Advertisement

Transportation

, Volume 45, Issue 3, pp 971–987 | Cite as

A business establishment fleet ownership and composition model

  • Taha Hossein Rashidi
  • Matthew J. Roorda
Article

Abstract

Commercial vehicle fleet ownership is a business decision that is fundamental to the transportation operations of a business establishment. This paper develops a model of fleet ownership and composition for the Region of Peel, Canada. A trivariate model is developed to represent a business establishment’s correlated decisions of how many passenger cars, pickups/vans, or trucks/tractors are owned by the company. The joint model is approximated into a closed-form formulation by using the composite marginal likelihood approach combined with copula functions. The models are estimated using establishment data from the Region of Peel Commercial Travel Survey, and land use and transportation infrastructure variables developed from GIS databases. Larger establishments, as measured by number of employees, are more likely to own vehicles, as are establishments with greater demand for goods and services. Differences are found between establishments in different industry classifications, with the construction industry favoring pickups/vans, and the office/service industries less inclined to own trucks or pickups/vans. Significant negative copulas between ownership of different vehicle types reveal a substitution effect. This also shows the importance of developing the joint model over the independent formulation for decisions on ownership of different types of vehicles.

Keywords

Business vehicle ownership Multivariate copula Composite marginal 

References

  1. Abdur Razzaque, M., Chen Sheng, C.: Outsourcing of logistics functions: a literature survey. Int. J. Phys. Distrib. Logist. Manag. 28(2), 89–107 (1998)CrossRefGoogle Scholar
  2. Arunapuram, S., Mathur, K., Solow, D.: Vehicle routing and scheduling with full truckloads. Transp. Sci. 37(2), 170–182 (2003)CrossRefGoogle Scholar
  3. Ball, M.O., Golden, B.L., Assad, A., Bodin, L.D.: Planning for truck fleet size in the presence of a common-carrier option. Decis. Sci. 14, 103–120 (1983)CrossRefGoogle Scholar
  4. Baltas, G.: A model for multiple brand choice. Eur. J. Oper. Res. 154(1), 144–149 (2004)CrossRefGoogle Scholar
  5. Bhat, C.R., Sener, I.N., Eluru, N.: A flexible spatially dependent discrete choice model: formulation and application to teenagers’ weekday recreational activity participation. Transp. Res. Part B Methodol. 44(8), 903–921 (2010)CrossRefGoogle Scholar
  6. Bhat, C.R., Eluru, N.: A copula-based approach to accommodate residential self-selection effects in travel behavior modelling. Transp. Res. Part B Methodol. 43(7), 749–765 (2009)CrossRefGoogle Scholar
  7. Bhat, C.R.A.: Multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions. Transp. Res. Part B 39(8), 679–707 (2005)CrossRefGoogle Scholar
  8. Bhat, C.R., Sen, S.: Household vehicle type holdings and usage: an application of the multiple discrete-continuous extreme value (MDCEV) model. Transp. Res. Part B 40(1), 35–53 (2006)CrossRefGoogle Scholar
  9. Boerkamps, J.H.K., van Binsbergen, A.J., Bovy, P.H.L.: Modeling behavioral aspects of urban freight movement in supply chains. Transp. Res. Rec. J. Transp. Res. Board 1725, 17–25 (2000)CrossRefGoogle Scholar
  10. Bovenkerk, M.: SMILE + , the new and improved Dutch national freight model system. In: European Transport Conference, Strasbourg, 3–5 October (2005)Google Scholar
  11. de Jong, G.C., Kitamura, R.: A review of household dynamic vehicle ownership models: holdings models versus transactions models. Transportation 36, 733–743 (2009)CrossRefGoogle Scholar
  12. de Jong, G.C., Fox, J., Pieters, M., Daly, A., Smit, R.: A comparison of car ownership models. Transp. Rev. 24(4), 379–408 (2004)CrossRefGoogle Scholar
  13. de Jong, G., Ben-Akiva, M.: A micro-simulation model of shipment size and transport chain choice. Transp. Res. Part B Methodol. 41(9), 950–965 (2007)CrossRefGoogle Scholar
  14. Di Febbraro, A., Sacco, N., Saeednia, M.: An agent-based framework for cooperative planning of intermodal freight transport chains. Transp. Res. Part C Emerg. Technol. 64, 72–85 (2016)CrossRefGoogle Scholar
  15. Dimitropoulos, A., van Ommeren, J.N., Koster, P., Rietveld, P.: Welfare Effects of Distortionary Tax Incentives under Preference Heterogeneity: An Application to Employer-Provided Electric Cars, Tinbergen Institute Discussion, Paper No. 2014-064/VIII. Amsterdam (2014)Google Scholar
  16. Fischer, M.J., Outwater, M.L., Cheng, L.L., Ahanotu, D.N., Calix, R.: An innovative framework for modeling freight transportation in Los Angeles County. Transp. Res. Rec. J. Transp. Res. Board 1906, 105–112 (2005)CrossRefGoogle Scholar
  17. Garikapati, V.M., You, D., Pendyala, R.M., Jeon, K., Livshits, V., Bhat, C.R.: Development of a Vehicle Fleet Composition Model System: Results from an Operational Prototype, Technical paper, School of Civil and Environmental Engineering, Georgia Institute of Technology (2015)Google Scholar
  18. Golob, T.F., Regan, A.C.: Trucking industry adoption of information technology: a multivariate discrete choice model. Transp. Res. Part C Emerg. Technol. 10(2), 205–228 (2002)CrossRefGoogle Scholar
  19. Göl, H., Çatay, B.: Third-party logistics provider selection: insights from a Turkish automotive company. Supply Chain Manag. Int. J. 12(6), 379–384 (2007)CrossRefGoogle Scholar
  20. Hensher, D.A., Ton, T.: TRESIS: a transportation, land use and environmental strategy impact simulator for urban areas. Transportation 29(4), 439–457 (2002)CrossRefGoogle Scholar
  21. Hoen, A., Koetse, M.: Preferences for Alternative Fuel Vehicles of Lease Car Drivers in The Netherlands, PBL Working Paper 4, PBL Netherlands Environmental Assessment Agency, The Hague, The Netherlands (2012)Google Scholar
  22. Holmgren, J., Davidsson, P., Persson, J.A., Ramstedt, L.: TAPAS: a multi-agent-based model for simulation of transport chains. Simul. Model. Pract. Theory 23, 1–18 (2012)CrossRefGoogle Scholar
  23. Hunt, J.D., Stefan, K.J.: Tour-based microsimulation of urban commercial movements. Transp. Res. Part B Methodol. 41(9), 981–1013 (2007)CrossRefGoogle Scholar
  24. Klincewics, J.G., Luss, H., Pilcher, M.G.: Fleet size planning when outside carrier services are available. Transp. Sci. 24(3), 169–182 (1990)CrossRefGoogle Scholar
  25. Lankford, W.M., Parsa, F.: Outsourcing: a primer. Manag. Decis. 37(4), 310–316 (1999)CrossRefGoogle Scholar
  26. Le Cessie, S., Van Houwelingen, J.C.: Logistic regression for correlated binary data. J. R. Stat. Soc. Ser. C (Appl. Stat.) 43(1), 95–108 (1994)Google Scholar
  27. Li, Z., Tao, F.: on determining optimal fleet size and vehicle transfer policy for a car rental company. Comput. Oper. Res. 37(2), 341–350 (2009)CrossRefGoogle Scholar
  28. Liedtke, G.: An Actor-Based Approach to Commodity Transport Modelling. Nomos Verlagsgesellschaft, Baden Germany (2006)Google Scholar
  29. Maltz, A.: Private fleet use: a transaction cost model. Transp. J. 32(3), 45–53 (1993)Google Scholar
  30. Mannering, F., Winston, C.: A Dynamic empirical analysis of household vehicle ownership and utilization. Rand J. Econ 16(2), 213–236 (1985)CrossRefGoogle Scholar
  31. Mohammadian, A., Rashidi, T.H.: Modeling household vehicle transaction behavior: a competing risk duration approach. Transp. Res. Rec. 2014, 9–16 (2007)CrossRefGoogle Scholar
  32. Montemayor, H.M.: Outsourcing transportation services: evidence from Mexican maquiladora industry. Int. J. Oper. Logist. Manag. 3(1), 48–57 (2014)Google Scholar
  33. Niraj, R., Padmanabhan, V., Seetharaman, P.B.: A cross-category model of households’ incidence and quantity decisions. Mark. Sci. 27(2), 225–235 (2008)CrossRefGoogle Scholar
  34. Pourabdollahi, Z., Mohammadian, A., Kawamura, K.: A conceptual framework for a behavioral freight transportation modeling system with logistics choices. In: 13th International Conference on Travel Behavior Research, Toronto, 15-20 July (2012)Google Scholar
  35. Oman, S.D., Landsman, V., Carmel, Y., Kadmon, R.: Analyzing spatially distributed binary data using independent-block estimating equations. Biometrics 63(3), 892–900 (2007)CrossRefGoogle Scholar
  36. Rashidi, T., Auld, J., Mohammadian, A.: Effectiveness of bayesian updating attributes in data transferability applications. Transp. Res. Rec. J. Transp. Res. Board 2344, 1–9 (2013)CrossRefGoogle Scholar
  37. Roorda, M.J., Cavalcante, R., McCabe, S., Kwan, H.: A conceptual framework for agent-based modelling of logistics services. Transp. Res. Part E 46(1), 18–31 (2010)CrossRefGoogle Scholar
  38. Roorda, M., McCabe, S., Kwan, H.: Design of a Shipper-Based Survey of Freight Movements in the Greater Golden Horseshoe. In: Proceedings of the Annual Canadian Transportation Research Forum (CTRF) Conference, Quebec, Quebec (2006)Google Scholar
  39. Roorda, M.J., McCabe, S., Kwan, H.: Comparing GPS and non-gps survey methods for collecting urban goods and service movements. In: CD Proceedings of the 8th International Conference on Travel Survey Methods, Annecy, France, May 25–31 (2008)Google Scholar
  40. Ruan, M., Lin, J.J., Kawamura, K.: Modeling urban commercial vehicle daily tour chaining. Transp. Res. Part E Logist. Transp. Rev. 48(6), 1169–1184 (2012)CrossRefGoogle Scholar
  41. Schroeter, J.R.: A model of taxi service under fare structure and fleet size regulation. Bell J. Econ. 14(1), 81–96 (1983)CrossRefGoogle Scholar
  42. Seetharaman, P.B., Chib, S., Ainslie, A., Boatwright, P., Chan, W., Gupa, S., Mehta, N., Rao, V., Strujnev, A.: Models of multi-category choice behavior. Mark. Lett. 16(3/4), 239–254 (2005)CrossRefGoogle Scholar
  43. Sklar, A.: Fonctions de répartition à n dimensions et leurs marges. Publ. Inst. Statist. Univ. Paris 8, 229–231 (1959)Google Scholar
  44. Talley, W.K., Anderson, E.E.: An urban transit affirm providing transit, paratransit and contracted out services. J. Transp. Econ. Policy 20(3), 353–368 (1986)Google Scholar
  45. Tavasszy, L.A., van de Vlist, M., Ruijgrok, C., van de Rest, J.: Scenario-wise analysis of transport and logistics with a SMILE. In: 8th WCTR Conference, Antwerp, 12–16 July (1998)Google Scholar
  46. Walters, R.G.: Assessing the impact of retail price promotions on product substitution, complementary purchase and inter-store displacement. J. Mark. 55(1), 17–28 (1991)CrossRefGoogle Scholar
  47. Walters, R.G., MacKenzie, S.B.: A structural equation model of the impact of price promotions on store performance. J. Mark. Res. 25(4), 551–563 (1988)Google Scholar
  48. Wang, Q., Holguin-Veras, J.: Investigation on the attributes determining trip chaining behavior in hybrid micro-simulation urban freight models. Transp. Res. Rec. J. Transp. Res. Board 2066, 1–8 (2008)CrossRefGoogle Scholar
  49. Wisetjindawat, W., Sano, K., Matsumoto, S., Raothanachonkun, P.: Micro-simulation model for modeling freight agents interactions in urban freight movement. In: CD Proceedings, 86th Annual Meeting of the Transportation Research Board, Washington DC, 21–25 January (2007)Google Scholar
  50. Yin, Y, Williams, I., Shahkarami, M.: Integrated regional economic and freight logistics modelling: results from a model for the Trans-Pennine Corridor, UK. In: European Transport Conference, Strasbourg, 3–5 October (2005)Google Scholar
  51. You, D., Garikapati, V.M., Pendyala, R.M., Bhat, C.R., Dubey, S., Jeon, K., Livshits, V.: Development of a vehicle fleet composition model system for implementation in an activity-based travel model. Transp. Res. Rec. J. Transp. Res. Board 2430, 145–154 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Civil and Environmental EngineeringUniversity of New South WalesSydneyAustralia
  2. 2.Department of Civil EngineeringUniversity of TorontoTorontoCanada

Personalised recommendations