SMART International Symposium for Next Generation Infrastructure:Agency in transport service: Implications of traveller mode choice objective and latent attributes using random parameter logit model

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A presentation conducted by Mr AHM Mehbub Anwar, University of Wollongong. …

A presentation conducted by Mr AHM Mehbub Anwar, University of Wollongong.
Presented on Wednesday the 2nd of October 2013.

This paper explains how principal-agent theory (PAT) can be used as an analytical tool to understand the traveller-Transport for NSW relationship and minimise the agency uncertainty in the relationship by examining traveller preferences for mode choices. The paper emphasises latent variables and objective attributes together during the choice process within the agency relationship, as a method by which
the utility of the principal (traveller) can be maximised and evaluated using a discrete choice experiment, i.e. random parameter logit (RPL) model. The probability of car useis significantly higher than public transport, which indicates that an agency uncertainty exists in the relationship and incorporating traveller preferences in the transport projects
may minimise this uncertainty.

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  • 1. ENDORSING PARTNERS Agency in transport service: Implications of traveller mode choice objective and latent attributes using random parameter logit model The following are confirmed contributors to the business and policy dialogue in Sydney: • Rick Sawers (National Australia Bank) • Nick Greiner (Chairman (Infrastructure NSW) Monday, 30th September 2013: Business & policy Dialogue 3rd Tuesday 1 October to Thursday, October: Academic and Policy Dialogue Presented by: Mr AHM Mehbub Anwar, University of Wollongong www.isngi.org www.isngi.org
  • 2. Agency in Transport Service: Implications of Traveller Mode Choice Objective and latent Attributes Using Random Parameter Logit Model A.H.M. Mehbub Anwar ISNGI 2013, Wollongong
  • 3. Principal-Agent (Agency) Theory  Focuses on a relationship between two parties  A relationship is understood when they involve in an association wherein one party (the principal) entrusts/delegates task and/or work to another party called agent to act on its behalf (Eisenhardt, 1989; Rungtusanatham et al., 2007).
  • 4. Principal-Agent (Agency) Theory: Assumptions  Potential goal/choice conflicts exist between principal(s) and agent(s);  Each party acts in its own self-interest; and  Informational asymmetry frequently exists between principals and agents.
  • 5. Principal-Agent (Agency) Problem  The assumptions reflect the agency problem, in fact.  This problem is appeared while the agent behaves opportunistically in such a way that works against the (goal) welfare of the principal (Barney & Hesterly, 1996).  The principal can’t monitor agent’s actions
  • 6. Traveller and TfNSW Relationship  When travellers (principal) entrust their desire for a mode of transport that is customer-focused (i.e. safe, reliable and low cost) to the TfNSW (agent), this creates a metaphorical contract between travellers and the TfNSW (Transport for NSW), known as an agency contract.  Due to experiences and skills of TfNSW, TfNSW is reasonably effective agent to fulfil the goals / expectations entrusted by travellers.
  • 7. Traveller and TfNSW Relationship  The tax and travel fares paid by the citizens (travellers) are the source of funding of TfNSW.  Maximisation of the travellers’ benefit  Traveller and TfNSW act most likely in their own selfinterest - thus the contract is often characterised by agency problem  Travellers may not trust the quality of services performed by the TfNSW
  • 8. Traveller Preference, Utility and Agency Relationship  Traveller preference and utility are regarded as key indicators of the traveller-TfNSW relationship  Utility is considered as a key indicator of traveller satisfaction/expectation.
  • 9. Hypotheses  Hypothesis 1 (H1): Traveller preferences influence TfNSW’s decisions on modal services.  Hypothesis 2 (H2): Individual specific attributes affect TfNSW’s planning of modal services.  Hypothesis 3 (H3): Mode specific attributes and nature of trips have an effect also on TfNSW’s decisions on modal service.
  • 10. Data and Methods • 2008/09 Household Travel Survey (HTS)- Bureau of transport statistics, Sydney • Sydney and Illawarra Statistical Divisions and the Newcastle SubStatistical Division • Data from Sydney Statistical Division (SSD) only (82121 trips)
  • 11. Data and Methods 1 LVs i) Comfort ii) Convenience iii) Safety iv) Flexibility v) Reliability vi) Satisfaction 2 LOS i) Travel time ii) Travel cost iii) Waiting time 3 SEC i) Age ii) Income iii) Family size iv) Gender v) Car ownership vi) Number of children vii) Number of full time workers 5 TOAs 1Latent 2 3 4 5 variables Level of service Socioeconomic characteristics Trip characteristics Traditional objective attributes Fig. 1: List of LV and TOAs 4 TC i) Trip rate, ii) Distance travelled iii) Trip purpose
  • 12. Data and Methods Table 1: Description of indicators of LVs Latent factors Comfort Convenience Safety Flexibility Reliability Satisfaction Explained by (indicators) - Enjoy time to read/relax on vehicle - Stressfulness on vehicle - Service slower - Mode availability - Accessibility (does not go where required) - Timetable availability - Safety response for mode used in 1st trip - Safety response for mode used in 2nd trip - Safety response for mode used in 3rd trip - Fixed start and finish times – each day can vary - Rotating shift - Roster shift - Variable hours - Frequency - Punctuality - Faster - Cleanliness - Travel time - Travel cost - Waiting time Definitions Importance with 1, otherwise 0 Importance with 1, otherwise 0 Importance with 1, otherwise 0 Importance with 1, otherwise 0 Importance with 1, otherwise 0 Importance with 1, otherwise 0 Importance with 1, otherwise 0 Importance with 1, otherwise 0 Importance with 1, otherwise 0 Importance with 1, otherwise 0 Importance with 1, otherwise 0 Importance with 1, otherwise 0 Importance with 1, otherwise 0 Importance with 1, otherwise 0 Importance with 1, otherwise 0 Importance with 1, otherwise 0 Importance with 1, otherwise 0 Travel time in minutes Travel cost in Australian dollar Waiting time in minutes
  • 13. Data and Methods*  A discrete choice analysis is the most popular method (Train, 2009). (1) Structural equation model (SEM) (2) Discrete choice model  MIMIC (Multiple Indicators and Multiple Causes) model  Software used: AMOS v.19  Random parameter logit (RPL) model  Software used: Nlogit v.4  The indicators of LVs have been evaluated and validated using factor analytic model (exploratory and confirmatory factor) (for details please see Anwar et al., 2011) *Similar methods have been used in Anwar et al. (in press)
  • 14. Data and Methods MIMIC Model Structural equation: ηijl = Σrαjlr * sijr + νijl (1) Measurement equation: yijp = Σlγjlp * ηijl + ζijp (2) ηijl = yijp = αjlr and γjlp = sijr = νijl and ζijp= Latent variables Indicators Vector parameter to be estimated Observed explanatory variables Error terms i = individual j = alternative mode of transport l = a LV r = explanatory variables to TOAs p = an indicator
  • 15. Data and Methods Comfort Indicator - y1 Indicator – y2 Specification of MIMIC Model Indicator – y3 Income Indicator – y4 Convenience Age Example: Comfort ij = α inc-com,j *Income i + α age-com,j *Age i + α gencom,j *Gender i + α car-com,j *Car ownership i + α ftw-com,j *Full time workers i + α dtcom,j *Distance travelled + α chicom,j *Having children + ν com,ij Indicator – y5 Gender Indicator – y6 Having children Indicator – y7 Car ownership Safety Indicator – y8 Indicator – y9 Travel time Indicator – y10 Travel cost Waiting time Indicator – y11 Flexibility Indicator – y12 Family size Indicator – y13 Full time worker Indicator – y14 Indicator – y15 Trip rate Reliability Trip purpose Indicator – y16 Distance travelled Indicator – y17 Indicator – y18 Indicator – y19 y y1,ij = γ y1,j * Comfort ij + ζ y1,ij Satisfaction Indicator – y20 Process of structural and measurement relationship (Anwar et al., in press)
  • 16. Data and Methods Hybrid discrete choice modelling (i) sequential (also known as two-step) approach and (ii) simultaneous approach Step 1: A MIMIC model (a type of regression model with a latent dependent variable(s) is estimated; and Step 2: A choice model with random parameters is estimated with incorporating the information from the first step.
  • 17. Data and Methods Hybrid discrete choice modelling Why sequential approach: (1) the estimated results were not statistically different from simultaneous (Raveau et al., 2010); (2) it is less cumbersome (Johansson et al. 2006) (3) travel decision itself is sequential
  • 18. Data and Methods Hybrid random parameter logit model Uij = Vij + εij, (3) Vij = Σkθjk * Xijk + Σlβjl * ηijl (4) Specifications of RPL Uij = xijβj Set of explanatory variables observed by the researcher. For example: SEC, LOS and TC = xijβj Deterministic component εij, + + (5) Variables not observed by the researcher. (Stochastic influence / error term) zijηi + Additional random term. It models the presence of correlation or heteroscedasticity among alternatives ϕij (6) Random component Problem: When β parameters vary in the population and the researcher is not able to explain it.
  • 19. Data and Methods Specifications of RPL Model  Model of logit Pr (j|η) = Lj (η) = (expXjβj+Zjη)/(ΣJexpXJβJ+ZJη) (7)  To derive RPL model from eq. 6 ϕ is assumed as IID extreme value η follows a general distribution, f(η|Ω).  As η is not given, the (unconditional) choice probability is this logit formula integrated over all values of η weighted by the density of η is the RPL model as below: P(j) = ∫η[(eXjβj+Zjη)/(ΣkeXkβk+Zkη)]f(ηΩ)∂η (8)  Estimating β (random parameter) and Ω (non-random parameter).
  • 20. Empirical Results Table 2: MIMIC model results (α): structural equations (t-values in the parenthesis) Travel time Comfort Convenience Flexibility Safety Reliability Satisfaction Model fit criteria GFI AGFI NFI CFI RMSEA Lower bound upper bound Travel cost Waiting time Age Income Family size Gender Car ownership No. child Full time Trip rate Distance travelled Trip purpose -0.055 (-2.10) -0.127 (-9.51) -0.171 (-7.52) -0.166 (-6.23) -0.444 (-5.24) -0.129 (-1.98) -0.202 (-5.77) -0.058 (-2.00) -0.004 (-1.99) -0.100 (-3.04) -0.022 (1.87) -0.155 (-6.66) -0.175 (-2.00) -0.222 (-4.35) -0.067 (2.99) -0.089 (-1.97) -0.107 (-3.33) -0.077 (-2.80) -0.014 (-11.1) -0.132 (-2.45) -0.184 (-4.12) -0.258 (-3.45) -0.142 (-4.44) -0.143 (-11.11) 0.145 (2.72) 0.189 (2.33) 0.082 (-3.50) -0.136 (-4.49) 0.026 (2.17) 0.028 (4.52) -0.008 (-3.15) -0.006 (-3.45) 0.021 (5.10) 0.011 (6.0) -0.009 (-2.10) -0.086 (-4.44) 0.054 (3.35) 0.189 (2.85) -0.106 (-3.13) -0.08 (-6.85) 0.074 (3.85) -0.086 (-3.45) 0.221 (5.00) 0.132 (5.63) -0.011 (-2.50) -0.087 (-6.78) 0.122 (3.21) 0.102 (6.19) 0.221 (4.21) 0.136 (2.89) -0.121 (-6.37) -0.121 (-6.37) 0.013 (4.25) 0.109 (15.25) 0.008 (2.03) 0.058 (4.68) 0.137 (3.43) 0.012 (2.00) 0.012 (2.00) 0.019 (3.17) 0.107 (17.83) 0.111 (4.84) 0.115 (2.05) 0.160 (8.00) 0.168 (6.41) 0.212 (3.45) 0.022 (7.33) 0.063 (1.75) 0.171 (2.00) 0.126 (10.5) 0.126 (5.73) 0.031 (2.58) 0.025 (2.08) Significant at 90% level of confidence if 1.960 > t ≥ 1.645; Significant at 95% level of confidence if 2.576 > t ≥ 1.960; Significant at 99% level of confidence if 2.810 > t ≥ 2.576; Significant at 99.5% level of confidence if 3.290 > t ≥ 2.810; Significant at 99.9% level of confidence if t ≥ 3.290. (Source: Anwar et al., 2011; Anwar et al., in press) 0.927 0.902 0.964 0.911 0.043 0.030 (90% CI of RMSEA) 0.051 (90% CI of RMSEA) 0.071 (3.44) -0.037 (-3.63) -0.037 (-3.44) 0.025 (3.13) 0.045 (5.63)
  • 21. Empirical Results Table 3 Results of random parameter logit models (t-values within the parenthesis) Attributes Travel cost (mean) Travel cost (st.dev.) Waiting time (mean) Waiting time (st.dev.) Age (mean) Age (st.dev.) Car ownership (mean) Car ownership (st.dev.) Having children (mean) Having child (st.dev.) Trip purpose (mean) Trip purpose (st.dev.) Comfort (mean) Comfort (st.dev.) Convenience (mean) Convenience (st.dev.) Safety (mean) Safety (st.dev.) Flexibility (mean) Flexibility (st.dev.) Reliability (mean) Reliability (st.dev.) Satisfaction (mean) Satisfaction (st.dev.) TRPL1 TRPL2 Random parameter in utility functions -3.14 (-2.11) -3.19 (-2.56) 1.07 (1.99) 1.02 (2.45) -1.72 (-2.12) -1.85 (-3.11) 0.08 (3.11) 0.03 (3.41) -0.22 (-1.89) 0.48 (1.66) 1.84 (3.52) 0.03 (3.51) -1.78 (-6.44) 0.11 (3.65) TRPL3 HRPL -3.20 (-5.55) 1.05 (3.45) -1.93 (-3.15) 0.004 (2.48) -0.11 (-1.11) 0.22 (2.01) 1.91 (5.21) 0.02 (4.21) -1.80 (-5.41) 0.26 (3.11) 0.07(3.44) 0.003 (2.33) -2.11 (-2.62) 1.06 (4.21) -1.75 (-3.14) 0.004 (2.99) -0.09 (-2.84) 0.58 (2.63) 1.89 (4.00) 0.04 (4.44) -1.77 (-5.02) 0.12 (2.87) 0.06 (2.15) 0.001 (3.63) 3.32 (7.89) 0.12 (5.66) 3.18 (4.66) 0.22 (5.66) 5.18 (11.11) 0.45 (9.84) 0.73 (1.00) 0.30 (2.16) 5.17 (11.10) 0.01 (9.15) 1.23 (2.66) 0.09 (2.99)
  • 22. Empirical Results Table 3 Results of random parameter logit models (t-values within the parenthesis) (Cont.) Attributes TRPL1 TRPL2 TRPL3 Nonrandom parameter in utility functions Age -0.08 (-0.99) Having children under 5 yrs -0.97 (-3.62) Car ownership 1.27 (3.91) Trip purpose 0.97 (2.89) 0.97 (2.91) Travel time -1.17 (-7.85) -1.17 (-8.77) -1.19 (-6.42) Gender 0.29 (1.89) 0.32 (2.13) 0.39 (2.15) Income 1.32 (1.85) 1.69 (1.11) 1.98 (1.91) Family size -0.94 (-0.45) 0.94 (1.01) 0.93 (0.99) Full time workers of HH 0.97 (0.32) 0.97 (1.45) 0.97 (0.85) Trip rate 0.91 (1.11) 0.91 (1.00) 0.91 (1.74) Distance travelled -0.19 (-1.89) -0.17 (-1.11) -0.78 (-1.01) Mode constant Car as a passenger (base) 0 0 0 Car as a driver -2.22 (-2.45) -2.23 (-2.54) -2.22 (-3.10) Train -1.00 (-1.99) -1.17 (-1.98) -2.18 (-3.41) Bus -0.11 (-0.52) -0.12 (-1.23) -0.14 (-1.22) HRPL -1.11 (-3.63) 0.21 (2.69) 1.50 (0.89) 0.94 (1.00) 0.97 (1.01) 0.91 (1.86) -0.24 (-1.12) 0 -2.41 (-9.00) -2.39 (-7.15) -0.10 (-1.53)
  • 23. Empirical Results Table 3 Results of random parameter logit models (t-values within the parenthesis) (Cont.) Attributes TRPL3 HRPL -0.12 (-3.62) -0.54 (-2.96) -0.08 (-1.98) 0.01 (3.01) -0.09 (-2.66) 0.01 (4.01) -0.01 (-3.99) -0.03 (-3.85) -0.12 (-2.14) 0.65 (5.14) -0.17 (-3.01) 0.05 (3.01) 0.09 (3.10) 0.10 (2.89) 0.45 (11.52) 0.05 (2.45) 0.31 (10.20) 0.08 (5.10) Log likelihood function McFadden Pseudo R-squared Akaike Information Criterion (AIC) Model statistics -812.41 -768.31 0.21 0.25 0.019 0.018 -715.28 0.27 0.017 -613.37 0.36 0.014 Car as a driver Car as a passenger Train Bus Modal choice probability 0.713 0.721 0.080 0.075 0.159 0.160 0.048 0.044 0.731 0.055 0.181 0.033 0.785 0.010 0.190 0.015 Travel cost :Income Waiting time :Income Age: Income Car ownership: Income Having child: income Purpose: Income Comfort: Income Convenience: Income Safety: Income Flexibility: Income Reliability: Income Satisfaction: Income TRPL1 TRPL2 Heterogeneity around the mean -0.11 (-4.21) -0.10 (-2.98) -0.54 (-3.56) -0.54 (-2.56) -0.11 (-1.89) 0.02 (3.12) -0.02 (-1.99)
  • 24. Empirical Results Significant at 90% level of confidence if 1.960 > t ≥ 1.645; Significant at 95% level of confidence if 2.576 > t ≥ 1.960; Significant at 99% level of confidence if 2.810 > t ≥ 2.576; Significant at 99.5% level of confidence if 3.290 > t ≥ 2.810; Significant at 99.9% level of confidence if t ≥ 3.290.
  • 25. Discussions and Conclusions • Percentage of car usage is notably high – presence of agency problem • Required to understand the lack of awareness about travellers’ utility • The HRPL mode is more powerful than the TRPL model • The LVs dominate the traveller choice process
  • 26. Discussions and Conclusions • Therefore, traveller choice attributes are the key issues in the traveller-TfNSW relationship • The hierarchy of importance of attributes are relevant in the context of transport policy responses • This study has clarified the nature of such a policy response by indicating which attributes of the traveller-TfNSW relationship are most important to travellers.
  • 27. Discussions and Conclusions • It is understood that traveller’s preference to mode choice is a fundamental factor to resolve the agency problem • Finally, TfNSW needs to be aware of those attributes of travellers’ choice process that should increase travellers’ utility the most. • Thus, the maximisation of traveller’s utility helps to rectify the agency problem.
  • 28. References • • • • • • • Anwar, A.H.M.M., Tieu, K., Gibson, P., Berryman, M., & Win, K.T. (2011). Structuring the influence of latent variables in traveller preference heterogeneity. Proceedings of the 16th International Conference of Hong Kong Society for Transportation Studies, Hong Kong, 141-148. Anwar, A.H.M.M., Tieu, K., Gibson, P., Win, K.T. & Berryman J.M. (in press). Analysing the heterogeneity of traveller mode choice preference using a random parameter logit model from the perspective of principal-agent theory. International Journal of Logistics Systems and management. Barney, J.B. & Hesterly, W. (1996). Organizational economics: Understanding the relationship between organizations and economic analysis. In handbook of organization, C. Stewart, H. Cynthia, and N. Walter R. (Ed.), London and Thousand Oaks: Sage Publications Eisenhardt, K.M., (1989). Agency theory: An assessment and review. Academy of Management Review, 14(1), 57-74. Johansson M.V, Heldt, T., & Johansson, P. (2006). The effects of attitudes and personality traits on mode choice. Transportation Research Part A: Policy and Practice, 40(6), 507-525. Raveau, S., Alvarez-Daziano, R., Yanez, M.F., Bolduc, D. and de Dios Ortuzar, J. (2010) ‘Sequential and simultaneous estimation of hybrid discrete choice models: some new findings’, Transportation Research Record, No. 2156, pp.131-139. Rungtusanatham, M., Rabinovich, E., Ashenbaum, B. & Wallin, C. (2007). Vendor-owned inventory management arrangements in retail: an agency theory perspective. Journal of Business Logistics, 28(1), 111-35.
  • 29. A.H.M. Mehbub Anwar Kiet Tieu Peter Gibson Khin Than Win Matthew J. Berryman PhD Student Professor Associate Professor Senior Lecturer Senior Research Fellow Email: ahmma324@uowmail.edu.au Email: ktieu@uow.edu.au Email: peterg@uow.edu.au Email: win@uow.edu.au Email: mberryma@uow.edu.au