Analysis of the acceptance of park-and-ride by users: A cumulative logistic regression approach

  • Kai Huang 1 Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, China 2 Department of Civil Engineering, Monash University, Clayton, Melbourne, Australia
  • Zhiyuan Liu 1 Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, China
  • Ting Zhu 1 Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, China
  • Inhi Kim 2 Department of Civil Engineering, Monash University, Clayton, Melbourne, Australia
  • Kun An 2 Department of Civil Engineering, Monash University, Clayton, Melbourne, Australia
Keywords: Park and Ride, Traveler behaviour, Stated Preference Survey, 5-Likert Scale, Cumulative Logistic Regression

Abstract

Park-and-ride (P&R) schemes are an important way of increasing the public transport mode share, which relieves the negative impact caused by excessive automobile usage. Several existing studies have been conducted in the past to explore the factors that can influence the acceptance of P&R by travelers. However, quantitative analyses of the pertinent factors and rates of traveler choice are quite rare. In this paper, the data collected from a survey in Melbourne, Australia, is used to analyze the acceptance of P&R by travelers going to the central business district (CBD). In particular, we explore the influence that specific factors have on the choice of travel by those who are currently using P&R. The results indicate that the parking fee in the CBD area, travel time on public transport, and P&R transfer time affect traveler use of P&R. A quantitative assessment of the impact of these three factors is conducted by using a cumulative logistic regression model. Results reveal that the P&R transfer time has the highest sensitivity while public transport travel time has the least. To maximize the use of P&R facilities and public transport, insights into setting parking fees and designing P&R stations are presented.

References

Boisjoly, G., Wasfi, R., & El-Geneidy, A. (2018). How much is enough? Assessing the influence of neighborhood walkability on undertaking 10-minutes walks. Journal of Transport and Land Use, 11(1), 143–151.

Cheng, L., Chen, X., Bi, X., & Yang, S. (2016). Exploring the impacts of location factors on the travel behavior of urban low-income residents in China. CICTP Proceedings, 2016, 2376–2389.

Cheng, L., Chen, X., Yang, S., Wu, J., & Yang, M. (2017). Structural equation models to analyze activity participation, trip generation, and mode choice of low-income commuters. Transportation Letters, 11(6), 341–349.

Clayton, W., Ben-Elia, E., Parkhurst, G., & Ricci, M. (2014). Where to park? A behavioral comparison of bus park and ride and city center car park usage in Bath, UK. Journal of Transport Geography, 36, 124–133.

Dijk, M., de Haes, J., & Montalvo, C. (2013). Park-and-ride motivations and air quality norms in Europe. Journal of Transport Geography, 30, 149–160.

Duncan, M., & Christensen, R. K. (2013). An analysis of park-and-ride provision at light rail stations across the US. Transport Policy, 25, 148–157.

Evans IV, J., & Pratt, R. (2003). Traveler response to transportation system changes. Washington, DC: Transportation Research Board.

Guo, Y., Li, Z., Wu, Y., & Xu, C. (2018a). Exploring unobserved heterogeneity in bicyclists’ red-light running behaviors at different crossing facilities. Accident Analysis & Prevention, 115, 118–127.

Guo, Y., Li, Z., Wu, Y., & Xu, C. (2018b). Evaluating factors affecting electric bike users’ registration of license plate in China using Bayesian approach. Transportation Research: Part F, 59, 212–221.

Hamer, P. (2010). Analyzing the effectiveness of park and ride as a generator of public transport mode shift. Road & Transport Research, 19(1), 51–61.

Hamid, N. A., Mohammad, J., & Karim, M. R. (2008). Travel behavior of the park-and-ride users and the factors influencing the demand for the use of the park-and-ride facility. Chemistry International, 37(2), 10–14.

Hensher, D. A., Rose, J. M., & Greene, W. H. (2005). Applied choice analysis: A primer. Cambridge, UK: Cambridge University Press.

Huang, K., Correia, G., & An, K. (2018). Solving the station-based one-way carsharing network planning problem with relocations and non-linear demand. Transportation Research Part C, 90, 1–17.

Huang, K., Liu, Z., Kim, I., Zhang, Y., & Zhu, T. (2019). Analysis of the influencing factors of carpooling schemes. IEEE Intelligent Transportation Systems Magazine. doi: 10.1109/MITS.2019.2919550

Islam, S. T., Liu, Z., Sarvi, M., & Zhu, T. (2015). Exploring the mode change behavior of park-and-ride users. Mathematical Problems in Engineering, 2015, 1–8.

Ji, Y., Fan, Y., Ermagun, A., Cao, X., Wang, W., & Das, K. (2017). Public bicycle as a feeder mode to rail transit in China: The role of gender, age, income, trip purpose, and bicycle theft experience. International Journal of Sustainable Transportation, 11(4), 308–317.

Karamychev, V., & van Reeven, P. (2011). Park and ride: Good for the city, good for the region? Regional Science and Urban Economics, 41(5), 455–464.

Li, Z. C., Lam, W., Wong, S., Zhu, D. L., & Huang, H. J. (2007). Modeling park-and-ride services in a multimodal transport network with elastic demand. Transportation Research Record, 1994, 101–109.

Lindstroem, A. (2003). Factors that influence choice of travel mode in major urban areas. The attractiveness of park & ride (TRITA-INFRA, 48). Stockholm: Department of Infrastructure, Royal Institute of Technology.

Liu, Z., Chen, X., Meng, Q., & Kim, I. (2018). Remote park-and-ride network equilibrium model and its applications. Transportation Research: Part B, 117, 37–62.

Liu, Z., & Meng, Q. (2012). Bus-based park-and-ride system: A stochastic model on multimodal network with congestion pricing schemes. International Journal of Systems Science, 45(5), 994–1006.

Liu, Z., Wang, S., Huang, K., Chen, J., & Fu, Y. (2019). Practical taxi sharing schemes at large transport terminals. Transportmetrica B: Transport Dynamics, 7(1), 596–616.

Mahmoud, M. S., Habib, K. N., & Shalaby, A. (2014). Park-and-ride access station choice model for cross-regional commuting: Case study of Greater Toronto and Hamilton area, Canada. Transportation Research Record, 2419(1), 92–100.

Mingardo, G. (2013). Transport and environmental effects of rail-based park and ride: Evidence from the Netherlands. Journal of Transport Geography, 30, 7–16.

Olaru, D., Smith, B., Xia, J. C., & Lin, T. G. (2014). Travelers’ attitudes towards park-and-ride (P&R) and choice of P&R station: Evidence from Perth, Western Australia. Procedia-Social and Behavioral Sciences, 162, 101–110.

Qin, H., Guan, H., & Zhang, G. (2012). Analysis of the travel intent for park-and-ride based on perception. Discrete Dynamics in Nature & Society, 2012, 2079–2092.

Qin, H. M., Guan, H. Z., & Wu, Y. J. (2013). Analysis of park-and-ride decision behavior based on Decision Field Theory. Transportation Research: Part F, 18, 199–212.

Qu, X., Wang, S., & Zhang, J., (2015). On the fundamental diagram for freeway traffic: A novel calibration approach for single-regime models. Transportation Research: Part B, 73, 91–102.

Shirgaokar, M., & Deakin, E. (2005). Study of park-and-ride facilities and their use in the San Francisco bay area of California. Transportation Research Record, 1927, 46–54.

Train, K. E. (2009). Discrete choice methods with simulation. Cambridge, UK: Cambridge University Press.

Wang, S. (2013). Efficiency and equity of speed limits in transportation networks. Transportation Research: Part C, 32, 61–75.

Wang, J. Y., Yang, H., & Lindsey, R. (2004). Locating and pricing park-and-ride facilities in a linear monocentric city with deterministic mode choice. Transportation Research: Part B, 38, 709–731.

Yan, S., Delmelle, E., & Duncan, M. (2012). The impact of a new light rail system on single-family property values in Charlotte, North Carolina. Journal of Transport & Land Use, 5(2), 60–67.

Ying, H., & Xiang, H. (2009). Study on influence factors and demand willingness of park and ride. Intelligent Computation Technology & Automation, ICICTA, Second International Conference, 4, 664–667.

Published
2019-07-29
How to Cite
Huang, K., Liu, Z., Zhu, T., Kim, I., & An, K. (2019). Analysis of the acceptance of park-and-ride by users: A cumulative logistic regression approach. Journal of Transport and Land Use, 12(1). https://doi.org/10.5198/jtlu.2019.1390
Section
Articles