Estimation of Travel Demand Using Traffic Counts and Other Data Sources

  • Ennio Cascetta
  • Alessandra A. Improta
Part of the Applied Optimization book series (APOP, volume 63)


Over the last decades, considerable work has been devoted to improve the quality of travel demand estimators by using cheap and easily collectable traffic counts.

In this paper it is first presented a review of the methodology for estimating within-day static O/D demand flows by efficiently combining traffic counts with all other available information, taking into account whether the information is experimental (Classic inference) or “a priori” (Bayesian inference). Within this framework, an analysis of different solution methods is carried out, both in case of link costs known and unknown (congested networks).

Subsequently, it is proposed an extension of previous results to the case of time-varying (within-day dynamic) demand and flows, through within-day dynamic assignment models, by using simultaneous and sequential estimators of O/D matrices.

Finally, the possibility of using aggregate information, namely traffic counts, in order to improve the estimation of demand models parameters is discussed, also considering extensions to the case of joint estimation of O/D flows and demand parameters.


Demand Model Travel Demand Congested Network Assignment Model Generalize Little Square 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer Science+Business Media Dordrecht 2002

Authors and Affiliations

  • Ennio Cascetta
  • Alessandra A. Improta

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