Analyzing Demand with Respect to Offer of Mobility

Abstract

A main key success for public transportation networks is their tuning by the analysis of mobility demand with respect to the offer in terms of public transportation means. Most of the solutions at the state of the art have strong limitations in taking into account: multiple contextual information as attractors/motivations for people movements, modalities of travel means, multiple operators, and a range of key performance indicators. For these reasons, a model for analyzing the demand with respect to the offer of mobility has been studied, and the corresponding tool DORAM developed. DORAM allows to perform the analysis of alternative scenarios, as what-if analyses, when the transport service offer and the mobility demand changed in the scenario, adopting a fast-computation strategy to compare scenarios with the aim of detecting/identifying motivations of crowded conditions on stops and on the vehicles. The analysis can exploit a wide range of data sources when computing a set of key performance indicators. The DORAM solution has been defined and developed in the MOSAIC research and development project with ALSTOM and other companies. The DORAM solution is validated by using real data and conditions in the Tuscany region.

Data Sources and Models for Integrated Mobility and Transport Solutions

“…Despite the relevance of mobility demand knowledge, it is typically quite difficult to acquire data to produce ODMs modeling the full spectrum of demand due to privacy concerns as regulated by the GDPR [ 22 ]. Therefore, alternative solutions to compute approximated ODMs may exploit different kinds of data, for example, IoT; vehicle density; cellular network data; census data; data coming from public transportation monitoring; partial mobile app data; and finally Action-Based data, which produce ODMs of the mobility demand according to city structures in terms of areas where people live and where they are interested in going to work, study, shop, etc., that can be extrapolated by using the POIs, the city structure and statistical information [ 23 , 24 ].…”

Vehicular Traffic Flow Reconstruction Analysis to Mitigate Scenarios With Large City Changes

“…In fact, once the dense TFR in one or more possible Road Graph has been estimated, it can be exploited to assess its impact on: (a) public transportation services, which could lead to moving bus stops out of the blocked area and changing lines/rides, timelines, etc. [47]; (b) parking lots availability, since some of the parking areas could be included within the blocked city area scenario or could be less easily accessible [48]; (c) pollutant changes on the basis of traffic flow, thus provoking more emissions of NO2 [49] and on CO2 [50], according to the related dense estimation [51]. In the following subsections, the most relevant aspects and steps of the above data flow architecture are described, while the computing of dense TFR with its corresponding TDM and KPI is described in Section 3.…”