Multi-source Data-driven Procedure for Traffic Analysis Zones Definition

The choice of an appropriate Traffic Analysis Zones (TAZ) system is a critical step of travel demand modeling which is often overlooked. Studies have approached this process as an optimization problem aiming to maximize the homogeneity of socio-economic and geographic characteristics of zones while minimizing, at the same time, the number of intrazonal trips. However, beyond the application of general guidelines and individual experience to specific case studies, the definition of a formal approach is still an unsolved issue. Nevertheless, the rapid ICT development and novel big data sources allow to enhance traditional models by exploiting additional land-use and spatio-temporal mobility features. This paper illustrates a multi-source data-driven method for an automatic TAZ definition procedure which aims to minimize the number of intrazonal trips. With the proposed approach, Floating Car Data (FCD) can be suitably paired with the TAZs in order to define the best configuration of origin/destination zones that can be directly used in travel demand forecasting models. The procedure is applied to the EUR district of the city of Rome (Italy), starting from a regular hexagonal division of the study area consisting of 161 zones, using data from an FCD dataset containing more than 1.500.000 trips carried out between September and December 2020.