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

Abstract

Purpose Traffic congestion is a large-scale problem in urban areas all over the world that can lead to substantial costs for travellers and business operations. This paper focus on how to measure the way in which congestion selectively affects different traffic streams, with special emphasis on light duty vehicles travelling around a city. Methods The idea is to integrate a dataset collecting Global Positioning System (GPS) vehicle traces with road side data sources related to traffic conditions in a road network, which on the other hand usually lack focus on specific traffic streams. The core of the data integration method is the creation of a specific indicator focusing on the time lost in congestion. This is a Key Performance Indicator (KPI) of an urban network that is of paramount importance as a decision support tool for policy makers, also because it has an impact on other key issues such as air pollution, noise emissions, energy efficiency and health problems. Then, a method is proposed to quantify the congestion KPI in a highly disaggregated fashion (each single vehicle travelling on each single link or street segment). Results This KPI can be used to inform a wide range of policy actions within the transport sector, both from the viewpoint of a city and from that of an individual actor of the transport system, such as the operator of a fleet of vehicles for urban freight deliveries. Some preliminary examples of how the aggregation of the KPI at different scales can provide insights into the transport system are presented. Document type: Article