Estimation of travel demand models with limited information: Floating car data for parameters’ calibration

This paper attempts to integrate data from models, traditional surveys and big data in a situation of limited information. The goal is to increase the capacity of transport planners to analyze, forecast, and plan passenger mobility. (Big) data are a precious source of information and substantial effort is necessary to filter, integrate, and convert big data into travel demand estimates. Moreover, data analytics approaches without demand models are limited because they allow: (a) the analysis of historical and/or real-time transport system configurations, and (b) the forecasting of transport system configurations in ordinary conditions. Without the support of travel demand models, the mere use of (big) data does not allow the forecasting of mobility patterns. The paper attempts to support traditional methods of transport systems engineering with new data sources from ICTs. By combining traditional data and floating car data (FCD), the proposed framework allows the estimation of travel demand models (e.g., trip generation and destination). The proposed method can be applied in a specific case of an area where FCD are available, and other sources of information are not available. The results of an application of the proposed framework in a sub-regional area (Calabria, southern Italy) are presented.