# Analyzing Demand with Respect to Offer of Mobility
## 2 Data Source and Requirements Analysis  
DORAM provides a model and tool for analyzing PTSs by comparing the service offer with respect to mobility demand. In this regard, the main requirements are presented and discussed in this section. **Req.1: matching/analyzing the mobility demand with respect to the offer** in terms of public transportation means, taking into account: multiple operators and modalities. **Req.2: changing the offer and the demand** in the scenarios. Performing the so-called what-if analysis by analyzing the impact of a (set of) change(s) in Actual Scenario to see the effects in alternative scenarios. Changes to the offer aims to verify if they may better satisfy the demand and can be automatically composed producing random changes around the present solution and taking a direction of action. Changes in the demand may be provoked by city changes, for example, the construction of a new stadium, the creation of 300 new apartments and entertainment services in a former industrial area. **Req.3: computing a number of KPIs** (e.g., drop-offs and pick-ups at a stop) and related information (e.g., passing lines in each time interval) which may allow to make a decision about the solutions proposed in changes at the offer. The most relevant aspects to be measured among the others are related to the number of people: (i) at the stops/stations to reduce the overcrowded situations, to improve the quality of service and for safety reasons; (ii) on the travel means (vehicles: busses, train, metro, ferry, cages, etc.) to reduce the overcrowded situations, to improve the quality of service and for safety reasons. **Req.4: exploiting a range of data sources** which may increase precision in the computational model, related to: the city structure and services, demand of mobility, sharing services. Please note that city structure and services may provide information to understand motivations to the city users to move. For example, a large residential area is a source of people in the morning, and an attractor during the late afternoon; a large shopping center, a stadium, a park area, and other attractors. See for example the study regarding the typical city user behavior in the different parts of the city [38], where the usage of Wi-Fi access points data permitted to understand the people flows and to reconstruct origin destination matrices for hour of the day in the week, over than 300 observation points. The **Data Sources** to perform the analysis are in accordance with the characteristics of the city (e.g., available transportation modalities, presence of tourists). In particular, the main aspects of _service offer_ can be obtained by considering provided services in terms of public transportation networks for moving in the city which may be obtained from (1) _GTFS data_, which includes different related information according with mobility operators, transportation modalities (e.g., intercity bus, city bus, ferry, tram, train), vehicle planned trips (e.g., paths, time tables), (2) _eventual real-time vehicle positions_, which could be not easy to obtain, and (3) _alternative transportation modalities_ (e.g., car sharing, bike sharing). Furthermore, the **mobility demand** can be computed to obtain origin destination matrices by considering a large data range:  
* **census data which includes the mobility** (and the public transportation means) of residents from/to the city for different purposes (e.g., work, study);
* **traffic flow** which includes counting (private) vehicles entering or exiting from the city over time which is typically performed on the city border using different methods (e.g., plate number recognition);
* **city structure and services** attract city users depending on daytime. For example, in the morning, residential areas and public parking spaces can be starting point of trips because the city users usually start from these places to go to work and study places and return in the late afternoon.