ISCTSC Workshop A7 Best Practices in Data Fusion

1. ISCTSCWorkshop A7 Best Practices in Data Fusion

2. Objectives • Indentify the state of the art and the state of practice • Identify key research challenges and opportunities • Identify tangible ways to accelerate methodological innovation and adoption in practice

3. What exactly is data fusion? • Using more than one data source to estimate a parameter of interest

4. What exactly is data fusion? • Using more than one data source to estimate a parameter of interest

5. SOP & SOA (1) • There is a long history of data fusion in transport, but very fragmented • Examples • Synthetic population generation • OD matrix updating • Data enrichment in discrete choice model estimation • Network state estimation • Activity pattern feature extraction from trace data • Use of multiple survey modes • Activity and time use survey consolidation • Population exposure modelling • Public transport (e.g. UK bus) OD matrix estimation

6. Summary: SOP & SOA (2) • Problem types: • Direct observation by multiple methods • Requires error model • Does not in general require system process model • Direct and indirect observation • Requires error model • Requires additionally a system process model to link indirect observations to parameters of interest • Methods: • ‘Record linking’ methods (e.g., statistical matching, data mining, imputation, fuzzy logic) • Model-based inference (e.g., FIML, filtering, Bayesian inference)

7. Research needs (1) • Enabling research • Better meta data (survey/data collection process + context) to support informed fusion (specially important in era of web 2.0) • More professional and disciplined protocols in reporting data treatments in published work • Better techniques of disclosure management • Understanding how to make the business case for data fusion • Benefits - sample size, precision; • Barriers – perception of ‘made up data’, threat to incumbent data providers

8. Research needs (2) • Methodological research • Detecting genuinely conflicting information (not fuseable) – a form of specification test • Better means of validating fused data • Better methods for modelling the propagation of data and model uncertainty during data fusion – enhance confidence in fused data • Are deterministic/’mean imputation’ approaches adequate – how seriously do they distort the covariance structure? • Better re-sampling/Bayesian methods in high dimensions • Integrate methods from SAE • Opportunities to reduce respondent burden by split designs and ex-post fusion (a la SP surveys and analysis) – question substitutability • For record matching, what are the key connecting variables?

9. Research needs (3) • Research infrastructure • Establish to more consistent and complete taxonomy of data fusion problems, methods, outcomes • Establish reference datasets and reference ‘cases’