# 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.  
public transportation systems; demand and offer algorithms; origin destination matrices; smart cities; what-if analysis +
Footnote †: journal: Accepted: 5 September 2022  
can be obtained from different resources (e.g., fare collection using ticket validation [10] or smart cards [11], boarding/alighting passengers [8; 13], census data, on board units of insurances, mobile phone data from the telecom operators, mobile phone data from mobile app operators providing navigators, census data, etc.) that are difficult or at least very expensive to have [13]. A certain amount of demand may be satisfied and oriented to exploit the public transportation offer, while others are oriented in using several kinds of private travel means: cars, bikes, motorbikes, etc. To this end, it is necessary to evaluate plans and strategies, including soft (e.g., incentives, providing information and feedback [14]) and hard ones (e.g., changing frequency of trips, ticket price, and line path [15]), designed to improve the efficiency of the offered services [13]. For instance, when complaints are received by a mobility officer about periodic crowding conditions on a bus line or on a (set of) segment(s) which connect(s) specific stops, the necessary actions to resolve the issue could include performing some on-site analysis, and then implementing possible solutions. Therefore, several solutions to the problem could not be socially, and/or economically efficient. Moreover, making modifications and verification to existing infrastructures may take a long time [16]. Therefore, to avoid ineffective on-site modifications, it is crucial to make a deep analysis to understand the impacts of changes in the current status (in most cases called _Actual Scenario_) of the public transportation system. Furthermore, the analysis of public transportation services, when alternative scenarios are defined by introducing a (set of) change(s) in the Actual Scenario, requires investigating a large amount of data both for the service offer and the mobility demand, and contextual changes in the road graph, when possible.