Transport System Models and Big Data: Zoning and Graph Building with Traditional Surveys, FCD and GIS

Abstract

The paper deals with the integration of data provided from traditional transport surveys (small data) with big data, provided from Information and Communication Technology (ICT), in building Transport System Models (TSMs). Big data are used to observe historical mobility patterns and transport facilities and services, but they are not able to assess ex-ante effects of planned interventions and policies. To overcome these limitations, TSMs can be specified, calibrated and validated with small data, but they are expensive to obtain. The paper proposes a procedure to increase the benefits of TSMs’ building in forecasting capabilities, on one side; and limiting the costs connected to traditional surveys thanks to the availability of big data, on the other side. Small data (e.g., census data) are enriched with Floating Car Data (FCD). At the current stage, the procedure focuses on two specific elements of TSMs: zoning and graph building. These processes are both executed considering the estimated values of an intensity function of FCDs, consistently with traditional methods based on small data. The data-fusion of small and big data, operated with a Geographic Information System (GIS) tool, in a real extra-urban context is presented in order to validate the proposed procedure.

Document type: Article

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The different versions of the original document can be found in:

• http://dx.doi.org/10.3390/ijgi8040187 under the license https://creativecommons.org/licenses/by

• https://www.mdpi.com/2220-9964/8/4/187/pdf under the license https://creativecommons.org/licenses/by/4.0

• https://dblp.uni-trier.de/db/journals/ijgi/ijgi8.html#CroceMRV19,

https://www.mdpi.com/2220-9964/8/4/187/pdf,

https://www.mdpi.com/2220-9964/8/4/187,

https://doi.org/10.3390/ijgi8040187,

https://academic.microsoft.com/#/detail/2938017005 under the license cc-by

• https://www.mdpi.com/2220-9964/8/4/187,

https://doaj.org/toc/2220-9964

• https://www.mdpi.com/2220-9964/8/4/187/pdf,

http://dx.doi.org/10.3390/ijgi8040187

under the license https://creativecommons.org/licenses/by/4.0/