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This study proposes a flexible and scalable single-level framework fororigin-destination matrix (ODM) inference using data from IoT (Internet ofThings) and other sources. The framework allows the analyst to integrateinformation from multiple data sources, while controlling for differences indata quality across sources. We assess the effectiveness of the frameworkthrough a real-world experiment in Greater Adelaide (GA), Australia. We infercar OD flows within the region using four separate data sources: site-leveltraffic counts from loop detectors, vehicle trajectories recorded by roadsideBluetooth sensors, partial OD flows based on data from in-vehicle navigationsystems, and journey-to-work data collected by the Australian Census. Wecompare our OD inferences with those from the current version of theMetropolitan Adelaide Strategic Transport Model (MASTEM), calibrated using datafrom traditional household travel surveys. We find remarkable consistencybetween our inferences and those from MASTEM, despite differences in input dataand methodologies. For example, for the morning peak period, we predict thetotal number of trips made within GA to be equal to 556,000, while thecorresponding prediction from MASTEM is equal to 484,000. The two predictionsare within 20 per cent of each other. When we compare the spatial distributionof trips, in terms of origins and destinations, we find that our inferred ODmatrix has an 86 per cent cosine similarity to the corresponding MASTEM matrix.In summary, our results show that the proposed framework can produce highlycomparable ODM, trip production and trip attraction patterns to those inferredfrom traditional household travel survey-based transportation demand modellingmethods.

A flexible and scalable single-level framework for OD matrix inference using multiple sources of transport information