Norm approximation method for handling traffic count inconsistencies in path flow estimator

Anthony Chen, Piya Chootinan, Will Recker
2009 Transportation Research Part B: Methodological  
Path flow estimator (PFE) is a one-stage network observer proposed to estimate path flows and hence origin-destination (O-D) flows from traffic counts in a transportation network. Although PFE does not require traffic counts to be collected on all network links when inferring unmeasured traffic conditions, it does require all available counts to be reasonably consistent. This requirement is difficult to fulfill in practice due to errors inherited in data collection and processing. The original
more » ... FE model handles this issue by relaxing the requirement of perfect replication of traffic counts through the specification of error bounds. This method enhances the flexibility of PFE by allowing the incorporation of local knowledge, regarding the traffic conditions and the nature of traffic data, into the estimation process. However, specifying appropriate error bounds for all observed links in real networks turns out to be a difficult and time-consuming task. In addition, improper specification of the error bounds could lead to a biased estimation of total travel demand in the network. This paper therefore proposes the norm approximation method capable of internally handling inconsistent traffic counts in PFE. Specifically, three norm approximation criteria are adopted to formulate three L p -PFE models for estimating consistent path flows and O-D flows that simultaneously minimize the deviation between the estimated and observed link volumes. A partial linearization algorithm embedded with an iterative balancing scheme and a column generation procedure is developed to solve the three L p -PFE models. In addition, the proposed L p -PFE models are illustrated with numerical examples and the characteristics of solutions obtained by these models are discussed. Published by Elsevier Ltd.
doi:10.1016/j.trb.2009.02.007 fatcat:waz7v537bjaunanr5rrjzbn34q