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Big Data fusion and parametrization for strategic transport demand models
Ever more observed data on destination and mode choices made by travelers is becoming available from e.g. GSM and ANPR data. For strategic transport demand modelling, this means that instead of estimating synthetic models and calibrating them on the limited set of available observations for a single study period definition, different data sources are fused to a 'common operational picture' of the total travel demand for many different study period definitions and this fused data is parametrized to a synthetic model for application in model forecasts. Three issues arise in the data fusion step. Firstly, inconsistencies between data sources and/or observations need to be detected and removed. Secondly, different data sources need to be weighted and normalized, often without (comparable or usable) reliability measures available. Thirdly, the data fusion problem is underspecified: the level of spatial detail of the transport models zoning system is usually higher than the observed data can provide. This paper proposes and demonstrates a method that solves all three data fusion problems by use of a multi-proportional gravity model to fuse all data into a single set of travel demand matrices. This set of demand matrices can be directly used in operational applications or parametrized to be used in tactical and strategical applications using a bi-level optimization method that is also described in this paper. The methodology is used to conduct OD matrix estimation using GSM data, observed modal splits, trip frequency distributions and synthetic trip generation, but can be used to fuse and parametrize any data source that relates to (aggregates of) mode-origin-destination combinations.
Big Data fusion and parametrization for strategic transport demand models
Ever more observed data on destination and mode choices made by travelers is becoming available from e.g. GSM and ANPR data. For strategic transport demand modelling, this means that instead of estimating synthetic models and calibrating them on the limited set of available observations for a single study period definition, different data sources are fused to a 'common operational picture' of the total travel demand for many different study period definitions and this fused data is parametrized to a synthetic model for application in model forecasts. Three issues arise in the data fusion step. Firstly, inconsistencies between data sources and/or observations need to be detected and removed. Secondly, different data sources need to be weighted and normalized, often without (comparable or usable) reliability measures available. Thirdly, the data fusion problem is underspecified: the level of spatial detail of the transport models zoning system is usually higher than the observed data can provide. This paper proposes and demonstrates a method that solves all three data fusion problems by use of a multi-proportional gravity model to fuse all data into a single set of travel demand matrices. This set of demand matrices can be directly used in operational applications or parametrized to be used in tactical and strategical applications using a bi-level optimization method that is also described in this paper. The methodology is used to conduct OD matrix estimation using GSM data, observed modal splits, trip frequency distributions and synthetic trip generation, but can be used to fuse and parametrize any data source that relates to (aggregates of) mode-origin-destination combinations.
Big Data fusion and parametrization for strategic transport demand models
Brederode, Luuk (author) / Pots, Mark (author) / Fransen, Ruben (author) / Brethouwer, Jan-Tino (author)
2019-06-01
745106 byte
Conference paper
Electronic Resource
English
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