Simulation-Based Origin-Destination Matrix Reduction: A Case Study of Helsinki City Area

Abstract

Estimation of a travel demand in a form of origin-destination (OD) matrix is a necessary step in a city-scale simulation of the vehicular mobility. However, an input data on travel demand in OD matrix may be available only for a specific set of traffic assignment zones (TAZs). Thus, there appears a need to infer OD matrix for a region of interest (we call it ‘core’ area) given OD matrix for a larger region (we call it ‘extended’ area), which is challenging as trip counts are only given for zones of the initial region. To perform a reduction, we explicitly simulate vehicle trajectories for the extended area and supplement trip values in ‘core’ TAZs based on the recorded trajectories on the border of core and extended areas. To keep validation results consistent between extended and core simulations, we introduce edge-based origin-destination assignment algorithm which preserves properties of traffic flows on the border of the core area but also keeps randomness in instantiating simulation for the core area. The experimental study is performed for Helsinki city area using Simulation of Urban MObility (SUMO) tool. The validation was performed using DigiTraffic data from traffic counting stations within the city area for workdays of autumn 2018. Validation results show that the reduced OD matrix combined with edge-based OD assignment algorithm keeps the simulated traffic counts in good agreement with results from the extended area simulation with average MAPE between observed and simulated traffic counts equal to 34%. Simulation time after reduction is equal to 20 minutes compared to 6 hours for the extended OD.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 9 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.