Skip to main content
Intended for healthcare professionals
Restricted access
Research article
First published online January 1, 2011

Computing Individual Path Marginal Cost in Networks with Queue Spillbacks

Abstract

“Individual path marginal cost” (IPMC) is defined as the change in travel cost of one unit of flow on a time-dependent path caused by one unit of flow on another time-dependent path. Knowledge of IPMC is central to dynamic transportation modeling, for instance, to compute system-optimal network performance, to solve a dynamic origin–destination (O-D) estimation problem, and to analyze equity issues for travelers with different origins and destinations. This paper proposes a method of approximating IPMC for general networks, in which a cell transmission model–based kinematic wave model is used to model traffic dynamics. By tracing the changes in the cumulative flow curves of the bottleneck links on which queues form during dynamic network loading, an approximation method is developed to obtain the IPMC for the cases of merge junctions, diverge junctions, and general junctions. This method was applied to compute the total path marginal cost in a network. The results showed that vehicles at the beginning of the congestion duration had significantly larger marginal travel costs than other vehicles. The method was then applied to solve a dynamic O-D estimation problem with partial link-flow counts and historical O-D trip tables. With the incorporation of IPMC into the estimation procedure, both the O-D demands and the observed path travel times were successfully reproduced.

Get full access to this article

View all access and purchase options for this article.

References

1. Ghali M., and Smith M. A Model for the Dynamic System Optimum Traffic Assignment Problem. Transportation Research Part B: Methodological, Vol. 29, No. 3, 1995, pp. 155–171.
2. Peeta S., and Mahmassani H. System Optimal and User Equilibrium Time-Dependent Traffic Assignment in Congested Networks. Annals of Operations Research, Vol. 60, No. 1, 1995, pp. 80–113.
3. Shen W., Nie Y., and Zhang H. M. On Path Marginal Cost Analysis and Its Relation to Dynamic System-Optimal Traffic Assignment. In Transportation and Traffic Theory 2007 (Allsop R. E., Bell M. G. H., and Heydecker Benjamin G., eds.), Proc., 17th International Symposium on Transportation and Traffic Theory, London, 2007, pp. 327–360.
4. Merchant D., and Nemhauser G. Model and an Algorithm for the Dynamic Traffic Assignment Problems. Transportation Science, Vol. 12, No. 3, 1978, pp. 183–199.
5. Merchant D., and Nemhauser G. Optimality Conditions for a Dynamic Traffic Assignment Model. Transportation Science, Vol. 12, No. 3, 1978, pp. 200–207.
6. Carey M. Optimal Time-Varying Flows on Congested Networks. Operations Research, Vol. 35, No. 1, 1987, pp. 58–69.
7. Friesz T., Bernstein D., Mehta N., Tobin R., and Ganjalizadeh S. Dynamic Network Traffic Assignment Considered as a Continuous Time Optimal Control Problem. Operations Research, Vol. 37, No. 6, 1989, pp. 893–901.
8. Smith M. A New Dynamic Traffic Model and the Existence and Calculation of Dynamic User Equilibria on Congested Capacity-Constrained Road Networks. Transportation Research Part B: Methodological, Vol. 26, No. 1, 1993, pp. 49–63.
9. Kuwahara M. Decomposition of the Reactive Dynamic Assignments with Queues for a Many-to-Many Origin–Destination Pattern. Transportation Research Part B: Methodological, Vol. 31, No. 1, 1997, pp. 1–10.
10. Ziliaskopoulos A. A Linear Programming Model for the Single Destination System Optimum Dynamic Traffic Assignment Problem. Transportation Science, Vol. 34, No. 1, 2000, pp. 37–49.
11. Daganzo C. F. The Cell Transmission Model: A Dynamic Representation of Highway Traffic Consistent with the Hydrodynamic Theory. Transportation Research Part B: Methodological, Vol. 28, No. 4, 1994, pp. 269–287.
12. Daganzo C. F. The Cell Transmission Model, Part II: Network Traffic. Transportation Research Part B: Methodological, Vol. 29, No. 2, 1995, pp. 79–93.
13. Nie Y. A Variational Inequality Approach for Inferring Dynamic Origin–Destination Travel Demands. PhD thesis. University of California, Davis, 2006.
14. Nie Y., and Zhang H. M. Solving the Dynamic User Optimal Assignment Problem Considering Queue Spillback. Networks and Spatial Economics, Vol. 10, No. 1, 2010, pp. 49–71.
15. Jin W., and Zhang H. M. On the Distribution Schemes for Determining Flows Through a Merge. Transportation Research Part B: Methodological, Vol. 37, No. 6, 2003, pp. 521–540.
16. Zhang H. M., Nie Y., and Qian Z. Estimating Time-Dependent Freeway Origin–Destination Demands with Different Data Coverage: Sensitivity Analysis. In Transportation Research Record: Journal of the Transportation Research Board, No. 2047, Transportation Research Board of the National Academies, Washington, D.C., 2008. 91–99.

Cite article

Cite article

Cite article

OR

Download to reference manager

If you have citation software installed, you can download article citation data to the citation manager of your choice

Share options

Share

Share this article

Share with email
EMAIL ARTICLE LINK
Share on social media

Share access to this article

Sharing links are not relevant where the article is open access and not available if you do not have a subscription.

For more information view the Sage Journals article sharing page.

Information, rights and permissions

Information

Published In

Article first published online: January 1, 2011
Issue published: January 2011

Rights and permissions

© 2011 National Academy of Sciences.
Request permissions for this article.

Authors

Affiliations

Zhen (Sean) Qian
Department of Civil and Environmental Engineering, University of California, Davis, Davis, CA 95616.
H. Michael Zhang
Department of Civil and Environmental Engineering, University of California, Davis, Davis, CA 95616.

Notes

Metrics and citations

Metrics

Journals metrics

This article was published in Transportation Research Record: Journal of the Transportation Research Board.

VIEW ALL JOURNAL METRICS

Article usage*

Total views and downloads: 24

*Article usage tracking started in December 2016


Altmetric

See the impact this article is making through the number of times it’s been read, and the Altmetric Score.
Learn more about the Altmetric Scores



Articles citing this one

Receive email alerts when this article is cited

Web of Science: 0

Crossref: 13

  1. Dynamic Routing of Heterogeneous Users After Traffic Disruptions Under...
    Go to citation Crossref Google Scholar
  2. A Bayesian Method for Dynamic Origin–Destination Demand Estimation Syn...
    Go to citation Crossref Google Scholar
  3. Estimating multi-class dynamic origin-destination demand through a for...
    Go to citation Crossref Google Scholar
  4. Path-based system optimal dynamic traffic assignment: A subgradient ap...
    Go to citation Crossref Google Scholar
  5. Estimating multi-year ...
    Go to citation Crossref Google Scholar
  6. A stochastic optimal control approach for real-time traffic routing co...
    Go to citation Crossref Google Scholar
  7. Efficient calibration techniques for large-scale traffic simulators
    Go to citation Crossref Google Scholar
  8. Development of a behaviorally induced system optimal travel demand man...
    Go to citation Crossref Google Scholar
  9. Eco-system optimal time-dependent flow assignment in a congested netwo...
    Go to citation Crossref Google Scholar
  10. Dynamic system optimal model for multi-OD traffic networks with an adv...
    Go to citation Crossref Google Scholar
  11. Improving the efficiency of repeated dynamic network loading through m...
    Go to citation Crossref Google Scholar
  12. A modification of local path marginal cost on the dynamic traffic netw...
    Go to citation Crossref Google Scholar
  13. Dynamic origin–destination demand flow estimation under congested traf...
    Go to citation Crossref Google Scholar

Figures and tables

Figures & Media

Tables

View Options

Get access

Access options

If you have access to journal content via a personal subscription, university, library, employer or society, select from the options below:


Alternatively, view purchase options below:

Purchase 24 hour online access to view and download content.

Access journal content via a DeepDyve subscription or find out more about this option.

View options

PDF/ePub

View PDF/ePub