The value of additional data for public transport origin–destination matrix estimation
place - europe, place - urban, mode - subway/metro, planning - methods
Dynamic origin–destination, OD estimation, Entropy maximization, Lagrangian relaxation, Smart card, Public transport
Passenger origin–destination data is an important input for public transport planning. In recent years, new data sources have become increasingly common through the use of the automatic collection of entry counts, exit counts and link flows. However, collecting such data can be sometimes costly. The value of additional data collection hence has to be weighed against its costs. We study the value of additional data for estimating time-dependent origin–destination matrices, using a case study from the London Piccadilly underground line. Our focus is on how the precision of the estimated matrix increases when additional data on link flow, destination count and/or average travel distance is added, starting from origin counts only. We concentrate on the precision of the most policy-relevant estimation outputs, namely, link flows and station exit flows. Our results suggest that link flows are harder to estimate than exit flows, and only using entry and exit data is far from enough to estimate link flows with any precision. Information about the average trip distance adds greatly to the estimation precision. The marginal value of additional destination counts decreases only slowly, so a relatively large number of exit station measurement points seem warranted. Link flow data for a subset of links hardly add to the precision, especially if other data have already been added.
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Ait-Ali, A., & Eliasson, J. (2022). The value of additional data for public transport origin–destination matrix estimation. Public Transport, Vol. 14, pp. 419–439.