Steering short-term demand for car-sharing: a mode choice and policy impact analysis by trip distance

Abstract

Car-sharing could have substantial benefits. However, there is not enough evidence about if more people choosing car-sharing would reduce private car usage or public transport demand. This work aims to bring forward some insights by studying short-term car-sharing choice behavior. A mode choice analysis is conducted first followed by a simulation analysis to evaluate modal substitution pattern. Policy implications are obtained in terms of the possible measures that could effectively bring down private car usage. The case study is Taiyuan-China; stated and revealed preference data are collected. Mixed nested logit models are developed to study the pooled SP/RP data. The analysis is conducted separately for a shorter trip case (2–5 km) and a longer trip case (more than 5 km) to examine if results would differ by distance. It is found that raising the cost of private car usage (travel cost, parking cost) should be prioritized for shorter trips since car is more difficult to be substituted when trip distance increases. Shorter trips also need such direct measures to help suppress the demand for private car when promoting a car-sharing service; otherwise car-sharing would attract more bus users instead. Longer trips need a more effective solution to bring down private car usage and that is discovered as making car-sharing service more appealing so that it can serve as a practical substitute to private car. A number of informative indicators (e.g. willingness to pay for travel time savings, direct and cross point elasticity) are also derived to enrich the findings.

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Notes

  1. 1.

    They are named as “medium” and “long” distances because the survey also collected short-distance (within 2 km) trip data. However, < 2 km trips are excluded from this research since car-sharing is not expected to be competitive within such a distance due to the associated access and alighting time (Martínez et al. 2017).

  2. 2.

    Recall the strategic-tactical choice framework in Le Vine et al. (2014), our survey did not address the strategic-level car-sharing choice behavior; this is because most car-sharing services in China do not require regular membership fee/long-term commitment, which makes the effect of strategic choice trivial.

  3. 3.

    As per the pilot survey feedback there was imperfect knowledge among Taiyuan citizens about what car-sharing really represents. Thus, the concept and key features of a free-floating car-sharing scheme were described in the survey to reduce the bias in their understanding.

  4. 4.

    Although an orthogonal design is not as advanced as several later proposed designs, such as the various forms of D-efficient design (Bliemer et al. 2009; Rose and Bliemer 2009; Bliemer and Rose 2010), we still employed this technique given the constraints we had on project cost (i.e. more advanced software such as Ngene is usually needed to handle an efficient design).

  5. 5.

    The difference is due to there are different number of attributes between short-dist scenarios and mid- & long-dist scenarios as a result of the different choice sets involved.

  6. 6.

    We also tested how many choice tasks being presented in the SP experiment were acceptable to respondents. In the pilot survey we included 10 for each individual to answer, and we found most respondents were averse to a number of scenarios larger than 8.

  7. 7.

    However, insignificant “policy variables” (or, level of service variables, such as travel times, travel costs, access times and app availability) are still included in light of the discussions in Ortúzar and Willumsen (2011).

  8. 8.

    Possession of a driving license is not an availability condition in this case since we allow the choices of car and car-sharing to come from both drivers and passengers.

  9. 9.

    The only exception is observed on the impact of trip purpose. When RP data is involved, bike-sharing is no longer a preferred mode for mid-dist commute trips while taxi and bus are no longer among the preferred modes for long-dist commute trips.

  10. 10.

    In fact, we found another nest (between car driver and car passenger) using only the RP data, where the t-statistic also shows significance; however, the nesting parameter μ has a value of 1.03 which is almost equivalent to an MNL specification. Thus, we discarded this nest by following the practice of Ortúzar and Willumsen (2011), in order to retain efficiency in model estimation.

  11. 11.

    λ was defined earlier in Eqs. 3 and 4.

  12. 12.

    Electric bike does not involve a perceived travel cost.

  13. 13.

    The simulation analysis only aims to reveal how people make trade-offs across the attributes; it does not intend to forecast market demand in the real world.

  14. 14.

    The findings on car correspond to the cross elasticity values. The probability to choose car is much more elastic to the changes in car-sharing’s attributes in the long-dist case (0.180 is much higher than the rest).

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Acknowledgements

We sincerely appreciate the support from Shanxi Transportation Research Institute in their funding and advice provided during the data collection. We would also like to express our gratitude to the following individuals who made the most significant contributions in the data recording task: Mr Li Peiyu from Shanxi Experimental Secondary School, Mr Hou Juntao from Peking University and Ms Zhao Helan.

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WL Literature Search and Review, Data Collection and Analysis, Manuscript Writing. MK Content planning, Data Collection and Analysis, Manuscript Editing.

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Correspondence to Weibo Li.

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Appendices

Appendix 1: An example of a mid-dist scenario and a long-dist scenario as seen in the survey (translated from Chinese)

Mid-dist: Travel within 2–5 km, to leisure, sunny day, 20 °C, with excellent air quality

  Car share Car Taxi Bus E-bike Bike share
  Travel 20 min Travel 15 min Travel 10 min Travel 20 min Ride 20 min Ride 30 min
  Cost ¥8 Fuel ¥3 Cost ¥18 Ticket ¥1   Cost ¥0
   Hard to park car     
   Parking ¥5/h     
  Walk 15 min to station    Walk 10 min to station   Walk 2 min to station
     Every 5 min   
  With app   With app Without app   With app
Your choice (please tick)       

Long-dist: Travel more than 5 km, to work/education, rainy day, 30 °C, with good air quality

  Car share Car Taxi Bus E-bike Bike share
  Travel 25 min Travel 20 min Travel 30 min Travel 30 min Ride 20 min Ride 60 min
  Cost ¥20 Fuel ¥5 Cost ¥25 Ticket ¥2   Cost ¥1.5
   Easy to park car     
   Parking ¥2/h     
  Walk 5 min to station    Walk 10 min to station   Walk 2 min to station
     Every 5 min   
  With app   Without app With app   With app
Your choice (please tick)       

Appendix 2: NL results for mid-dist case and long-dist case

Mid-dist
  SP data SP and RP data
Coef. t-stat Coef. t-stat
αcarshare (SP) − 1.91 − 8.38 − 1.68 − 7.18
αcar (SP) − 0.63 − 2.28 − 0.52 − 1.98
αtaxi (SP) − 2.48 − 7.66 − 2.38 − 7.46
αbus (SP) − 0.53 − 1.96 − 0.27 − 1.03
αbikeshare (SP) 2.45 9.27 2.52 9.60
αcardriver (RP)    2.29 4.86
αcarpassenger (RP)    − 0.89 − 3.10
αtaxi (RP)    0.19 0.72
αbus (RP)    2.48 5.15
αebike (RP)    1.88 3.76
αbike (RP)    0.37 0.90
Natural environmental conditions
Air pollution-carshare (SP) 0.0094 9.38 0.0096 9.63
Air pollution-car (SP) 0.0033 3.47 0.0034 3.63
Air pollution-taxi (SP) 0.0035 2.72 0.0027 2.06
Air pollution-bus (SP) 0.0015 1.67* 0.0012 1.35*
Air pollution-bikeshare (SP) − 0.0177 − 13.36 − 0.0175 − 13.21
Rain-ebike (SP and RP) − 0.94 − 4.74 − 0.64 − 4.18
Temperature-taxi (SP) − 0.01 − 2.16 − 0.01 − 2.14
Temperature-ebike (SP) 0.02 4.38 0.02 4.06
Trip and mode attributes
Commute-carshare (SP) − 0.62 − 3.94 − 0.66 − 4.32
Commute-taxi (SP and RP) − 1.20 − 6.14 − 1.02 − 5.45
Commute-ebike (SP and RP) 0.50 4.64 0.42 4.31
Commute-bikeshare (SP and RP) 0.32 2.39 0.14 1.27*
Travel cost-carshare (SP) − 0.03 − 2.69 − 0.03 − 2.76
Travel cost-car (SP and RP) − 0.06 − 0.56* − 0.19 − 2.47
Travel cost-taxi (SP and RP) − 0.05 − 3.35 − 0.05 − 3.26
Travel cost-bus (SP and RP) − 0.10 − 0.90* − 0.08 − 0.82*
Travel cost-bikeshare (SP and RP) − 0.38 − 3.72 − 0.46 − 4.70
Parking cost-car (SP) − 0.06 − 4.14 − 0.06 − 3.86
Parking space-car (SP) 0.14 1.24* 0.04 0.36*
Travel time-carshare (SP) − 0.01 − 1.49* − 0.03 − 2.92
Travel time-car (SP and RP) − 0.01 − 0.34* − 0.01 − 0.24*
Travel time-taxi (SP and RP) − 0.01 − 0.26* − 0.03 − 1.88
Travel time-bus (SP and RP) − 0.02 − 1.88 − 0.03 − 4.21
Travel time-ebike (SP and RP) − 0.04 − 3.72 − 0.01 − 0.99*
Travel time-bikeshare (SP and RP) − 0.15 − 11.51 − 0.14 − 11.38
Travel time-bike (RP) − 0.01 − 0.17*
Waiting time-bus (SP) − 0.03 − 4.02 − 0.03 − 3.84
Access time-carshare (SP) − 0.04 − 2.78 − 0.04 − 2.69
Access time-bikeshare (SP) − 0.25 − 12.03 − 0.24 − 11.74
App availability-carshare (SP) 0.18 2.10 0.18 2.18
App availability-taxi (SP) 0.32 2.26 0.40 2.88
App availability-bus (SP) 0.16 2.24 0.16 2.16
App availability-bikeshare (SP) 3.11 10.98 3.28 11.62
Systematic taste heterogeneity
Air pollution * Male-bus (SP) − 0.0022 − 5.77 − 0.0022 − 5.76
Air pollution * Lower age-taxi (SP) 0.0027 3.45 0.0025 3.20
Air pollution * Lower age-bus (SP) 0.0028 6.46 0.0028 6.66
Air pollution * Lower education-carshare (SP) − 0.0034 − 4.67 − 0.0032 − 4.42
Air pollution * Lower education-taxi (SP) − 0.0030 − 3.35 − 0.0017 − 1.92
Commute * Lower education-carshare (SP) 0.54 3.24 0.46 2.80
Commute * Lower education-taxi (SP and RP) 0.48 2.06 0.03 0.14*
Inter-alternative correlation
μselfdriven (SP) 2.81 14.75# 2.80 17.53#
Scaling factor (RP) 0.76 4.27#
Number of observations 6848 11655
Initial log-likelihood − 10738.4 − 15408.3
Final log-likelihood − 9038.7 − 12705.8
Likelihood ratio test 3399.3 5405.0
Adjusted rho-bar squared 0.15 0.17
  1. *Parameter values not meeting the 90% significance level
  2. #t-test against base value of 1
Long-dist
  SP data SP and RP data
Coef. t-stat Coef. t-stat
αcarshare (SP) − 2.61 − 5.32 − 1.79 − 6.32
αcar (SP) − 1.22 − 3.01 0.10 0.58
αtaxi (SP) − 2.26 − 5.33 − 0.50 − 2.67
αbus (SP) 1.07 2.62 1.58 8.49
αebike (SP) − 0.81 − 2.05 − 0.12 − 0.74
αcardriver (RP) − 0.23 − 7.33
αcarpassenger (RP) − 0.32 − 7.62
αtaxi (RP) − 0.21 − 6.35
αbus (RP) − 0.16 − 6.96
αebike (RP) − 0.21 − 7.07
αbike (RP) − 0.17 − 5.74
Natural environmental conditions
Air pollution-carshare (SP) 0.0088 14.49 0.0060 13.02
Air pollution-car (SP) 0.0062 13.17 0.0044 11.53
Air pollution-taxi (SP) 0.0050 13.28 0.0042 12.82
Air pollution-bikeshare (SP) − 0.0217 − 6.58 − 0.0130 − 6.97
Rain-car (SP and RP) 0.39 3.67 0.07 4.58
Rain-taxi (SP and RP) 0.59 4.60 0.07 4.09
Rain-bus (SP and RP) 0.21 2.29 0.08 5.12
Rain-ebike (SP and RP) − 0.14 − 1.23* − 0.06 − 3.84
Rain-bikeshare (SP and RP) − 0.54 − 2.56 − 0.06 − 4.09
Temperature-carshare (SP) − 0.03 − 4.40 − 0.03 − 4.86
Temperature-taxi (SP) − 0.04 − 7.60 − 0.03 − 6.50
Temperature-bus (SP) − 0.04 − 11.04 − 0.02 − 8.07
Temperature-bikeshare (SP) 0.05 4.45 0.01 3.00
Trip and mode attributes
Commute-carshare (SP) 1.34 8.85 0.99 8.87
Commute-taxi (SP and RP) 0.33 2.99 − 0.03 − 2.99
Commute-bus (SP and RP) 0.22 2.08 − 0.09 − 6.39
Commute-bikeshare (SP and RP) − 2.33 − 5.47 − 0.07 − 6.37
Travel cost-carshare (SP) − 0.04 − 4.61 − 0.04 − 5.97
Travel cost-car (SP and RP) − 0.02 − 1.11* − 0.01 − 7.30
Travel cost-taxi (SP and RP) − 0.02 − 3.14 − 0.02 − 10.25
Travel cost-bus (SP and RP) − 0.61 − 13.77 − 0.03 − 4.03
Travel cost-bikeshare (SP and RP) − 0.78 − 5.04 − 0.04 − 0.59*
Parking cost-car (SP) − 0.07 − 4.98 − 0.05 − 3.98
Parking space-car (SP) 0.27 3.14 0.09 1.44*
Travel time-carshare (SP) − 0.07 − 7.52 − 0.04 − 6.16
Travel time-car (SP and RP) − 0.03 − 2.87 − 0.01 − 5.88
Travel time-taxi (SP and RP) − 0.02 − 2.07 − 0.02 − 9.69
Travel time-bus (SP and RP) − 0.01 − 0.82* − 0.01 − 5.61
Travel time-ebike (SP and RP) − 0.02 − 6.54 − 0.01 − 5.72
Travel time-bikeshare (SP and RP) − 0.04 − 6.19 − 0.01 − 7.17
Travel time-bike (RP) − 0.01 − 4.25
Waiting time-bus (SP) − 0.02 − 1.90 − 0.05 − 8.26
Access time-carshare (SP) − 0.04 − 4.10 − 0.04 − 4.32
Access time-bus (SP) − 0.16 − 15.42 − 0.10 − 11.95
Access time-bikeshare (SP) − 0.09 − 2.89 − 0.04 − 1.51*
App availability-carshare (SP) 0.53 3.37 0.97 8.83
App availability-taxi (SP) 0.24 2.80 0.13 1.86
Systematic taste heterogeneity
Air pollution * Male-bikeshare (SP) 0.0041 2.95 0.0030 2.72
Air pollution * Lower income-car (SP) − 0.0017 − 5.34 − 0.0018 − 6.01
Air pollution * Lower education-car (SP) − 0.0013 − 4.57 − 0.0008 − 2.98
Temperature * Male-carshare (SP) − 0.01 − 2.99 − 0.01 − 2.70
Temperature * Male-bus (SP) − 0.01 − 5.05 − 0.01 − 5.20
Temperature * Lower age-carshare (SP) 0.01 3.90 0.01 4.41
Temperature * Lower age-taxi (SP) 0.02 5.48 0.02 4.86
Commute * Lower income-bus (SP and RP) 0.24 2.88 0.08 6.22
Commute * Lower education-carshare (SP) − 0.24 − 3.67 − 0.22 − 3.44
Inter-alternative correlation
μsharingeconomy (SP) 2.71 7.58# 2.49 8.13#
Scaling factor (RP) 1.29 8.10#
Number of observations 11925 21824
Initial log-likelihood − 18938.3 − 35361.5
Final log-likelihood − 15438.8 − 27555.7
Likelihood ratio test 6999.2 15611.6
Adjusted rho-bar squared 0.18 0.22
  1. *Parameter values not meeting the 90% significance level
  2. #t-test against base value of 1

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Li, W., Kamargianni, M. Steering short-term demand for car-sharing: a mode choice and policy impact analysis by trip distance. Transportation 47, 2233–2265 (2020). https://doi.org/10.1007/s11116-019-10010-0

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Keywords

  • Car-sharing policy
  • Mode choice
  • Value of time
  • Mixed NL model
  • Pooled SP/RP data
  • China