Investigating Spatial Interdependence in E-Bike Choice Using Spatially Autoregressive Model
Increased attention has been given to promoting e-bike usage in recent years. However, the research gap still exists in understanding the effects of spatial interdependence on e-bike choice. This study investigated how spatial interdependence affected the e-bike choice. The Moran’s I statistic test showed that spatial interdependence exists in e-bike choice at aggregated level. Bayesian spatial autoregressive logistic analyses were then used to investigate the spatial interdependence at individual level. Separate models were developed for commuting and non-commuting trips. The factors affecting e-bike choice are different between commuting and non-commuting trips. Spatial interdependence exists at both origin and destination sides of commuting and non-commuting trips. Travellers are more likely to choose e-bikes if their neighbours at the trip origin and destination also travel by e-bikes. And the magnitude of this spatial interdependence is different across various traffic analysis zones. The results suggest that, without considering spatial interdependence, the traditional methods may have biased estimation results and make systematic forecasting errors.
Cherry C, Weinert J, Yang XM. Comparative Environmental Impacts of Electric Bikes in China. Transportation Research Part D: Transportation and Environment. 2009;14(5):281-290. Available from: http://www.sciencedirect.com/science/article/pii/S1361920908001387
Cherry C, Cervero R. Use characteristics and mode choice behavior of electric bike users in China. Transport Policy. 2007;14:247-257. Available from: http://www.sciencedirect.com/science/article/pii/S0967070X07000169
Chiu Y, Tzeng G. The market acceptance of electric motorcycles in Taiwan experience through a stated preference analysis. Transportation Research Part D: Transportation and Environment. 1999;4:127-146. Available from: http://www.sciencedirect.com/science/journal/13619209
Cherry C, Yang H, Jones L, He M. Dynamics of Electric Bike Ownership and Use in Kunming China. Transport Policy. 2016;45:127-135. Available from: http://www.sciencedirect.com/science/article/pii/S0967070X15300524
Jones LR, Cherry C, Vu TA, Nguyen QN. The effect of incentives and technology on the adoption of electric motorcycles: A stated choice experiment in Vietnam. Transportation Research Part A: Policy and Practice. 2013;57:1-11. Available from: http://www.sciencedirect.com/science/article/pii/S0965856413001675
Zhang Y, Li Y, Yang X, Liu Q, Li C. Built Environment and Household Electric Bike Ownership: Insights from Zhongshan Metropolitan Area, China. Transportation Research Record. 2013;2387:102–111. Available from: http://trrjournalonline.trb.org/doi/10.3141/2387-12
Popovich N, Gordon E, Shao Z, Xing Y, Yang Y, Handy S. Experiences of electric bicycle users in the Sacramento, California area. Travel Behaviour and Society. 2014;1:37-44. Available from: http://www.sciencedirect.com/science/article/pii/S2214367X13000185
Heyvaert S, Vanhaverbeke L, Knapen L, Declercq K, Coosemans T, Joeri VM. Choosing an Electric Vehicle as a Travel Mode: Travel Diary Case Study in a Belgian Living Lab Context. Presented at the Transportation Research Board 94th Annual Meeting; 2015.
Lee A, Molin E, Maat K, Sierzchula W. Electric Bicycle Use and Mode Choice in the Netherlands. Presented at the Transportation Research Board 94th Annual Meeting; 2015.
Montgomery BN. Cycling Trends and Fate in the Face of Bus Rapid Transit: Case Study of Jinan, Shandong Province, China. Transportation Research Record. 2000;2193:28-36. Available from: http://trrjournalonline.trb.org/doi/ref/10.3141/2193-04
Dill J. Rose G. Electric bikes and transportation policy insights from early adopters. Transportation Research Record. 2012;2314:1-6. Available from: http://trrjournalonline.trb.org/doi/10.3141/2314-01
Fyhri A, Sundfør HB. Ebikes-who wants to buy them, and what effect do they have? TØI Report 1325/2014. Institute of Transport Economics, Oslo; 2014. Available from: https://www.toi.no/getfile.php/Publikasjoner/T%C3%98I%20rapporter/2014/1325-2014/sum-1325-2014.pdf
Fyhri A, Fearnley A. Effects of e-bikes on bicycle use and mode share. Transportation Research Part D: Transportation and Environment. 2015;36:45-52. Available from: http://www.sciencedirect.com/science/article/pii/S1361920915000140
Goetzke F. Network effects in public transit use: evidence from a spatially autoregressive mode choice model for New York. Urban Studies. 2008;45:407-417. Available from: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.364.2358
Adjemian MC, Williams LJ. Estimating spatial interdependence in automobile type choice with survey data. Transportation Research Part A. 2010;44:661-675. Available from: http://www.sciencedirect.com/science/article/pii/S0965856410000911
Li Q, Lam WHK, Tam M L. Vehicle Travel Time Prediction in Spatio-Temporal Space. Applied Mechanics and Materials. 2013;253:1662-1665. Available from: http://www.scientific.net/AMM.253-255.1645
Lawson A. Bayesian Disease Mapping Hierarchical Modeling in Spatial Epidemiology. CRC; 2009.
Gelman A, Carlin J, Stern H, Rubin D. Bayesian Data Analysis. 2nd ed. London: Chapman and Hall; 2004.
Ntzoufras I. Bayesian Modeling Using WinBUGS. Wiley; 2009.
Washington SP, Karlaftis MG, Mannering FL. Statistical and Econometric Methods for Transportation Data Analysis. Boca Raton, FL: Chapman & Hall/CRC; 2003.
Weiss GM, Provost F. Learning when training data are costly: The effect of class distribution on tree induction. Journal of Artificial Intelligence Research. 2003;19:315-354. Available from: http://citeseerx.ist.psu.edu/viewdoc/summary?-doi=10.1.1.60.8100
Li Z, Wang W, Liu P, Ragland D. Physical environments influencing bicyclists’ perception of comfort on separated and on-street bicycle facilities. Transportation Research Part D: Transportation and Environment. 2012;17:256-261. Available from: http://www.sciencedirect.com/science/article/pii/S1361920911001556
Li Z, Wang W, Shan XF, Jin J, Lu J, Yang C. Analysis of bicycle passing events for LOS evaluation on physically separated bicycle roadways in China. Presented at 89th Annual Meeting of the Transportation Research Board. Washington, DC; 2010. Available from: https://trid.trb.org/view/2010/C/910355
Copyright (c) 2017 Chengcheng Xu, Chen Wang, Wei Wang, Jie Bao, Menglin Yang
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).