Approximation model to estimate joint market share in off-hour deliveries: William H. Hart Professor
The main objective of this paper is to develop an approximation model to estimate the joint carrier–receiver response to off-hour delivery policies. The model’s main intent is to bypass the need to use more complex approaches that require expensive data for model calibration. Having access to such approximation models would make it easier for transportation agencies and metropolitan planning organizations to analyze and design off-hour deliveries programs and policies. In its first part, the paper discusses carrier–receiver interactions concerning delivery time decisions and the conditions under which both carrier and receivers would agree to off-hour deliveries. Some of the key findings are that the typical receivers would participate only if provided with a financial incentive that covers the costs associated with the off-hour operations and that the carrier would find the off-hour delivery operation profitable if a large number of receivers switch to the off-hours. The latter provides an important piece of information to support the development of the approximation model introduced in the paper. The proposed model estimates the joint market share in off-hour deliveries by computing the joint probability that all receivers in a typical tour of length M agree to off-hour deliveries, the probability that the carrier operation is profitable, and finally the joint market share. The model’s inputs are the probability that a typical receiver would participate in off-hour deliveries, the statistical distribution of tour lengths, and the probability that the carrier operation is profitable for a given number of receivers. The results indicate that the model provides the same results than other more complex methodologies for the practical range of values of receiver participation. For the high end of receiver participation (+80%), the formulation underestimates carrier participation. Because of its simplicity and practicality, the model provides an excellent way to estimate participation in off-hour delivery programs.
KeywordsFreight pricing Off-hour deliveries City logistics
The research reported here was supported by the United States Department of Transportation’s (USDOT) “Integrative Freight Demand Management in the New York City Metropolitan Area,” (DTPH56-06-BAA-0002) funded by the Commercial Remote Sensing and Spatial Information Technology Application Program. This support is both acknowledged and appreciated. This paper does not represent the official position of the USDOT.
- 2.Bureau of Transportation Statistics (2010) Transportation statistics annual report. http://www.bts.gov/publications/transportation_statistics_annual_report/2010/pdf/entire.pdf
- 6.Hicks SK (1977) Urban freight. In: Hensher D (ed) Urban transport economics. Cambridge University Press, Cambridge, pp 100–130Google Scholar
- 7.Holguín-Veras J (2004) On the estimation of the maximum efficiency of the trucking industry: implications for city logistics. In: Taniguchi E, Thomson R (eds) City logistics III. Elsevier, Amsterdam, pp 123–134Google Scholar
- 10.Holguín-Veras J, Ozbay K, Kornhauser AL, Shorris A, Ukkusuri S (2010) Integrative freight demand management in the New York city metropolitan area. http://transp.rpi.edu/~usdotp/OHD_FINAL_REPORT.pdf
- 11.Holguín-Veras J, Ozbay K, Kornhauser AL, Ukkusuri S, Brom M, Iyer S, Yushimito W, Allen B, Silas M (2011) Overall impacts of off-hour delivery programs in the New York city metropolitan area. Transp Res Record (in press)Google Scholar
- 16.Menon APG, Lam SH, Fan HSL (1993) Singapore’s road pricing system: its past, present and future. ITE J 63(12):44–48Google Scholar
- 17.National Surface Transportation Infrastructure Financing Commission (2009) Paying our way: a new framework for transportation finance. http://financecommission.dot.gov/
- 18.Nelson JC (1962) The pricing of highways, waterways, and airways facilities. Am Econ Rev (74th annual meeting of the American economic association) 52(2): 426–435Google Scholar
- 19.Ogden KW (1992) Urban goods movement. Ashgate Publishing, LondonGoogle Scholar
- 20.Port Authority of New York and New Jersey (2009) PANYNJ 2008 toll schedule. Posted: Retrieved March 23, 2009, from http://www.panynj.gov/COMMUTINGTRAVEL/tunnels/html/tolls.html
- 21.Rasmusen E (2001) Games and information: an introduction to game theory. Blackwell Publishers, MaldenGoogle Scholar
- 25.Vickrey WS (1963) Pricing and resource allocation in transportation and public utilities: pricing in urban and suburban transport. Am Econ Rev 53(2):452–465Google Scholar