp. 17–33. https://doi.org/10.1016/J.TRA.2020.12.015.

71.

Loa

Nurul Habib

Examining the Influence of Attitudinal Factors on the Use of Ride-Hailing Services in Toronto. Transportation Research Part A: Policy and Practice, Vol. 146, 2021, pp. 13–28. https://doi.org/10.1016/J.TRA.2021.02.002.

72.

Hensher

D. A.

Beck

M. J.

Wei

Working from Home and Its Implications for Strategic Transport Modelling Based on the Early Days of the COVID-19 Pandemic. Transportation Research Part A: Policy and Practice, Vol. 148, 2021, pp. 64–78. https://doi.org/10.1016/J.TRA.2021.03.027.

73.

Zhou

Zhang

Peng

Zhang

Exploring the Determinants of Public Transport Usage and Shared Mobilities: A Case Study from Nanchang, China. Sustainable Cities and Society, Vol. 87, 2022, p. 104146. https://doi.org/10.1016/J.SCS.2022.104146.

74.

Orvin

M. M.

Bachhal

J. K.

Fatmi

M. R.

Modeling the Demand for Shared E-Scooter Services. Transportation Research Record: Journal of the Transportation Research Board, 2022. 2676: 429–442.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.01 MB