Transportation

, Volume 38, Issue 2, pp 227–247 | Cite as

The importance of being early

  • Pavithra Parthasarathi
  • Anupam Srivastava
  • Nikolas Geroliminis
  • David Levinson
Article

Abstract

This research quantifies the relationship between the cost of earliness and lateness by empirically observing commute trips from two different sources. The first empirical analysis uses individual level travel survey data from six metropolitan regions while the second analysis uses traffic data from the Twin Cities freeway network. The analysis conducted in this research provides a method to estimate the ratio of the costs of earliness to lateness for different datasets. This can be a useful tool for traffic engineers and planners, to assist them in the development and implementation of improved control strategies for congested cities. The results also corroborates the hypothesis of earliness being less expensive than lateness and show that the finding holds steady over time and across different regions and levels.

Keywords

Earliness to lateness Congestion pricing Macroscopic traffic model 

References

  1. Arnott, R.J., De Palma, A., Lindsey, R.: Departure time and route choice for the morning commute. Transp. Res. B Methodol. 24B, 209–228 (1990)CrossRefGoogle Scholar
  2. Cassidy, M., Windover, J.: Methodology for assessing dynamics of freeway traffic flow. Transp. Res. Rec. Natl. Res. Counc. 1484, 73–79 (1995)Google Scholar
  3. Daganzo, C.: Urban gridlock: macroscopic modeling and mitigation approaches. Transp. Res. B 41, 49–62 (2007)CrossRefGoogle Scholar
  4. Geroliminis, N., Daganzo, C.: Macroscopic modeling of traffic in cities. In: 86th Annual Meeting of the Transportation Research Board, pp. 07-0413. Washington, DC (2007)Google Scholar
  5. Geroliminis, N., Daganzo, C.: Existence of urban-scale macroscopic fundamental diagrams: some experimental findings. Transp. Res. B 42, 759–770 (2008)CrossRefGoogle Scholar
  6. Geroliminis, N., Levinson, D.: Cordon pricing consistent with the physics of overcrowding. In: Proceedings of the 18th International Symposium on Transportation and Traffic Theory (2009)Google Scholar
  7. Hendrickson, C., Plank, E.: The flexibility of departure time for work trips. Transp. Res. A 18, 25–36 (1984)CrossRefGoogle Scholar
  8. Hollander, Y.: Direct versus indirect models for the effects of unreliability. Transp. Res. A 40, 699–711 (2006)Google Scholar
  9. Jou R.C., Kitamura R., Weng M.C., Chen C.C.: Dynamic commuter departure time choice under uncertainty. Transp. Res. A 42, 774–783 (2008)Google Scholar
  10. Levinson, D., Harder, K., Bloomfield, J., Carlson, K.: Waiting tolerance: ramp delay vs. freeway congestion. Transp. Res. F 9, 1–13 (2006)Google Scholar
  11. Levinson, D., Krizek, K.: Planning for Place and Plexus: Metropolitan Land Use and Transport. Routledge, New Yrok (2008)Google Scholar
  12. Levinson, D., Zofka, E.: The metropolitan travel survey archive: a case study in archiving. In: Stopher P., Stecher C. (eds.) Travel Survey Methods: Quality and Future Directions, Proceedings of the 5th Intenational Conference on Travel Survey Methods, pp. 223–238. Emerald Group Pub Ltd, Bingley (2006)Google Scholar
  13. Metropolitan Council of the Twin Cities Area: 2000 Travel behavior inventory home interview survey: data and methodology. Metropolitan Council, St. Paul, MI (2003)Google Scholar
  14. Muñoz, J., Daganzo, C., Center, T., University of California (System): Fingerprinting traffic from static freeway sensors. University of California Transportation Center, University of California, California (2000)Google Scholar
  15. Noland, R., Small, K., Koskenoja, P., Chu, X.: Simulating travel reliability. Reg. Sci. Urban Econ. 28, 535–564 (1998)CrossRefGoogle Scholar
  16. Regional Transportation Management Center: http://www.dot.state.mn.us/tmc/tmctools.html. Accessed October 2008
  17. Sarvi, M., Horiguchi, R., Kuwahara, M., Shimizu, Y., Sato, A., Sugisaki, Y.: A methodology to identify traffic condition using intelligent probe vehicles. In: Proceedings of 10th ITS World Congress, Madrid, pp. 17–21 (2003)Google Scholar
  18. Small, K.: The scheduling of consumer activities: work trips. Am. Econ. Rev. 72, 467–479 (1982)Google Scholar
  19. Tilahun, N., Levinson, D.: A moment of time: valuing reliability using stated preference. J. Intell. Transp. Syst. (2006, in press)Google Scholar
  20. University of Minnesota: Metropolitan Travel Survey Archive. http://www.surveyarchive.org (2003). Accessed Oct 2008
  21. Vickrey, W.: Pricing in urban and suburban transport. Am. Econ. Rev. JSTOR 53, 452–465 (1963)Google Scholar
  22. Vickrey, W.: Congestion theory and transport investment. Am. Econ. Rev. 59, 251–260 (1969)Google Scholar
  23. Wu, X., Levinson, D., Liu, H.: Perception of waiting time at signalized intersections. Transp. Res. Rec. 2135, 52–59 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2010

Authors and Affiliations

  • Pavithra Parthasarathi
    • 1
  • Anupam Srivastava
    • 1
  • Nikolas Geroliminis
    • 2
  • David Levinson
    • 3
  1. 1.Department of Civil EngineeringUniversity of MinnesotaMinneapolisUSA
  2. 2.Urban Transport Systems LaboratoryÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
  3. 3.Network, Economics, and Urban Systems Research Group, Department of Civil EngineeringUniversity of MinnesotaMinneapolisUSA

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